AI and Robots for Advanced Tribology
A Special Issue of AI for Engineering (ISSN 3042-8831).
Deadline for manuscript submissions: 20 August 2027 | Viewed by 540
Editors
Interests: wear mechanism of super hard materials; lubricating performance of general lubricants under high contact pressures; friction and tribology in bio-systems
Special Issues, Collections and Topics in MDPI journals
Interests: bionanocomposties; micro/nanomechanics; sensors; nanofluid; surface/interface engineering; tribology; finite element analysis; hydrogen energy
Special Issues, Collections and Topics in MDPI journals
2. Advisory Board Chair and Adjunct Professor, Department of Chemical Engineering, State University of New York, Stony Brook, NY, USA
3. Advisor, Department of Mechanical Technology, State University of New York, Farmingdale, NY, USA
4. Industrial and Professional Advisory Board member, The Pennsylvania State University, State College, PA, USA
5. Advisory Board Chair, Tribology Minor Program, Department of Mechanical Engineering, Auburn University, Auburn, AL, USA
Interests: AI-driven tribology; machine learning for lubricants and fuels; intelligent petroleum testing systems; smart laboratory instrumentation; digitalization of ASTM test methods condition monitoring and diagnostics using AI; applied AI in chemical engineering
Special Issues, Collections and Topics in MDPI journals
Interests: surface properties-behavior relations; (nano) tribology; tribochemistry; bio-nanointerface; biomaterials; nanomanufacturing; CMP
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The field of tribology is undergoing a profound digital transformation, evolving from a data-rich inductive science into an era of autonomous, predictive intelligence. This Special Issue explores the convergence of Artificial Intelligence and Robotic Automation as catalysts for overcoming long-standing challenges in friction, wear, and lubrication. We are moving beyond manual "trial-and-error" experimentation toward a tribo-informatics framework where machine learning models—ranging from physics-informed neural networks to generative molecular design—accelerate the discovery of sustainable lubricants and wear-resistant coatings. Simultaneously, the rise of autonomous robotic laboratories is redefining experimental precision, enabling high-throughput screening and closed-loop discovery cycles that remove human bias and enhance reproducibility. By integrating real-time sensor fusion and digital twins, this issue aims to bridge the gap between fundamental surface science and industrial reliability, paving the way for self-adjusting mechanical systems and advanced predictive maintenance.
You may choose our Joint Special Issue in Lubricants.
Dr. Huaping Xiao
Dr. Sunghan Kim
Dr. Raj Shah
Prof. Dr. Hong Liang
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI for Engineering is an international peer-reviewed open access quarterly 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 1000 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
- intelligent tribology
- tribo-informatics
- neural networks
- machine learning
- autonomous tribotesting and tribochemistry
- materials discovery
- lubricants
- AI methodologies
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