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Procedia CIRP 2 (2012) 64 – 69

1st CIRP Global Web Conference: Interdisciplinary Research in Production Engineering

Digital Manufacturing Cell Design for Performance Increase A. Caggiano*, R. Teti Fraunhofer Joint Laboratory of Excellence for Advanced Production Technology, Dept. of Materials & Production Engineering, University of Naples Federico II, P.le Tecchio 80, 80125, Naples, Italy * Corresponding author. Tel.: +39 3289223274; fax: +39 081 7682362 E-mail address: [email protected] .

Abstract Digital simulation tools are jointly employed for the design of an existing aircraft engine components manufacturing cell to be enhanced through automated robotic deburring. The application of 3D Motion Simulation is illustrated for layout and material handling system design. Discrete Event Simulation is applied to analyze different scenarios and improve the cell performance with regards to two key objectives: (a) optimization of the batch throughput time for the part number fabricated in the manufacturing cell; (b) utilization increase of the automated deburring station by processing additional part numbers coming from other manufacturing cells in the same production department. 2012 The Published byPublished Elsevier BV. Selection and/or peer-review under responsibility of Dr. Ir. Wessel Wits W. Wits © 2012 Authors. by Elsevier B.V. Selection and/or peer-review under responsibility of Dr.W. Ir. Wessel Keywords: Manufacturing Cell, Discrete Event Simulation, 3D Motion Simulation, Digital Factory, Industrial Robot

1. Introduction Modern manufacturing systems should be capable to meet several challenges and satisfy requirements and constraints that rapidly vary over time. Following the concept of product life cycle, the concept of manufacturing systems life cycle has emerged in the last years. This approach considers the manufacturing system, and the entire factory, as a product characterized by several stages. These stages start from the initial system design, and proceed through the implementation, operation, and subsequent re-design/reconfiguration of the manufacturing system [1-3]. New market requirements and technology improvements impose very short time for manufacturing system design and reconfiguration in order to win the competition at global scale. This requires the critical examination of several alternative solutions to support optimal design decision making. In the literature, several analytical methods have been proposed over time and are currently employed [4-5]. However, as up-to-date manufacturing systems tend to be very complex, these

methods can be notably demanding in terms of computing time and resources. In the last years, Information Technology (IT) based approaches have become essential to handle this complexity and simultaneously reduce time and cost for manufacturing innovation and productivity enhancement by supporting several stages in the design, development, production and operation of novel manufacturing systems [6-7]. The recent IT-based concept of Digital Factory entails the employment of digital methods and tools as an instrument for low cost and fast analysis to investigate the complexity and evaluate the different configurations of a given manufacturing system [8-12]. Simulation techniques represent a central tool in the Digital Factory concept implementation, since they allow for the experimentation and validation of different scenarios and configurations for new as well as existing manufacturing systems design and performance evaluation [13-16]. There are several digital simulation tools to choose from, both during the design and the operation of a manufacturing system. Facility layout as well as material

2212-8271 © 2012 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of Dr. Ir. Wessel W. Wits http://dx.doi.org/10.1016/j.procir.2012.05.041

A. Caggiano and R. Teti / Procedia CIRP 2 (2012) 64 – 69

handling system selection and configuration can be supported by tools such as 3D Motion Simulation. System capacity, throughput analysis, and other relevant metrics can be evaluated through Discrete Event Simulation (DES), which is particularly helpful in order to investigate the manufacturing system performance [17]. In this research work, both DES and 3D Motion Simulation are jointly employed for the design development of an existing manufacturing cell. The latter cell is dedicated to the production of aircraft engine components and needs to be enhanced through a new automated robotic deburring station. 2. Case Study The examined case study consists of an existing manufacturing cell dedicated to the fabrication of a single aircraft engine component part number. On each component serial number, two successive grinding operations, called Phase 1 and Phase 2, are carried out, each followed by a corresponding deburring operation. The manufacturing cell is composed of the following elements: x A grinding machine tool provided with a loading/unloading robot x A Coordinate Measuring Machine (CMM) x A manual deburring station To date, deburring operations are performed manually by a human operator. However, this process requires notable experience, manual ability and mind concentration. An incorrect procedure or operator distraction can produce severe damages to the component. Since much material has already been removed and the component tolerances are quite tight, these damages cannot be eliminated by repair machining. As a consequence, the component is exposed to high rejection costs due to the expensive raw material as well as the significant processing already performed on it. Moreover, ergonomics considerations prove that manual material removal processes, such as deburring or polishing, can often determine worker's injuries that could be avoided by introducing a higher level of automation based on devices such as robots. To reduce these risks and simultaneously improve the manufacturing cell performance, an automated deburring station equipped with an industrial robot has been designed in order to be integrated in the manufacturing cell for the deburring operation. 2.1. Manufacturing cell new configuration In the new manufacturing cell configuration, the grinding machine and the CMM are associated to an

automated deburring station provided with a robot that performs deburring, inspection and transfer of components. The deburring station is composed of a rotating table for components input/output, an inspection post provided with a touch probe to verify component positioning with respect to the robot, a deburring device with several tools for different component features, and a tool changer for the robot gripper. Furthermore, a human labor is employed in the manufacturing cell to carry out assembly and disassembly of components and fixtures on the grinding machine input/output buffers and to transfer parts between grinding machine, CMM and automated deburring station. The new manufacturing cell elements are listed in Tab. 1 and the general layout is shown in Fig. 1. Table 1. Manufacturing cell elements. N.

MANUFACTURING CELL ELEMENTS

1.

Input Component Storage

2.

Component and Fixture Assembly Station

3.

Handling Robot

4.

Grinding Machine

5.

Rotating Table

6.

Handling/Deburring Robot

7.

Inspection Post

8.

Deburring Device

9.

Tooling Storage

10.

CMM – Coordinate Measuring Machine

11.

Output Component Storage

Fig. 1. Manufacturing cell layout.

3. Digital Simulation Tools for Manufacturing Cell Design The design of a new manufacturing cell configuration is typically carried out via a decision making process requiring several steps as numerous issues have to be taken into consideration. Digital simulation tools and

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their joint employment are very effective in this procedure, as they allow to deal with a number of aspects as diverse as facility layout, material handling system design, system capacity and throughput analysis. In this case study, two different digital simulation tools are utilized for the design of the manufacturing cell: 3D Motion Simulation and Discrete Event Simulation (DES).

correct modeling of the system of interest and the identification of the most significant hypotheses to investigate. The employment of DES tools can considerably reduce the time and cost required for decision making on cell design. In this case study, DES was employed to examine different scenarios for the manufacturing cell under study with the aim of improving specified performance objectives with the layout shown in Figs. 3 and 4.

3.1. 3D Simulation for manufacturing cell layout and robot motion With the aim to analyze the layout of the new manufacturing cell configuration, with particular attention to the automated deburring station, 3D Motion Simulation was employed. The robot selected for the deburring operations is the 6 axis ABB IRB 2400-16 robot with a payload of about 20 kg. The maximum reach is 1.55 m and the weight is 380 kg; its repeatability is 0.03 - 0.07 mm. A 3D model of the robot with the corresponding kinematics was obtained from a robot data base. The robot gripper was designed to handle the components by inserting two prongs in the available part slots. The kinematics modules of the 3D Motion Simulation software allowed to simulate robot kinematics and collision detection was employed to plan safe paths within the deburring station. Tasks were created for all the steps of the deburring station production cycle, from table rotation, to robot component grabbing, component positioning control, component deburring and final release. This simulation allowed to determine the distance required between the cell elements and the robot, and thus the overall dimension of the deburring station (the bounded area requires a maximum of 4000 mm in one direction and 4100 mm in the other direction). The layout of the manufacturing cell employed for the 3D Motion Simulation, including the grinding machine, the CMM and the automated deburring station, is shown in Fig. 2.

Fig. 2. 3D Motion Simulation model of the manufacturing cell.

Fig. 3. Top view of the manufacturing cell DES model.

3.2. DES for manufacturing cell performance analysis and optimization The design phase of the new manufacturing cell, beyond layout and material handling system configuration, involves the analysis and improvement of the system performance in order to achieve the desired outcome. Several mathematical models of different complexity have been developed for this purpose [4-5]. However, DES represents a valuable tool through which it is possible to study and analyze different what-if scenarios in a digital framework and with limited computational effort. On the basis of the performance indicators to take into consideration, the main task is the

Fig. 4. 3D view of the manufacturing cell DES model.

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3.2.1. DES for throughput time optimization The first aim was to evaluate different alternatives to improve the throughput time for a batch of the part number fabricated in the manufacturing cell. Two different hypotheses were identified, both focused on the grinding machine processing sequence, as the grinding process is the longest one carried out in the manufacturing cell, and thus has a strong impact on the overall throughput time. In the first simulation case (Case A), the grinding machine starts carrying out a Phase 1 operation on a new component. When the Phase 1 operation is completed, another component is assembled on the Phase 1 fixture and is processed. After this, the grinding machine performs alternatively a Phase 1 operation and a Phase 2 operation. In this way, only one fixture per grinding phase is required in Case A simulation: while the Phase 1 fixture with the component for the Phase 1 operation is inside the grinding machine, the component for the Phase 2 operation is assembled on the Phase 2 fixture at the assembly station located at the entrance of the grinding machine (element N. 2 in Figs. 1 and 3). In the second simulation case (Case B), the grinding machine performs all the Phase 1 operations for the whole batch, and only when all the components have undergone the Phase 1 operation, it starts carrying out the Phase 2 operations. In Case B simulation, in order to carry out assembly/disassembly of components and fixtures while the grinding machine is working, two fixtures for each grinding phase are required. In both simulation cases, 3 shifts of 8 hours each, with breaks distributed during the day, were considered for the human labor, and the maximum availability of the machine was set to 85%. The results of the simulation runs in Case A and case B show a very similar throughput time for a whole batch of components: 64.3 hrs against 64.1 hrs. As a consequence, Case A seems to be the optimal solution for several reasons; even if two fixtures per each grinding phase are available in Case B, there is no significant advantage in terms of throughput time. This is because no setup time is required on the grinding machine to switch between Phase 1 and Phase 2 operations, so that there is no significant benefit in processing subsequent components with the same phase operation rather than alternating Phase 1 and Phase 2 operations. Moreover, the Work in Progress (WIP) of the manufacturing cell is much higher in Case B: the maximum number of components in the system is equal to the batch, and the first fully finished component is obtained only after completing all the Phase 1 operations on the whole batch. Therefore, Case B requires a higher

investment in terms of fixtures cost, a larger buffer to collect the components waiting for Phase 2 operations and a longer time to have available a fully finished component. Table 2 shows the comparison between the two simulation cases in terms of utilization of elements (machines, robots and labours): it can be noticed that the values are very close for the two cases. In particular, the grinding machine is always the bottleneck of the cell with a utilization around 84%, (see first row of Table 2). The utilization of the handling/deburring robot is quite low for both cases, as it is around 13 %. Table 2. Manufacturing cell elements utilization: Case A and Case B. Element

Case A utilization (%)

Case B utilization (%)

Grinding machine

84.0

84.3

Deburring station

12.1

12.2

CMM

9.3

9.4

Inspection post

0.9

0.9

Handling/deburring robot

13.2

13.3

Labor

15.0

16.8

3.2.2. DES for elements utilization improvement In order to justify the investment required by the automated deburring station, a higher utilization for its elements should be achieved. To further exploit the capacity of the handling/deburring robot, additional part numbers, coming from other manufacturing cells in the same production department, were introduced in the automated deburring station for simulation. As long as the additional part numbers are geometrically comparable to the ones fabricated in the manufacturing cell, the robot is able to perform deburring with only a slight variation of the cycle time (as the latter is related to component dimensions). In order to verify this hypothesis, starting from the cell configuration for Case A, new elements and logics were introduced in the DES model of the manufacturing cell. New logics for component routing were set up in particular for the entrance of an external part number, as this should not interfere with the production cycle of the original part number. As an example, the remaining time to the end of the grinding process is taken into consideration as a decisional parameter for component routing. The grinding machine is the bottleneck of the system and it should never keep waiting because of the additional part


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numbers, as this would increase the entire batch throughput time. Two different hypotheses were simulated through DES, respectively called Case C and Case D. In Case C, the external part numbers are introduced into the cell as a unique final batch requiring Phase 1 and Phase 2 deburring operations to be performed on the same component one immediately after the other. In Case D, the external part numbers are introduced into the cell as two subsequent batches: first a Phase 1 batch and then a Phase 2 batch to be deburred separately. Table 3. Manufacturing cell elements utilization: Case C and Case D. Element

Case C utilization (%)

Case D utilization (%)

Grinding machine

84.0

84.0

Deburring station

26.3

56.8

CMM

9.3

9.3

Inspection post

2.0

3.9

Handling/deburring robot

28.6

61.4

Labour

16.5

22.7

The simulation runs carried out in Case C and Case D provided new results about the utilization of the cell elements, as shown in Table 3. The handling/deburring robot utilization was significantly increased from 13% to 61% of batch throughput time in Case D. As regards the number of deburred components of the external part number, it is much higher in Case D than in Case C: 115 against 36. These results are due to the fact that when the additional part numbers are deburred using two separate batches, one for Phase 1 deburring and the other for Phase 2 deburring, their impact on the production cycle time of the original part number fabricated in the manufacturing cell is much lower because their shorter deburring cycle times are easier to manage. 4. Conclusions The design of an existing aircraft engine components manufacturing cell to be enhanced through automated robotic deburring was presented as a case study for the application of digital simulation tools. 3D Motion Simulation for layout and material handling system design was illustrated. Discrete Event Simulation was applied to analyze different scenarios and improve the cell performance. Two objectives were identified: (a) the optimization of the batch throughput time for the part number fabricated in the manufacturing cell; (b) the improvement of the automated deburring station utilization by introducing into the cell, only for the

deburring operations, a number of additional external part numbers without interfering with the original part number batch throughput time. The optimal solution was to alternate Phase 1 and Phase 2 operations on the grinding machine and to deburr the additional external part numbers separately for Phase 1 and Phase 2 deburring using the new automated deburring station. Acknowledgements This research work was carried out within the framework of the Executive Program of Scientific and Technological Co-operation between Italy and Hungary, Ministry of Foreign Affairs, under the Joint Project on: “Digital Factory”, in collaboration with the Budapest University of Technology & Economics, Budapest, Hungary (2011-2013). The Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh - J_LEAPT) at the Department of Materials and Production Engineering, University of Naples Federico II, is gratefully acknowledged for its support to this research activity. References [1]

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