Intelligent CNC Tool Path Optimization for Sculptured Surface Machining Through a Virus-Evolutionary Genetic Algorithm

Abstract

Priorities for manufacturers worldwide include their attempt towards optimizing modern manufacturing systems to satisfy the needs of their customers. Major goal of the proposed study is to present a novel optimization methodology based on Artificial Intelligence using the Virus Theory of Evolution. The methodology implements a Virus-Evolutionary Genetic Algorithm to undertake sculptured surface tool path optimization in terms of geometrical machining error to reflect part quality and machining time to reflect productivity for both 3-and 5-axis sculptured surface machining. The algorithm implements its virus operators to create efficient solution representations, to rabidly reproduce enhanced schemata during the evaluations' loops, and finally come up with the optimum machining parameters based on the available resources and constraints ought to be imposed. Through a fully automated environment, time-consuming activities and repetitive tasks are no more of the CNC programmers' concern since the algorithm handles the CAM system's

A virus-evolutionary multi-objective intelligent tool path optimization methodology for 5-axis sculptured surface CNC machining

“…On the contrary if virus fitness is degraded the corresponding infection rate is reduced. Information about the representation of these assessment metrics and their programming philosophy can be found in [22], [28] as well as in [29]. The rest of the algorithm's structure involves the chromosome representation, the conventional genetic operators (selection, crossover, mutation) and fitness function evaluation.…”