Manufacturing optimization through intelligent techniques

Manufacturing Optimization through Intelligent Techniques

Volumn , Issue , 2006, Pages 1-208

  Saravanan, Rajendran ^a  

^a KUMARAGURU COLLEGE OF TECHNOLOGY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85056984895     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (101)

1. Agapiou, J.S., The optimization of machining operations based on a combined criterion, part 1: the use of combined objectives in single pass operations, Transactions of ASME: Journal of Engineering for Industry, 114, 500–507, 1992.
2. Agapiou, J.S, The optimization of machining operations based on combined criterion, part 2: multipass operations, ASME Journal of Engineering for industry, 114, 508–513, 1992.
3. Arora, J., Introduction to Optimum Design, McGraw-Hill International, Singapore, 1989.
4. Box, G.E.P., Evolutionary operation: A method of increasing industrial productivity, Applied Statistics, 6, 81–101, 1957.
5. Box, M.J., A new method of constrained optimization and comparison with other methods, Computer Journal, Vol. 7, 42–52, 1965.
6. Deb, K., Optimization for Engineering Design — Algorithms and Examples, Prentice Hall of India (Pvt.) Ltd., New Delhi, 1998.
7. Nelden, J.A. and Mead, R., A simplex method for function minimization, Computer Journal, 7, 308–313, 1965.
8. Rao, S.S., Engineering Optimization: Theory and Practice, Wiley Eastern, New Delhi, 1984
9. Saravanan, R. et al., Comparative analysis of conventional and non-conventional optimisation techniques for CNC turning process, International Journal of Advanced Manufacturing Technology, 17(7), 471–476, 2001.
10. Baker, J.E, Adaptive selection methods for genetic algorithms: Proceedings of the first international conference on genetic algorithms and their applications, Erlbaum, 1985.
11. Baker, J.E, Reducing bias and inefficiency in the selection algorithm, Genetic algorithms and their applications: Proceedings of the second international conference on genetic algorithm, Erlbaum, 1987.
12. De Jong, K., An analysis of the behaviour of a class of genetic adaptive systems, Ph.D. Thesis, University of Michigan, Ann Arbor, 1975.
13. Dorigo, et al., The ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems Man and Cybernetics, B 26(1), 1–13, 1996.
14. Eberhart, R. and Kennedy, J., A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science (Nagoya, Japan), IEEE Service Center, Piscataway, NJ, 39–43, 1995.
15. Eberhart, R.C. and Shi, Y., Comparing inertia weights and constriction factors in particle swarm optimization, Congress on Evolutionary Computing, 1, 84–88, 2000.
16. Glover, F., Future paths for integer programming and links to artificial intelligence, Computers and Operations Research, 19, pp. 533–549, 1986.
17. Glover, F., Tabu search, part II, ORSA Journal of Computers, 2, 4–32, 1990.
18. Goldberg, D.E., Genetic Algorithm in Search, Optimization and Machine Learning, Addision Wesley, Singapore, 2000.
19. Goldberg, D.E., and Deb, K., A comparative analysis of selection schemes used in genetic algorithm, Proceedings of Foundations of Gas and Classifier Systems, Indiana University, Bloomington, IN, 69–74, 1990.
20. Goldberg, D.E., and Deb, K., A comparative analysis of selection schemes used in genetic algorithm, Foundations of genetic algorithms, Morgan Kaufmann, 1991.
21. Holland, John, Genetic algorithms and the optimal allocation of trials, SIAM Journal of Computers, 2, 88–105, 1974.
22. Jayaraman, V.K., et al., Ant colony framework for optimal design and scheduling of batch plants, International Journal of Computers and Chemical Engineering, 24, 1901–1912, 2000.
23. Kennedy, J. et al., Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks, IV, IEEE Service Center, Piscatway, NJ, 1942–1948, 1995.
24. Maniezzo, V. et al., The ant system applied to the quadratic assignment problem, IEEE Transactions on Knowledge and Data Engineering, 11(5), 769–778, 1999.
25. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., and Teller, E., Equation of state calculations by fast computing machines, Journal of Chemical Physics, 21, pp. 1087–1092, 1953.
26. Mitchell, M., An Introduction to Genetic Algorithms, Prentice Hall of India (Pvt.) Ltd., New Delhi, 1998.
27. Rajasekaran, S. and Vijayalakshmi Pai, G.A., Neural Networks, Fuzzy Logic and Genetic Algorithms Synthesis and Applications, Prentice Hall of India (Pvt.) Ltd., New Delhi, 2004.
28. Tanese, R., Distributed genetic algorithms for function optimization, Ph.D Thesis, Electrical Engineering and Computer Science Department, University of Michigan, 1989.
29. Arora, J., Introduction to Optimum Design, McGraw-Hill International, Singapore, 1989.
30. Bras, B.A. and Mistree, F., Designing design processes in decision-based concurrent engineering, SAE Transactions, Journal of Materials and Manufacturing, 100, 5, 451–458, 1991.
31. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, West Sussex, 2001.
32. Deb, K., Optimization for Engineeering Design: Algorithms and Examples, Prentice Hall of India (Pvt.) Ltd., New Delhi 1995.
33. Deb, K. and Goyal, M., A combined genetic adaptive search (GeneAS) for engineering design, Computer Science and Informatics, 26 (4), pp.30– 45, 1996.
34. Donaldson, C., Le Cain, C.H. and Goold, V.C., Tool Design, McGraw-Hill, New York, 1973.
35. Dudley, D.W., Practical Gear Design, McGraw-Hill, New York, 1954.
36. Dudley, D.W., Practical Gear Design: The Design, Manufacture, and Application of Gears, McGraw-Hill, New York, 1962
37. Faculty of Mechanical Engineering Department, “Design data,” PSG College of Technology, Coimbatore, India, 1985.
38. Iwata, K. et al., Optimization of cutting conditions for multi-pass operations considering probabilistic nature in machining processes, Journal of Engineering for Industry, 99, 210–217, 1977.
39. Johnson, R.C., Optimal Design of Machine Elements, John Wiley & Sons, New York, 1961.
40. Karandikar, H.M. and Mistree, F., The integration of information from design and manufacture through decision support problems, Applied Mechanics Reviews, 44(10), 150–159, 1991.
41. Pakala, R. and Jagannatha Rao, J.R., Study of concurrent decision-making protocols in the design of a metal cutting tool using monotonicity arguments, Systems Design Laboratory, Department of Mechanical Engineering, University of Houston, Houston, TX, http://www.egr.uh.edu/me/research/sdl/research/rama-tool-94.ps.
42. Papalambros, P.Y. and Wilde, D.G., Principles of Optimal Design, Cambridge University Press, New York, 1988.
43. Shaw, M.C., Metal Cutting Principles, Clarendon Press, Oxford, 1984.
44. Taraman, S.R., and Taraman, K.S., Optimum selection of machining and cutting tool variables, Proceedings of the First International Material Removal Conference, Detroit, MI, SME Technical Paper MR83-182, 1983.
45. Askin, R.G. and Goldberg, J.B., Economic optimization in product design, Engineering Optimization, 14, 139–152, 1988.
46. Carpinetti, L.C.R. and Chetwynd, D.G., Genetic search methods for assessing geometric tolerances, Computer Methods in Applied Mechanics & Engineering, 122, 193–204, 1995.
47. Chase, K.W., Greenwood, W.H., Loosli, B.G., and Hauglund, L.F, Least cost tolerance allocation form mechanical assemblies with automated process selection, Manufacturing Review 3, 49–59, 1990.
48. Choi Hoo-Gon, R., Park, M. H., and Selisbury, E. Optimal tolerance allocation with loss function, Journal of Manufacturing Science and Engineering, 122, 529–535, 2000.
49. Deb, K., Optimization for Engineering Design: Algorithm and Examples, Prentice Hall, New Delhi, India, 1995.
50. Greenwood, W. and Chase, K.W., Worst case tolerance analysis with nonlinear problems, Journal of Engineering Industry, 110, 232–235, 1988.
51. Lee, J. and Johnson, G.E., Optimal tolerance allotment using a genetic algorithm and truncated Monte Carlo simulation, Computer Aided Design, 25, 601–611, 1993.
52. Lee, W.J. and Woo, T.C., Optimization selection of discrete tolerance, ASME Journal of Mechanism, Transmissions and Automation in Design, III, 243–251, 1989.
53. Li, W., Bai, G., Zhang, C., and Wang, B., Optimization of machining datum selection and machining tolerance allocation with genetic algorithm, International Journal of Production Research, 38, 1407–1424, 2000.
54. Michael, W. and Siddall, J.N., Optimization problem with tolerance assignment and full acceptance, ASME Journal of Mechanical Design, 103, 842–848, 1981.
55. Partkinson, D.B., Assessment and optimization of dimensional tolerance, Computer Aided Design, 17(4), 191–199, 1985.
56. Speckhart, F.H., Calculation of tolerance based on a minimum cost approach, ASME Journal of Engineering for Industry, 94(2), 447–453, 1972.
57. Spotts, M.F., Allocation of tolerance to minimize cost of assembly, ASME Journal of Engineering for Industry, August, 762–764, 1973.
58. Agapiou, J.S., The optimization of machining operations based on a combined criterion, part 1: the use of combined objectives in single pass operations, Transactions of ASME, Journal of Engineering for Industry, 114, 500–507, 1992.
59. Agapiou, J.S., The optimization of machining operations based on combined criterion, part 2: multipass operations, ASME Journal of Engineering for Industry, 114, 508–513, 1992.
60. Amitay, G., Adaptive control optimization of grinding, ASME Journal of Engineering for Industry, v103, 103–108, 1981.
61. Asokan, P., et al., Machining parameters optimization for turning cylindrical stock into a continuous finished profile using genetic algorithm (GA) and simulated annealing (SA), International Journal of Advanced Manufacturing Technology, 21(1), 1–9, 2003.
62. Gupta, R. et al., Determination of optimal subdivision of depth of cut in multipass turning with constraints, International Journal of Production Research, 33(9), 2555–2565, 1994.
63. Jha, N.K. and Rao, V.R.K., Opimization of milling process through geometric programming, Proceedings of the Third International Conference on Production Engineering, Kyoto, Japan, 1977.
64. Malkin, S., Practical approaches to grinding optimization, in M.C. Shaw, Grinding Symposium, ASME Winter Annual Meeting, Miami Beach, FL, 289–299, 1985.
65. Saravanan, R. et al., Comparative analysis of conventional and non-conventional optimization techniques for CNC turning process, International Journal of Advanced Manufacturing Technology, 17(7), 471–476, 2001.
66. Saravanan, R., et al., Genetic algorithm (GA) for multivariable surface grinding process optimization using a multi-objective function model, International Journal of Advanced Manufacturing Technology, 17(5), 330–338, 2001.
67. Saravanan, R., Vengadesan, S., and Sachithanandam, M., Selection of operating parameters in surface grinding process using genetic algorithm (GA), Proceedings of the 18th All-India Manufacturing Technology, Design and Research Conference, Kharagpur, India, 167–171, 1998.
68. Shin, Y.C. et al., Optimization of machining conditions with practical constraints, International Journal of Production Research, 30, 2907–2919, 1992.
69. Vijayakumar, K. et al., Optimization of multipass turning operations using ant colony system, International Journal of Advanced Manufacturing Technology, 43(15), 1633–1639, 2003.
70. Hsu, W., and Lee, C. S. G., Fuzzy Application in Tolerance Design, Proc. of IEEE International Conference on Fuzzy Systems, Orlando, Florida, June 26–July 2, 1994.
71. Nachtmann, H., and Chimka, R.J., Fuzzy reliability in conceptual design, Proceedings of the Annual Reliability and Maintainability Symposium, University of Arlcansas, Fayelteville, AR, Vol. 29, 360–364, 2003.
72. Patterson, J.L. and Dietrich, D., Dirichlet binomial attribute testing model: A Bayesian approach to estimating reliability decay, Proceedings of the Annual Reliability and Maintainability Symposium, Ottawa, Canada, 393–400, 2001.
73. Tsai, C.K., Hong, S.H., and Zhang, H.C., Design for Manufacturability and Design for X: Concepts, Applications and Perspectives, Elsevier Science Ltd., Burlington, VT, 241–260, 2001.
74. Vintr, Z., Optimization of reliability requirements from manufacturer’s point of view, Proceedings of the Annual Reliability and Maintainability Symposium, Miltary Academy, Brno, Czech Republic, 183–189, 2001.
75. Baker, K.R., Introduction to Sequencing and Scheduling. John Wiley & Sons, New York, 1974.
76. Balasubramaniam, H., Monch, L., Fowler, J., and Pfund, M., Heuristic scheduling of jobs on parallel batch machines with incompatible job families and unequal ready times, International Journal of Production Research, 42(18), 1621, 2004.
77. Cheng, R. and Gen, M., Parallel machine scheduling problems using memetic algorithms, Computers and Industrial Engineering, 33(3-4), 761–764, 1997.
78. Cheng, R., Gen, M., and Tozawa, T., Minimax earliness/tardiness scheduling in identical parallel machine system using Genetic Algorithms, Computers and Industrial Engineering, 1(4), 513–517, 1995.
79. French, S., Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop, Ellis Horwood, Chichester, UK, 1982.
80. Ishibuchi, H., Misaki, S., and Tanaka, H., Modified simulated annealing algorithms for the flow shop sequencing problems, European Journal of Operational Research, 81, 388–398, 1995.
81. Johnson, D.S., Argon, C.R., McGeoch, L.A., and Schevon, C., Optimisation by simulated annealing: an experimental evaluation, part 1 — graph partitioning, Operations Research, 37, 865–891, 1989.
82. Kim, Y.D., Heuristics for flowshop scheduling problems minimizing mean tardiness, Journal of Operational Research Society, 44, 19–29, 1993.
83. Kirkpatrick, S., Gelatt, C.D., Jr., and Vecchi, M.P., Optimization by simulated annealing, Science, 220, 671–680, 1983.
84. Logendran, R. and Nudtasomboon, N., Minimizing the makespan of a group scheduling problem: a new heuristic, International Journal of Production Economics, 22, 217–230, 1991.
85. Min, L. and Cheng, W., A genetic algorithm for minimizing the makespan in the case of scheduling identical parallel machines, Artificial Intelligence in Engineering, 13(4), 399–403, 1999.
86. Mokotoff, E., Parallel machine scheduling: a survey, Asia-Pacific Journal of Operational Research, 18(2), 193, 2001.
87. Ogbu, F.A. and Smith, D.K., The application of the simulated annealing algorithm to the solution of the flowshop problem, Computers and Operations Research, 17, 243–253, 1990.
88. Rajendran, C., Heuristic algorithm for scheduling in a flowshop to minimize total flow time, International Journal of Production Economics, 29, 65–73, 1993.
89. Sridhar, J. and Rajendran, C., Scheduling in a cellular manufacturing system: a simulated annealing approach, International Journal of Production Research, 31, 2927–2945, 1993.
90. Van Laarhoven, P.J.M. and Aarts, E.H.L., Simulated Annealing: Theory and Applications, Reidal, Dordrecht, Netherlands, 1987.
91. Boctor, F.F., A linear formulation of the machine-part cell formation problem, International Journal of Production Research, 29(2), 343–356, 1991.
92. Capi, G., Kaeko, S., Mitobe, K., Barolli, L., and Nasu, Y., Optimal trajectory generation for a biped robot using genetic algorithm, Journal of Robotics and Autonomous Systems, 38(2), 119–128, 2002.
93. Garg, D.P. and Kumar, M., Optimization techniques applied to multiple manipulators for path planning and torque minimization, Engineering Applications of Artificial Intelligence, 15, 241–252, 2002.
94. Groover, M.P., Industrial Robotics, McGraw-Hill, Singapore, 1987.
95. Hartly, J. Robots at Work, IFS Publishing, HAR, U.K., 1983.
96. Hourtash, A. and Tarokh, M. Manipulator path planning by decomposition algorithm and analysis, IEEE Transactions on Robotics and Automation, 17(6), December 2001.
97. Hwang, K.-S. and Ming-Yi Ju, A propagating interface model strategy global trajectory planning among moving obstacles, IEEE Transactions on Industrial Electronics, 49(6), December 2002.
98. Ineg, D.Y. and Chen, M., Robot trajectory planning using simulation, Journal of Robotics and Computer Integrated Manufacturing, 3(2), 121–129, 1997.
99. Katz, Z. and van Niekerk, T., Implementation aspects of intelligent machining, Proceedings of the Institution of Mechanical Engineers, 217, 601–613, 2003.
100. Moshan, S., A Robot Engineering Textbook, Harper & Row, New York, 1987.
101. Xiangrong, Xu. and Chen, Y., A method for trajectory planning of robot manipulators in Cartesian space, IEEE Proceedings on Intelligent Control and Automation, Hefei, China, July 2, 2000.