Next-Generation EDM: Physics-Informed Artificial Intelligence from Embedded Process Principles to Industrial Control
A Special Issue of Machines (ISSN 2075-1702) belonging to the section "Advanced Manufacturing".
Deadline for manuscript submissions: 30 April 2027 | Viewed by 67
Editor
Interests: design for assembly and AI automation; applied robotics; electric discharge machining; stochastic process control; AI-driven cad design; giga-press injection moulding and blow moulding design; closed-loop industrial AI
Special Issue Information
Dear Colleagues,
Electrical discharge machining (EDM) is essential for producing high-value components from difficult-to-machine materials. However, due to its stochastic, nonlinear, and multi-physics behaviour, it continues to challenge predictive modelling, process stability, and industrial control. Thus, this Special Issue focuses on physics-informed artificial intelligence as an emerging approach for embedding EDM process principles into data-driven models, improving interpretability, robustness, transferability, and practical usability.
We invite all contributions that integrate EDM domain knowledge, including discharge energy, spark-gap dynamics, thermal effects, plasma-channel behaviour, material removal mechanisms, electrode wear, dielectric flushing, surface integrity, and recast-layer formation, into machine learning, deep learning, soft computing, surrogate modelling, optimisation, monitoring, or adaptive control frameworks. Relevant topics include data-efficient modelling, explainable AI, hybrid physics-data approaches, real-time process monitoring, intelligent parameter selection, uncertainty-aware prediction, and AI-assisted industrial control of EDM, WEDM, micro-EDM, powder-mixed EDM, and hybrid EDM processes.
The aim is to advance scientifically grounded and industrially relevant AI methods that move EDM beyond black-box prediction toward intelligent, trustworthy, and controllable manufacturing systems.
Dr. Sergio Almeida
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- electrical discharge machining
- physics-informed artificial intelligence
- machine learning
- process monitoring
- adaptive control
- intelligent manufacturing
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