Companies operating in today's machining environment are focused on improving their product quality and decreasing manufacturing cost and time. In their attempts to meet these objectives, the machining processes optimization is of prime importance. Among the traditional optimization methods, in recent years, modern meta-heuristic algorithms are being increasingly applied to solving machining optimization problems. Regardless of numerous capabilities of the Monte Carlo method, its application for solving machining optimization problems has been given less attention by researchers and practitioners. The aim of this paper is to investigate the Monte Carlo method applicability for solving single-objective machining optimization problems and to analyze its efficiency by comparing the optimization solutions to those obtained by the past researchers using meta-heuristic algorithms. For this purpose, five machining optimization case studies taken from the literature are considered and discussed.
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