Efficient genetic algorithm for multi-objective robust optimization of machining parameters with taking into account uncertainties

Abstract

The respect of the machined piece quality and productivity is closely related to the mastery of uncertain factors. Indeed, the efficient solutions obtained from the machining parameter optimization based on classical methods are assigned of uncertain deviations which affect the cutting process. In the present paper, we propose multi-and monoobjective optimization approach of parameter turning with taking into account both production constraints related to piece quality, to machine power, or to tool life, than uncertainty factors related to the tool wear and to piece geometry defaults. To this end, we developed and implemented an efficient genetic algorithm, based on an evaluation mechanism of "objective" functions, which integrate the Monte Carlo simulations to calculate the robustness of objective function and different constraints. Our approach has been validated by two applications implemented with Matlab™ for the minimization of cost and machining time, which has allowed obtaining simultaneously efficient and robust results and offering the possibility to choose beforehand a compromise between efficiency and robustness of solutions.

Bi-objective robust optimization of machined surface quality and productivity under vibrations limitation

“…Thereafter, a comparison between the robust optima (under uncertainty) and efficient optima (classical) is processed. For this, the developed genetic algorithm MC-GA is used for its efficiency to treat the optimization problems under uncertainty [9]. The proposed "robust" genetic algorithm contains the same steps as the "deterministic" genetic algorithm.…”

“…The two parameters are the (9) tool feed ( f ) and depth of cut (a p ). Their variabilities are assumed to follow a uniform distribution.…”

“…To relate theory and practice, we have built models of substitution by multiple linear regressions from design of experiments for the prediction and optimization of cutting parameters. Using the developed genetic algorithm [9] that based on a robust evaluation mechanism of chromosomes by Monte-Carlo simulations, the influence and interest of the uncertainties integration in the machining optimization will be demonstrated.…”

New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm

“…Moreover, an efficient optimum is defined as the global minimum of the objective function. Thus, upon the resolution of robust optimization problems, particularly in [12,13], the use of safety coefficients under another name (penalty factors) is taken up in the penalistic formulation of constraint functions. But, the penalty factor is not related directly to the requirements of a reliable machining.…”

“…But, the penalty factor is not related directly to the requirements of a reliable machining. As stated in [13], overestimation of the penalty factor can lead to less efficient solutions of the optimization problem, and even that there would be no solutions because of the closure or distortion of search space. To remove this ambiguity, an approach to determine the optimum cutting conditions with consideration of uncertainty, called 'probabilistic optimization', has emerged.…”