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Application of soft computing techniques in machining performance prediction and optimization: a literature review

  • M. Chandrasekaran
  • M. Muralidhar
  • C. Murali Krishna
  • U. S. DixitEmail author
ORIGINAL ARTICLE

Abstract

Machining is one of the most important and widely used manufacturing processes. Due to complexity and uncertainty of the machining processes, of late, soft computing techniques are being preferred to physics-based models for predicting the performance of the machining processes and optimizing them. Major soft computing tools applied for this purpose are neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. The present paper reviews the application of these tools to four machining processes—turning, milling, drilling, and grinding. The paper highlights the progress made in this area and discusses the issues that need to be addressed.

Keywords

Machining Optimization Process models Soft computing 

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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • M. Chandrasekaran
    • 1
  • M. Muralidhar
    • 1
  • C. Murali Krishna
    • 1
  • U. S. Dixit
    • 2
    Email author
  1. 1.North Eastern Regional Institute of Science and TechnologyItanagarIndia
  2. 2.Department of Mechanical EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia

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