Version 1
: Received: 5 August 2024 / Approved: 6 August 2024 / Online: 8 August 2024 (11:50:55 CEST)
How to cite:
Li, P.; Chen, M.; Ji, C.; Zhou, Z.; Lin, X.; Yu, D. An Agent-Based Method for Feature Recognition and Path Optimization of CNC Machining Trajectories. Preprints2024, 2024080453. https://doi.org/10.20944/preprints202408.0453.v1
Li, P.; Chen, M.; Ji, C.; Zhou, Z.; Lin, X.; Yu, D. An Agent-Based Method for Feature Recognition and Path Optimization of CNC Machining Trajectories. Preprints 2024, 2024080453. https://doi.org/10.20944/preprints202408.0453.v1
Li, P.; Chen, M.; Ji, C.; Zhou, Z.; Lin, X.; Yu, D. An Agent-Based Method for Feature Recognition and Path Optimization of CNC Machining Trajectories. Preprints2024, 2024080453. https://doi.org/10.20944/preprints202408.0453.v1
APA Style
Li, P., Chen, M., Ji, C., Zhou, Z., Lin, X., & Yu, D. (2024). An Agent-Based Method for Feature Recognition and Path Optimization of CNC Machining Trajectories. Preprints. https://doi.org/10.20944/preprints202408.0453.v1
Chicago/Turabian Style
Li, P., Xusheng Lin and Dong Yu. 2024 "An Agent-Based Method for Feature Recognition and Path Optimization of CNC Machining Trajectories" Preprints. https://doi.org/10.20944/preprints202408.0453.v1
Abstract
In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), often resulting in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models, analyze the key geometric information in the G-code generated during CNC machining, and uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bezier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. Taking the optimization of a gear model’s process file as an example, the effectiveness of the proposed method is verified. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths, reduce machining defects, and has substantial application value.
Keywords
CNC system; intelligent elements; process analysis; path optimization; deep learning; feature recognition
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
