Modelling and Simulation in Manufacturing

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Course Contents

Basic concepts in modeling and simulation of manufacturing processes- conventional and unconventional manufacturing processes; Different approaches of modeling – Analytic and numerical techniques.

Empirical and semi-empirical – Design of Experiments (DOE), Analysis of variance (ANOVA), Regression analysis; Numerical methods – Finite Difference and Finite Element Methods.

Newer approaches –Fuzzy Logic, Artificial Neural Networks, Genetic Algorithms. Typical case studies covering different manufacturing processes.

Text/Reference Books

1. S.S. Sastry, Introductory  Methods  of Numerical Analysis Prince Hall of India, New Delhi, 1993.
2. Krishnamoorthy C. S., `Finite Element Analysis`, Tata McGraw Hill, 1987.
3. David E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Professional; 2000
4. Lotfi Zadeh, Janusz Kacprzyk, Fuzzy Logic for the Management of Uncertainty, Wiley-Interscience, 1992
5. James A. Anderson, An Introduction to Neural Networks, The MIT Press, London, 1997
6. Simon Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall; 2 edition, 1998
7. Timothy J. Ross, Fuzzy Logic With Engineering Applications, John Wiley, 2004