Multi Parametric Optimization of Dry Turning of Titanium Alloy (Ti6Al4V Graded 5) using Coated Carbide Insert: A Novel Hybrid RSM-Artificial Gorilla Troop Optimization and Dingo Optimization Algorithm

Abstract

AbstractThe present research piloted dry turning of titanium alloys (Ti-6Al-4V Grade 5) utilizing modern titanium carbonitrides chemical vapor deposition (MT-CVD) coated carbide tool inserts. The impacts of cutting parameters such as feed rate, cutting speed, and depth of cut on output responses such as cutting forces, flank wear, and surface roughness were investigated using a Box-Behnken design based on response surface methodology (RSM). To find the optimum cutting condition, a newly developed hybrid optimization method, namely RSM-linked Artificial Gorilla Troop Optimization Algorithm and Dingo Optimization Algorithm, was utilized. The flank wear along with cutting force was recorded when the depth of cut was less while better surface finish was achieved with lower cutting speed. Additionally, analysis of variance was used to determine the most important component in each of the three responses, followed by a confirmatory test that revealed a high degree of agreement between anticipated and experimental results. As per the analysis of variance (ANOVA) results, the depth of cut was determined to be the most crucial component in attaining the lowest cutting force, flank wear, and surface roughness responses. The results obtained using the artificial gorilla optimization algorithm and the dingo optimization algorithm are found to be more precise than those obtained using the RSM-designed experimentation of dry turning operations, as cutting force, surface roughness, and flank wear have been minimized by utilizing the factor settings achieved using both the artificial gorilla optimization algorithm and the dingo optimization algorithm.KeywordsArtificial gorilla troop optimization algorithmDingo optimization algorithmDry turningRSMTitanium alloy