A review of empirical modeling techniques to optimize machining parameters for hard turning applications

Author:

Dureja JS1,Gupta VK1,Sharma Vishal S2,Dogra Manu3,Bhatti Manpreet S4

Affiliation:

1. Department of Mechanical Engineering, Punjabi University, Patiala, Patiala, India

2. Department of Industrial and Production Engineering, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, India

3. SSG Panjab University Regional Centre, Hoshiarpur, India

4. Department of Botanical and Environmental Sciences, Guru Nanak Dev University, Amritsar, India

Abstract

There has been a tremendous development in the field of modeling and optimization methods starting from Taylor’s tool life model. Use of costly tools such as polycrystalline cubic boron nitride, polycrystalline diamond and ceramics in high-end computer numerical control machining forces the researcher to minimize the experimental runs to achieve the best cutting conditions with minimum tool wear and overall production cost. Machining process optimization to achieve said objectives comprises selecting optimum cutting parameters by applying low-cost mathematical models. This article attempts to evaluate the applicability of various modeling and optimization methods to specific response parameters in hard turning problems. Various empirical modeling techniques such as linear regression modeling, artificial neural networks, polynomial and fuzzy modeling along with process optimization through Taguchi, response surface methodology and genetic algorithm for hard turning applications have been discussed in length to provide the production engineers a ready database to compare relative merits and suitability of these techniques for a particular machining application. Also, article discusses integration of different modeling and optimization techniques to achieve desired goals when a single optimization technique is not able to provide the acceptable solution. The last part of the article highlights the current trends in hard turning applications and research priorities for future work.

Publisher

SAGE Publications

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

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