Optimization of Cutting Conditions Using Regression and Genetic Algorithm in End Milling

Abstract

End milling is a key machining operation in industrial world, particularly in manufacturing of dies and similar products. Although, such products require high degree of surface roughness, milling operation is taken to be the enough for the cost wise if considering further finishing operations. Thus optimizing the cutting conditions to achieve the optimal surface roughness is becoming a vital issue. Several authors tackled this problem. In this paper the same case is investigated but with an advanced algorithm using regression and genetic methodology. The results obtained which ended by deducing a general equation combining the effect of various parameters on surface roughness highlighted the factors involved in achieving the surface roughness and proved to be good tool to predict the optimal cutting conditions.

Design optimization of a three-stage transmission using advanced optimization techniques

“…Such definitions are the rudder of the search process and their formulation impacts the rate of convergence to a solution and the certainty of optimality. Literature [24][25][26] shows that there is increasing application of computing and optimization algorithms to solve design engineering and scientific problems. Such investigations have resulted in discovery of new optimal values while using less resources and time.…”