| Abstrakt: |
The Computer Numerical Control (CNC) of a Lathe Machine helps in shaping hard materials like metal and wood, while rotating on two axes and the amount of materials removed per unit time during the production process provides the material removal rate (MRR). Hence, the objective of this paper was to optimize the Material Removal Rate in a Computer Numerical Control Lathe Machine in Turning AISI 1040 Steel while focusing on cutting parameters such as depth of cut, cutting speed, and feed rate. Employing a central composite design with twenty experimental runs, ANOVA analysis revealed cutting speed (F-value: 80.40) as the most influential parameter on MRR. Initially, Artificial Neural Networks (ANN) predicted MRR, yielding optimal parameters: depth of cut (0.25 mm), cutting speed (250 m/min), feed rate (0.20 mm/rev), resulting in MRR of 27.88 mm3/min. Genetic Algorithm (GA) optimization surpassed ANN, yielding higher MRR (29.07442 mm3/min) with optimal parameters: depth of cut (0.65 mm), cutting speed (221.09 m/min), and feed rate (0.21 mm/rev). Confirmatory tests validated predictions. This study provides insights into enhancing CNC turning efficiency and productivity. [ABSTRACT FROM AUTHOR] |
