Artificial neural network optimizing a novel nano cutting tool via powder metallurgy with a machine learning-based Taguchi approach

In this investigation, the performance of powder-metallurgically manufactured cutting tools made of Fe, 2% C, 2% W, and 1.5 Ti will be assessed. The making of the cutting tool involves the processes of hot forging, sintering, machining, and induction hardening. The induction hardening procedure contributes to the better endurance of the cutting tool. The synthetic cutting tool is evaluated using the L9 orthogonal array. Tool wear and surface roughness are reaction parameters, whereas cutting speed, feed rate, and depth of cut are machining parameters. When turning AISI 1018 low carbon steel, it was discovered that the tool made of powder metallurgy had a superior surface polish than the traditional HSS tool. The experiment is carried out using the Taguchi technique, which is based on machine learning. The Signal to Noise ratio and the ANOVA statistical techniques were used to examine the impact and contribution of the machining parameter on the response outcome. An ANOVA revealed that feed rate has a greater effect on surface quality than cutting speed does on tool wear. Findings from confirmation tests show that the P/M cutting tool performed satisfactorily under the intended ideal machining conditions. The neural network that has been created predicts results more precisely.