Drilling process on CFRP: Multi-criteria decision-making with entropy weight using grey-topsis method

Abstract

Moisture strongly affects the quality and mechanical specificity of carbon fiber reinforced plastic (CFRP) when using lubrication fluids during machining, and the significant impact of the cutting tool geometry and cryogenic gas cooling on CFRP machining capabilities are observed. The main body of this paper aims at making decisions about the optimum parameter of the drilling process while machining on CFRP base on the grey relational coefficient embed to the technique for order of preference by similarity to an ideal solution (Grey-TOPSIS). The entropy method was used to determine the weight of decision-making for handling a multiple measure decision-making response. The twist angle of the tool drill, lubrication, and feed rate were used as the input variables, and were analyzed while taking into account several multi-response outputs, such as the surface roughness, uncut fiber, and delamination. The result showed that a feed rate of 228 mm/min, the high-helix twist angle, and cryogenic CO2 lubrication leads the calculated value to close the relative value, which minimizes the value of the surface roughness, the uncut fiber, and the delamination. Finally, verification of the valid effect of each parameter process was conducted using analysis of variance. The results indicated that the lubrication was the highest remarkable criterion on the uncut fiber, the delamination, and the surface roughness. By integrating the advantage of grey systems theory, and the technique for order preference by similarity to an ideal solution, to evaluate and optimize the machining parameter, the results indicate that the proposed model is useful to facilitate the multi-criteria decision-making problem under the environment of uncertainty and vagueness. This relatively advanced approach is very effectual in rejecting process variation and a great assistive strategy than other multi-criteria decision-making approaches.