A Genetic Algorithm for Cost Estimating Turned Parts in an Interactive DFM Environment

This paper presents a method for the optimization of machining parameters, essential for the determination of an economical operating point for multi-pass turning operations. Optimal selection of cutting parameters such as the number of passes, depth of cuts, cutting speeds and feed rates are critical to process planning and cost optimization. This in turn creates a crossing point between product design and manufacturing. Generally, machining models are complex since they are highly non-linear. This research utilizes genetic algorithms as an optimization technique in order to maximize the accuracy of results, the computational achievement, and to minimize the influence of initial conditions and part geometry. A new approach that uses conventional turning process modeling along with genetic algorithms to rapidly search and optimize the feasible workspace is considered. The total production cost minimization is achieved by adding together the minimum cost of each roughing pass and the final finishing pass. In addition, the preventive tool replacement policy used in practice is incorporated. Finally, the results obtained for test cases are evaluated and compared.