Experimental, AI, and Computational Methods for Modern Manufacturing
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 January 2026 | Viewed by 27
Special Issue Editors
Interests: robotics; robotization of industrial processes; mechanical assembly; assembly sequence determination and optimization; production optimization
Interests: cutting process; surface roughness; micromachining; tool wear; carbide tools; mechanical properties; advanced materials; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue welcomes contributions that bridge practical experimentation, artificial intelligence, and computational modeling to advance contemporary production technologies. We invite empirical studies that deploy innovative experimental designs, real-time data acquisition, and adaptive process control alongside AI techniques—such as machine learning, neural networks, and data analytics—to enhance precision, productivity, and quality in manufacturing systems. Complementary computational works may include multi-scale simulations, digital twin development, optimization algorithms, and virtual prototyping for materials, processes, and supply chains.
This Special Issue targets a wide audience—mechanical and materials scientists, computer engineers, industrial practitioners, and interdisciplinary researchers—and therefore encourages submissions across diverse topics, including additive and subtractive manufacturing, hybrid production, smart factories, process monitoring, sustainability assessment, and predictive maintenance. Both foundational research and application-driven case studies that demonstrate seamless integration from lab-scale experiments to industrial implementation are sought. Review articles, comparative benchmarks, and methodological surveys that critically evaluate the convergence of experimental, AI-driven, and computational approaches are also encouraged. By fostering collaboration among academia and industry, this Special Issue aims to accelerate the development of flexible, efficient, and resilient manufacturing solutions suited to the challenges of Industry 4.0 and beyond.
Dr. Marcin Suszynski
Prof. Dr. Szymon Wojciechowski
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. 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
- experimental methods
- artificial intelligence
- computational modeling
- digital twins
- smart manufacturing
- Industry 4.0
- machine learning
- process optimization
- simulation
- experimental design
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