Abstract

In this paper, a new method is presented for the optimal selection of cutting tools and operating conditions for end milling. An end milling model developed by Armarego is used to determine the cutting forces and power. This model is coupled with a microgenetic algorithm to determine the optimal tooling and operating conditions. Unlike other more conventional optimization techniques, genetic algorithms can be used for discrete data. In this paper, the optimal tool is identified from a manufacturer’s catalog of milling cutters along with the optimal speed and feed to achieve both minimum production cost and minimum production time criteria. In addition, constraints are imposed on the cutting speed, feed, maximum cutting force, and available power to simulate realistic machine tool constraints.

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