Optimization of cutting parameters of NiTi memory alloys based on NSGA-II and EWM-GRA-TOPSIS hybrid decision models

During the cutting machining of nickel-titanium shape memory alloy (NiTi-SMA), unreasonable machining parameters will lead to severe tool wear and poor surface quality. This article establishes the prediction model relating cutting speed (vc), feed rate (f) and cutting depth (ap) to surface roughness (Ra), material removal rate (MRR) and tool wear (VB) based on the Genetic algorithm-optimized BP neural network (GA-BP) by designing full-factor cutting experiments. Experimental findings indicate that the model’s fitting degree is 98.57%, and the maximum relative error is 6.2%. On this basis, the NSGA-II is used for multi-objective optimization and Pareto optimal solution search. The EWM-GRA-TOPSIS method is used to calculate the similarity between the randomly selected 30 sets of Pareto optimal solutions and the ideal solution and sort. The optimal cutting parameters were finally determined to be vc = 79 m/min, f = 0.06 mm/r and ap = 0.12 mm. Using the optimized machining parameters for cutting experiments, the Ra value obtained at a processing length of 40 m is 0.459 μm, the VB is only 0.049 mm and the MRR reaches 9.48 mm³/s. The maximum relative errors between the experimental and predicted Ra, VB and MRR of the optimal parameters are 2.7%, 4.1% and 0.6%, respectively.