Multi-objective optimization of carbon fiber–reinforced polymer drilling process based on grey fuzzy reasoning grade analysis

Abstract

With outstanding mechanical advantages such as high strength, light weight, high rigidity, low thermal expansion, good resistance corrosion, and high vibration resistance, carbon fiber–reinforced polymer (CFRP) is becoming increasingly popular in the automotive, aerospace, and medical spheres. For the CFRP machining, the biggest drawback is the effect of moisture on the mechanical properties caused by the cooling liquid and tool geometry. The appreciable body of study on the influence of wetness on the mechanical quality of carbon fiber–reinforced plastic has shown that there is no suitable use for lubrication fluids in the composite industry. However, there is a significant impact on the CFRP machining capabilities caused by the geometry of the cutting tool and cryogenic liquid cooling. This paper investigated the optimal choice of the parametric combinations in the CFRP drilling process. The optimization method was proposed using the Fuzzy interference system based on grey relation analysis. The experiment data was designed according to the Taguchi method. The twist angle of the drill, the coolant gas, and the feed rate were considered as the parameter inputs while observing several multi-responses such as thrust force and temperature generated during drilling process. The optimal result, as well as the case with highest grey fuzzy reasoning grade (GFRG), indicated that the low feed rate combined with the high twist angle drill in the hybrid cryogenic CO2-N2 gas condition tended to minimize the value of the response output. The verification experiment results showed a good correlation with the optimal analytical result. The analysis of variance results also showed that the reliability of the data was satisfactory.