Optimizing turning parameters for improved surface quality and productivity of Al-MMC under dry, wet, and MQL conditions

Abstract

The machining of Aluminum-based Metal Matrix Composites (Al-MMCs) is challenging due to their inhomogeneity, anisotropic nature, and dynamic cutting forces. In this paper, the effect of machining parameters, including cutting speed, feed rate, and depth of cut, on surface quality (Ra) and main (Normal) cutting forces (Fc) during turning of Al-MMCs under different cutting conditions (DRY, WET, and MQL) was investigated. Statistical analysis tools were used to analyze the experimental results, and ANOVA and RSM techniques were used to model the relationships between machining parameters and responses. The results showed that feed rate significantly affected both Ra and Fc for all machining conditions. The DRY machining mode was optimal for surface finish, and the MQL mode was effective in reducing cutting forces due to its cooling and lubrication properties. A feed rate of 0.03 mm rev−1 was found to be the most favourable for all cutting environments as it resulted in lower Ra and Fc values. Turning in the WET condition also produced lower Ra due to the effective flushing of chips and cleaning of the cutting zone. In contrast, turning in the MQL condition reduced cutting forces by 17% and 50% as compared with dry and Wet conditions respectively under optimal conditions. Similarly, surface roughness improves under dry conditions by 33% and 3% compared to Wet and MQL conditions.

Optimization of responses in single and multi-point machining processes using Dragon-fly algorithm coupled with TOPSIS and VIKOR decision-making approaches

“…On the basis of the microscopic flank wear investigations, tool wear analysis was conducted. Umesh et al [14] studied the effect of process parameters on surface finish and cutting forces in single point turning operation of Al-MMCs under dry, wet and minimum quantity lubrication conditions. They used analysis of variance and response surface method (RSM) techniques on the experimental responses for analysis and modeling.…”