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

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.

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends

“…The use of cutting tool inserts such as cubic boron nitride, polycrystalline diamond and ceramic was intended to prevent rapid TW; however, high tool costs were observed. Recently, however, the most suitable cutting conditions have been developed to ensure minimum TW and minimum production cost [ 132 , 133 , 134 , 135 ]. In addition, modeling and optimization techniques such as artificial neural network, fuzzy logic, multiple regression, response surface methodology, variance analysis, and Taguchi method were used in the development of these conditions [ 36 ].…”

A cloud-based, knowledge-enriched framework for increasing machining efficiency based on machine tool monitoring

“…The simulation models are usually used in order to get results such as the cutting forces during the process, the stability of the cutting process and the resulting surface quality of the produced part [34,35,36] Traditional approaches in the field of optimization of cutting conditions include the optimization of cutting conditions using dynamic programming, genetic algorithms, simulated annealing, particle swarm optimization, and neural networks [37]. Some of the most recent optimization models include the selection of cutting condition, based on experimental data and Design of Experiment (DoE) methods, such as the Taguchi method or RSM methods [38,39,40,41]. In [42] the authors used experimental data to calculate a regression model for describing the surface roughness in milling, which in turn was used as an objective function for optimizing the cutting conditions using a genetic algorithm.…”

Optimum selection of tool materials for machining of high-strength steels based on fuzzy comprehensive evaluation method

“…Thus, the fuzzy comprehensive evaluation method can be used for tool selection. 18 In this research, the workpiece is 42CrMo highstrength steel (AISI 4140/4142 steel), which is typically used as forged gears and shafts, spindles, fixtures, jigs, and collars because of the high fatigue and torsional strength, abrasion and impact resistance, and toughness. Based on the fuzzy comprehensive evaluation theory and experimental data, the cutting performance of tools with different materials was comprehensively evaluated with considerations of multiple evaluation indicators, and the optimum tool materials with the best cutting performance were selected.…”