Prediction Of Nano-Coated Tool Wear Using Bat And Whale Optimization Algorithms

In the industrial machining process, there have been major advances in near-net-shaped forming, which leads machining to be considered a significant modern phenomenon. Machining turns a huge number of metals into chips every year. This study aimed to determine the wear and mechanical properties of various cutting inserts. Polycrystalline diamond (PCD) and Ceramic Inserts were selected as coated inserts. It was discovered that tool wear at the cutting edge impacts various factors, including the amount of cutting forces created during machining; the surface finish of the workpiece is also compromised, resulting in reduced tool life. Owing to the frequent replacement of cutting tools, the decreased wear rate of cutting tools exponentially raises the costs that companies/machine shops would incur. After the second iteration, this insert began to develop crater wear, which resulted in a poor surface finish and high heat generation. However, the surface finish of this instrument was discovered to be the best during the first iteration. From the outcome, the PCD coated tool with feed speeds and low depth of cuts performed the efficient machining process. The surface finish is also accurate for PCD coated tool. The bat and whale algorithms’ optimization involved to find the best technical parameters to achieve the lowest possible error value based on rake and face wear. The bat and whale algorithms were used to determine the optimized rake and face wear values. The bat algorithm outperforms the whale algorithm in terms of wear value predictions.