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RESEARCH PRODUCT

Implementing some Evolutionary Computing Methods for Determining the Optimal Parameters in the Turning Process

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In this paper, we comparatively present two heuristics search methods – Simulated Annealing and Weighted Sum Genetic Algorithm, in order to find optimal cutting parameters in turning operation. We consider five different constraints aiming to achieve minimum total cost of machining. We developed a customizable software application in Microsoft Visual Studio with C# source code, flexible and extensible that implements the optimization methods. The experiments are based on real data gathered from S.C. “Compa” S.A Sibiu, a company that manufactures automotive components and targets improving of product quality and reducing cost and production time. The obtained results show that, although the Weighted Sum Genetic Algorithm does not guarantee the optimality of finals solution despite of a high probability to be, it is superior to that provided by Simulated Annealing.