Multi-objective Optimization of Cutting Conditions When Turning Aluminum Alloys (1350-O and 7075-T6 Grades) Using a Genetic Algorithm

Abstract

In machining, many output parameters are used to diagnose machinability. Parameters such as tool wear, tool life, cutting temperature, machining force components, power consumption, surface integrity, and chip thickness ratio are regularly employed. The aim of this work is to investigate the behavior of the machining force (Fu), chip thickness ratio (CTR) and chip disposal when turning ductile (1350-O grade) and of high strength (7075-T6 grade) aluminum alloys at various cutting conditions (cutting speed: Vc, feed rate: f, and depth of cut: doc). A central composite design (CCD) of experiments was used that generated second order models of the parameters: [Fu (Vc, doc, f ); CTR (Vc, doc, f )]. Surface response methods and level curves were used for studying the effects of the cutting conditions on the output parameters: Fu and CTR. The cutting conditions (Vc, doc, f ) that simultaneously minimize the machining force (Fu) and the chip thickness ratio (CTR) was determined with the help of the genetic algorithm method (GAM). The results showed that all the input parameters acting both individually or in combination with each other had significant effect on the responses. Moreover, the machining force (Fu) followed a similar trend as the chip thickness ratio (CTR) when the input parameters were varied, i.e., they decreased when the cutting speed increases and increased at higher feed rate and depth of cut.