Artificial Intelligence in Non-Traditional Machining: A Comprehensive Review of Process Optimization, Prediction, Monitoring, and Intelligent Manufacturing
Year: 2026
Type: article
Abstract: Non-traditional machining processes are increasingly used for difficult-to-machine alloys, brittle materials, intricate geometries, and micro-scale features where conventional cutting may be limited. Their performance, however, depends on strongly coupled electrical, thermal, chemical, mechanical, and fluidic phenomena. Artificial intelligence (AI) provides a data-driven layer for modelling these nonlinear relationships, predicting quality, selecting process parameters, identifying unstable states, and supporting adaptive control. This paper reviews the role of AI across major non-traditional machining processes, with emphasis on electrical discharge machining, wire electrical discharge machining, electrochemical machining, laser beam machining, abrasive water jet machining, and related processes. The review organizes existing work into four functions: process optimization, prediction, monitoring, and intelligent control. Representative studies using artificial neural networks, support vector machines, fuzzy systems, genetic algorithms, particle swarm optimization, ensemble learning, and deep learning are examined. Recent evidence shows that AI can reduce the need for repeated physical trials and improve prediction of material removal rate, surface roughness, kerf characteristics, and other quality indicators. At the same time, small experimental datasets, material-specific models, weak external validation, sensor variability, limited interpretability, and the gap between offline prediction and real-time control remain important barriers. The paper therefore argues for physics-informed learning, uncertainty-aware prediction, digital twins, multimodal sensing, and closed-loop validation as the next stage of intelligent non-traditional machining.
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225292146