Advanced Machine Learning Algorithm Based EDM Machines for Aerospace Application

DOI

10.2991/978-94-6463-754-0_45How to use a DOI?

Keywords

EDM; Tolerance; Statistical variables; MRR

Abstract

Now a days, most of the industries especially in aerospace sector, non-conventional machining processes plays a vital role. In these industries output should be zero defect and with minimum accuracy tolerance. This can be easily achieved in non-conventional machining manufacturing process. Electrical Discharge Machining, EDM function is the contactless process also known as non-conventional method, where can monitor and control the statistical variables. The process method of the conductive materials in the die-sinker EDM technology is very sensible for the technology which is used to do the manufacturing process. The main purpose of this paper is to evolve a progressive EDM inspection process in the perspective of giving zero defect output along with the estimated cycle time. Based on the correlation between the die sinker EDM machine statistical variable and workpiece/electrode combination, a supervised machine learning model can predict the estimated cycle time. This cycle time calculation will be very helpful for the production line people for the target achievement and for planning their production schedule. MRR, Material removal rate, in the EDM process is one of the fundamental aspects because cycle time and MRR are directly proportional. MRR will vary for the different combination of workpiece and electrodes.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Arunkumar
PY  - 2025
DA  - 2025/06/30
TI  - Advanced Machine Learning Algorithm Based EDM Machines for Aerospace Application
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 513
EP  - 520
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_45
DO  - 10.2991/978-94-6463-754-0_45
ID  - Arunkumar2025
ER  -