Generative Process Planning with Reasoning based on Geometrical Product Specification Mariusz Deja1, a, Mieczyslaw Siemiatkowski1, b * 1
Department of Manufacturing Engineering and Automation,
Mechanical Engineering Faculty, Gdansk University of Technology, 11/12 Narutowicza Str., 80-233 Gdansk, Poland, a
[email protected],
[email protected] * corresponding author
Keywords: CAPP, generative methods, machining features, Geometrical Product Specification (GPS)
Abstract. The focus of this paper is on CAPP for parts manufacture in systems of definite process capabilities, involving the use of multi-axis machining centers for parts shaping and grinding machines for finishing. It presents in particular a decision making scheme for setup determination as a part of generative process planning. The planning procedure consists of two stages. The first stage is associated with generic setup determination applying tool accessibility analysis to machined features and reasoning based on geometrical product specification (GPS). The second stage involves machine specific setup planning considering the availability and capability of machines on a shop floor through setup merging. The impact of dimensional and geometric tolerance as well as the surface roughness of relatively complex mechanical element on the generated process plan with several setups was presented. The relevant reasoning mechanism within a decision making scheme on generated process alternatives is shown based on a numeric case. Introduction The problems of automated process planning have attracted a great deal of research attention and there exists an abundant amount of literature on this subject. Despite significant progress, particularly the issues related to the integration of CAD and CAPP systems still remain to be fully solved. Much of current research effort is devoted to features recognition that plays an important role in the integration of CAD, CAM and CAPP. The determination of proper machining precedence becomes more difficult when features being considered interact with each other, as clearly shown in [1]. The research work [2] in turn presents a scheme for recognising composite features with automatic process planning, involving precedence relations among features derived from CAD data model, considering their topological sorting. The problem of embedding, in applied CAD models, non-geometric technological information, such as: dimensional and geometric tolerance, surface roughness and hardness, and indispensable for CAPP, is discussed in e.g. [3, 4]. More importantly, decision making in process design for the conditions of current manufacturing practice, frequently might involve the use of uncertain and imprecise information. Dantan et al. in [5] propose a model for the communication of geometrical information which can come from design, manufacturing or inspection. This model takes into account, not only the specification on isolated parts, but also on related assemblies with the integration of a relevant quantifier concept. This research work is intended to present the extended solution of the developed framework [4, 6], including the additional, non-geometric technological information, typically found on the technical drawing, into the data input model. Consequently, the impact of dimensional and geometric
tolerance as well as the surface roughness of relatively complex mechanical elements on the generated process plan is further discussed. Input data structure with geometric dimension and tolerance (GD&T) model for generative process planning A conceptual framework for the generation of process alternatives in the form of a network is outlined in [4, 6]. It forms the basis for further development and includes a relevant hierarchical procedure proposed for creating such a network and extracting optimal process plan solutions from the viewpoint of their operational performance. The system utilises a matrix recording of input data sets that comprise machining system capabilities, process constraints and feature based product data model. The feature precedence relationships are coded within the feature precedence matrix FPM = [fij] i≤m, j≤n, where: m – the maximum number of required preceding features for a specific feature, n – the total number of features, with the strict correlation between the value of a single element fij and the location of the corresponding feature #j on the feature precedence tree [6]. The processing alternatives for individual part features are given in the form of the binary process capability matrix PCM = [pij] i≤k, j≤n, where: k – the number of available machining systems. The limitations related to the necessity of machining the datum feature in one of alternative systems beforehand to continue the machining process in a system with specific fixture type using this datum are defined in the unique constraints matrix for the availability of machining systems, and formulated as: CM = [cij] i≤h, j≤k, where: h – the maximum number of required preceding machining systems for a given machining system. The detailed heuristic procedure for the identification of the optimal or near-optimal process plan solution process selection and operation sequencing, with the analysis of the data written in FPM, PCM and CM matrices, is presented in [4, 6]. In mechanical engineering, functional requirements of a part are normally expressed by geometrical dimensions and tolerances, related to size, form, profile, orientation and run-out. It implies the need for the inclusion Geometric Dimension and Tolerance (GD&T) specification into informational data model for use in integrated procedure of design and process planning [7]. The Geometrical Product specification (GPS) assumed in this research is consequently based on relative degrees of freedom (DoFs) of geometric entities, such as: feature axes, edges, faces and so-called features-of-size (i.e. holes, pins, slots, pockets, bosses, etc.). Individual translational - (TDoF) and rotational degrees of freedom (RDoFs) related to parts machined may be constrained by datum feature references (DFRs) in an instance-adequate feature control frame [7, 8]. Meeting the criteria for compatibility with the ASME and ISO Y14.5 standard [9] the adopted GPS model enables capturing the designers’ GD&T scheme based on the part functionalities, and further transfer the scheme to machining features extracted automatically by feature recognition and consequently determine the GD&T of machining volumes. Thus, functionally coupled machining features might be included into separate group and machined in an individual part setup, based on appropriate DRF. The frame is determined by the functional relations in terms of tolerance types (classes) among the identified machining features understood as a reference coordination system selected to secure the location of other features in the same part component [8]. Decision-making scheme for setup generation in CAPP Following the assumptions made with regard to input data model formulation, and GD&T modelling in particular, a two-stage scheme towards setup determination as a crucial part of featurebased generative process planning is proposed (Fig. 1). The first stage of the hierarchical scheme comprises the analysis of part design representation in terms of features, including feature interactions, and performed in the environment of CAD solid modelling. The activity is aimed at clustering features into basically three-axis generic setups with determined primary locating direction It is achieved by application of machine-neutral feature-based reasoning according to the tool accessibility analysis of machining features, geometrical product specifications and machining
technologies. Critical sequence generation based on datum references and manufacturing constraints are also accomplished at this stage. The second stage in the proposed scheme is associated with generating machine-specific part setups, considering the availability and capability of machines on a shop floor. The task is performed under the consideration of the possibilities for setup merging across multiple available machines, as manufacturing a part may require in practice a few operations. Thus decision-making with generating the final solution for part setups and optimal or near-optimal process sequencing entails the detailed considerations of part fixturing strategies and machining constraints. In this regard the solution assumed in this research work is generally similar to the one reported recently by [10].
Machine - specific setup determination and process sequencing
Feature - based part design representation and related analysis
Input 3D CAD model with design specification
Feature recognition activity
GD&T data model specification and reasoning based on DoFs and DRF
Feature mapping by GD&T and their clustering into generic setups using tool access directions (TADs)
Determination of feature precedence relations by FPM matrix
Analysis of fixturing and machining constraints for setup merging
Assignment of machining features (volumes) to machine - related setups using CM matrix
Machining systems (machine tools,fixtures and tools) represented by PCM matrix Fixturing strategies
Generation of optimal / near-optimal process plan solutions
Fig. 1. The two-stage scheme for setup determination with GD&T based reasoning model in generative process planning Illustrative case study Methodical approach being under development underwent to numerical validation. An exemplary test part with 13 machining features was chosen for the case study of generic setup planning considering GPS specification (Fig. 2). The features of different types (e.g. F3) required one or two general feature states obtained after semi-finishing (e.g. F3s) or after finishing (e.g. F3f), depending on the individual specification (Tab. 1). Each machining feature includes the information of its ID and type as well as the tool access directions (TADs). In this case study, machining features F1s and F3s were selected as the primary locating surfaces machined at the beginning of
the process with the reference to features WPC-F2 and WPC-F6 located on the blank and related geometrically to features F2 and F6. Based on the GPS specification and process capabilities all the reference features were identified, what is presented in the last column of Tab. 1. That was necessary to assume the feature precedence relationships, coded within the feature precedence matrix FPM and to specify the processing alternatives (PCM matrix) with the fixture type (CM matrix). The application of the developed algorithm allowed generating the alternatives of manufacturing process, with the exemplary process plan with seven setups (Fig. 3). As mentioned earlier, a generic setup plan was created for three-axis machines. The second stage of the tested procedure for machine-specific setup plans generation assumes the occurrence of a CNC grinding machine apart from the multi-axis turning centre (with z, x and C controlled axes), while considering possible setup merging across machines. b)
a)
y
x
z
c)
Figure 2: Analysed real work part (a); a solid model of the part with indicated geometrical axes (b); engineering drawing with geometrical and selected technological requirements, along with the specification of machining features under consideration (c)
Table 1. Machining features of the test part in an illustrative case Feature ID/ type
Feature state
Tool access direction
Reference features
F1/Face
F1s
(0, 0, -1)
WPC-F2
F2/Face
F2s
(0, 0, 1)
F1s, F3s
F3s
(0, 0, -1)
WPC-F6
F3f
(-1, 0, 0)
F6s
F4s
(-1, 0, 0)
F1s, F3s
F5s
(0, 0, -1)
F3s
F5f
(-1, 0, 0)
F3f
F6s
(0, 0, -1)
F3s
F6f
(-1, 0, 0)
F3f
F7/Cylindrical pocket
F7s
(0, 0, -1)
F1s
F8/Tapped hole
F8s
(0, 0, -1)
F7s
F9/Tapped hole
F9s
(0, 0, -1)
F7s
F10s
(0, 0, -1), (0, 0, 1)
F3f, F7s
F10f
(-1, 0, 0)
F10s
F3/Cylinder F4/Groove F5/Cylinder F6/Cylinder
F10/Through hole
F11s
F11/Eccentric boss
F10f, F2s
(0, 0, -1)
F11f
F10f, F11s
F12/3-sided pocket
F12s
(0, 0, -1)
F10f
F13/3-sided pocket
F12s
(0, 0, -1)
F10f
//
Setup-2
F2s
Setup-7 Setup-5
F1s
F7s
Setup-6 F12s
F8s
F10s
F10f
F11f F13s
F9s
F4s
F11s
F9s F9s F5s F5f
F3s F6s
F3f F6f
Setup-1
Setup-3 Setup-4
– datum reference features; light grey shaded areas mean consecutive setups and dark grey shaded areas groups sharing the same tool types
Fig. 3. Results of generic setup planning for the exemplary part type considering GPS specification Concluding remarks Generating the final solution for part setups and optimal or near-optimal process sequencing entails the detailed considerations of part fixturing strategies and machining constraints. This research work is intended to present the extended solution of the developed framework for the
generation of process alternatives in the form of a network, including the additional, non-geometric technological information, typically found on the technical drawing. The impact of dimensional and geometric tolerance as well as the surface roughness of relatively complex mechanical element on the generated process plan with several setups was presented. For the generation of the process plan, a modified developed algorithm analysing data sets related to geometrical part specifications and some non-geometric technological information, has been used. The usability of the approach is studied numerically with regard to a case study of a complex part machined derived from the food processing industrial practice, with the use of multi-axis centres and grinding machines, equipped with relevant fixtures, as far as the technological specification was concerned. The proposed system is appropriated for prismatic and rotational part components. The experimental results reveal its capability of identifying the machining process design, considering actual production conditions with regard to the constraints of a fixturing strategy, and the availability of a specific tool set. References [1] Z. Liu, L. Wang, Sequencing of prismatic machining features for process planning, Computers in Ind. 58 (2007) 295-303. [2] C. Gologlu, Machine capability and fixturing constraints-imposed automatic machining set-ups generation, J. of Mater. Proc. Technol. 148 (2004) 83-92. [3] H. Paris, D. Brissaud, Modelling for process planning: the links between process planning entities, Robotics and Comput. Integr. Manuf. 16 (2000) 259-266. [4] M. Deja, M. Siemiatkowski, Generation of optimal process plan alternatives for manufacturing mechanical components, Solid State Phenomena 165 (2010) 250-255. [5] J.Y. Dantan, A. Ballu, L. Mathieu, Geometrical product specifications–model for product life cycle, Computer-Aided Design 40 (2008) 493-501. [6] M. Deja, M. Siemiatkowski, Feature-based generation of machining process plans for optimised parts manufacture, J. Intell. Manuf. 24 (2013) 831-846. [7] Z. Shen, J.J. Shah, J.K. Davidson, Analysis neutral data structure for GD&T, J. Intell. Manuf. 19 (2008) 455-472. [8] J.J. Shah, Y. Yan, B-C. Zhang, Dimension and tolerance modeling and transformations in feature based design and manufacturing, J. Intell. Manuf. 9 (1998), 475-488. [9] ASME Y 14.5, Dimensioning and tolerancing. An International Standard, The American Society of Mechanical Engineers, ASME Intl. 2009. [10] L. Wang, N. Cai, H.-Y Feng, J. Ma, ASP: An adaptive setup planning approach for dynamic machine assignments, IEEE Trans. on Automation Science and Eng. 7 (2010) 2-14.
