Abstract
In manufacturing industries, selecting the appropriate cutting parameters is essential to improve the product quality. As a result, the applications of optimization techniques in metal cutting processes is vital for a quality product. Due to the complex nature of the machining processes, single objective optimization approaches have limitations, since several different and contradictory objectives must be simultaneously optimized. Multi-objective optimization method is introduced to find the optimum cutting parameters to avoid this dilemma. The main objective of this paper is to develop a multi-objective optimization algorithm using the hybrid Whale Optimization Algorithm (WOA). In order to perform the multi-objective optimization, grey analysis is integrated with the WOA algorithm. In this paper, Stainless Steel 304 is utilized for turning operation to study the effect of machining parameters such as cutting speed, feed rate and depth of cut on surface roughness, cutting forces, power, peak tool temperature, material removal rate and heat rate. The output parameters are obtained through series of simulations and experiments. Then by using this hybrid optimization algorithm the optimum machining conditions for turning operation is achieved by considering unit cost and quality of production. It is also found that with the change of output parameter weightage, the optimum cutting condition varies. In addition to that, the effects of different cutting parameters on surface roughness and power consumption are analysed.
Highlights
Modern day manufacturing industries operate in a demanding environment of constant competition, requiring them to focus heavily on ways in which they could improve their processes
genetic algorithm (GA) appeared on numerous occasions in literature where it was employed to study the effects of cutting parameters on other output characteristics such as surface roughness and energy efficiency [8,9,10]
The results show anticipated trends supporting previous studies, and it is further shown that changing the weightage factors assigned to each output parameter may change the optimal combination of parameters entirely
Summary
Modern day manufacturing industries operate in a demanding environment of constant competition, requiring them to focus heavily on ways in which they could improve their processes. The choice is ever more important as the cutting parameters tend to be quite extensive, since slight variations in them can lead to significant improvements in the output quality characteristics. Relying solely on experience or machine tool manuals for choosing the input parameters does not necessarily guarantee the best selection [2]. This is because the impact of each parameter tends to depend on the particular machining environment of the production line. This justifies the need for effective optimization techniques, many of which have been proposed and demonstrated in past studies [3]. GA appeared on numerous occasions in literature where it was employed to study the effects of cutting parameters on other output characteristics such as surface roughness and energy efficiency [8,9,10]
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