Optimization Of Process Parameters In The Abrasive Waterjet Machining Using Bees Algorithm

Abstract

Despite the demonstrated capacity of abrasive waterjet machining to cut materials in the most cost-effective and ecologically friendly manner, the industry is suffering from significant problems associated with product failures. In this study, the Bees Algorithm (BA) is used to anticipate the process parameters that would result in the lowest feasible machining efficiency numbers. The product's cutting quality is determined by examining the average surface roughness value (Ra). The best solution for the process parameters is generated from the optimization of the ideal values of the process parameters, which produces the lowest possible Ra value with fewer repetitions. When compared to experimental data, the findings of this study demonstrate that the suggested BA approach accurately predicts the process parameters that result in the highest value of machining efficiency. The optimal solution requires fewer iterations (68 iterations) and has a lower optimum value of surface roughness (Ra = 1.5223 µm) when V = 50 mm/min, P = 125 MPa, h = 1.5447 mm, d = 102.5056 µm/min, and m = 0.5 g/s.