Machine Learning in Manufacturing: Digital Twins, Optimization and Control

Dear Colleagues,

Machine learning (ML) has become a powerful tool for studying and optimizing complex engineering systems. However, the increasing demand for reliability and interpretability in safety-critical applications highlights the limitations of purely black-box approaches. This Special Issue focuses on the transition toward Physics-Aware/ Physics-Informed Machine Learning (PAML/PIML) as a core enabling technology for next-generation Digital Twins and engineering optimization in Industry 5.0.

In this context, PAML/PIML serves as a critical bridge: it augments data-driven models with physical constraints to create high-fidelity Digital Twins. These digital replicas, in turn, enable robust process optimization and resilient decision support that go beyond simple automation. By advancing the industrialization of hybrid modeling, we can achieve the human-centric and sustainable goals of modern manufacturing.

This Special Issue invites original research and reviews with a clear focus on interpretable, physics-aware/physics-informed approaches that empower Digital Twins and multi-objective optimization. We aim to provide a platform for sharing how hybrid intelligence improves manufacturing understanding, control, and sustainability.

Topics include, but are not limited to:

• Physics-aware/physics-informed machine learning for complex manufacturing and multi-physics problems.
• ML-enabled Digital Twins: Hybrid modeling (ML + mechanistic models) for predictive and resilient manufacturing.
• Physics-driven engineering optimization: Multi-objective parameter selection (quality–cost–energy trade-offs) constrained by physical principles.
• Trustworthy Decision Support: Explainable AI (XAI) and uncertainty quantification for high-value manufacturing assets.
• Advanced Control Strategies: ML-driven adaptive and model predictive control within Digital Twin frameworks.
• Sustainable & Circular Manufacturing: ML for energy/emissions prediction and resource efficiency optimization.
• Industrial Case Studies: Implementation of PAML/PIML and optimization in additive, conventional, and non-conventional manufacturing processes.

You may choose our Joint Special Issue in Mathematics.

Dr. Angelos P. Markopoulos
Dr. Emmanouil Lazaros Papazoglou
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Industries is an international peer-reviewed open access quarterly 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 1000 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.