MECHANICAL & APPLIED SCIENCE DOMAIN
Published February 19, 2024 | Version v1
Journal article Open

A Review: Advancements in Automation Techniques for EDM (Electrical Discharge Machining)

Authors/Creators

  • 1. Asst. Professor, Dr. Daulatrao Aher College of Engineering, Karad, Maharashtra, India

Description

Electrical Discharge Machining (EDM) has gone through critical headways with the mix of robotization strategies. This paper examines the most recent advancements in EDM process automation with an emphasis on increasing productivity, precision, and efficiency. The abstract discusses various automation strategies such as robotic handling, adaptive control systems, AI-based optimization, and sensor integration. Furthermore, it examines the impact of automation on reducing manual intervention, enhancing machining capabilities, and addressing challenges related to complex part geometries and material properties. The abstract concludes with insights into the future direction of automation in EDM and its potential implications for manufacturing industries.

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References

  • Chen, Y., Zhang, L., & Zhang, H. (2019). Automated programming system for EDM based on feature recognition and optimization algorithm. International Journal of Advanced Manufacturing Technology, 102(1-4), 407-418.
  • Khan, A., Arif, M., & Rehman, M. (2018). Development of adaptive control system for automated EDM process. Journal of Manufacturing Processes, 32, 206-215.
  • Liang, X., Li, C., Zhang, S., & Wang, C. (2021). AI-based optimization of EDM process parameters using machine learning techniques. Journal of Manufacturing Science and Engineering, 143(6), 061005.
  • Zhang, J., Liu, Y., & Zhu, H. (2020). Development of robotic handling system for automated EDM operations. Robotics and Computer-Integrated Manufacturing, 62, 101851.
  • Kulkarni, A., Pawade, R., & Rao, R. (2019). Integration of CAD/CAM system with EDM for automated programming and machining. Journal of Manufacturing Systems, 50, 153-165.
  • Chen, H., & Jiang, Y. (2018). Automated programming of EDM process based on feature recognition and optimization. The International Journal of Advanced Manufacturing Technology, 96(1-4), 305-315.
  • Tahir, A., & Tan, S. (2019). Development of automated EDM programming system using CAD/CAM integration. Procedia Manufacturing, 36, 284-291.
  • Khan, M. A., Anwar, S., & Ahmad, S. (2020). Artificial intelligence-based optimization of EDM process parameters using genetic algorithms. Journal of Intelligent Manufacturing, 31(1), 237-248.