Meta-Reinforcement Learning of Machining Parameters for Energy-Efficient Process Control of Flexible Turning Operations

Energy-efficient machining has become imperative for energy conservation, emission reduction, and cost saving of manufacturing sectors. Optimal machining parameter decision is regarded as an effective way to achieve energy efficient turning. For flexible machining, it is of utmost importance to determine the optimal parameters adaptive to various machines, workpieces, and tools. However, very little research has focused on this issue. Hence, this paper undertakes this challenge by integrated meta-reinforcement learning (MRL) of machining parameters to explore the commonalities of optimization models and use the knowledge to respond quickly to new machining tasks. Specifically, the optimization problem is first formulated as a finite Markov decision process (MDP). Then, the continuous parametric optimization is approached with actor-critic (AC) framework. On the basis of the framework, meta-policy training is performed to improve the generalization capacity of the optimizer. The significance of the proposed method is exemplified and elucidated by a case study with a comparative analysis. Note to Practitioners-Here, we consider a real-world application problem of energy-aware machining parameter optimization encountered in flexible turning operations, namely, design of a parametric optimization method that can be generalized to various machining tasks where multiple objectives and constraints varying with the machining configurations. This paper presents a novel meta-reinforcement learning (MRL)-based optimization method to improve the generalization by training optimizer with multiple machining tasks. To the best of our knowledge, this is the first MRL-based method of adaptive parameter decision for energy-efficient flexible machining. It should be highly emphasized that technologists benefit from the reduced decision-making time and the improved energy saving opportunity.

Automated summary

This paper integrates meta-reinforcement learning of machining parameters to explore optimization model commonalities and apply them to energy-efficient flexible machining. The proposed method improves optimizer generalization by training with multiple machining tasks, representing the first MRL-based adaptive parameter decision for energy-efficient machining. Technologists benefit from reduced decision-making time and improved energy-saving opportunities.