Knowledge graph with deep reinforcement learning for intelligent generation of machining process design

Abstract

In the manufacturing industry, the design of machining processes plays a pivotal role in determining the quality, efficiency, and cost of product manufacturing. Machining process design is progressing towards intelligence to meet the high demands for efficiency and effectiveness in smart manufacturing. Building upon traditional computer-aided process planning, intelligent technologies, such as machine learning and knowledge graphs, have emerged as key drivers in advancing intelligent process design. To address knowledge accumulation, inflexible reuse, and fragmented reasoning in machining process design, this paper organically integrates the structured representation characteristics of the knowledge graph and the perceptual reasoning capabilities of deep reinforcement learning. It introduces an intelligent generation method for machining process design based on knowledge graph and deep reinforcement learning, aiming to achieve unified representation and reasoning reuse for historical process cases and general process rules. The effectiveness of the proposed method is validated through a case study involving the generation of machining process solutions for a specific model of diesel engine components. This research contributes valuable insights to overcoming the limitations of traditional methods and enhancing the efficiency of the machining process design.