A Data-Driven and Physically Constrained Approach to Parameter Optimization for Milling Complex Surface

Optimizing the milling parameters for complex surface can improve the machining quality. However, existing methods are oriented toward a simple machining path and single machining process. In this paper, a milling parameter optimization method for complex surface is proposed by combining the data-driven models and physical constraints. Initially, the chatter indicator is derived from the tool vibration using Variational Mode Decomposition. Subsequently, multiple few-shot prediction models are developed based on real machining data using deep neural networks. A mathematical optimization model is then constructed by combining multiple prediction models and physical constraints, which aims to minimize the machining failure rate and maximize the material removal rate under multiple constraints. The spindle speed, feed speed, cutting depth, and path spacing are the optimization parameters. Finally, a Hypervolume-based Multi-Objective Optimization (HMOO) algorithm is proposed to solve the optimization model. The solution set produced by HMOO exhibits superior convergence and diversity compared to the S-Metric Selection Based Evolutionary Multi-Objective Algorithm (SMS-MOEA). In experiments, a five-axis machine tool is employed to mill turbine blades with complex surface. Experimental results demonstrate that the proposed prediction models achieve higher accuracy than widely used regression algorithms. Integrating the high-precision prediction models with HMOO significantly enhances blade machining quality while guaranteeing reliable machining efficiency, resulting in a 5.25% reduction in the machining failure rate. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i>—Methods designed for simple machining paths and single machining processes are inadequate for optimizing milling parameters in complex surfaces. Given the challenging machining characteristics and stringent precision demands of complex surfaces, this paper proposes a milling parameter optimization approach for complex surface by integrating data-driven models with real physical constraints. The approach employs neural network-based prediction models to map the relationships among milling parameters, machining conditions, and machining accuracy, while accounting for actual physical constraints in the optimization process. Our approach enhances the machining accuracy of complex surfaces while ensuring reliable machining efficiency.