Optimisation of cutting force using metaheuristics

Abstract

The significance of this research is to optimise the cutting force in turning by optimising the cutting parameters like cutting speed, feed, and depth of cut. Cutting force is one of the essential characteristic variables to be watch and controlled in the cutting processes to optimise tool life and surface roughness of the workpiece. The principal presumption was that the cutting forces increase due to the wearing of the tool. Cutting force is optimised by the metaheuristic, i.e., genetic algorithm (GA) and teaching-learning based optimisation (TLBO) algorithm. The analysis of the result shows that the optimal combination for low resultant cutting force is low cutting speed, low feed and low depth of cut. This study finds that by adjusting machining parameters, tool life can be enhanced because cutting forces increase due to the wearing of the tool. So, cutting forces have been used to maximise the tool life because cutting force increased rapidly as tool life finished. As a result, the production cost can be minimised and be extending the tool usage and subsequently, the machining time is reduced, and the tool usage can be extended.