Meta-modeling Optimization of the Cutting Process During Turning Titanium Metal Matrix Composites (Ti-MMCs)

The Outstanding characteristics of titanium metal matrix composites (Ti-MMCs) have brought them up as promising materials in different industries, such as aerospace and biomedical. They exhibit high mechanical and physical properties, in addition to their low weight, high stiffness and high wear resistance. The presence of the ceramic reinforcements in a metallic matrix further contributes to these preferable properties. However, the high abrasive nature of the ceramic particles limits greatly the machinability of this class of material, as they induce significant tool wear and poor surface finish. In this study an attempt is made to find the optimum cutting conditions in terms of minimizing the tool wear and surface roughness during machining Ti-MMCs. Meta-modeling optimization in performed to achieve the goal.In this study the three independent parameters under consideration are the cutting speed, feed rate and the depth of cut. The response parameters are the surface roughness and the tool wear rate. The independent parameters are divided into a set of levels at which the experiments are conducted. At each experimental condition the two response parameters are measured. Kriging meta-modeling technique is used to fit a model to the response parameters in the multi-dimensional space. These models are used, in turn, within a multi-objective optimization algorithm to find the optimum cutting condition space. The above-mentioned algorithm is based on an evolutionary multi-objective search technique known as SPEA (Strength Pareto Evolutionary Algorithm).