PT Journal
AU Lusi, N
   Fiveriati, A
   Afandi, A
   Gusti Ngurah Bagus Catra Wedarma, I
   Yuliandoko, H
   Darsin, M
   Qutaba, S
TI Predictive Modelling on Machining Performance of ECDM Using Artificial Neural Network and Particle Swarm Optimization
SO Manufacturing Technology Journal
PY 2023
BP 649
EP 662
VL 23
IS 5
DI 10.21062/mft.2023.076
DE ANN; PSO; ECDM; Surface roughness; Tungsten Carbide
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.
ER