Advances in Smart Manufacturing and Industry 4.0
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 46
Special Issue Editor
Interests: hybrid twin (real-time computational control) for the manufacturing process; data-driven machine learning/deep learning approach for advanced manufacturing; computational mechanics for process–structure–property predictions
Special Issue Information
Dear Colleagues,
Additive manufacturing (AM) technologies have revolutionized design flexibility and functional integration across industries. This Special Issue of Machines focuses on integrated process monitoring and control strategies for AM, encompassing AM techniques, including, but not limited to, Laser Powder Bed Fusion (PBF-LB), Directed Energy Deposition (DED), Binder Jetting, and Fused Deposition Modeling (FDM). We seek original research and comprehensive reviews on data-driven machine learning and deep learning approaches for real-time quality control, closed-loop feedback, and anomaly detection in AM systems. Contributions exploring computational modeling, multi-physics simulation, and topology optimization for accurate process–structure–property predictions are particularly welcome. The development and validation of Digital Twin frameworks that combine in situ sensor fusion, high-fidelity simulations, and virtual commissioning to optimize process parameters and predict mechanical performance are of particular interest. Topics of interest also include hybrid manufacturing systems, reinforcement learning for adaptive control, explainable AI methods for defect diagnosis, and sustainable AM practices through process energy efficiency and material reuse. By bringing together cutting-edge advances in monitoring, modeling, and digitalization, this Special Issue aims to accelerate the adoption of robust, intelligent, and efficient AM workflows for industrial applications. We also welcome studies on real-time defect detection, multi-modal data analytics, and cyber-physical security in AM platforms.
Dr. Abdullah Al Amin
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 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. Machines is an international peer-reviewed open access monthly 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
- digital twin
- machine learning
- process–structure–property modeling
- explainable AI
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