Research on intelligent CNC programming technology of tool electrode in EDM machining

Ruijun Yang; Jiaqi Li

doi:10.1117/12.3030490

5 June 2024 Research on intelligent CNC programming technology of tool electrode in EDM machining

Ruijun Yang, Jiaqi Li

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131637R (2024) https://doi.org/10.1117/12.3030490
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

In EDM, the cavity is directly replicated and molded by the tool electrode through galvanic corrosion, so the electrode design is directly related to the machining accuracy of the part. However, in the current CAD/CAM system, the processing of tool electrodes is done manually by technicians based on their experience and knowledge, which is very dependent on their professional skills and experience, and also unable to reuse the process, resulting in a large waste of process resources. To solve this problem, an intelligent CNC programming method for tool electrode is proposed, which organizes the historical machining cases of tool electrode to form a CNC parameter dataset, and then constructs a GA-BP network model to train and determine the CNC parameters required in the machining process, and finally generates the machining toolpaths of tool electrode in NX software. The experimental results prove that the integration of GA-BP neural network can effectively reuse the resources of manual programming process of tool electrode, reduce the dependence on technicians' professional skills, and promote the development of tool electrode machining programming in a more intelligent direction.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ruijun Yang and Jiaqi Li "Research on intelligent CNC programming technology of tool electrode in EDM machining", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131637R (5 June 2024); https://doi.org/10.1117/12.3030490

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KEYWORDS

Electrodes

Computer programming

Neural networks

Education and training

Data modeling

Graphite

Copper

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