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

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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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
JournalPaladyn
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Jan 2023

Keywords

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

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Developmental Neuroscience
  • Cognitive Neuroscience
  • Artificial Intelligence
  • Behavioral Neuroscience

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