Experimental Investigation and Parametric Optimization of Turning Process Using Grey Relational Analysis
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Cooling method type significantly affects the turning process performance. In turning processes, there are several input parameters. However, the mode of application of the cutting fluid and the amount of cutting fluid used doesn’t have enough attention. The aim of this work is to investigate the effect of different cooling methods such as minimum quantity lubricant (MQL), wet cooling and dry cutting on turning process performance during machining of EN19 steel material. In the present, an attempt will be made to select the optimum turning process parameters using multi objective optimization called Grey relational analysis (GRA). Experiments were conducted based on the Taguchi L12 orthogonal array (OA) design. In this work, cutting speed, feed rate, depth of cut and cooling method will be considered as turning process variables whereas Cutting temperature, tool wear and surface roughness were taken as output characteristics. Grey relational analysis (GRA) optimization technique was applied to select the optimum cutting parameters. From results, the optimum turning process variables determined as spindle speed 800 rpm, feed rate 0.16mm/rev, depth of cut 0.4mm, coolant type MQL respectively using GRA for concurrent optimization of cutting temperature, surface roughness and tool wear.
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