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

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.