Identification of tool wear and surface morphology measurements in sustainable milling of Al 6082 hybrid metal matrix composite

Aluminium hybrid metal matrix composites have significantly increased in many advanced applications owing to their unique properties. However, these hybrid composites are difficult to machine because of hard reinforcements in the matrix. After identifying the optimum cutting conditions, it's crucial to identify the tool wear mechanisms to keep surface roughness within the desired range, especially with hybrid composites. Hence, the present work mainly focuses on identifying the wear mechanism of PVD-coated TiAlN cutting tool inserts during the end milling of Al 6082 hybrid metal matrix composites. MQL, CO2, and a combination of MQL + CO2 cooling conditions were employed over the cutting zone to decrease tool wear. In the context of 300 mm of cutting length, employing CO2, MQL, and MQL + CO2 conditions led to a significant reduction in tool wear by 34.5 %, 50.1 %, and 74.7 %, respectively, compared to dry cutting. The results showed that the MQL + CO2 condition produced better results than the other cooling conditions, improving tool life and surface quality

Optimization of Process Parameters for Turning Hastelloy X under Different Machining Environments Using Evolutionary Algorithms: A Comparative Study

In this research work, the machinability of turning Hastelloy X with a PVD Ti-Al-N coated insert tool in dry, wet, and cryogenic machining environments is investigated. The machinability indices namely cutting force (CF), surface roughness (SR), and cutting temperature (CT) are studied for the different set of input process parameters such as cutting speed, feed rate, and machining environment, through the experiments conducted as per L27 orthogonal array. Minitab 17 is used to create quadratic Multiple Linear Regression Models (MLRM) based on the association between turning parameters and machineability indices. The Moth-Flame Optimization (MFO) algorithm is proposed in this work to identify the optimal set of turning parameters through the MLRM models, in view of minimizing the machinability indices. Three case studies by considering individual machinability indices, a combination of dual indices, and a combination of all three indices, are performed. The suggested MFO algorithm’s effectiveness is evaluated in comparison to the findings of Genetic, Grass-Hooper, Grey-Wolf, and Particle Swarm Optimization algorithms. From the results, it is identified that the MFO algorithm outperformed the others. In addition, a confirmation experiment is conducted to verify the results of the MFO algorithm’s optimal combination of turning parameters