Artificial Intelligence (AI) and Machine Learning in Mechanical and Industrial Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 October 2025 | Viewed by 86
Special Issue Editor
2. Department of Mechanical and Industrial Engineering (MC 251), University of Illinois at Chicago, Chicago, IL 60661, USA
Interests: equipment health monitoring and fault diagnosis; prognostics and health management (PHM); failure analysis; reliability and quality engineering; manufacturing systems; signal processing; acoustic emission
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue on ‘Artificial Intelligence (AI) and Machine Learning in Mechanical and Industrial Engineering’ will explore the recent advancements, applications, and future prospects in integrating AI technologies into these engineering domains. AI and Machine Learning have revolutionized traditional mechanical and industrial engineering methods by offering intelligent, adaptive solutions capable of handling complex, high-dimensional problems efficiently. This Special Issue will highlight how AI-driven algorithms can optimize engineering designs, enhance predictive modeling, and significantly improve decision-making processes in areas such as manufacturing, automation, robotics, supply chain management, and advanced material design.
Featured contributions should demonstrate state-of-the-art approaches including large language models (LLMs), deep learning models, neural network techniques, reinforcement learning, transfer learning, and data-driven predictive analytics, emphasizing their potential in system diagnostics, predictive maintenance, quality control, process optimization, and smart manufacturing. Furthermore, this Special Issue will address critical challenges such as computational complexity, data scarcity, interpretability, and the ethical implications of AI deployment in mechanical and industrial engineering. Researchers and practitioners are invited to examine practical case studies, theoretical advancements, and innovative frameworks presented in this Special Issue, offering insights into harnessing AI effectively to foster technological growth, sustainability, and resilience in these engineering disciplines. This compilation will serve as an essential resource to guide future research trajectories and encourage interdisciplinary collaboration for enhanced engineering solutions.
Prof. Dr. David He
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. 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
- artificial intelligence
- machine learning
- engineering applications
- predictive analytics
- optimization algorithms
- deep learning
- intelligent systems
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