Manufacturing Technology 2023, 23(5):649-662 | DOI: 10.21062/mft.2023.076

Predictive Modelling on Machining Performance of ECDM Using Artificial Neural Network and Particle Swarm Optimization

Nuraini Lusi ORCID...1, Anggra Fiveriati ORCID...1, Akhmad Afandi ORCID...1, I Gusti Ngurah Bagus Catra Wedarma ORCID...1, Herman Yuliandoko ORCID...2, Mahros Darsin ORCID...3, Syed Qutaba ORCID...4
1 Department of Mechanical Engineering, Politeknik Negeri Banyuwangi, 68461 Banyuwangi, Indonesia
2 Department of Informatic Engineering, Politeknik Negeri Banyuwangi, 68461 Banyuwangi, Indonesia
3 Department of Mechanical Engineering, University of Jember, Jalan Kalimantan 37 Jember 68121, Indonesia
4 Department of Textile, BUITEMS, Quetta 87100, Pakistan

The electrochemical discharge machining (ECDM) process is developing into a potentially useful method of performing micromachining in conductive or non-conductive materials. The materials are machined using a combination of chemical and thermal energy. This paper examines the effect of Artificial Neural Network (ANN) architectures combined with particle swarm optimization (PSO) on the predictive ability of tungsten carbide machining. Material removal rate (MRR) and surface roughness (SR) is the response used to evaluate the performance of the ECDM process. The four selected process parameters are voltage, gap width, electrode type, and type of electrolyte, with each parameter has two levels. The 4-9-1 structure was chosen to obtain pre-dictions in the form of an optimal formula based on the statistical values for surface roughness: MSE 0.001, RMSE 0.025, MAPE 1.36, and R2 0.99.

Keywords: ANN, PSO, ECDM, Surface roughness, Tungsten Carbide
Grants and funding:

The authors are grateful to the Center of Research and Community Service State Polytechnic of Banyuwangi for financial support under  PB RIP grant No 2543.22/PL36/LT/2022

Received: December 4, 2022; Revised: October 16, 2023; Accepted: October 26, 2023; Prepublished online: November 28, 2023; Published: December 6, 2023  Show citation

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Lusi N, Fiveriati A, Afandi A, Gusti Ngurah Bagus Catra Wedarma I, Yuliandoko H, Darsin M, Qutaba S. Predictive Modelling on Machining Performance of ECDM Using Artificial Neural Network and Particle Swarm Optimization. Manufacturing Technology. 2023;23(5):649-662. doi: 10.21062/mft.2023.076.
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