Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—Proper selection of cutting parameters results in better performance of cutting force. For this purpose, a series of meticulous experiments were carried out to determine the effect of machining parameters on the cutting force during hard milling of SKD 61 steel under SiO2 nanofluid minimum quantity lubrication (MQL). The cutting parameters selected include cutting speed, feed-rate, depth-of-cut and hardness of workpiece. The Taguchi method and the Response Surface Methodology (RSM) were integrated to design the experiment and analyze the influences of the input factors on the output factor. The L27 orthogonal array developed by G. Taguchi was used to design the experiment. Three cutting force components were measured by using a dynamometer, and then analysis of variance (ANOVA) was carried out. It is found that cutting force components becomes affected primarily by depth-of-cut and feed-rate. A quadratic mathematical model was established to predict the cutting force components during hard milling under nanofluid MQL condition. In order to obtain the maximum material removal rate (MRR) and minimum cutting force, the multi-objective optimization was conducted to find out the optimal cutting parameters.
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