Generalizing knowledge enabled fast-adaptive optimization for advanced machining systems

The infusion of generalized knowledge into machining systems holds significant potential for enhancing optimization processes, rendering them fast-adaptive, flexible, and goal-oriented. To foster the autonomous evolution of central control systems and effectively address multi-objective requirements, this paper introduces a novel approach: a generative manifold-based policy-gradient method tailored for approximating the continuously distributed Pareto frontier in advanced machining system optimization. This method seamlessly integrates multi-pass operations into a multi-policy Markov Decision Process to adeptly respond to dynamic changes in machining configurations. Moreover, it leverages a multi-layered generator to effectively map the high-dimensional policy manifold from a simple Gaussian distribution, thereby simplifying intricate computations. Experimental findings in various cutting scenarios underscore the superior effectiveness of the proposed method compared to meta-heuristics in tackling the challenges of advanced machining optimization.