A Novel Method Based on Deep Learning for Hole Group Machining Process Path Optimization
Keywords: hole group machining, deep learning, tool path, optimization, efficiency
Abstract: Hole group machining is a critical operation in modern manufacturing, and its tool path optimization plays a key role in
improving efficiency, reducing costs, and ensuring product quality. Traditional methods for tool path optimization rely on heuristic algorithms or manual intervention, which are often time-consuming and sub-optimal for complex workpieces. This paper proposes a novel deep learning-based method for optimizing the machining process path for hole group processing. A deep learning framework is built. A deep learning-based process optimization experiment for hole group machining is carried out. By using advanced neural network architectures and reinforcement learning techniques, the proposed method achieves significant improvements in path
efficiency, reduces non-cutting movements, and enhances tool lifespan. The experimental results demonstrate that the method proposed
in this paper can effectively improve the efficiency.
Submission Number: 7
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