Machine Learning-Based Prediction of EDM Material Removal Rate and Surface Roughness

Isam Qasem, Amjad Alsakarneh

Journal of Manufacturing and Materials Processing 2025 article cited by 11

importance 4/10 ai assisted Advanced Machining and Optimization Techniques precision manufacturing stage: research

Predicting material removal rate and surface roughness in electrical discharge machining (EDM) is difficult because of complex physical events in the electrode gaps.

Accurate prediction reduces costly trial-and-error in precision manufacturing.

Multiple machine learning models were trained on experimental EDM data to map process parameters to responses; GPR performed best.

GPR achieved average RMSE of 0.9234 (MRR) and 3.0216 (Sa), outperforming other ML models across materials.

The evidence supports ML as an effective tool for modeling EDM performance, though deployment is only proposed.

Venue Journal of Manufacturing and Materials Processing
Published 2025
Work type article
Authors Isam Qasem, Amjad Alsakarneh
Citations 11 (OpenAlex, at import time)
References 34
Model or method GPR (Gaussian Process Regression) among multiple ML models
Organization not stated in source
Primary source https://doi.org/10.3390/jmmp9080274
Open access gold
Keywords EDM, machine learning, GPR, material removal rate, surface roughness
4 / 10

real but narrower advance

Accurate EDM prediction reduces costly trial-and-error in precision manufacturing; a real but narrow process-modeling advance with no stated scale of impact.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

# Paper Year Citations Importance
1 Scaling deep learning for materials discovery
Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, G...
2023 1,471
8
2 Application of Machine Learning in Material Synthesis and Property Prediction
Guannan Huang, Yani Guo, Ye Chen, Zhengwei Nie
2023 165
7
3 Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
Kai Li, Jifeng Wang, Yuanyuan Song, Ying Wang
2023 115
7
4 Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review
Gennaro Scarselli, Francesco Nicassio
2025 44
7
5 Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
Mihail D. Senyuk, Murodbek Safaraliev, Firuz Kamalov, Hana Sulieman
2023 42
7