The interaction between the machine tool, controller and cutting process disturbances are discussed .... The development of high speed machine tools requires.
Virtual Machine Tool Y. Altintas1 (1), C. Brecher2, M. Weck2 (1), S. Witt2 Manufacturing Automation Laboratory-The University of British Columbia Department of Mechanical Engineering, Vancouver, Canada 2 Laboratory for Machine Tools and Production Engineering, Chair for Machine Tools Aachen University of Technology, Aachen, Germany 1
Abstract This paper presents current state of Virtual Machine Tool Technology and related ongoing research challenges. The structural analysis of machine tools using Finite Element models and their experimental calibration techniques are presented. The kinematic analysis and optimisation of machine tool elements are discussed with sample examples. The interaction between the control of the feed drives, cutting conditions and machine tool structure is presented. Multi-body dynamic models of the machine, which allow integrated simulation of machine kinematics, structural dynamics and control techniques, are discussed. The interaction between the machine tool, controller and cutting process disturbances are discussed with sample examples. The simulation of machining operation and its impact on the dynamics of the machine tool and CNC are elaborated. The paper presents both the summary of current and past research, as well as research challenges in order to realise a fully digitised model of the machine tool. Keywords: Simulation, Machine Tools, Virtual Prototype 1 INTRODUCTION The goal of present manufacturing technology is to produce even the first part correctly in a shortest time and most cost effective way. Since the product complexities increase and the competitive product life cycle times are reduced, the realisation and testing of physical prototypes become major bottlenecks for the successful and economically advantageous production of modern machine tools [54], [114]. Presently, the machine tool builders can no longer afford the time- and cost-intensive manufacturing and testing of physical prototypes to detect weak spots and optimise
the design. Instead, the design processes of modern machine tools employ “virtual prototyping” technology to reduce the cost and time of hardware testing and iterative improvements of the physical prototype. The virtual prototype of a machine tool is a computer simulation model of the physical product that can be presented, analysed and tested like a real machine. Iterative changing of a virtual model of the machine tool during the design process and exercising design variations until the performance requirements are achieved, reduce the whole product development time and cost significantly. The advantages and the potentials of time savings by virtual prototypes are illustrated in Figure 1.
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Figure 1: Comparison of the traditional design process and the design process with virtual prototypes.
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Figure 2: The mechatronic system “machine tool”. To ensure that the first physical prototype of the machine tool meets the requirements in the best possible way, it is essential that every design step is evaluated with simulations of the virtual prototype. 2.1 Integrated design of modern machine tools Initiated mainly by the automotive and aircraft industry, the development of modern software tools for the simulation of product properties has been enhanced significantly
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2 THE VIRTUAL MACHINE TOOL Modern machine tools are very complex mechatronical systems. The capability and efficiency of a machine tool are mainly determined by its kinematics, structural dynamics, computer numerical control system and the machining process as shown in Figure 2.
in recent years [114]. Advanced software and hardware systems allow design engineers to evaluate and optimise critical product characteristics with virtual prototypes before the first physical prototype is built. A wide range of software tools is available for the different design-stages of a machine tool [114], [124] as shown in Figure 3.
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If the possibility of comprehensive simulations during the entire design process is not available the optimisation of physical prototypes is often based on trial and error based on the past design experience, which leads to a costly and lengthy development process. In the virtual prototype approach, engineers are able to realistically simulate the kinematic, static and dynamic behaviour of the whole machine tool system including the cutting operations. Thus it is possible to quickly analyse multiple design variations until achieving an optimised prototype which satisfies the machining requirements in the best possible manner. The virtual design engineering is enabled by the use of high performance computer technology and software engineering tools. The virtual prototypes are not only helpful for the design process but also for the virtual initial start-up of the machine tool or the simulation of the machining operations on the digital model of the machine tool. This paper presents the design, analysis, optimisation and operation of machine tools in a virtual environment. The paper is organised as follows: The concept of virtual machine tool design and testing is presented in Section 2. Finite Element, kinematics, structural analysis and optimisation of the machine tool elements are explained. The simulation model of the CNC system is presented in Section 3. Trajectory generation, axis control laws and tool path simulation with collision detection are discussed. The simulation of machining operations is given in Section 4. The predictions of cutting loads as well as the stresstemperature simulation in the chip and tool wedge are explained. Section 5 covers the integration of process and machine tool simulation, which is the ultimate goal in realising a complete digital model of the machine tool during machining of a part. The present research challenges which has to be solved for the full realisation of virtual machine tool system are discussed in Section 6. The paper is concluded by assessing the effectiveness and future trends in “Virtual Machine Tool and Machining Systems”.
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Figure 3: Integrated development of modern machine tools with virtual prototypes. Computer aided design and kinematics studies During the concept stage, simplified simulation models can be used to estimate the influence of general design parameters on the machine performance. The kinematic configuration or the geometry and widths of guideways can be given as examples for general design parameters. Especially the machine tools with parallel kinematics the kinematic behaviour needs to be simulated and optimised during the early design stage. The machine tools with complex kinematic configuration are much more sensitive to slight variations of geometric parameters than traditional cartesian machine tools, and thus offer huge potential for optimisation. The 3D-CAD-Model of the machine tool is exported to a kinematic analysis software environment. The optimisation of the kinematic behaviour and the simulation with rigid multi-body simulation during the early design stages are illustrated in Sections 2.2 and 2.3. Finite-Element-Analysis The Finite-Element-Analysis (FEA) is used to calculate static stiffness or dynamic characteristics of the machine tool, e.g. natural-frequencies and mode shapes. Powerful optimisation methods, which are based on the FiniteElement-Method, are used effectively to find optimum design variants under given restrictions, e.g. the minimisation of masses of moving machine components or the maximisation of the static stiffness. The Finite-ElementAnalysis as well as the application of structural optimisation methods are discussed in Sections 2.4 and 2.5 . Coupled flexible Multi-Body-Simulation The development of high speed machine tools requires light-weight design in combination with sufficient stiffness of the structural components. Moreover, the machine control must be capable of dealing with the high-speed position changes at acceptable accuracy. Therefore, the interaction between structural dynamics and control loops must be considered during the design of modern machine tools. The coupled flexible multi-body simulation is illustrated in Section 2.6.
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The second step is most important since the performance is highly influenced by the geometric dimensions of a machine tool with parallel kinematics. A poor topology which is optimally designed may perform better than a mechanism with appropriate topology but poor design [66], [106]. The choice of the right dimensions for the design parameters with respect to a given application is a difficult task: There are many performance values which have to be taken into account and which are often antagonistic to the design parameters, i.e. kinematic stiffness vs. workspace.
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Many performance values are of the type "best case worst case" over an up to six-dimensional workspace.
Since the performance characteristics vary within a workspace of complex shape a simple and unique performance comparison of either parallel with serial kinematics or different parallel mechanisms becomes most difficult. To achieve an optimal kinematic configuration in a short time, the designer has to be supported by suitable analysis- and optimisation tools. A classical way of finding the required design parameters is to define a cost function, consisting of the weighted sum of the performance values as a function of the design parameters. A numerical procedure is then used to find the design parameters which minimise the cost-function with respect to an initial estimate. This strategy is limited by the definitions of the weight factors, e.g. in terms of priority [66]. In addition, finding the global optimum cannot be guaranteed due to the complexity of the optimisation problem. To avoid these limitations, different approaches have been proposed. The parameter space approach estimates all satisfying solutions within a multidimensional design-space for each performance requirement [65]. The intersections of these individual solutions contain the sets of design parameters which will meet all requirements.
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2.2 Optimisation of the kinematic behaviour During the early stages of the design process of machine tools the type of the kinematic as well as the desired workspace dimensions have to be defined. The efficiency of machine tools is basically determined by these characteristics. Especially machine tools with parallel kinematics are characterised by their non-linear transmission of movements and forces from joint- to task-space [106]. These transmission characteristics are influenced by the kinematic topology of the mechanism and its geometric configuration. Thus, the following two steps are most important during design [66].
Thus, the optimal solution is either chosen intuitively by the designer or estimated by the classical cost-function approach. An approach based on pareto-optimal design is proposed in [57], [112]. The idea is to estimate all sets of design parameters with genetic algorithms, in which the individual performance values can only be maximised by a weakening of another performance requirement. Within the resulting sets of pareto-optimal design parameters, the optimal configuration for a given task can be chosen. The optimisation with the help of genetic algorithms is illustrated in Figure 4. The design parameters which were optimised are the dimensions of the platform, as well as the position of the joints at the machine bed under given restrictions [111].
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Calibration of the Simulation Models To realise a good correlation between the results of measurements and Multi-Body-Simulation, the parameters of the simulation model, e. g. the damping and stiffness parameters of guiding systems and bearings, must be calibrated. Especially the correct prediction of damping parameters in machine tools is very difficult because of the dependency on a large number of different influences, e.g. the pre-load, temperatures, assembly conditions and many others. The calibration of simulation models with results of measurements are discussed in Section 2.7.
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Figure 4: Optimisation of the kinematic performance. It can be observed, that the development of design tools for machine tools is still ongoing research. While tools for the performance analysis are widely established, the estimation of an optimal layout for a given application has to be automated to establish conceptual capabilities in terms of modularity and reconfigurability. 2.3 Simulation of rigid multi-body models During the early design stages the kinematic behaviour of the machine tool can be simulated with the multi-body simulation (MBS) as a rough estimation [113], [123] using rigid bodies. This kind of simulation enables the design engineer to make a first, quick prediction of the kinematic behaviour and estimations of the influence of parameter variations in the model, as, for example, the length of an actuator in machine tools with parallel kinematics [123]. Each individual element within the multi-body model consists of rigid bodies. In this context rigid bodies are parts that have mass and inertia properties but cannot deform. These rigid bodies can be imported from 3D-CAD-Models through interfaces using standard formats such as IGES, STEP, DXF/DWG and Parasolid or can even be generated within the multi-body environment. Constraints define how the parts are attached and how they move relative to each other. Multi-body simulation tools usually provide a library of constraints including for example [64]: •
Idealised joints that have a physical counterpart, such as a revolute (hinge) or translational (sliding dovetail) joint.
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Joint primitives that place a restriction on relative motion, such as forced parallel movement of two parts.
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Motion generators that drive the model through a prescribed distance, velocity or acceleration profile as a function of time.
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Associative constraints that define how pairs of constraints move, such as couplers or gears.
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Two-dimensional curve constraints that define how a point or curve moves along another curve.
Furthermore, forces that act on the model can be defined. These forces will affect part motion and reaction forces on constraints. Multi-body simulation tools provide libraries of forces that usually include: •
Flexible connectors, such as spring-dampers and bushings, which provide pre-defined, compliant force relationships.
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Applied forces that allow the writing of algorithms to represent a wide variety of different force relationships.
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The analysis options in the established multi-body simulation systems consists of the following types [90]: •
Assembly analysis
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In the assembly analysis, the MBS-software tries to assemble the mechanism in the modelled configuration. This means that the underlying non-linear equation system is solved. If necessary, minor variations of the initial positions owing to the numerical precision of the input data are applied. This analysis step is carried out before each simulation. During a kinematic simulation, the position of all bodies of the mechanism is analysed depending on the time. During such a simulation the movements of one or more bodies are described by a law of motion. This kind of analysis is used to simulate the reachable kinematic performance, e.g. the acceleration capability of the design over the complete workspace. The model of a machine tool with parallel kinematics and some results of such a kinematic simulation are shown in the following Figure 5.
2.4 Finite Element Analysis of machine tools After the concept of the machine tool and the dimensions of the kinematics have been defined the structural behaviour has to be analysed and optimised [64], [113], [124]. The structural behaviour under static, dynamic and thermal loads is evaluated to derive an optimal machine design with respect to minimum structure mass and highest machining precision. The Finite-Element-Analysis (FEA) is an established tool to evaluate the properties mentioned above. It is applicable for single components such as columns or spindle housings as well as for complete machine tools. The most common types of the Finite-Element-Analysis for structural problems are illustrated in Figure 6. Apart from these analysis types the Finite-Element-Analysis is also applicable for other physical problems, e.g. in hydraulic, electromagnetic and casting simulations. Analysen Finite-Elemente-Methode Analysis typesder of the Finite-Element-Analysis Lineare Statik Linear Static
Rigid Multi-Body Multi-Body Model of a Machine Tool’s kinematics Screw Joint
In this example the multi-body model is used to simulate the dependency between reachable acceleration and necessary jerk setting for positioning operations of the kinematics [112]. In the dynamic analysis, the position of all bodies of the mechanism is determined as a result of time-dependent forces applied from outside. Generally, kinematic constraints are replaced by flexible connectors like 3dimensional spring-damper-elements. With the help of this analysis the simulation of expected load histories of machine components can be estimated for the dimensioning [20], [90], [76], [108]. During the analysis of inverse dynamics, the motion pattern of one or more bodies is specified and the drive and the internal forces of the joints and flexible connectors are calculated. This kind of simulation is especially useful for the dimensioning of the drive systems during the early design stages. The static calculation is traced back to a dynamic calculation where the MBS-system determines the state of equilibrium [64], [90]. The multi-body simulation provides an easy way to analyse the kinematic behaviour over the complete workspace of a machine tool as well as to determine load histories of components or joints [64], [90], [123]. In addition, it helps to choose proper elements or detect weak spots of a machine tool in the early design stages. However, the flexibility and strain of single machine parts cannot be considered with the pure multi-body simulation using rigid body models [113], [64].
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For structural problems the types of analysis can be divided into three groups as depicted below: namely the linear and non-linear static analysis, the dynamic analysis and the thermal analysis [30].
A static analysis calculates the effects of steady load conditions on a structure, while ignoring inertia and damping effects caused by time-varying loads. A static analysis can, however, include steady inertia loads (such as gravity and rotational velocity), and time-varying loads that can be approximated as static equivalent loads. Static analysis is used to determine the displacements, stresses, strains, and forces in structures or components caused by loads that do not induce significant inertia and damping effects. Steady load and response conditions are assumed in static analysis; i.e., the loads and the structure’s response are assumed to vary slowly with time. A static analysis can be either linear or non-linear. The linear static approach is selected when small, elastic deformations occur on the structure. In general, the analysis refers to the classical calculation of elasticity problems that can also be described analytically in the case of very simple structures. In this context buckling problems can be analysed that match the classical Euler solution. In non-linear analysis different types of non-linearity are allowed such as large deformations, plasticity (non-linear material properties), creep, stress stiffening, contact (gap) elements or hyperelastic elements. Contrary to the static case, the dynamic analysis allows the examination of a structure with respect to time-varying effects. For machine tools the most important aspect is the analysis of normal mode dynamics to determine the vibration characteristics (natural frequencies and mode shapes) of a structure or a machine component in the frequency domain, as well as analysis of time domain response of the machine [30]. Apart from the mechanical aspects the influence of heat sources on the machine’s structure is another most relevant topic that can be examined using the thermal FiniteElement-Analysis. In most cases the basis for thermal analysis is a heat balance equation obtained from the principle of the conservation of energy. In a thermal simulation the three primary modes of heat transfer can be considered: conduction, convection and radiation. For machine tools the most important results in a FiniteElement-Analysis are [20]: •
Deformations, e.g. deflection of the tool centre point (TCP) under process loads, deflection of guideways, reaction forces, e.g. forces in bearings or guidingsystems
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Linear normal modes of vibration
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Flexibility frequency response (with limitation)
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Stress distribution, e.g. in highly loaded tool interfaces under additional rotational loads
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The detailed procedure of a Finite-Element-Analysis for machine tools according to Figure 7 is exemplified in the following. For the effective use of simulations during the design process Finite-Element programs are often integrated into CAD-systems or provide standard interfaces, such as IGES, STEP or Parasolid in order to transfer existing geometry models. In a first step it is necessary to prepare the CAD model for the following Finite-Element-Analysis (pre-processing). Geometric details, such as chamfers, small holes and radii that only have a local influence on the structural behaviour are neglected. After simplifying the geometry,
the geometric model is split into surface patches (partitions). By this, a complex structure is fractionalised into simple base geometry elements that allow easy meshing. Defeaturing of the 3D-CAD-Model and setup of the FEA-Model Calculation & Optimisation of the static behaviour Calculation & Optimisation of the dynamic behaviour
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Figure 7: Steps of a FEA-Analysis of a machine tool. Next, the prepared geometric structure is reproduced by finite elements. Depending on the simulation problem and the desired calculation accuracy, the FEA programs offer a variety of different elements that are specific to the analysis (static, dynamic, thermal). The finite elements are connected by nodes that make up the complete finite element mesh. Each element type contains information on its degree-of-freedom set (e.g. translational, rotational, thermal), its material properties and its spacial orientation (1D-, 2D-, 3D-element types). Thin-walled structural components of machine tools, like columns or machine beds, are usually meshed with shellelements (2D-element types). The wall thickness of the structure is contained as a physical property of each element. Compact parts are typically meshed with solid elements (3D-element types) [30]. Semi-automatic mesh generators are widely used in practise, helping the engineer to reduce the model generation effort. In the semi-automatic meshing process, also called mapped meshing, regular FEA meshes made of quadrilateral or hexahedral elements are generated. These kind of FEA meshes are distinguished by balanced element proportions and smooth dimensional transitions. On the contrary, with fully automatic meshing methods, only the generation of irregular FEA meshes is possible, which in general provide lower calculation accuracy compared to the mapped meshes. While the real structure components of machine tools are commonly connected by guidance systems and drives, e.g. ball-screws, the meshed structural components are connected using spring elements with corresponding stiffness values. These spring elements represent the connection stiffness of real machine components with adequate accuracy. They may also contain local damping properties, if those are needed for direct dynamic calculations and if they are known for the corresponding machine component. Finally, boundary and load conditions are added to fully describe the simulation model. Boundary conditions are applied to give specified displacements and to describe symmetry conditions. The boundary conditions are defined by fixing the various translational and, rotational degrees of freedom, or by constrained mesh’s nodes. Loads are added to describe the machine tool loading scenarios such as machining forces or heated motors.
Therefore, loads can be of a structural (forces), thermal (heat sources) or fluid (pressures) nature. For machine tools the static and dynamic behaviour is of major interest, as illustrated in Figure 7. In postprocessing, the calculation results can be reviewed and load cases of different operating conditions can be superimposed. While displaying the calculation results (e.g. static, dynamic) in the post-processing program, the machine model can be examined with respect to displacements, stresses, reaction forces, mode shapes or natural frequencies, which allows the designer to evaluate the machine properties in the design phase. Albertz [1] and Schneider [87] presented applications of the Finite-Element-Analysis for the simulation of the static and the dynamic behaviour of machining centres during the design process. Zatarain [134] used a FE-Model with movable joints between the structural components for a modular synthesis of the static and dynamic behaviour of machine tools at several positions in the workspace. Groche [46] used Finite-Element-Analysis for the optimisation of a forming press under dynamic loads. The industrial application of Finite-Element-Analysis as a tool for computer aided engineering is illustrated by many different examples [20], [76], [125], [108]. However a single analysis of the actual state of a machine tool (analysis of weak spots) during the design process is usually of little help. Rather, in most cases, continuous improvements to the design are necessary in order to improve the static and dynamic behaviour of suboptimal components, to reduce masses of moving parts. These improvements of the machine performance can be achieved cost-effectively by the use of modern optimisation methods based on the Finite-Element-Method. These methods will be explained and discussed in the next section. 2.5 Optimisation of structural components In machine tool design, optimisation offers the possibility of improving different properties of the design by using numerical optimisation [20], [84], [89], [91], [114], [124]. The numerical optimisation of structural components is generally based on the Finite-Element-Method and can thus easily be integrated in the design process [89]. Depending on the necessary level of detail, different methods are used to find or improve the design of structural components of machine tools. The topology optimisation is used to define the best material distribution in a given design space. Thus this method is mainly used in the early design stages supporting the engineer in finding a design concept with regard to given demands [79], [84], [87], [91], [92]. As a result of this optimisation an optimal material distribution in the given design space is calculated. For the design of machine tools this method is often used to determine the design of machine beds or columns in terms of light weight design. Some examples and applications of the topology optimisation will be discussed in Section 2.5.1. The parameter optimisation is used for the optimisation of more detailed designs of machine tools. This numerical method is used to optimise parameters of Finite-Elements (2D-element types) considering different constraints, e.g. maximal allowed deformation [13], [89], [113], [124]. A typical application is the optimisation of the wall thickness of machine beds or columns for machine tools. Generally the overall weight of the structural components is minimised with regard to a desired static stiffness at the tool
centre point. The application of the parameter optimisation will be illustrated in Section 2.5.2. The following Figure 8 illustrates the most common methods for structural optimisation. Methods of structural optimisation Topology Optimisation
optimisation with regard of the casting core draw directions
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Figure 8: Methods of structural optimisation. The topography as well as the shape optimisation are of secondary importance for the design and optimisation of structural components for machine tools. 2.5.1 Topology Optimisation The topology optimisation supports the designer in the task of finding a preliminary rough design based on minimum design specifications, wherein the mass of the component is distributed with load-orientation in the solution space [69], [79], [84], [91], [123]. The topology optimisation requires no design plan as an initial solution. Starting from the available design space and the requirements for the component, a basic design of the component is determined. The objective of a topology optimisation mostly lies in designing the component with a minimum mass with simultaneous adherence to the boundary conditions, such as stiffness specifications. Moreover, some topology optimisation systems allow the formulation of reference stress and natural frequencies as goal and restriction functions [89]. The topology of a component created in this way must finally be smoothed and transformed into a CAD model to be able to reuse it [13], [113]. Hessel [50], [113] developed an approach to transfer the results of a topology optimisation back to the CAD-based design process. The mesh of the optimisation result is transferred into a surface model consisting of NURBSsurfaces and standard geometry. These models can be exported in a standard format, like STEP or IGES, and can thus be used for the detail design-engineering work. Fleischer et al. [44], [70], [92], [124] developed an approach for the topology optimisation of structural components which is called “coupled hybrid multi-body simulation with topology optimisation” (HMBS-TO). This software environment uses the multi-body simulation to calculate the loads and the FEA-software in combination with the optimisation software. The advantage of this approach is the automatic load and inertia update which guarantees a fully automated optimisation loop [70]. The workflow for the new dynamic topology optimisation is illustrated in the following Figure 9. After the preparation of the flexible bodies, a modal reduction has to be carried out to reduce the degrees of freedom of the hybrid multi-body simulation model. This can be achieved by means of computing the CraigBampton modes [31], which are described in detail in Section 2.6.3.2.
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wall thickness values of shell elements for models of structural components
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fibre orientation angles of shell elements for models of light-weight design
The parameter optimisation is a useful tool for the design engineer to meet the demands of light weight design especially for moving parts of highly dynamic machine tools. The results of such an optimisation of a HighPerformance-Cutting (HPC) machine tool are presented in Figure 10. vertical table - bottom vertical table - top pallet carrier 3 pallete 2 column head Sprint Z3 fixation
2.6 Coupled Simulation of structural dynamics and control loops of machine tools Generally, the requirements on modern highly dynamic machine tools can be summarised as follows [21], [22], [116]: •
high static and dynamic stiffness to ensure high accuracy of the finished workpieces
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high dynamic properties of the feed drives to realise highly dynamic positioning operations and movements to decrease the processing time of each workpiece
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low path deflection during the chip removal
These ambitious demands on machine tools can only be fulfilled employing small moving masses with sufficient static and dynamic stiffness of the structural components as well as high adjustable controller parameters of the drives [22], [116]. This leads to interactions between structural dynamics and feed drive controls. Natural frequencies of the feed drives are coupled with lower natural frequencies of the machine structure. To avoid instabilities the control parameters have to be reduced, whereby the bandwidth of the feed axes decreases. This leads to a limitation of the productivity of the machine tool. Despite these known interactions the dimensioning of the feed drives and the design of the structural components of the machine tool nowadays still take place decoupled from each other. Different approaches are known to simulate these interactions during the early design stage of the machine tool [33], [48], [80], [81], [116], [131]. Figure 11 illustrates the most common approaches for the coupled simulation of structural dynamics and control loops used today. Replacing Models Finite Element Analysis (FEA)
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tion the thickness of each wall of the structural components was defined as a design parameter which could be varied within limits. The optimisation led to a noticeable improvement of the dynamic behaviour resulting in a significant increase of the first natural-frequency.
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reduced model of the mechanic [M]; [K]; [C]
Design Element
Frozen Element
2. Computation of Craig Bampton Modes
Rigid Body Element
reduced model of the drives mred, kred, cred
1. Preparation of Flexible Body
-
100
FEA
DBS
80
Coupled rigid Multi-Body Simulation (MBS)
60
MBS
Coupled flexible Multi-Body Simulation MBS
180 160
optimisation of the design comparison of different design-versions consequently realisation of light weight design parameter optimisation of the wall thickness by the use of Finite-Element-Analysis
percent [%]
1. eigenfrequency
optimisation table
z
Optimisation of a Milling Machine
over-all mass
140 120 100 80 60 40 20 0
Start design Optimised design
interface
-
s
-
-
interface interface
forces
0 y
x
position, velocity
7
1
interface
forces
20
position, velocity
40
-
-
DBS
s
-
-
-
DBS
Co-Simulation
Figure 11: Methods of coupled simulation of structural dynamics and control loops. over-all mass
1. eigenfrequency
Figure 10: Parameter optimisation of a machining centre. The over-all weight of the structural components could be reduced significantly by maintaining a constant static stiffness at the tool centre point [22]. During the optimisa-
The different approaches can be classified as the simulation with replaced models and the co-simulation of the dynamic behaviour. The simulation with replaced models uses either analogue models of the control loop for the FEA-Model of the structure or analogue models of the mechanics for the simulation of the control loop [48].
In the context of the co-simulation, two independent simulation environments, one for the control loops and one for the machine structure, are coupled via interfaces during the simulation [33], [48], [73], [131]. Within the research project MECOMAT (FP5 Growth Programme of the European Union) [103] an computer aided engineering tool was developed for the mechatronic design of machine tools, which supports the conceptual design as well as the detailed verification. The different approaches will be explained with some examples within the next sections. 2.6.1 Coupled rigid multi-body simulation The rigid coupled multi-body simulation can be used to simulate the kinematic behaviour of the machine tool while considering the control loops of the drives [20], [76], [125]. The models of the structural components are stiff and cannot deform under load, and are connected by idealised joints. The simulation is valid for any possible position of the machine tool in the workspace. Therefore it is possible to simulate positioning operations in the workspace with this approach. Pritschow et al. [73], [74], [75] developed a simulation environment which is illustrated in Figure 12. The environment was developed for the coupled simulation of a rigid multi-body model and control loop models of a PKM machine tool. MBS-Model
PC-NC Model
Velocity
CAD-Model
desired feed rates
Neugebauer et al. [71] developed models to describe the interaction of machine and hydraulic drive system of forming machines. The methods use numerical simulations for the hydraulic systems. 2.6.2 Coupled Finite-Element simulation Another approach is the coupled Finite-Element simulation with reduced models of the control loops of the drives. Within this procedure the reduced stiffness, damping and mass of the drive system are calculated with the help of a digital block-simulation and modelled with special elements in the FEA-model [20], [48], [76], [131]. Some FEA-programs provide special linear control elements to represent the analogue model of the control loops. In this case only the settings of the controllers have to be specified as parameters of the elements in the FEAmodel [17]. These kinds of elements are handled in the same way as conventional finite elements. The simulation of the dynamic behaviour of the x-slide of a turning centre with a linear direct drive is depicted in the following Figure 13 [20]. To simulate the error at the toolcentre-point during a positioning, a trajectory profile was generated as an input signal for the controller element. The signal of the measuring system was used as an additional input signal. This signal was measured between two nodes at the two parts to which the measuring system is mounted on the real machine. At each simulation step of the dynamic analysis the controller element calculated the force of the linear direct drive which was applied as a pair of forces (action=reaction) on the primary and the secondary parts.
Tigger 1
Kv
+-
+-
Xsoll
1/Ks
Kf
1/1000
-+
1/Tpc
feed force on the secondary parts
1 Fsoll
PI 1/2
counteracting force on the primary parts
Xist 2
Displacements Forces
Forces Displacements Velocities Velocities
error X in the measuring system [mm]
vber
error. [mm]
Time
Model of the control loops
TCP
0.01 0 -0.01
Design in 3D-CAD-Systems -> Import into MBS-Software (MSC.ADAMS) adaptable level of detail coupled model of the control loops (Matlab/Simulink) of the drives desired feed rates with PC-NC model
0
controller
measuring system 10 required acceleration [m/s²]
0.04
-10
The multi-body model of the machine tool is imported with the aid of an interface from the CAD-system into the MBS-environment. This approach enables the update of the model during the different design stages; if the layout is detailed during the design process these changes can easily be included [74]. The model is coupled with models of the control loop for each drive. The displacement and velocity of the measuring systems in the model as well as the forces of the drives are exchanged with the aid of interfaces between the MBS-environment and the Computer-Aided-ControlEngineering program. In addition the control loop models are coupled with a PC-based model of the numerical control, which generates the desired feed rate of each individual drive. Especially in the field of machine tools with parallel kinematics the possibility to perform test runs of the numerical control before implementing new functionalities, like algorithms for path preparation, collision checks or coordinate transformations into the real machines is a significant improvement to avoid physical damage [75]. Rehsteiner et al. [83] used the multi body simulation to optimise the accuracy of machine tools under acceleration loads for the demands of high-speed-machining.
0.01 error [mm]
0
0
0.1
time [s]
0.3
0.4
required position [m] 0.08
Figure 12: Coupled simulation of a rigid multi-body model and control loop models of a PKM machine tool.
0.1 time [s] 0.3
error X at TCP [mm]
0.4
0
0
0.1
time [s]
0.3
0.4
0 -0.01 0
0.1 time [s] 0.3 0.4
source: Gildemeister / Siemens Linear Motor Systems GmbH & Co. KG
Figure 13: Coupled FEA-simulation with control loops This approach enabled the investigation of the influence of the position of the measuring system as well as different orientations of the linear direct drive. Thus the designer was able to optimise the drive of the x-slide in an early design stage and minimise the occurring errors during machining. Such changes of the principle design would be extremely expensive if they had to be realised at a physical prototype, or impossible if the surrounding design space did not allow such changes. Berkemer [16], [17], [18] demonstrated the industrial use of the methodology for tuning of the SIEMENS controllers in a virtual environment, as well as recommending the modification of the machine tool dimensions to minimise inertial excitation of the machine during high speed contouring where large accelerations occur. Van Brussel et al. [104], [105] proposed to treat the complete machine tool and control as an integrated mechatronics design system. The Finite-Element-Model of the machine tool and control algorithms are integrated in the simulation environment as shown in Figure 14.
The aim of the strategy is to optimise the machine tool’s mechanical components as well as the control laws during the design stage of the machine tool simultaneously. Control models (Matlab® / Simulink):
Structural models (Finite element model): • • •
Structural elements Drive elements Non-linear phenomena (friction,…)
• • •
Control laws Digital implementation (DAC, ADC) Measurement devices, filters In1 Out1
Structure-control integration: Desired trajectories
Finite-Element Model
®
Matlab / Simulink
Resulting outputs
Figure 14: Integration of structural and controller models.
have a strong influence on the dynamic behaviour of the machine tool. These components are modelled by three dimensional spring-damper-elements [126]. Guiding systems The guiding systems are used to determine a defined movement of different machine components relative to each other. Guiding systems are also modelled by 3Dspring-damper-elements. Parameters of these elements are the stiffness in two directions, perpendicular and transverse to the direction of movement. The stiffness in the direction of movement is nearly zero. The damping of such a guiding system is considered in three directions [126], [14]. DRIVES
fixed bearing F k
Zäh et al. [130], [131], [132] developed a Finite-ElementModel of the feed drive and simulated the performance of the axis control law under the influence of structural vibrations received by the position sensor. 2.6.3 Coupled flexible multi-body simulation The coupled flexible multi-body simulation is used to simulate the dynamic behaviour of the machine taking into account the behaviour of the control loops of the drives [48], [80], [115]. The models of the single components of the machine tool can represent the static as well as the dynamic behaviour and are coupled by flexible connectors. In reality, guiding systems and bearings appear as joints between the components. These joints are approximated by spring-damper-elements in the flexible multi-body model. For example, for each guide shoe between two structural components one spring-damper element with stiffness and damping values in the X, Y and Z-direction is defined. To consider the influence of the individual drives of the machine tool on the dynamic behaviour, the flexible multibody model is coupled with a model of the control loops via an interface [14], [115], [126]. Different research activities in the field of coupled flexible multi-body simulation have been done by Reinhart et al. [14], [80], Weck et al. [108], [109], [110], [115], [116], [126], Großmann et al. [47], [49], Denkena et al. [33], [34] [100] and Turna . The model set-up as well as the different types of simulations are discussed in the next sections. 2.6.3.1 Model configuration Each structural component of the machine tool is modelled as a so-called flexible body [31], [115], [116]. The different elements which are used to connect the structural components, such as guiding systems, mounting devices or ball-screw-drives, are modelled as a combination of flexible connectors and joints depending on the specific configuration [14]. The individual flexible components of the multi-body model are connected by these flexible connectors depending on the direction of the internal force of the component (1D-element or 3D-element). The different model techniques of the different connectors in multi-body models are pictured in Figure 15. Some typical modelling techniques of popular machine components are specified below. Mounting devices In most technical applications the machine tool is mounted with special mounting devices onto the foundation. The stiffness and the damping in three directions
D
ball screw spindle L(t)
+
spindle nut
+
F
k=
nut stiffness
F
E⋅A ,D L
F M
h
F
+
k
D F
⎛2⋅π⎞ Fa = ⎜ ⎟ ⋅M ⎝ h ⎠
GUIDING SYSTEMS F k
D
{F} = [k ] ⋅ {u} + [D]⋅ {u& } + {Fv }
D
{F} = [k ]⋅ {u} + [D] ⋅ {u& } + {Fv }
F
MOUNTING DEVICES F k F
Figure 15: Model configuration for the flexible MBS. Ball-screw-drives These drives are used to realise translational movement of machine axes. Different components are used in such a drive system. The bearings and the ball screw-nut are modelled with 3D-spring-damper-elements with stiffness and damping parameters in all directions. The screw is modelled using flexible beam elements, which are able to rotate about the pitch attitude. The rotation of the screw, which is caused by the model of the servodrive in the control model, is transformed into a translational movement by the use of a nut. Thus it is possible to simulate the dynamic behaviour of such systems [113], [115], [116]. 2.6.3.2 Generation of flexible multi-bodies To consider the flexibility of the machine components during the multi-body simulation, data from natural vibration and deformation calculations of the individual components, the so-called Superelement Creation, are integrated in the multi-body model through an interface of the multi-body simulation program to popular Finite-ElementPrograms [14], [76], [115] [116]. Superelement Creation uses a Finite-Element-Model to define a component of a complex structure, and a connection degree of freedom set (DOF) to specify the interface nodes, or attachment points, of the component to other components of the structural system and points where forces are applied. The software calculates fixed normal modes and static constraint modes to approximate the general behaviour of the component at those “interface node degrees of freedom”. The fixed normal modes contain the dynamic response of the superelement when all “connection degrees of freedom” are fixed. The static constraint modes contain the static response assumed by the component when one degree of freedom of one interface point is given a unit deflection while fixing all other “interface degrees of free-
Constraint modes
Φ Φ
physical DOF boundary DOF interior DOF Identity and zero matrices physical displacements of the interiot DOF in the constraint modes physical displacement of the interior DOF in the normal modes modal coordinates of the constraint modes modal coordinates of the normal modes
IC
IN
qC qN
f=1240 Hz f=2275 Hz
Figure 16: The Craig-Bampton theorem for the flexible multi-body simulation. For the Craig-Bampton (CB) solution option, processing concludes at this point; the reduced mass and stiffness matrices as well as the fixed normal modes and static constraint modes are stored in an output file for the interface to the multi-body simulation program. Tönshoff et al. [100] developed an alternative approach to model the elasto-kinetic behaviour of machine tool structures based on the theory of flexible multi-bodies. 2.6.3.3 Coupling of multi-body models with control loops To consider the dynamic behaviour of the control loop a coupling to commercial Computer-Aided-Control-Engineering (CACE) programs is possible with common multibody simulation programs [34], [48], [110]. Especially for machines with linear direct drives, where no mechanical transfer elements occur, the consideration of the control loops is necessary for the approximation of the drive system stiffness [108], [110], [126]. The drive control loops generated in the CACE environment can communicate with the complete machine model in the multi-body system. Figure 17 depicts the general structure of this coupling for the coupled flexible multi-body simulation of machine tools. control loop of the direct drive (x-y) control loop of the direct drive (x-y) KF
force excitation TCP
iA
RA,Tel u Ki ,Tni isoll A - xist
Fa
Force
KE x ist
0
Time
1,0
KM
RA,LA
A
uA
0
z
nist
y
zist
Ki, Tni
isoll -
i ges
Kp,Tnp -
zsoll
estimation of interactions between mechanical structure and control influence of the controller settings on the dynamic behaviour at the tool centre point (TCP) overshooting of the feed drives
0
100 Frequency [Hz]
200
Figure 18: Simulated frequency response function Especially for machine tools with small workspace dimensions, the potential of the installed drive power can only be used efficiently at high jerk settings. To optimise the dynamic behaviour of machine tools the coupled flexible multi-body simulation can be used to analyse the maximum jerk settings of the feed drives. Therefore an inputsignal for the control loops of the drives can be generated by a virtual controller. The simulation of such a positioning operation is illustrated in Figure 19. jerk
KL
simulation of frequency response functions (FRF)
with control loop without control loop
Positioning operation of the Z-unit (5mm)
acceleration
position controller
KE
1,0
ssoll 1 -
Simulation Results
Lineardrive K F =103 N/A K L =70 1/s K P =3500 As/m Tnp =8 ms
velocity controller
-
Time
KL -
control loop of the ball screw drive (z)
displacement TCP
0
Kp,Tnp
-
x ist 0
Displacement
s
current controller
Z-Axis K M =0,93 Nm/A K L =70 1/s K P =2,71 Nms/rad Tnp =10 ms
0
M (t)
0
-
current controller velocity controller 0
position controller
Time
0
Time
position
velocity
x
control loop loop Input control z-axis z-axis
[m]
Figure 17: Coupling of flexible multi-boidy models and control loops.
[m/s]
flexible multi-body model
00
The entire control system (incl. all non-linearities) delivers the resulting drive power of each axis to the multi-body system. The control loop itself is closed with the help of the velocities and displacements of the axes determined from the multi-body system.
Time
∆=0
00
Time
F (t)
u uB uI E,0
Fixed-boundary normal modes
0 ⎤ ⎧qC ⎫ ⋅⎨ ⎬ Φ IN ⎥⎦ ⎩qN ⎭
control loop loop control linear drive direct1 drive 1 control loop loop control drive 2 linear direct ∆= 0 drive 2
F (t)
⎧u ⎫ ⎡ E u = ⎨ B⎬ = ⎢ ⎩ uI ⎭ ⎣Φ IC
Unit translation of the guideway in x-direction
Unit translation of the hinge in x-direction
x
[m/s²]
z y
[m/s³]
Boundary-conditions (Craig-Bampton)
2.6.3.4 Results of the coupled flexible multi-body simulation For the simulation of flexibility frequency response functions of the coupled flexible multi-body model, an excitation signal must additionally be defined. For this purpose, so-called INPUTS and OUTPUTS have to be generated. In the INPUT, a value is controlled from the outside for each time step during the calculation. Through the OUTPUT that can be applied as a force in the X-, Y- and Z-direction at any location of the multi-body model, the outer signal is directed into the structure [14], [47], [108], [110], [115], [126]. In case of machine tools an excitation at the machining interface (tool centre point) is useful, because it corresponds to the method for experimental investigations and best depicts the excitation through machining forces in the chip removal process [108], [114], [115]. Basically, sinus wobbles, noise or an impulse are considered as excitation signal types [114]. These frequency response functions are useful for the estimation of the interaction between the mechanical structure and the control during the design stage, as well as for the estimation of the influence of the controller parameters on the dynamic behaviour at the tool centre point [126], see Figure 18.
Compliance [um/N]
dom”. The solver performs Superelement Creation much like normal modes analysis using the Lanczos method, then uses the Craig-Bampton method to generate the superelement [31]. The different modes of a super-element creation are illustrated in Figure 16.
Figure 19: Simulation of a positioning operation. The influence of the jerk on the path deviation during a positioning operation was investigated in this case. The
desired path of the Z-unit was generated by a model of the controller and used as an input-signal for the control loop of the z-axis with different jerk settings. The z-unit started at standstill and was accelerated to the maximum speed of the z-drive. After a short movement with constant velocity the drive was decelerated to standstill. The results of this simulation are shown in Figure 20.
lytically before they are implemented in the current design or in the next machine generation. Probe • inductive
Force probe x
F
position sensor
Excitor
• strain gauge • piezo sensor
• impulse hammer • piezo actuator • hydraulic actuator
• accelerometer
Bode diagram compliance [µm/N]
Gxx Gyy Gzz
Amplifier
locus
Amplifier
0.001 180
0,1
A/D converter
-0,1 -0,1
FFT analyser
real 0,1 [µm/N]
coherencephase [°]
T3 = f 3
FRF
imaginary [µm/N]
r=730 m/s³ r=650 m/s³ r=550 m/s³ r=450 m/s³ desired path
Compliance [um/N]
Displacement [mm]
displacement
0 -180 1 0
0
200
frequency [Hz] 800
Time [sec]
Figure 21: Measuring of a frequency response function.
eigenfrequency of the machine
Frequency [Hz]
Figure 20: Simulation results of a positioning operation. Such positioning operations always excite natural frequencies of the machine tool, which can lead to deviations of the desired tolerances of the workpiece or even to damaged tools dependent on the amplitude of the vibration [14], [108], [110], [126]. The evaluation of the simulated vibration signals enables the allocation of the excited natural frequencies and the derivation of arrangements for improvements during the design process. 2.7 Validation and optimisation of the simulation models Despite the rapid development of the available software tools in recent years, the correct estimation of the simulation parameters is still a problem, which limits the accuracy of the results [107]. The prediction of stiffness and especially of the damping characteristics of machine components is extremely difficult due to their dependence on many different influences, like lubrication, pre-loads or tolerances [53], [68]. Measurements of the dynamic behaviour of similar machine tools or components and the validation of existing simulation models can help to find better initial values for future simulations. The measurement of the dynamic properties of machine tools usually targets two characteristics [111]: • •
The Frequency Response Function (FRF) of the compliance at the tool centre point (TCP) The mode shapes of the machine with their associated resonance frequencies and dynamic amplitudes as well as the phase shift
The calibration of simulation models, especially the parameters of spring-damper-elements (stiffness- and damping-coefficients) is extremely difficult and very timeconsuming. For the described example of the machine tool in Figure 15 the flexible multi-body model contains 48 different parameters to model the mounting devices, the guiding systems, different bearings and the mechanical components of the ball screw drive. It is obvious that a manual calibration of such complex simulation models of machine tools is nearly impossible. Witt and Brecher [24] developed an approach for an automated optimisation of simulation models with the help of measured frequency response functions. To match the results of the simulation and the measuring it is possible to model the stiffness and damping parameters as design variables and optimise them by using numerical optimisation methods, e.g sequential quadratic programming (SQP). The design goal of this optimisation is the minimisation of the deviation of the measured and the simulated frequency response function. The principle approach of this optimisation is illustrated in the following Figure 22. Coupled flexible MultiMulti-Body Model of the machine tool
Measuring of the Frequency Response Function kax
dax + krot
Measured FrequencyFrequencyResponseResponseFunction
0
Simulated FrequencyFrequencyResponseResponseFunction
Measured FRF
no
[K]start [D]start
Convergence Convergence
yes 0 Frequency [Hz]
Both characteristics can be measured with special experiments as depicted in Figure 21 for the FRF measurement. For the determination of the FRF, the TCP is excited with a dynamic actuator and the reaction of the TCP is measured. Via Fast Fourier Transformation (FFT), a frequency spectrum or a locus curve can be generated. The results of both examinations can help the design engineer to validate the simulation models in order to find realistic values for the stiffness and damping behaviour of the machine components. Design modifications to improve weak points of the machine can be assessed ana-
drot
Matching Measuring Measuring // Simulation Simulation Matching
Compliance [µm/N]
Excitation of the third
1
10 Iteration
Optimised parameters parameters of of Optimised the model: model: [K] [K]actual the actual,, [D]actual [D] actual
Figure 22: Automated model update with measured frequency response functions. This approach enables the calibration of the machine tool models for the simulation of the interaction between machine tool and process. This kind of simulations requires models of the machine tools which represents the real static and dynamic behaviour in the best possible manner.
2.8 Virtual reality in the development process The virtual reality (VR) is mainly used in the automotive industry and as a marketing technique for the consumer goods industry at the present [88], [96], [133]. The automotive industry uses the virtual reality increasingly in the field of design and development as a tool for the investigation and error diagnostics of complex 3D-CAD designs [86]. Another application is the benchmarking with the help of virtual products [96]. Krause et al. [58] used the virtual reality for the simulation and evaluation of complex assembly and disassembly processes. Furthermore different approaches are known to use the virtual reality for the visualisation of simulation results, e.g. crash-tests or flow investigation in a virtual wind tunnel [19], [58], [86]. In the field of the machine tool industry Tönshoff et al. [99] used the virtual reality for the visualisation of NCprogramming simulations in combination with a force feedback for a realistic impression for the user. Weck et al. [23], [108] developed an automated visualisation environment for the evaluation of the machine kinematics as well as for the results of Finite-ElementAnalysis. Figure 23 illustrates the VR-environment for the investigation of machine tools. Import of the FE-Data Extraction of the components Triangulation of the FE-mesh
geometric update of the workpiece as the tool cuts the material at each NC block. In addition, the solid model of the machine tool, its multi-axis kinematics and the location of fixtures can be displayed in the CAD environment [127], [128]. 3.1 NC-path simulation The present technology allows the prediction of tool collision spots and correctness of the NC program by checking path errors and gauging on the workpiece surface graphically. Lauwers et al. [59], [60], [61], [62] take the CL file from the CAD system and simulate the machine motion by modelling the kinematics of the machine tool for collision detection and avoidance. In some commercial controllers machining simulation systems are integrated. During machining the simulation system runs a number of blocks (e.g. 100) ahead, and if there is a danger of a collision, the controller stops the machine immediately, see Figure 24. Postprocessor module CLDATA file
x,y,z,i,j,k
Kinematics engine
NCformatting
NCsimulation
Collision avoidance
NCprogram
N100 G01 X..Y..Z..A..B..
Workpiece
VR-Cave
Tool Collision Area Clamping Table
Machine Head
Part Program Tape
Derivation of the kinematics ball screw drive (kx)
movement in x direction
3D-Visualisation in the “VR”
Figure 23: VR environment for the investigation of machine tools. This environment enables the engineer to import FiniteElement-Models of machine tools. The software automatically extracts the single structural components and enables the engineer to get a realistic impression of the design.
Look Ahead Distance
guiding system (ky , kz, ϕx , ϕy , ϕz )
Section Index
Section Index
N 50 G17 N 55 F1000 S1000 G00 X-364.94 Y-61.67 Z150. M03 N 60 G00 Z100. N 65 G01 Z0. N 70 X-359.94 Section being N 75 G03 X-324.94 Y-26.67 I0. J35. machined N 80 G01 Y84. N 85 G03 X-346. Y105.06 I-21.06 J0. N 90 G01 X-506.303 N 95 G03 X-513.416 Y87.887 I0. J-10.06 N 100 G01 X-480.648 Y55.118 N 105 G02 X-477.729 Y48.678 I-7.099 J-7.099 N 110 G03 X-460.589 Y-115.162 12134.642 J140.491 N 115 G02 X-463.428 Y-123.689 I-9.938 J-1.428 N 120 G01 X-487.625 Y-147.887 N 125 G03 X-480.511 Y-165.06 I7.113 J-7.113 N 130 G01 X-357 N 135 G03 X-324.94 Y-133. I0. J32.06 N 140 Go1 Y-22.33 N 145 G03 X-359.94 Y12.67 I-35. J0. N 150 G01 G40 X-364.94 N 155 G00 Z100. N 160 G00 X204.94 Y-34.755 N 165 G01 Z0. N 170 G41 X199.94 Section being N 175 G03 X164.94 Y-69.755 I0. J-35. Simulated N 180 G01 Y-162.132 N 185 G03 X167.887 Y-169.246 I10.06 J0. N 190 G01 X185.557 Y-186.916 N 195 G03 X190.362 Y-186.519 I2.234 J2.234 N 200 G01 X193.875 Y-181.606 N 205 G02 X195.145 Y-180.149 I8.167 J-5.84 N 210 G01 X206.296 Y-169.61 N 215 G02 X231.316 Y-166.338 I14.767 J-15.625 N 220 G02 X243.573 Y-174.115 I-50.726 J-93.491 N 225 G03 X248.965 Y-175.801 I5.152 J7.011 N 230 G03 X251.841 Y-174.534 I-0.115 J4.158
CNC
Figure 24: Integration of NC-simulation and controller. 3 SIMULATION OF THE CNC SYSTEM The CNC system consists of a computer, power electronics components, such as motor amplifiers and electronic circuits, and servo actuators. The computer control unit receives ISO standard NC-programs which describe the tool path geometry, tool number, feed and spindle speed at each path segment [72], [76]. Simulation of the CNC system involves virtual modelling of the machine tool kinematics and feed drive dynamics, update of the workpiece geometry as the material is removed and motions of the drives and auxiliary units, such as tool and pallet changes. In short, the rigid body motion of the machine tool and the CNC functions must be predicted as the workpiece is produced in order to realize a Virtual CNC system. Once the NC Program is generated in a CAD/CAM environment, the present Virtual CNC technology allows the
However, a realistic simulation of machine tool motion and accurate prediction of final part geometry requires the inclusion of real time trajectory generation, dynamic behaviour of actuators under axis control laws and cutting process disturbances. The architecture of the tool motion processing sequence in a typical CNC system is given by Altintas [4], [41], [42] [43], see Figure 25. The path segment is broken into discrete position commands as a function of jerk, acceleration and feed speed by the trajectory generation algorithms of the CNC. Here, it is important not to violate jerkacceleration and speed of individual drives which participate in moving the tool along the specified path. If the axes limits are violated, the saturation of the actuators may cause deviations from the commanded path as well
as feed fluctuations which lead to poor surface finish marks on the workpiece. Smooth trajectory generation, especially in multi-axis contour machining of sculptured surfaces, are still subject to intensive research for high speed machining of dies, molds and aerospace parts to achieve good surface finish [41]. CAD Model
Tool path geometry
CL/APT File
CAD/CAM Software
N1 G00 X5 Y1 N2 G01 X3 Y3 N3 G03 X4 Y3 I1 J0 ...
y
Interpreter
y
y
s
s x
x
x
Trajectory Generation Position closed-loop
S Displacement
t
.
S
Feedrate
..
S Re-process
...
S
Acceleration Jerk
t t
Reference position
Axis Control Law
.
r(t), r (t), .. ... r (t), r (t)
Control signal
t Feedback
Optimization Process -Reschedule Feedrate, Accel./Decel., Jerk Limit -Contour Error Reduction
Predicted Trecking Error
+ -
Feed Drive Servo
Feedback Measurements
Actual Position
Actual Position
Feed motion planning
Simulate the contour errors generated from Servo Control in „Virtual“ enviroment
Figure 25: Virtual model of trajectory generation and control of axes positions. There has been research activities to integrate machine motions and geometric removal of the material from the workpiece so that the part accuracy can be predicted ahead of actual production. Altintas et al. [127], [128] developed a reconfigurable, modular Virtual CNC simulation system by porting the experimentally proven real time algorithms from an actual open CNC. Ball screw or linear motor driven feed drives can be defined by specifying mechanical dimensions, servo motor and amplifier parameters, position-velocity-acceleration sensors and their resolution, friction field between the guide and drives and time varying cutting force disturbances. The type of trajectory generation algorithm, such as “jerk continuous with actuator limits”, can be selected as well as the axis control law. Experimental Result
spots on the workpiece and the correct cycle time, by including acceleration and deceleration, as well as the time history of axes tracking errors and the accelerationvelocity-displacement of each drive. The Virtual CNC has built in auto-tuning of control laws, and they are currently extending the CNC to 5 axes systems and integrating structural dynamic models of the feed drives to the virtual CNC system [127]. An experimentally verified simulation of a tool path within the Virtual CNC is shown in Figure 26 for a spiral part. The green zones represent the tolerance violations caused by the contouring errors of the CNC [128]. Pritschow et al. [76], [77], [78] presented the simulation of an entire machine behaviour under real CNC system control. The actual CNC sends time stamped position commands to a model of the complete machine. Since the position commands contain velocity, acceleration and jerk, they excite the structural dynamics of the machine. The resulting vibrations are sent back to the CNC by mimicking an encoder measurement contaminated with machine tool vibrations. 3.2 Optimisation of NC-Programs for five-axis milling While it is satisfactory in three-axes machining to generate NC-programs without considering the axial-specific dynamic parameters, practical experience has shown that it is insufficient for five-axis milling. The reasons for this are the highly variant dynamics of the involved rotation, panning, and translation axes [32], [119]. The analysis of NC-programs on different machines with respect to the required axis velocity and acceleration shows that, at positions with high feed rate drops, the dynamic limits of the rotation axes have to be considerably higher in order to follow the programmed path. This discrepancy arises because the CAM-system does not consider the dynamic capabilities of the machine while generating NC-tool paths. Weinert et al. [32], [119], [120], [129] developed an approach for the harmonisation of the rotation and swivel movements. As an intermediate step between CAMprogramming and the milling process, the tool path is adjusted, so that at no time are the limits of the dynamic capabilities violated. The principle of this approach is illustrated in Figure 27. Feed
Simulation
Workpiece
Workpiece
Figure 27: Adjustment of tool movement to satisfy dynamic limits of the five axis machine tool.
Figure 26: Simulation of milling a spiral part on virtual CNC with marked tolerance violations caused by CNC. The virtual CNC reads the CL file imported from the CAD/CAM system, and processes the NC program by simulating the physical behaviour of the machine in the prescribed CNC model. It predicts the tolerance violation
In addition to the general dynamic parameters especially the control-specific characteristics, which describe the behaviour of consecutive NC-steps, are considered. The manipulation of axes setting values may cause in principle an originally collision-free NC-program to contain collisions between tool, tool holder, machine components, and workpiece. To prevent this, in addition to the optimisation algorithm, a process simulation is used, which calculates the intersection of the involved objects during a movement along the NC-path on the basis of a volume model [32], [120], [129].
4 SIMULATION OF METAL CUTTING The manufacturing process research should lead to improved design of tools, machine tool structures, spindle and feed drives and the optimal planning of individual machining operations based on physical constraints. The research activities and industrial applications of metal cutting process simulation are presented in the following sections. The amplitude and frequency of cutting forces, torque and power are used in sizing machine tool structures, spindle and feed drive mechanisms, bearings, motors and drives as well as the shank size of the tools and the fixture rigidity. The stress and temperature field in the cutting tool edge, chip and finished work piece surface are used in designing the cutting edge shape as well as in optimising feed, speed and depth of cut to avoid residual stresses on the finished surface. Modelling the interaction between the cutting process and structural vibrations of machine tool, cutting tool and fixture leads to the identification of weak links in the machine structure and to the determination of chatter vibration free spindle speeds and depths of cut [5]. The complete model of the machining process is therefore used in both design of cutting tools and machine tools, as well as in planning of machining operations for maximum productivity and accuracy. 4.1 Analytical modelling of cutting processes The first step is to model the cutting process as a function of work material, tool geometry and material, chip load and cutting speed. The macro-mechanics of cutting lead to the identification of cutting coefficients, which are used in predicting the cutting forces, torque, power and chatter stability limits for a specified tool geometry and work material. The cutting coefficients can be modelled using either orthogonal cutting mechanics or mechanistic models [6]. The micro-mechanics of metal cutting on the other hand, are used to predict the stress, strain and temperature distribution in the chip and tool. This simulation results are primarily used for tool design, the analysis of material behaviour under high strain and temperature, and optimal selection of chip load and speed to avoid tool chipping, tool wear, and residual stresses left on the finished surface. The directions of cutting forces in turning and milling are given in Figure 28 [8].
dFa
Y n
dFr Z
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dFt
Fr
Fa
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f
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n
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f c
φex
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Figure 28: Prediction of cutting forces for turning and milling operations.
The major cutting forces (Ff) act in the direction of cutting speed, followed by the thrust force (Fr) acting in the direction of chip thickness and the axial force (Fa). The cutting forces are proportional to the instantaneous chip area which is expressed as a product of depth of cut (a) and uncut chip thickness (h). The cutting forces are typically expressed by shear (Ftc, Frc, Fac) and flank contact/ploughing (Fte, Ffe. Fae) edge components as Ft = Ftc + Fte = K tc ah + K te a Fr = Frc + Fre = Krc ah + Krea Fa = Fac + Fae = K ac ah + K ae a
where the chip shearing, cutting force coefficients ( Ktc, Krc, Kac) can be expressed as a function of tool’s rake angle, work material shear stress and average friction coefficient between the chip and tool rake face. The edge force coefficients (Kte, Kre, Kae) are found from cutting tests by extrapolating the measured forces at zero cut thickness (h = 0) intercept. The theory of this approach of analytical modelling of the cutting process can be found in [8]. It is also customary to use nonlinear cutting force coefficients as proposed by Kienzle [55]: Ft = K t ah Fr = Kr ah Fa = K a ah
where the cutting force coefficients (Kt, Kr, Ka) are usually expressed as a function of rake angle and chip thickness. It is most important to have a cutting coefficient data base which allows the user to select a work material for a variety of tool geometries. 4.2 Numerical simulation of cutting processes For cutting processes involving geometrically defined cutting edges, high speed cutting (HSC) is widely used in aerospace, and the die and mold machining industry. High speed machining allows the operation of machine tool spindles in large stability pockets where deeper cuts are possible. While keeping small chip loads to avoid thermal overload of the tool edge and mechanical overload of the spindle power limits, high material removal rates can be achieved with high spindle speeds and table feeds while maintaining a good surface finish on the part. However, the practical application of HSC methods depends on empirical cutting data which has to be obtained through cost- and time-consuming cutting experiments. The Finite-Element-Method (FEA) is a tool that is suited for optimisation of the cutting edge geometry and material. Hence the cutting edge can withstand high thermal and impact loads during machining [29]. Finite-ElementAnalysis belongs to the class of micro-mechanics of metal cutting and is widely used by the cutting tool industry. However, the key bottle neck is to model the flow stress of the work material reflecting high strain, strain rate and temperature experienced in metal cutting processes. The thermo-plastic properties of the material is usually evaluated under high strain rate conditions using either Orthogonal Cutting Tests or Hopkinson Bar tests [67]. Three main methods of mechanical formulation are commonly used in Finite-Element-Modelling of metal cutting [12], [122]: •
Eulerian formulation, where the grid is not attached to the material, is computationally efficient but needs the updating of the free chip geometry [55].
•
Lagrangian formulation, where the grid is attached to the material, requires updating of the mesh (remeshing algorithm) or the use of a chip separation criterion to form a chip from the workpiece [97].
•
Arbitrary Lagrangian Eulerian (ALE) formulation, where the grid is not attached to the material and it can move to avoid distortion and update the free chip geometry [67].
A 3D FEA-Simulation of a Milling Process [85], [122] is presented in Figure 29. 3D CAD-Model
FEA-Model
The geometric model of the part, blank and NC tool path in the form of a standard CL file are imported from current CAD/CAM systems using IGES or STEP NC standards. The cutter – part intersection along the tool path is evaluated at feed rate increments using solid modelling techniques. The intersection geometry is required to solve machining process simulation algorithms [93]. The machining process simulation engine is based on the laws of metal cutting mechanics and dynamics, it pulls the required machine tool and work material parameters from the data base and predicts the cutting forces, torque, power, static and dynamic deformations of the machine tool-part-fixture along the tool path. For a given set of constraints, such as maximum power-torque-dynamic stiffness of the machine and chip thickness limit of the cutting edge, the speed and feed can be optimised to maximise the material removal rate. CAD MODEL
Tool, Material, Machine-Tool Data Base
NC Tool Path Cutter Geometry
3D Simulation of a Milling Process
Cutter-part intersection calculations
FINAL PROCESS PLAN Optimized Speed, Feed, Depth, Width, Error Compensation
Virtual VirtualMachining Machining process simulation MONITORING MONITORINGAND AND CONTROL CONTROLDATA DATA
PATH PLANNER CL File
Path Strategy Analysis
Peak force, torque, power, tracking error, modal frequencies
M A C H I N E T O O L
Figure 30: Virtual machining process simulation and optimisation architecture. Figure 29: 3D FEA simulation of a Milling Process. A successful simulation is dependent on the accurate knowledge of the boundary conditions and the materialbehaviour which is different from simple metal models obtained from tensile tests due to the influence of large strain, strain rate, and temperature. In order to achieve an accurate prediction of chip flow, stress and temperature distribution within the chip and tool, an accurate model of flow stress of the material and friction between the rake face of the tool and chip is absolutely necessary. The validity of all numerical models is proven experimentally by comparing predicted forces, average shear angles and shear stresses in metal cutting tests. 5
INTEGRATED SIMULATION OF MACHINE AND PROCESS Current NC tool path and machining simulation systems consider only the rigid body kinematics of the machine tool, and do not take the physics of the machining process into consideration. The magnitude of cutting forces, torque, power and thermal energy produced during machining depends on the tool geometry, structural dynamics between the workpiece and the tool, work material properties, and cutting conditions such as feed, speed and depth of cut. Currently, the cutting conditions are selected from either tool manufacturers’ handbooks or experience, which may or may not lead to productive and accurate production of parts. The objective of next generation CAM systems is to include the physics of manufacturing processes in order to produce the first part accurately and optimally. A sample architecture for Virtual Machining Process simulation was proposed by Altintas et al. [2] as shown in Figure 30.
Although intensive research efforts are under way at present, there are several key requirements, that have to be met before a virtual simulation of the machining process can be realised. The cutter-part intersection along the feed increments requires intensive computational time since the part geometry must be updated as the material is removed at feed increments [45]. Researchers used Constructive Solid Geometry – CSG [93], Boundary Representation – Brep [51], and z- buffer techniques to model material removal [15]. The computational time is rather unaffordable and long at the present time, and considerable research efforts are directed towards developing efficient computational models and parallel processing of algorithms at multiple central processing units (CPUs). Although some commercial NC Simulation systems provide feed optimisation, their algorithms are not based on the laws of cutting mechanics, hence they do not represent the true process. However, considerable effort has been undertaken to integrate the true process physics into NC program optimisation. Altintas and Spence presented a 2 ½ axis end milling process simulation system [94]. Altan et al. [15], Spence et al.[95], Weinart et al. [118], [121] and Lazoglu et al. [26] presented a process simulation and optimisation strategy for dies and molds. They illustrated that the machining cycle time can be decreased significantly by scheduling feed rates along the tool path while respecting tool deflection, tool breakage, torque and power limits of the machine tool. Altintas et al. [7] presented algorithms which can handle arbitrary cutter shapes in predicting the forces, torque, power and chatter vibrations during milling.
Kapoor and Devor [40], Armarego et al. [11] and a number of researchers presented mechanics of cutting models to predict the cutting forces for milling, turning, drilling, boring and tapping operations. The aim of the present research is to integrate the mechanics of machining into a CAD/CAM system so that the process of machining a complete part can be simulated as shown in Figure 31 [51].
... N9 X-8.0056 N10 X- 7.9655 Y49.3901 N11 X-6.3125 N12 G3 X28.2708 Y49.1355 I17.3496 J7.7454 N13 G1 X42.8735 N14 G3 X102. Y- 7.5278 I67.1265 J10.8645 N15 G1 Y-8. N16 X23.083 N17 Y-3.2 N18 Y1.6 ...
Spindle nose
Cutting Tool Toolpath Workpiece
Tool Toolholder Housing
4 FRF-Magnitude[m/N]
NC Code:
material removal rate at the desired speed is preferred for dedicated machine tools for mass production of parts like in the automotive industry. Once the spindle is designed, its performance can be tested in the virtual environment by applying cutting forces at the tool tip. The results are illustrated in Figure 32.
x 10
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-8
Experiment Simulation
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Figure 31: Simulation of virtual machining of a part with features.
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Figure 32: FRF-Simulation of a spindle. The stiffness changes and contact forces at the bearings and static and dynamic displacements along the spindle shaft assembly can be simulated instead of manufacturing and testing the spindle on a real machine which is a lengthy and costly process. Figure 33 shows the optimisation of the bearing locations to achieve maximum depth of cut at 9000 rev/min spindle speed for a four fluted end mill machining aluminium alloy, and a simulation of bearing contact loads during milling with the same tool [10]. The spindle was unstable at the desired spindle speed of 9000 rev/min before the optimisation of bearing locations.
Depth of cut [mm]
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5.1 Simulation of chatter vibrations in cutting The dynamics of the machine tool have a major influence on the productivity of machine tools. The designers must consider the interaction between the process and the structure in the virtual environment so that the optimal dynamic stiffness is achieved during the design stage of the machine and spindle system [9]. While the major parts of the machine tool, such as column, headstock and table dynamics influence the stability of low speed machining with large cutters, the stability of high speed machining is usually determined by the dynamic behaviour of the spindle-bearing-system and the tool. The dynamic stiffness of the spindle-bearing-tool assembly can be improved by optimising the locations of the bearing and direct drive motor along the shaft [63]. Typically, a Finite Element model of the prototype spindle is modelled by including kinematics of the angular contact bearings, speed effects and preload. The validity of the Finite Element model is tested experimentally, and the mathematical model is improved until realistic results are obtained. Only the damping ratios of the spindle are borrowed from the measurements collected from past spindle designs, since it is not possible to predict the damping analytically. The FE model as well as the predicted and measured Frequency Response Function of a sample spindle are given in Figure 32 [27]. The locations of the bearings are automatically optimised either to achieve maximum dynamic stiffness in all major natural modes, or a stable stability pocket is created at the desired speed for a given spindle and cutting tool pair, see Figure 32 [63]. While maximising the dynamic stiffness is preferred for machines which need to use multiple tools, maximum
Bearing
3
0
Similar to machining, the process forces during forming and grinding have also been studied, however mainly for process and machine design purposes. The goal of virtual production is to integrate all steps of the manufacturing cycle into the simulation environment in order to achieve a true digital factory.
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After cutting force is applied
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Figure 33: Virtual design and testing of spindles.
Modular FE-Model of the Spindle-Bearing-System
Stability Simulation GFxx
G [µm/N]
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Fx(t)
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Figure 34: Simulation of stability chart for the milling of aluminium aerospace parts. For HPC processes of aluminium parts typically tools with two or three cutting edges are used which are characterised by a time varying behaviour. In this case the time varying behaviour is caused by the change of the cutting force direction. For this reason time domain simulation techniques are used for the simulation of the stability of the cutting process. For this simulation the simulated dynamic behaviour of the spindle-bearing-system is used as an input. With the help of this simulation chain the theory of the stability behaviour of cutting processes which is known for a long time becomes applicable for end-users in the area
of HPC, i.e. the manufacturing of aluminium parts for the aircraft industry with high material removal rates. Different results of a simulation of stability lobes for the HPC machining of aluminium are shown in Figure 35. Bad Correlation between Measurement and Simulation 15
Stable Process Chattering Experimental Results Simulation
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Another example is shown in Figure 34 where the spindle and tooling are specifically designed to machine aluminium aerospace parts [22], [117]. The stability of HPC-processes with high spindle speeds is mainly determined by the dynamic behaviour of the spindle-bearing-system and the tool. In this case the dynamic behaviour of the structural components of the machine tool is of secondary importance. But especially the static and dynamic flexibility of spindle-bearingsystems for high rotational speeds up to 30,000 min-1 can hardly be optimised, because an increase of the spindle diameter is limited by the kinematic and thermal behaviour of the spindle bearings. However, the process stability can be significantly improved by a selective setting of the machining parameters. In particular, the variation of the spindle speed according to so-called stability charts is an effective method to enhance the performance of machining processes. Stability charts can either be determined experimentally or they can be calculated on the basis of the dynamic flexibility behaviour given in the form of a flexibility frequency response function. Due to the fact that the experimental measurement of the dynamic behaviour of a spindle-bearing-system for each tool is very time-consuming, a simulation software was developed to calculate the flexibility frequency response function of spindle bearing systems on the basis of a beam FEA model. The bearings are modelled as springdamper-elements in the FEA model. It is useful to match the stiffness and the damping parameters of the bearings with the results of a measurement for one spindle tool configuration. Using this matched model the dynamic flexibility behaviour even for a large number of different tools can be determined efficiently without timeconsuming measurements [117]. With a supplementary program for the simulation of the stability behaviour of milling processes a complete simulation chain for the calculation of stability lobes is available, as illustrated in Figure 34.
179
Figure 35: Simulated and measured stability lobes for the HPC machining of aluminium. As illustrated in Figure 35 the results of the stability simulation of high performance processes often have variations in the accuracy. An essential part of the cutting process and stability simulation are the time varying direction factors diFj, which project the forces at each cutting edge into the machine coordinates. Furthermore, the cutting forces are determined by the cutting force coefficient kcb and the cutting depth b. The theoretical background of these effects are still unexplored for HPCmachining processes and requires intensive investigations [117]. 5.2 Frequency and Time domain simulation of machine tool and process The simulation of machining process is done in two modes: rigid or flexible models of the machine tool. The rigid simulation does not consider the interaction between the machine structure and the cutting process, hence the predicted cutting forces, torque and power only can be used for basic process planning of the machining operations. As discussed earlier, the cutter – part intersection along the tool path must be identified at feed rate increments for the process simulation [2], [3], [28]. However, in realistic process planning as well as machine tool/spindle/tool design, the relative elastic displacements between the cutting tool and part must be considered. The vibrations lead to changes in the chip thickness, which in turn vary the cutting forces that excite the structure. If the process becomes unstable with chatter vibrations, the cutting load on the machine may grow a few times more than the rigid case and leads to poor surface finish, short tool life and damage on the spindle/machine structure [6]. While Frequency Domain chatter stability solutions provide a direct relationship between the dynamic stiffness of the machine and the process, the time domain simulation allows prediction of dynamic cutting forces and dimensional surface errors for complex tools and processes while machining a specific part under defined cutting conditions. A sample prediction of stability lobes in both frequency and time domain for an indexed cutter milling aluminium alloy is shown in Figure 36. The simulation also shows predicted and experimentally measured dimensional form errors at one specific cutting condition [7].
Stability Lobes for Bull Noes Cutter and Al7075 6
Analytical Time domain
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machine tool and process has to be carried out in time domain. The aim of the research project SindBap is to develop an approach for the integrated simulation and optimisation of industrial processes [25]. This co-operative project is founded by the German Federal Ministry of Education and Research. For the integrated analysis and optimisation of industrial production processes time domain simulation models of the process and the machine tool as well as the workpiece are coupled. The cutting forces cause a relative displacement between tool and workpiece which changes the instantaneous chip area which affects the cutting process again. This approach enables the investigation of effects of the machine tool, the workpiece and the process. Denkena et al. [33], [35], [37], [101] developed the cutting simulation system CutS which combines different simulation environments, see Figure 38. The approach for a coupled simulation of the manufacturing process is to combine separate simulation models via interfaces and also to include the supporting software tools, e.g. FEA-systems for the simulation of the manufacturing process [36].
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Figure 36: Chatter stability, force, vibration and surface error prediction in milling. The details of the chatter vibrations for metal cutting and grinding are given by Tlusty [98], Altintas et al. [9], and Inasaki et al. [52] in previous CIRP key note papers. Nowadays the simulation of single processes or machine characteristics is state of the art. Generally, these simulations are carried out separately for the process as well as for the ma-chine tool. Interactions between machine tool, workpiece and process cause variations of the tolerances and characteristics of the workpiece, which are not taken into account by common simulation approaches [25]. It would be of great economic interest for the design of machine tools as well as for process planning if the resulting quality of the workpiece was predictable prior to the start of production. The principle procedure of an integrated Simulation of machine tool, workpiece and process in time domain is shown in Figure 37. CNC G01 x100 y50 z10 G01 x101 y50 z10
S
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& S
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Integrated Simulation of Machine Tool, Workpiece and Process
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t
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result
Process Stability Depth of Cut [mm]
reference
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Figure 37: Integrated simulation of machine tool, workpiece and process. An analysis and optimisation of the production process is only possible if all interactions between machine tool, workpiece and process can be simulated accurately. Due to its time-dependent behaviour the simulation of the
Data exchange
Figure 38: Environment for the coupled simulation of machine tool and process. The advantage of such an architecture is a relatively simple exchangeability of single simulation sub models. Through variation of model parts, modelling and calculation techniques the possibility of studies concerning model complexity and extent is given. Due to the nonlinear system behaviour, the simulation has to be solved in the time domain [37], [101]. The data flow of such a coupled simulation is shown in Figure 39. Input data for such a system in general is the NC-code derived from a CAM-system which is converted in the virtual NC-kernel. In this part simulation, nominal values for the drives of the machine tool are generated. The simulation module of the Control/Drives generates the force of each drive which act on the model of the machine tool [37], [101]. The process forces are applied on the machine structure which results in a displacement at the tool centre point. This displacement changes the instantaneous chip area which leads to changed cutting forces. To simulate these interactions between machine tool and process, forces and displacements are exchanged via interfaces at each simulation step between the modules [37], [101].
For the simulation of the manufacturing process either an analytical, an empirical or a semi-empirical approach can be integrated.
Rough Part
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To realise the industrial application of the integrated simulation of workpiece properties both the simulation of the machine process interaction and the simulation of the workpiece properties have to be improved in the future.
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Integrated IntegratedSimulation simulationand andOptimisation optimisationofofthe theProcess processChain chain Forging
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Figure 40: Scenario of a simulated process chain. Figure 39: Principle approach for the coupled simulation of cutting process and machine tool. Machine process interaction is facing the challenge to increase the speed of both the single simulation models and the data exchange. Apart from the uncertainties within the separate simulation modules concerning e.g. damping in machine tools [38] or the material parameters for cutting [39], the main problem is the high amount of calculation operations in the material removal scenario due to the high resolution of the material removal scenario. 6
RESEARCH CHALLENGES: “THE VIRTUAL WORKPIECE PRODUCTION” As mentioned above the interaction between machine tool and manufacturing process causes variations of the characteristics of the workpieces. It would be of great economic interest for the design of machine tools as well as for the design of single processes or complete process chains if the resulting quality of the workpiece was predictable prior to the start of production. The aim is to determine the ideal process parameters for each step of the process chain to fulfill the required tolerances and characteristics of the workpiece. Especially for the large-volume production and the production of extremely complex, or very expensive components, the simulation and optimisation of single processing steps as well as the complete process chain is of particular importance. Nowadays different simulations of single processes and machines are state of the art in many industrial fields. The quality of the simulation result depends on the respective standard of knowledge. Many research activities today concentrate on the coupled simulation of manufacturing process and machine tool, but without any industrial application. Until now the integrated simulation of the interaction between machine tool, manufacturing process, workpiece, fixture, and the history of the single manufacturing processes is not realised. Thus it is not possible to simulate the workpiece quality in consideration of the individual steps of an industrial process chain. A possible scenario of a simulated process chain for the manufacturing of a gearshaft is shown in Figure 40.
For an integrated modelling of the machine tool and manufacturing system, first research studies exist. The necessary further developments are integrated methods, improved models for machine tools, processes, workpieces, clamping systems, controls and tools as well as models for the entire process chain. 7 CONCLUSIONS The aim of virtual machine tool engineering is to design, test, optimise, control and machine parts in a computer simulation environment. The machine is designed in a CAD environment. The CAD model is exported to Finite Element system for the structural analysis of the machine tool statically and dynamically. The Finite Element model is reduced to a multi-body model of the machine which consists of rigid links connected via flexible springs. The rigid and flexible machine tool models are analysed under various jerk, acceleration, velocity and control profiles at high speeds. The interaction between the specific CNC control model and machine tool structure can be simulated, and either the machine tool or control system, or both, can be modified based on the simulation. The digital model of the machine tool is integrated to the numerical simulation of the cutting process, hence the machine tool can be tested to machine particular parts under desired cutting conditions. The present technology allows Finite Element, multi-body, kinematics and control engineering concepts. However, the virtual machine tool technology still requires fundamental research in the area of process simulation, integration of all analysis modules in a user friendly simulation program for the users. This goal is being rapidly realised by the research community at the present. 8 ACKNOWLEDGMENTS The authors wish to thank Professors Arrazola, van Brussel, Denkena, Fleischer, Groche, Klocke, Lauwers, Pritschow, Weinert and all colleagues and industrial companies who sent valuable contributions for the preparation of the article.
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[1]
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[2]
Altintas, Y., Spence, A., 1991, End Milling Force Algorithms for CAD Systems, Annals of the CIRP, 40/1:31-34.
[3]
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