Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction Read chapter Read first chapter

2011 | OriginalPaper | Chapter

3. State-of-the-Art Multi-Objective Optimisation of Manufacturing Processes Based on Thermo-Mechanical Simulations

Authors : Cem Celal Tutum, Jesper Hattel

Published in: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Publisher: Springer London

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

During the last couple of decades the possibility of modelling multi-physics phenomena has increased dramatically, thus making simulation of very complex manufacturing processes possible and in some fields even an everyday event. A consequence of this has been improved products with respect to properties, weight/stiffness ratio and cost. However this development has mostly been based on “manual iterations” carried out by the user of the relevant simulation software rather than being based on a systematic search for optimal solutions. This is, however, about to change because of the very tough competition between manufacturers of products in combination with the possibility of doing these highly complex simulations. Thus, there is a crucial need for combining advanced simulation tools for manufacturing processes with systematic optimisation algorithms which are capable of searching for single or multiple optimal solutions. Nevertheless, despite this crucial need, it is interesting to notice the very limited number of contributions in this field and consequently this makes us wonder about the underlying reasons for it. The understanding of the physical phenomena behind the processes, the current numerical simulation tools and the optimisation capabilities which all mainly are driven by the industrial or academic demands as well as computational power and availability of both the simulation and the multi-objective optimisation oriented software on the market are the main concerns to look for. These limitations eventually determine what is in fact possible today and hence define what the “state-of-the-art” is. So, seen from that perspective the very definition of the state-of-the-art itself in the field of optimisation of manufacturing processes constitutes an important discussion. Moreover, in the major research fields of manufacturing process simulation and multi-objective optimisation there are still many issues to be reserved.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Multi-Objective Optimisation in Manufacturing Supply Chain Systems Design: A Comprehensive Survey and New Directions
next chapter Many-Objective Evolutionary Optimisation and Visual Analytics for Product Family Design
Literature
1.
Tekkaya, E. (2000). State-of-the-art of simulation of sheet metal forming. Journal of Materials Processing Technology, 103, 14–22.CrossRef
2.
Tutum, C. C., & Hattel, J. H. (2010). Optimisation of process parameters in friction stir welding based on residual stress analysis: A feasibility study. Journal of Science and Technology of Welding and Joining, 15(5), 369–377.CrossRef
3.
Tutum, C. C., & Hattel, J. H. (2010). Multi-objective optimization of process parameters in friction stir welding. In Genetic and evolutionary computation conference (GECCO 2010), Portland, Oregon (pp. 1323–1324).
4.
Tutum, C. C., & Hattel, J. H. (2010). A multi-objective optimization application in friction stir welding: considering thermo-mechanical aspects. In IEEE congress on evolutionary computation (IEEE CEC 2010), Barcelona, Spain (pp. 427–434).
5.
Kotas, P., Tutum, C. C., Snajdrova, O., Thorborg, J., & Hattel, J. H. (2010). A casting yield optimization case study: Forging ram. International Journal of Metalcasting, 4(4), 61–76.
6.
Dulikravich, G. S., Egorov, I. N., & Colaco, M. J. (2008). Optimizing chemistry of bulk metallic glasses for improved thermal stability. Modelling and Simulation in Materials Science and Engineering, 16(7), 1–19.CrossRef
7.
Dulikravich, G. S., Sikka, V. K., & Muralidharan, G. (2006). Development of semi-stochastic algorithm for optimizing alloy composition of high-temperature austenitic stainless steels (H-series) for desired mechanical and corrosion properties. Technical Report, U.S. Department of Energy, (http://​www.​osti.​gov/​bridge).
8.
Wei, L., & Yuying, Y. (2008). Multi-objective optimization of sheet metal forming process using Pareto-based algorithm. Journal of Materials Processing Technology, 208, 499–506.CrossRef
9.
Schenk, O., & Hillmann, M. (2004). Optimal design of metal forming die surfaces with evolution strategies. Journal of Computers and Structures, 82(20–21), 1695–1705.CrossRef
10.
Jansson, T., Andersson, A., & Nilsson, L. (2005). Optimization of draw-in for an automotive sheet metal part–an evaluation using surrogate models and response surfaces. Journal of Materials Processing Technology, 159, 426–434.CrossRef
11.
Oduguwa, V., & Roy, R. (2002). Multi-objective optimisation of rolling rod product design using meta-modelling approach. In Genetic and evolutionary computation conference (GECCO 2002), San Francisco, CA, USA (pp. 1164–1171).
12.
Katayama, T., Nakamachi, E., Nakamura, Y., Ohata, T., Morishita, Y., & Murase, H. (2004). Development of process design system for press forming–multi-objective optimization of intermediate die shape in transfer forming. Journal of Materials Processing Technology, 155–156, 1564–1570.CrossRef
13.
Han, Z. X., Xu, L., Wei, R., Wang, B. P., & Reinikainen, T. (2004). Reliability-based design optimization for land grid array solder joints under thermo-mechanical load. In 5th international conference on thermal and mechanical simulation and experiments in micro-electronics and micro-systems (EuroSimE2004) (pp. 219–224).
14.
Kor, J., Chen, X., & Hu, H. (2009). Multi-objective optimal gating and riser design for metal-casting. In IEEE international symposium on intelligent control, Saint Petersburg, Russia (pp. 428–433).
15.
Esparza, C. E., Guerrero-mata, M. P., & Ríos-mercado, R. Z. (2006). Optimal design of gating systems by gradient search methods. Computational Materials Science, 36, 57–467.CrossRef
16.
Poloni, C., Poles, S., Odorizzi, S., Gramegna, N., & Bonollo, F. (2002). MAGMAfrontier: State of the art of an optimisation tool for the MAGMASOFT environment. MAGMASOFT international user meeting.
17.
Hahn, I., & Hartmann, G. (2008). Automatic computerized optimization in die casting processes. Casting Plant & Technology, 4, 2–14.
18.
19.
Kirk, D. B., & Hwu, W.-M. W. (2010). Programming massively parallel processors–a hans-on approach. San Francisco, USA: Morgan Kaufmann Publishers.
20.
21.
Chapman, B., Jost, G., & Pas, R. V. D. (2008). Using openMP: Portable shared memory parallel programming. Cambridge: The MIT Press.
22.
Pacheco, P. S. (1997). Parallel programming with MPI. San Francisco, USA: Morgan Kaufmann Publishers, Inc.MATH
23.
Talbi, E.-G., Mostaghim, S., Okabe, T., Ishibuchi, H., Rudolph, G., & Coello, C. A. C. (2008). Parallel approaches for multiobjective optimization. In J. Branke et al. (Eds.), Multiobjective optimization, LNCS 5252 (pp. 349–372). Berlin: Springer.
24.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Chichester: Wiley.MATH
25.
Deb, K. (2008). Introduction to evolutionary multiobjective optimization. In J. Branke et al. (Eds.), Multiobjective optimization, LNCS 5252 (pp. 59–96). Berlin: Springer.
26.
Betounes, D. (1998). Partial differential equations for computational science: With Maple and vector analysis. New York: Springer.MATHCrossRef
27.
Cook, R., Malkus, D., Plesha, M., & Witt, R. (2001). Concepts and applications of finite element analysis. New York: Wiley.
28.
Simo, J. C., & Hughes, T. J. R. (1998). Computational inelasticity. New York, USA: Springer.MATH
29.
Crisfield, M. (1991). Non-linear finite element analysis of solids and structures—volume 1: Essentials. Chichester: Wiley.
30.
Crisfield, M. (1991). Non-linear finite element analysis of solids and structures—volume 2: Advanced topics. Chichester: Wiley.
31.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197.CrossRef
32.
Zitzler, E., & Thiele, L. (1998). An evolutionary algorithm for multiobjective optimization: The strength pareto approach. Technical Report 43, Swiss Federal Institute of Technology (ETH) Zurich.
33.
Knowles, J., & Corne, D. (1998). The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimization. Parallel problem solving from nature—PPSN V. In 5th International Conference (pp. 250–259).
34.
Horn, J., Nafpliotis, N., & Goldberg, D. (1994). A niched Pareto genetic algorithm for multiobjective optimization. In First IEEE conference on evolutionary computation. IEEE world congress on computational intelligence (pp. 82–87).
35.
Corne, D., Knowles, J., & Oates, M. (2000). The pareto envelope-based selection algorithm for multiobjective optimization. In 6th international conference on parallel problem solving from nature (pp. 839–848).
36.
Fonseca, C., & Fleming, P. (1993). Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In 5th international conference on genetic algorithms, USA.
37.
Knowles, J., Corne, D., & Deb, K. (2008). Multi-objective problem solving from nature. Natural computing series. Heidelberg: Springer.
38.
Coello, C. A. C., Pulido, G., & Montes, E. (2005). Current and future research trends in evolutionary multiobjective optimization. Advanced Information and Knowledge Processing, pp. 213–231.
39.
Miettinen, K., Deb, K., Jahn, J., Ogryczak, W., Shimoyama, K., & Vetschera, R. (2008). Future challenges. In J. Branke et al. (Eds.), Multiobjective optimization, LNCS 5252 (pp. 435–461). Berlin: Springer.
40.
Poles, S., Vassileva, M., & Sasaki, D. (2008). Multiobjective optimization software. In J. Branke et al. (Eds.), Multiobjective optimization, LNCS 5252 (pp. 329–348). Berlin: Springer.
41.
Miettinen, K., & Mäkelä, M. M. (2006). Synchronous approach in interactive multiobjective optimization. European Journal of Operational Research, 170(3), 909–922.MATHCrossRef
42.
Hattel, J. H. (2005). Fundamentals of numerical modelling of casting processes. Kgs. Lyngby: Polyteknisk Forlag.
43.
Lindgren, L. (2007). Computational welding mechanics: Thermomechanical and microstructural simulations. Cambridge: Woodhead Publishing, Ltd.CrossRef
44.
Boley, B., & Weiner, J. (1960). Theory of thermal stresses. New York: Dover.MATH
45.
Hattel, J. H., Schmidt, H. B., & Tutum, C. C. (2008). Thermomechanical modelling of friction stir welding. In 8th international conference on trends in welding research conference, ASM, Atlanta, USA.
46.
Tutum, C. C. (2009). Optimization of thermo-mechanical conditions in friction stir welding. Ph.D. thesis, ISBN: 978-87-89502-89-2.
47.
48.
Mishra, R. S., & Ma, Z. Y. (2005). Friction stir welding and processing. Materials and Science Engineering A, 50, 1–78.MATH
49.
Nandan, R., DebRoy, T., & Bhadeshia, H. (2008). Recent advances in friction stir welding—process, weldment structure and properties. Progress in Materials Science, 53, 980–1023.CrossRef
50.
Tutum, C. C., Schmidt, H. N. B., & Hattel, J. H. (2008). Assessment of benchmark cases for modelling of residual stresses and distortions in friction stir welding. In 7th international symposium friction stir welding, TWI, Awaji Island, Japan.
51.
Tutum, C. C., Schmidt, H., Hattel, J., & Bendsøe, M. (2007). Estimation of the welding speed and heat input in friction stir welding using thermal models and optimization. In 7th world congress on structural and multidisciplinary optimization, Seoul (pp. 2639–2646).
52.
Tutum, C. C., Deb, K., & Hattel, J. H. (2010). Hybrid search for faster production and safer process conditions in friction stir welding. In The eighth international conference on simulated evolution and learning (SEAL-2010), IITK Kanpur, India (accepted).
53.
Liao, T. W., & Daftardar, S. (2009). Model based optimization of friction stir welding processes. Science and Technology of Welding and Joining, 14(5), 426–435.CrossRef
54.
Larsen, A. A., Bendsøe, M. P., Hattel, J. H., & Schmidt, H. N. B. (2009). Optimization of friction stir welding using space mapping and manifold mapping—an initial study of thermal aspects. Structural and Multidisciplinary Optimization, 38(3), 289–299.MathSciNetCrossRef
55.
Nandan, R., Lienert, T. J., & DebRoy, T. (2008). Toward reliable calculations of heat and plastic flow during friction stir welding of Ti-6Al-4V alloy. International Journal of Materials Research, 99(4), 434–444.
56.
Schmidt, H. N. B., & Hattel, J. H. (2008). Thermal modelling of friction stir welding. Scripta Materialia, 58, 332–337.CrossRef
57.
Schmidt, H. N. B., & Hattel, J. H. (2005). Modelling heat flow around tool probe in friction stir welding. Science and Technology of Welding and Joining, 10(2), 176–186.CrossRef
58.
Schmidt, H. N. B., Hattel, J. H., & Wert, J. (2004). An analytical model for the heat generation in friction stir welding. Modeling and Simulation in Materials Science and Engineering, 12(1), 143–157.CrossRef
59.
Haimes, Y. Y., Lasdon, L. S., & Wismer, D. A. (1971). On a bi-criterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man and Cybernetics, 1(3), 296–297.MathSciNetMATHCrossRef
60.
Deb, K. (2003). Unveiling innovative design principles by means of multiple conflicting objectives. Engineering Optimization, 35(5), 445–470.CrossRef
61.
Deb, K., & Srinivasan, A. (2006). Innovization: Innovating design principles through optimization. In Genetic and evolutionary computation conference (GECCO 2006), New York, NY, USA (pp. 1629–1636).
62.
Deb, K., & Chaudhuri, S. (2005). Automated discovery of innovative designs of mechanical components using evolutionary multi-objective algorithms. In Evolutionary machine design: methodology and applications (pp. 143–168).
63.
Bandaru, S., & Deb, K. (2010). Automated discovery of vital knowledge from Pareto-optimal solutions: First results from engineering design. In IEEE congress on evolutionary computation (CEC 2010) (pp. 1224–1231).
64.
Michaleris, P., & Sun, X. (1997). Finite element analysis of thermal tensioning techniques mitigating weld buckling distortion. Welding Journal, 76, 451–457.
65.
Michaleris, P., Dantzig, J., & Torterelli, D. (1999). Minimization of welding residual stress and distortion in large structures. Welding Journal, 78, 361–366.
66.
Richards, D. G., Prangnell, P. B., Williams, S. W., & Withers, P. J. (2008). Global mechanical tensioning for the management of residual stresses in welds. Materials Science and Engineering, 489, 351–362.CrossRef
67.
Hatamleh, O., Lyons, J., & Forman, R. (2007). Laser and shot peening effects on fatigue crack growth in friction stir welded 7075–T7351 aluminum alloy joints. International Journal of Fatigue, 29(3), 421–434.CrossRef
68.
Hatamleh, O. (2008). The effects of laser peening and shot peening on mechanical properties in friction stir welded 7075–T7351 aluminum. Journal of Materials Engineering and Performance, 17, 688–694.CrossRef
69.
Richards, D. G., Prangnell, P. B., Withers, P. J., Williams, S. W., Nagy, T., & Morgan, S. (2008). Simulation of the effectiveness of dynamic cooling for controlling residual stresses in friction stir welds. In 7th international symposium friction stir welding, TWI, Japan.
70.
Feng, Z., Wang, X., David, S. A., & Sklad, P. (2007). Modelling of residual stresses and property distributions in friction stir welds of aluminium alloy 6061–T6. Science and Technology of Welding & Joining, 12(4), 348–356.CrossRef
71.
Chao, Y. J., & Qi, X. H. (1999). Thermal and thermo-mechanical modelling of friction stir welding of aluminium alloy 6061–T6. Journal of Materials Processing Manufacturing Science, 7, 215–233.CrossRef
72.
Zhu, X. K., & Chao, Y. J. (2004). Numerical simulation of transient temperature and residual stresses in friction stir welding of 304L stainless steel. Journal of Materials Processing Technology, 146, 263–272.CrossRef
73.
Shi, Q., Dickerson, T., & Shercliff, H. (2003). Thermal-mechanical FE modeling of friction stir welding of Al-2024 including tool loads. In Proceedings of the 4th international symposium on FSW, TWI, Utah, USA.
74.
Chen, C. M., & Kovacevic, R. (2006). Parametric finite element analysis of stress evolution during friction stir welding. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 220(8), 1359–1371.CrossRef
75.
Bastier, A., Maitournam, M. H., Van, K. D., & Roger, F. (2006). Steady state thermomechanical modelling of friction stir welding. Science and Technology of Welding and Joining, 11, 278–288.CrossRef
76.
Li, T., Shi, Q. Y., & Li, H. K. (2007). Residual stresses simulation for friction stir welded joint. Science and Technology of Welding and Joining, 12(8), 634–640.CrossRef
77.
Dubourg, L., Doran, P., Larose, S., Gharghouri, M. A., & Jahazi, M. (2010). Prediction and measurements of thermal residual stresses in AA2024–T3 friction stir welds as a function of welding parameters. Materials Science Forum, 638–642, 1215–1220.CrossRef
78.
Qin, X., & Michaleris, P. (2009). Thermo-elasto-viscoplastic modelling of friction stir welding. Science and Technology of Welding and Joining, 14(7), 640–649.CrossRef
79.
Xu, S., & Deng, X. (2003). Two and three-dimensional finite element models for the friction stir welding process. In 4th international symposium on friction stir welding, UT, USA.
80.
Schmidt, H. N. B., & Hattel, J. H. (2005). A local model for the thermomechanical conditions in friction stir welding. Modelling and Simulation in Materials Science and Engineering, 13, 77–93.CrossRef
81.
Zhang, H., & Zhang, Z. (2007). Numerical modelling of friction stir welding process by using rate-dependent constitutive model. Journal of Materials Science and Technology, 23(1), 73–80.MATHCrossRef
82.
Hamilton, R., MacKenzie, D., & Li, H. (2011). Multi-physics simulation of friction stir welding process. Engineering Computations, 27(8), 967–985.CrossRef
83.
Bastier, A., Maitournam, M. H., Roger, F., & Dang Van, K. (2008). Modelling of the residual state of friction stir welded plates. Journal of Materials Processing Technology, 200, 25–37.CrossRef
84.
Grujicic, M., Arakere, G., Yalavarthy, H. V., He, T., Yen, C.-F., & Cheeseman, B. A. (2010). Modeling of AA5083 material-microstructure evolution during butt friction stir welding. Journal of Materials Engineering and Performance, 19(5), 672–684.CrossRef
85.
Vuyst, T. D, Madhavan, V., Ducoeur, B., Simar, A., Meester, B. D., & D’Alvise, L. (2008). A thermo-fluid/thermo-mechanical modeling approach for computing temperature cycles and residual stresses in FSW. In 7th international symposium on friction stir welding, Japan.
86.
Altenkirch, J., Steuwer, A., Peel, M., Richards, D. G., & Withers, P. J. (2008). The effect of tensioning and sectioning on residual stresses in aluminum AA7749 friction stir welds. Materials Science and Engineering A, 488, 16–24.CrossRef
87.
Hattel, J. H., & Tutum, C. C. (2011). Modelling residual stresses in friction stir welding of Al-alloys - A review of possibilities and future trends. International Journal of Advanced Manufacturing Technology (under review).
88.
Zhu, X. K., & Chao, Y. J. (2002). Effects of temperature-dependent material properties on welding simulation. Computers & Structures, 80, 967–976.CrossRef
89.
90.
91.
92.
Groover, M. P. (2002). Fundamentals of modern manufacturing–materials, processes, and systems. Hoboken: Wiley.
93.
94.
Flender, E., & Sturm, J. (2010). Thirty years of casting process simulation. International Journal of Metalcasting, Spring, 10, 7–23.
95.
Winterscheidt, D. L., & Huang, G. X. (2002). Fundamentals of casting process modeling. In Yu Kuang-o (Ed.), Modeling for casting and solidification processing (pp. 17–54). New York, USA: Marcel Dekker, Inc.
96.
Chandra, U., & Ahmed, A. (2002). Stress analysis. In Yu Kuang-o (Ed.), Modeling for casting and solidification processing (pp. 55–93). New York, USA: Marcel Dekker, Inc.
97.
Suri, V., & Yu, K.-O (2002). Defects formation. In Yu Kuang-o (Ed.), Modeling for casting and solidification processing (pp. 95–122). New York, USA: Marcel Dekker, Inc.
98.
Stefanescu, D. M. (2002). Microstructure evolution. In Yu Kuang-o (Ed.), Modeling for casting and solidification processing (pp. 123–187). New York, USA: Marcel Dekker, Inc.
99.
Cleary, P. W., Ha, J., Prakash, M., & Nguyen, T. (2006). 3D SPH flow predictions and validation for high pressure die casting of automotive components. Applied Mathematical Modelling, 30, 1406–1427.CrossRef
100.
Thorborg, J., Hattel, J. H., & Bellini, A. (2006). Thermo-mechanical conditions in heat treated aluminium cast parts. In Proceedings of 11th modelling of casting, welding and advanced solidification processes (pp. 193–200).
101.
Campbell, J. (2003). Castings, (2 nd ed.). Oxford: Butterworth Heinemann.
102.
Hansen, S. S. (2007). Reduced energy consumption for melting in foundries. Ph.D. thesis, Technical University of Denmark, ISBN 978-87-91035-63-5.
103.
Niyama, E., Uchida, T., Morikawa, M., & Saito, S. (1982). Method of schrinkage prediction and its application to steel casting practice. AFS International Cast Metals Journal, 7(3), 52–63.
104.
Carlson, K. D., & Beckermann, Ch. (2009). Prediction of schrinkage pore volume fraction using a dimensionless Niyama criterion. Metallurgical and Materials Transactions A, 40A, 163–175.CrossRef
105.
Carlson, K. D., Ou, S., & Beckermann, C. (2005). Feeding of high-nickel alloy castings. Metallurgical Materials Transactions B, 36B, 843–856.CrossRef
106.
Jain, N., Carlson, K. D., & Beckermann, C. (2007). Round robin study to assess variations in casting simulation Niyama criterion predictions. In Proceedings of the 61st technical and operating conference, Steel Founders’ Society of America, Chicago.
107.
Carlson, K. D., Ou, S., Hardin, R. A., & Beckermann, C. (2001). Development of a methodology to predict and prevent leaks caused by microporosity in steel castings. In Proceedings of the 55th technical and operating conference, SFSA, Chicago.
108.
Hardin, R. A., Ou, S., Carlson, K. D., & Beckermann, C. (2002). Development of new feeding distance rules using casting simulation; Part I: Methodology. Metallurgical Materials Transactions B, 33B, 731–740.
109.
Beckermann, C. (2002). Modelling of macrosegregation: Applications and future needs. International Materials Reviews, 47, 243–261.CrossRef
110.
Beeley, P. (2001). Foundry technology (2 nd ed.). Oxford: Butterworth Heinemann.
111.
Porter, D. A., & Easterling, K. E. (2004). Phase transformations in metals and alloys, (2 nd ed.). London: Taylor & Francis Group.
112.
113.
Hattel, J. H., Nielsen, K. L., & Tutum, C. C. (2010). The effect of post-welding conditions in friction stir welds: From weld simulation to ductile failure. European Journal of Mechanics - A/Solids (under review).
114.
Fleming, P. J., Purshouse, R. C., & Lygoe, R. J. (2005). Many-objective optimization: An engineering design perspective. In C. A.Coello Coello et al. (Eds.), EMO 2005, LNCS 3410 (pp. 14–32). Berlin: Springer.
115.
Deb, K., & Saxena, D. K. (2005). On finding Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. KanGAL Report Number 2005011.
116.
Saxena, D. K., Ray, T., Deb, K., & Tiwari, A. (2009). Constrained many-objective optimization: A way forward. In IEEE congress on evolutionary computation (CEC 2009) (pp. 545–552).
117.
Brockhoff, D., & Zitzler, E. (2006). Are all objectives necessary? On dimensionality reduction in evolutionary multiobjective optimization. In T. P. Runarsson et al. (Eds.), PPSN IX, LNCS 4193 (pp. 533–542). Berlin: Springer.
118.
Corne, D., & Knowles, J. (2007). Techniques for highly multiobjective optimisation: Some nondominated points are better than others. In Genetic and evolutionary computation conference (GECCO 2007), London, England, United Kingdom (pp. 773–780).
119.
Saxena, D. K., Duro, J. A., Tiwari, A., Deb, K., & Zhang, Q. (2010). Objective reduction in many-objective optimization: Linear and nonlinear algorithms. KanGAL Report Number 2010008.
120.
Deb, K., Sundar, J., Rao, U. B., & Chaudhuri, S. (2006). Reference point based multi-objective optimization using evolutionary algorithms. International Journal of Computational Intelligence Research, 2(3), 273–286.MathSciNetCrossRef
121.
Deb, K., & Kumar, A. (2007). Light beam search based multi-objective optimization using evolutionary algorithms. KanGAL Report 2007005.
122.
Jin, Y. (2005). A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing, 9(1), 3–12.CrossRef
123.
Branke, J. (2008) Consideration of partial user preferences in evolutionary multiobjective optimization. In J. Branke et al. (Eds.), Multiobjective optimization, LNCS 5252 (pp. 157–178). Berlin: Springer.
124.
Sigmund, O. (2001). A 99 line topology optimization code written in Matlab. Structural and Multidisciplinary Optimization, 21, 120–127.CrossRef
125.
Bendsøe, M. P., & Sigmund, O. (2003). Topology optimization—theory, methods and applications. Berlin: Springer.
126.
Jahn, J. (2004). Introduction to the theory on nonlinear optimization. Heidelberg: Springer.
127.
Krog, L., Tucker, A., & Rollema, G. (2002). Application of topology, sizing and shape optimization methods to optimal design of aircraft components. Seattle: Altair Engineering Ltd.
128.
Krog, L., Tucker, A., Kemp, M., & Boyd, R. (2004). Topology optimization of aircraft wing box ribs. In The Altair technology conference (pp. 6.1–6.16).
129.
Colegrove, P. A., & Shercliff, H. R. (2004). Development of trivex friction stir welding tool. Part 1–two-dimensional flow modeling and experimental validation. Science and Technology of Welding and Joining, 9, 345–351.CrossRef
130.
Vlahinos, A., & Kelkar, S. G. (2002). Designing for six-sigma quality with robust optimization using CAE. In Proceedings of the 2002 SAE international body. Deb, K. (2007). Current trends in evolutionary multi-objective optimization. International Journal for Simulation and Multidisciplinary Design Optimization, 1, 1–8.
131.
Vlahinos, A., Suryatama, D., Ullahkhan, M., TenBrink, J. T., & Baker, E. (2002). Robust design of a catalytic converter with material and manufacturing variations. In Powertrain & fluid systems conference & exhibition, San Diego, California USA.
132.
ANSYS Inc: Probabilistic design techniques.
133.
Ferreira, J., Fonseca, C. M., Covas, J. A., & Gaspar-Cunha, A. (2008). Evolutionary multi-objective robust optimization. Advances in evolutionary algorithms (pp. 261–278). Vienna, Austria: I-Tech Education and Publishing.
134.
Wiesmann, D., Hammel, U., & Back, T. (1998). Robust design of multilayer optical coatings by means of evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 2(4), 162–167.CrossRef
135.
Du, X., & Chen, W. (1998). Towards a better understanding of modelling feasibility robustness in engineering design. In Proceedings of DETC 99, Las Vegas, USA.
136.
Arrold, D. V., & Beyer, H.-G. (2003). A comparison of evolution strategies with other direct search methods in the presence of noise. Computational Optimization and Applications, 24(1), 135–159.MathSciNetCrossRef
137.
Deb, K., & Gupta, H. (2005). Searching for robust pareto-optimal solutions in multi-objective optimization. In C. A. C. Coello et al. (Eds.), EMO 2005, LNCS 3410 (pp. 150–164). Heidelberg: Springer.
138.
Daum, D. A., Deb, K., & Branke, J. (2007). Reliability-based optimization for multiple constraints with evolutionary algorithms. In IEEE congress on evolutionary computation (CEC 2007), Singapore (pp. 911–918).
Metadata
Title
State-of-the-Art Multi-Objective Optimisation of Manufacturing Processes Based on Thermo-Mechanical Simulations
Authors
Cem Celal Tutum
Jesper Hattel
Copyright Year
2011
Publisher
Springer London
Book
Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Print ISBN: 978-0-85729-617-7

Electronic ISBN: 978-0-85729-652-8

Copyright Year: 2011

DOI
https://doi.org/10.1007/978-0-85729-652-8_3

Premium Partners

    Image Credits