Multi-objective method integrated with back propagation neural network analysis for surface quality in wood–plastic composite milling

Abstract

In wood–plastic composites (WPCs) milling, achieving optimal material removal rates and surface roughness levels are critical objectives. In this study, WPCs milling experiments were conducted, and a back propagation (BP) neural network was applied to develop a predictive model for surface roughness. A geometric method was used to derive the calculation formula for the material removal rate. Subsequently, a multi-objective approach was adopted to determine the optimal combination of control factors, including spindle speed n, feed rate U, milling depth h, for WPCs milling. The findings indicate that an increase in spindle speed reduced surface roughness, whereas higher feed speed and milling depth resulted in increased surface roughness. Variance analysis revealed that milling depth had the greatest impact on surface roughness, contributing 34.66%, followed by feed speed at 30.77% contribution and spindle speed at 30.55% contribution. A BP prediction model for surface roughness was established with high accuracy, exhibiting a maximum error of 4.89%. Furthermore, a multi-objective particle swarm optimization algorithm was applied to optimize the objectives of minimizing surface roughness and maximizing material removal rate. Based on the obtained Pareto front, the milling parameter combination of n = 12,000 r/min, U = 3.23 m/min, and h = 0.4 mm is recommended for roughing. For semi-finishing, the optimal parameters are n = 12,000 r/min, U = 4.76 m/min, and h = 0.4 mm. For finishing, the suitable combination is n = 12,000 r/min, U = 6 m/min, and h = 0.72 mm. Experimental verification demonstrated a maximum predictive error of 16.87%, confirming the feasibility of the multi-objective optimization approach.