A novel method based on deep reinforcement learning for machining process route planning

Efficient and high-quality machining process route planning is crucial in the realm of manufacturing. Traditional methods heavily rely on human-computer interaction, which can be inefficient. To enhance the efficiency of machining process route planning, this paper introduces a novel framework based on deep reinforcement learning (DRL), designed to automatically generate machining process routes for designated parts. The framework treats machining process route planning as a Markov decision process, making it amenable to DRL techniques. For effective representations of parts, the framework utilizes directed attributed adjacency graphs, wherein nodes represent machining features and edges represent their relationships. To effectively process the graphs, convolutional graph neural networks are employed as the underlying neural networks in the framework. After training, the framework is able to generate efficient machining process routes while adhering to machining process rules. Experimental studies conducted on a number of aircraft structural parts serve as examples to show the feasibility and effectiveness of the proposed approach. The experimental results underscore the effectiveness of the proposed method in machining processes planning for parts and its ability to overcome limitations in traditional methods. As a result, this study contributes to the enhancement of process planning efficiency within the manufacturing domain, carrying practical implications.