Artificial neural network-based prediction assessment of wire electric discharge machining parameters for smart manufacturing

Itagi Vijayakumar Manoj, SannaYellappa Narendranath, Peter Madindwa Mashinini, Hargovind Soni, Shanay Rab Shanay Rab, Shadab Ahmad, Ahatsham Hayat

Research output: Contribution to journalArticlepeer-review


Artificial intelligence (AI), robotics, cybersecurity, the Industrial Internet of Things, and blockchain are some of the technologies and solutions that are combined to produce “smart manufacturing,” which is used to optimize manufacturing processes by creating and/or accepting data. In manufacturing, spark erosion technique such as wire electric discharge machining (WEDM) is a process that machines different hard-to-cut alloys. It is regarded as the solution for cutting intricate parts and materials that are resistant to conventional machining techniques or are required by design. In the present study, holes of different radii, i.e. 1, 3, and 5 mm, have been cut on Nickelvac-HX. Tapering in WEDM is a delicate process to avoid disadvantages such as wire break, wire bend, wire friction, guide wear, and insufficient flushing. Taper angles viz. 0°, 15°, and 30° were obtained from a unique fixture to get holes at different angles. The study also shows the influence of taper angles on the part geometry and area of the holes. Next, the artificial neural network (ANN) technique is implemented for the parametric result prediction. The findings were in good agreement with the experimental data, supporting the viability of the ANN approach for the evaluation of the manufacturing process. The findings in this research provide as a reference to the potential of AI-based assessment in smart manufacturing processes and as a design tool in many manufacturing-related fields.
Original languageEnglish
Article number20220118
Number of pages11
JournalPaladyn, Journal of Behavioral Robotics
Issue number1
Publication statusPublished - 29 Jul 2023


  • cutting speed
  • EDM
  • manufacturing
  • dwell time
  • ANN
  • machining


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