Cutting Force Optimization Under ANN and QRCCD

The prediction analysis of cutting force in the cutting process is very significant in the manufacturing of products for industrial use. Cutting force is one of the responses in machining operations that affect energy consumption via computer numerical control of the manufacturing process. The focus of this chapter is to employ artificial neural network (ANN) and quadratic rotatable central composite design (QRCCD) to carry out prediction and optimisation to study the cutting force to minimised the energy consumption. The data employed in this study is obtained from experimental machining operation of AA 8012 alloy under vegetable oil TiO₂ biodegradable nano-lubricant for sustainable machining process. The result obtained showed that the minimum cutting force occurs at the optimal machining parameter of spindle speed of 2351 rpm, 101 mm/min feed rate, 1 mm depth of cut, 20 mm length of cut, and 60° helix angle and the minimum cutting force of 31 N with the desirability of 0.968. Also, the generated models developed for the cutting force using ANN and QRCCD predicted the experimental results with 97.5% and 95.56% accuracy under the biodegradable TiO₂ nano-lubricant. This result has proven that the implementation of Heuristic and Metaheuristic Techniques for Advanced Nano-lubricant Machining Optimization is a sustainable manufacturing process.