A computer-aided-design system for forming processes

Transcription

1 Journal of Materials Processing Technology 89±90 (1999) 1±7 A computer-aided-design system for forming processes Y.-T. Im * Computer Aided Materials Processing Laboratory, Department of Mechanical Engineering, ME3227 Korea Advanced Institute of Science and Technology, 373-1, Kusong-dong, Yusong-gu, Taejon , South Korea Abstract The evolution of modern technology demands ever-increasing international competitiveness of manufacturing processes and products, requiring less design and manufacturing costs. In order to achieve this goal the design and manufacturing engineers work together as a team to come up with an economical solution meeting given constraints. As a rst-step solution, new materials and processes or technologies to be applied will be sought with the help from a computer-aided-design system that is based on either arti cial intelligence techniques or numerical simulation tools. Some issues involved with this approach in developing forming processes of multi-stage forming, bar rolling, rolling, injection molding, and compression molding, are discussed in this paper. # 1999 Elsevier Science S.A. All rights reserved. Keywords: Computer-aided-design system; Expert system; Fuzzy algorithm; Finite element; Finite difference 1. Introduction *Tel.: ; fax: ; ytim@convex.kaist.ac.kr Recent international market demands are that mechanical parts should be produced to net-shape or near net-shape with improved mechanical properties, a smooth surface nish, good dimensional accuracy and material savings, depending on service conditions. Thus, near net-shape manufacturing has been evolved into a `must' condition for industries in meeting the new demands for reducing manufacturing costs together with better mechanical quality. In practice, manufacturing engineers are faced with the problem of determining the proper design of dies and molds to transform an initial simple shape into more complex product geometries without causing any surface or internal defects at a lower manufacturing cost, depending on the material, the part geometry, and the process. Since a decision made at the design process level has a profound effect on die and tool design, manufacturing, maintenance, mechanical properties, and on life cycle, a great deal of effort is being made to reduce experience-based process development. According to recent remarkable advances in computer technology, the applications of computers in manufacturing have been growing rapidly and independently in the area of computer aided design/computer aided manufacturing (CAD/CAM), computer aided engineering (CAE), computer aided process planning (CAPP) and rapid prototyping to aid design and manufacturing activities in present-day industries [1±3]. In addition, a knowledge-based manufacturing system becomes practical using qualitative and ambiguous human experiences due to the development of knowledge treatment techniques. Based on this approach, expert systems, neural networks or fuzzy theory, and arti cial intelligence (AI) are used with the help of the manufacturing engineer's experiences [4±7]. It is widely known that the expert system has emerged as one of the most active and fruitful areas of research in the application of human knowledge for problems that do not lend themselves to solution by conventional methods because of a lack of quantitative data. Since these computer-aided techniques have been developed independently, there is the necessity of bridging these techniques and verifying the developed system for real usage. Recently, researches on incorporating numerical simulation with arti cial intelligence techniques have been actively carried out in academia and industries. Therefore, a systematic integration of computer-aided-design is considered to be the most promising approach to improve the producibility of near net-shape manufacturing parts with low cost, high quality, and short lead-time. In the present paper, some paradigms of such activities carried out at the laboratory level will be demonstrated in the areas of multi-stage /99/$ ± see front matter # 1999 Elsevier Science S.A. All rights reserved. PII: S (99)

2 2 Y.-T. Im / Journal of Materials Processing Technology 89±90 (1999) 1±7 forming, bar rolling, rolling, injection molding, and compression molding. 2. AI techniques in forming 2.1. Expert system for multi-stage forming and bar rolling In the eld of the development of CAD systems for multistage cold forming processes, knowledge- or rule-based expert systems were developed initially [7,8]. However, these systems were not exible enough to handlevarious manufacturing conditions such as variations of forming-machine capacities, the number of forming stages, the shearing diameter of the coil, the open upsetting diameter, and the knockout lengths of the die and punch. In order to solve this problem, searching techniques have been applied recently [9,10]. In their approach, A * and depth- rst searches which are one of the AI techniques, have been used in obtaining exible and optimal forming sequences for an axi-symmetric pin made of AISI They were trying to generate optimal sequences meeting the various design constraints provided by design engineers from the developed expert system, as shown in Fig. 1. The designed sequences were checked numerically. Ductile failures of aluminum alloys such as Al1100-O, Al2024-T3, Al6061-T4, and Al7075-T4 were also investigated experimentally and numerically at room temperature, see Figs. 2 and 3, in order to implement proper ductile fracture criteria into the developed expert system to generate defect-free forming sequences [11]. This activity was extended to shape rolling by Kim et al. and Kwon et al. [12,13]. In shape rolling, the billet is formed through several roll passes with different roll grooves. To improve production ef ciency and product quality, process Fig. 2. Experimental results of the pin shape forging of: (a) Al1100-O; (b) Al6061-T4. designers must optimize the amount of elongation at each roll pass and roll groove shape to achieve the designer's goal. Therefore, they have tried to automatically determine roll pass and roll groove design. Fig. 1. Designed process sequences of AISI1045 optimizing: (a) the effective strain; (b) the forging load distributions, at each stage. Fig. 3. Comparisons of the effective strain distributions for the case of: (a) Fig. 1(a); (b) Fig. 1(b).

3 Y.-T. Im / Journal of Materials Processing Technology 89±90 (1999) 1±7 3 Fig. 6. Comparison of roll separating forces between production data and the predictions of the developed system. Fig. 4. Front-tension variations obtained from the developed fuzzy tension-control system Fuzzy control algorithm in rolling Shape control in producing thin- cold or hot-rolled strip is complex because of the non-linearity of the process. In order to minimize the operator's intervention, fuzzy and neural network theories have been implemented on an IBM PC by Jung et al. [14±16]. In order to develop the fuzzy algorithm, they have utilized the production and numerical data obtained by nite element (FE) simulations also. The developed fuzzy algorithm has determined processing parameters such as the front and back tension variations, the roll separating forces, and the roll speeds, to automatically control the process, as displayed in Figs. 4±7. This approach was found to be successful in reducing the irregularity of the cross-sectional shape of the rolled thin steel strip for both the steady and the non-steady state. A similar approach can easily be extended to identify the correlation between controlled variables and design parameters in other forming process development. 3. Development of three-dimensional simulation programs The rigid±thermo-viscoplastic FE approach proposed by Kobayashi et al. [1] has been used widely in the eld of metal forming analyses. This approach is essentially a coupled procedure of solving equilibrium and energy equations, in which a rigid±thermo-viscoplastic constitutive model with Mises yield criterion was used. In order to handle the complex die geometry involved with threedimensional forming analyses, the modeling of complex die surfaces and the contact treatment algorithm at the dies and workpiece interface are very important. Thus, Kim et al. [17,18] have used Ferguson surface patches for surface modeling and Jacobian inversion and line-surface intersection algorithms for contact treatment algorithm, respectively. In Fig. 8, grid deformations of the closed-die forging of an aluminum swash plate at room temperature are shown. The material was an annealed Al brick elements and 2080 nodes were used for the simulation. In Fig. 9, simulation results of the non-isothermal shape rolling of cross-sectional H-beam of common carbon steel are given in terms of grid deformation and temperature distribution. In Fig. 5. Back-tension variations obtained from the developed fuzzy tension-control system. Fig. 7. Comparison of roll and strip speeds predicted by the fuzzy system and by FE analysis at each stand.

4 4 Y.-T. Im / Journal of Materials Processing Technology 89±90 (1999) 1±7 Fig. 10. Grid deformation before and after remeshing at different deformation stages. Fig. 8. The deformed shapes of the workpiece at various strokes: (a) 3.86 mm; (b) 5.87 mm. simulation, the roll velocity and friction constant were set to 0.3 and 0.8 rev/s, respectively. The initial roll and billet temperatures were set to 313 and 1425 K, respectively. Fig. 10 demonstrates the bench-mark test simulation for 1994 Conference of Metal Forming Process Simulation in Industry at Baden-Baden in Germany, using hexahedral mesh systems generated by a three-dimensional mesh generator developed by Kwak et al. [19]. In most three-dimensional simulations with large deformations, remeshing is necessary. Therefore, adaptive mesh generation should be carried out to nish the three-dimensional analysis economically. In order to apply new materials such as polymers and plastics with or without reinforcing bers, injection-molding simulations might be necessary. Thus, Han et al. [20] have applied a hybrid of FE and nite-different schemes to analyze lling and post- lling based on the generalized Hele±Shaw model for a non-newtonian uid, as shown in Fig. 11. In addition, hybrid closure approximations were modi ed by Han et al. [21] for better prediction of the threedimensional ber orientation distribution in injection molded parts, as shown in Fig. 12. The residual stress can also be determined numerically to handle warpage of the molded parts. The calculated results are compared to the experimental data available in the literature in Fig. 13. In compression molded products the mechanical properties vary depending on the volume ber distributions. Thus, Fig. 11. Flow-front distribution in the injection molding of polypropylene. Fig. 9. Presenting (a) the grid deformation shape; (b) temperature distribution; in H-beam shape rolling analysis at t ˆ s. Fig. 12. Distribution of the three-dimensional fiber orientations near to the mold wall during the injection molding of polypropylene.

5 Y.-T. Im / Journal of Materials Processing Technology 89±90 (1999) 1±7 5 Fig. 13. Comparison of the in-plane residual stress distribution between predicted and measured values for an injection-molded part. Fig. 15. Comparison of the predicted fiber volume fraction ratio distributions with image processing results for five regions of a SMC flat plate [23]. an image processing technique and a numerical algorithm to predict the volume ber distributions for various processing conditions have been developed by Kim et al. and Cheon et al. [22±24], as indicated in Figs. 14 and 15. As seen in these gures, the local distribution of bers in the compression-molded parts is not uniform, resulting in different exural strength and impact resistance. 4. Paradigms of the system approach of computeraided-design As pointed out earlier, there is a necessity to bridge the AI technique and simulation tools systematically. As a rst example, system integration between an expert system for determining multi-stage forming sequences and FE simulations was made. In the present application, the level of forming load and the effective strain distributions at each forming stage were subjected to be optimized in the expert system, as mentioned earlier. The designed process sequences were checked numerically by subsequent FE simulations. For veri cation of the reliability of the developed system, forming experiments with specimen of AISI1045 were conducted, as shown in Fig. 16. Based on this investigation, it was found that the systematic integration of the exible expert system and the FE simulation program was achieved, including the implementation of a ductile fracture criterion. As a second example, bar rolling was selected to demonstrate improvement of the design process by utilizing iteratively the developed expert system and three-dimensional rigid thermo-viscoplastic FE programs, as shown in Fig. 17(a, b). In the former gure under- lling occurred according to the initial try-out sequence obtained from the developed expert system based on the design rules currently available, whilst this was eliminated by the iterative feedback obtained from FE simulations, as displayed in the latter gure. Since the texture distribution can be determined by the developed system, it can also be optimized by utilizing a similar approach to economically improve process development in future work. Fig. 14. Comparison between (a) original scanned image; (b) image obtained from image processing. Fig. 16. Experimental results of the pin-shape forging of AISI1045.

6 6 Y.-T. Im / Journal of Materials Processing Technology 89±90 (1999) 1±7 that the one-piece composite hood was of preferable design and manufacture, compared to currently used composite hood made in two pieces, in terms of reducing in weight and manufacturing cost, without losing the required stiffness. In order to develop this prototype, the product designer, material supplier, manufacturing company, research engineer, and end user had been working together in several elds such as the optimization of the material preparation, the characterization of the material behavior, structural analysis, the development of simulation tools, the application of rapid prototyping using epoxy molds, and the development of better surface nish based on an in-mold coating process. 5. Conclusions Fig. 17. Comparisons of the final cylindrical cross-section between (a) under-filled, and (b) filled; cases. Third, ber glass reinforced composites such as sheet molding compounds have recently been used widely in the fabrication of two-piece automobile hoods for passenger cars. Jeong et al. [25] have developed a one-piece composite hood with reinforcing substructures which was optimally designed through structural analyses. The prototype was manufactured by resin transfer molding in order to reduce manufacturing cost, as shown in Fig. 18. It was found that the weight-saving effect obtained by introducing the optimally designed one-piece composite hood was 37% compared to the conventional steel hood. It was also con rmed Fig. 18. Prototype of a one-piece composite hood manufactured by resin transfer molding. In the present paper, some examples of systematically utilizing computer-aided-design tools have been demonstrated at development level. In modern manufacturing industry, such a systems approach for the design and manufacturing of dies and molds, integrating available knowledge and various techniques, might enable the achievement of cost-effective process development. Only this approach can meet the new world market demand of the ever-changing manufacturing environment. In terms of three-dimensional applications, there is yet the need of various efforts to come up with ef ciency and userfriendliness. Acknowledgements A research fellowship from the Alexander von Humboldt Foundation is greatly appreciated. References [1] S. Kobayashi, S.I. Oh, T. Altan, Metal Forming and the Finite Element Method, Oxford University Press, New York, [2] C.C. Gallagher, W.A. Knight, Group Technology, Butterworth, [3] L. Wood, Rapid Automated Prototyping: An Introduction, Industrial Press, New York, [4] R. Maus, J. Keyes, Handbook of Expert Systems in Manufacturing, McGraw-Hill, [5] M.M. Nelson, W.T. Illingworth, A Practical Guide to Neural Nets, Addison Wesley, [6] P.H. Winston, Artificial Intelligence, McGraw-Hill, Singapore, [7] C.R. Liu, T.C. Chang, R. Komanduri, Computer-Aided/Intelligent Process Planning, ASME, PED, vol. 19, [8] H.S. Kim, Y.-T. Im, J. Mater. Proc. Tech. 54 (1995) 271. [9] H.S. Kim, Y.-T. Im, Trans. NAMRC, 24 (1996) 161. [10] H.S. Kim, Y.-T. Im, Expert system for cold forging process design based on depth-first search, J. Mater. Proc. Tech., in press. [11] H.S. Kim, Y.-T. Im, M. Geiger, Prediction of ductile fracture in cold forging of aluminum alloy, Trans. ASME, J. Manufac. Sci. Eng., in press.

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