Modeling Turning Parameters for PCD Cutting Tool for Decision Process by Evolutionary Multi-Objective Optimization Techniques Using Micro-Genetic Algorithm

Many, if not most, real-world decision-making problems from operations research/management science are very complex in nature and quite hard to solve by classical optimization techniques. Such problems are often confronted with multi-objective decision-making problems, which involve multiple objectives and may produce tradeoffs among different objectives. A solution that is extreme with respect to one objective requires a compromise in other objectives. This restricts choosing a solution which is optimal with respect to only one objective. In this paper, for solving the problem, micro-Genetic Algorithm procedures are used, which are characterized as one of the Evolutionary Algorithms (EA) conceptual tools, to obtain the pareto-optimal zone. In this zone, the decision maker has to choose the best solution after he has made a ranking with all potential solutions. A decision support system shell is developed in order to classify all solutions. Further, the nonlinear relationship between the selected parameters from the process and the tool life and Material Removal Rate (MRR), which are mutually dependent objectives, needs to be optimized simultaneously