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.

中文翻译：

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加工参数的元强化学习，用于灵活车削操作的节能过程控制

节能加工已成为节约能源，减少排放和节省制造成本的必要条件。最佳的加工参数决定被认为是实现节能车削的有效方法。对于柔性加工，最重要的是确定适用于各种机器，工件和工具的最佳参数。但是，很少有研究集中在这个问题上。因此，本文通过对加工参数进行综合元强化学习（MRL）来应对这一挑战，以探索优化模型的共性，并利用该知识快速响应新的加工任务。具体来说，首先将优化问题表述为有限马尔可夫决策过程（MDP）。然后，使用actor-critic（AC）框架实现连续的参数优化。在该框架的基础上，执行元策略培训以提高优化程序的泛化能力。通过案例研究并进行比较分析，阐明并阐明了所提出方法的重要性。给从业者的注意-在这里，我们考虑在灵活的车削操作中遇到的能源敏感的加工参数优化的实际应用问题，即设计一种参数优化方法，该方法可以推广到各种加工任务，其中多个目标和约束随加工配置。本文提出了一种新颖的基于元强化学习（MRL）的优化方法，通过训练具有多个加工任务的优化器来提高泛化能力。据我们所知，这是用于节能柔性加工的第一个基于MRL的自适应参数决策方法。应当特别强调的是，技术人员将从减少的决策时间和增加的节能机会中受益。