Six sigma optimization of multiple machining characteristics in hard turning under dry, flood, MQL and solid lubrication

The manufacturing industry, especially of automotive sector requires a robust strategy to become cost effective. As a tool for process improvement and reduction of defects while obtaining a major increase in quality, a statistical method can be successfully implemented. It permits removing variability from a process by a field strategy of define –measure – analyze – improve – monitor. This study was devoted to originally develop a robust manufacturing process using a statistical optimization i.e. Six sigma to obtain machining with accurate machined surface roughness while keeping the cutting temperature in control to reduce waste of energy. As subject operation, the turning of hardened steel under dry, flood cooling, minimum quality lubrication, and solid lubrication with compressed air condition is considered. The experiments were performed according to Taguchi L8 orthogonal array. The olive oil and graphite were used as liquid and solid lubricant, respectively. It was found that the dry condition can generate good quality surface, but the temperature is dispersive. On the other hand, the MQL showed reduction of temperature gradient. Continuous process control is required to check and balance the depth of cut and cutting speed as they are the two most dominant factors.