PT  - JOURNAL ARTICLE
AU  - Lusi, Nuraini
AU  - Fiveriati, Anggra
AU  - Afandi, Akhmad
AU  - Gusti Ngurah Bagus Catra Wedarma, I
AU  - Yuliandoko, Herman
AU  - Darsin, Mahros
AU  - Qutaba, Syed
TI  - Predictive Modelling on Machining Performance of ECDM Using Artificial Neural Network and Particle Swarm Optimization
DP  - 2023 Dec 6
TA  - Manufacturing Technology Journal
PG  - 649--662
VI  - 23
IP  - 5
AID - 10.21062/mft.2023.076
IS  - 12132489
AB  - 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.