Multi-objective optimization of the trimming operation of CFRPs using sensor-fused neural networks and TOPSIS

Abstract

This work presents the results of an optimization study of trimming carbon fiber composite by technique of order preference by similarity to ideal solution (TOPSIS). Edge trimming was conducted at different levels of cutting speed, feed speed, and radial depth of cut using a 2k factorial design. The effects of cutting parameters on tool wear, surface roughness and tool temperature were analyzed and discussed. Data generalization was performed by training multiple neural network structures (NN) where the input vector of the neural networks contained sensor data in addition to typical process parameters. TOPSIS was used to select the optimal cutting conditions that could generate minimum tool wear, surface roughness, and tool temperature simultaneously while maintaining high production rates. The results show that the NN models offer better results when sensor data is fused in the input vector. TOPSIS optimum conditions were confirmed by validation experiments and were found to be accurate.