Robotic abrasive cloth flap wheel polishing system and multivariate parameter decision-making method for blade leading and trailing edges

The surface quality of the blade leading and trailing edges (LTE) impacts jet-engine performance. This study constructs a robotic abrasive cloth flap wheel (ACFW) polishing system and proposes a multivariate parameter decision-making method for the optimal polishing surface quality of the blade LTE. The robotic polishing system, including machine vision, offline programming, and constant force control, is first developed, and the blade polishing process, including blade clamping, on-machine measurement, position compensation, and polishing strategies, is then analyzed. Finally, a multivariate parameter decision-making method is proposed based on the surface roughness regression model (RM) and adaptive genetic algorithm-backpropagation (AGA-BP) network. The surface roughness RM, influencing factors, and the response curve are determined through a full factorial design (FFD) and the response surface methodology (RSM). Meanwhile, the AGA-BP network, which integrates the adaptive genetic algorithm (AGA) and backpropagation neural network (BPNN), is proposed to model and predict the roughness of the blade surface. Based on the optimal parameters, the surface roughness of the blade LTE will reach Ra = 0.142μm, which illustrates that the developed robotic polishing system is highly efficient and feasible. Furthermore, the mean error percentages of the RM, BPNN, and AGA-BP predictions are 17.946%, 9.633%, and 1.495%, respectively, for four random test datasets. The maximum error for the AGA-BP network is 1.995%, while the minimum is 0.758%. This network model can accurately predict surface roughness for the robotic polishing system of the Ti-6Al-4V blade LTE.