Special Issue "Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 31 March 2024 | Viewed by 168

Special Issue Editors

Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
Interests: machine learning; aritificial intelligence; aircraft optimal design
Special Issues, Collections and Topics in MDPI journals
Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA
Interests: computational fluid dynamics; machine learning; optimization

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) makes the core of the Industry 4.0 revolution. AI, especially subset machine learning (ML), has been advancing the mechanical engineering area. In particular, ML could help fine-tune product quality and optimize operations during the manufacturing process for improving product quality and reducing time to market. In addition, ML-based predictive failure enables optimal maintenance time, which saves cost and time. Furthermore, optimal control incorporated with reinforcement learning plays a key role in scheduling in production, supply chain, and Industry 4.0 systems. In the meantime, stakeholders achieve optimal product management through novel optimization architectures enabled by ML surrogate modeling. In summary, ML, optimal control, and optimization together have been pushing forward the leading edge in manufacturing and Industry 4.0.

This Special Issue on “Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0” targets original and novel research products on ML, control, and optimization, with application emphasis on practical mechanical engineering problems.

Topics include, but are not limited to:

  1. Novel ML algorithm development demonstrated on mechanical engineering problems (including manufacturing, aerospace engineering, etc.).
  2. State-of-the-art ML methods introduced for large-scale optimal control or practical mechanical engineering applications.
  3. Challenging analysis or design under uncertainty for mechanical engineering problems through ML methods.

Dr. Xiaosong Du
Dr. Devina P. Sanjaya
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.


  • machine learning
  • mechanical engineering
  • engineering design optimization
  • optimal control
  • aerospace engineering
  • design under uncertainty
  • reinforcement learning
  • surrogate modeling
  • manufacturing

Published Papers

This special issue is now open for submission.
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