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 11
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
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 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
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
- 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
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.