Intelligent Metal Forming: AI Modeling, Simulation, and Digital Twins
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 56
Special Issue Editors
Interests: AI in manufacturing; metal forming; constitutive modelling; microstructure transformation
Interests: AI in manufacturing; explainable AI; Industry 4.0; digital twins; ultra-precision manufacturing; additive manufacturing
Special Issues, Collections and Topics in MDPI journals
Interests: metal forming; materials engineering; manufacturing engineering
Special Issue Information
Dear Colleagues,
Modern metal forming is inherently complex, consisting of nonlinear thermo-mechanical behaviour, dynamic tribology evolution, and intricate microstructure–property relationships. Concurrently, industry demands high-throughput and low-carbon operation, achieving first-time-right quality with minimal variation. Traditional modelling, often reliant on simplifying assumptions and limited state variables, remains computationally intensive and can overlook key phenomena, constraining rapid design exploration and in-process decision-making.
As universal approximators, AI models are well suited to optimising strongly multiparametric forming processes, where many interacting factors must be accounted for. Integrating AI with FE modelling enables (i) higher predictive accuracy, (ii) deeper insights into underlying process physics, (iii) direct utilisation of industrial data for training, (iv) significantly reduced simulation time. However, many current AI applications remain fragmented, validated only in controlled settings and limited to non-generalizable solutions, highlighting the need for further research on holistic, production-ready deployment.
In this context, a key focus of this Special Issue is establishing the place and role of AI in modern metal forming: what it enables, where it augments physics-based models, and how to deploy it reliably (with generalisation, uncertainty quantification, physical consistency, and shop-floor integration). We invite contributions on AI-enabled predictive modelling, physics-informed scientific ML, AI-enabled digital twins, in situ sensing and data fusion, defect and forming-limit prediction, friction/lubrication and tool wear, and multiscale links between processes, microstructure evolution, and properties, including industrial case studies and open benchmarks.
Dr. Olga Bylya
Dr. Abhilash Puthanveettil Madathil
Dr. Evgenia Yakushina
Prof. Dr. Andrew Sherlock
Guest Editors
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Keywords
- metal forming
- artificial intelligence
- machine learning
- Industry 4.0
- physics-informed machine learning
- digital twins
- in situ sensing and data fusion
- surrogate models
- process optimisation and control
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