Sustainable machining of Inconel 718 using minimum quantity lubrication: Artificial intelligence-based process modelling.

Governments and industries are developing aggressive policies to reduce carbon emissions and shift from fossil fuels to renewable energy. On the other hand, industries struggle to reduce energy consumption and depend on production lot sizes to control energy requirements. In this regard, energy-efficient processing through CNC machine tools can potentially influence energy demand and requires energy-aware power consumption strategies for machining processes.

Sustainability metrics targeted optimization and electric discharge process modelling by neural networks.

Aluminium and its alloys, especially Al6061, have gathered significant interest among researchers due to its less density, great durability, and high strength. Due to their lightweight properties, the precise machining of these alloys can become expensive through conventional machining operations for intricate products. Therefore, non-traditional machining such as electric discharge machining (EDM) can potentially be opted for the cutting of Al6061.