Machine Learning for Predictive Modeling and Optimization of Manufacturing Processes
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".
Deadline for manuscript submissions: 10 December 2025 | Viewed by 492
Special Issue Editors
Interests: artificial intelligence models; unsupervised learning; data preprocessing; evolutionary optimization methods
Special Issue Information
Dear Colleagues,
Machine learning (ML) has become a powerful tool in advancing predictive modeling and optimization, especially within the manufacturing sector. The Special Issue "Machine Learning for Predictive Modeling and Optimization of Manufacturing Processes" aims to gather cutting-edge research focused on the development and application of ML techniques to improve efficiency, quality, and decision-making in manufacturing processes.
This Special Issue invites contributions that explore innovative machine learning methodologies, optimization frameworks, and real-world implementations specifically targeted at manufacturing applications. Topics of interest include but are not limited to supervised and unsupervised learning, deep learning architectures, reinforcement learning, ensemble models, feature engineering, and explainable AI as applied to manufacturing systems.
We particularly encourage submissions addressing challenges such as process parameter prediction, defect detection, process control, energy optimization, predictive maintenance, and smart manufacturing. Contributions related to data preprocessing, model interpretability, and ML-integrated decision support systems tailored to manufacturing are also welcome.
Through this Special Issue, we aim to promote interdisciplinary collaboration and foster novel insights into how ML can revolutionize manufacturing processes. We welcome original research articles, comprehensive reviews, and practical case studies that contribute to this rapidly evolving field.
Dr. Atiq Ur Rehman
Dr. Fawad Ali
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. Processes 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
- machine learning
- predictive modeling
- manufacturing processes
- process optimization
- smart manufacturing
- deep learning
- reinforcement learning
- predictive maintenance
- process control
- defect detection
- feature engineering
- explainable AI
- data-driven manufacturing
- Industry 4.0
- digital twins
- sustainable and smart manufacturing
- heuristic optimization methods
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