Impact of Material Properties, Tooling, and Process Conditions on Mechanical and Tribological Performance
A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).
Deadline for manuscript submissions: 30 April 2026 | Viewed by 26
Special Issue Editors
Interests: mathematical modelling and computer simulations; contact and computational mechanics in materials processing; the application of FEM in metal-forming processes; analysis of the thermo-mechanical and tribological properties of materials; the application of artificial intelligence in materials design and manufacturing
Interests: multi-scale mechanics in metal manufacturing; multi-scale materials processing engineering; tribology; FEM analysis of heat transfer and thermo-mechanical and mechanical properties; high-precision mathematical models in metal-forming processes; the application of artificial intelligence in materials manufacturing processes; the development of novel steel composites for mining equipment
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Special Issue Information
Dear Colleagues,
Recent advances in materials design and manufacturing have showcased the possibility of enhancing mechanical and tribological behaviour through innovative materials, treatments, coatings, and processing technologies, leading to improvements in the wear resistance, durability, performance, and sustainability of components and systems in various industries. This Special Issue will bring together studies exploring the failure mechanisms of such materials through the deployment of advanced characterisation and testing techniques, as well as studies adopting theoretical, numerical, and/or experimental approaches to investigate novel materials and technologies with the capacity to strengthen mechanical and tribological performance, including, but not limited to, nanomaterials and composites, high-entropy alloys, heat treatment, surface alterations, advanced coatings, and innovative nanolubricants.
Artificial intelligence (AI) is an emerging keystone in materials science and advanced manufacturing, offering value in market research, product design, process control and optimisation, and quality assurance through its capacity to improve product quality, reduce costs, and increase innovation. This Special Issue also welcomes research related to the application of AI in data analysis and design processes to facilitate the prediction and optimisation of material properties for a wide variety of manufacturing operations and engineering applications.
Dr. Feijun Qu
Prof. Dr. Zhengyi Jiang
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. Journal of Manufacturing and Materials Processing 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 1800 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
- tribology
- fatigue
- materials characterisation and testing
- failure analysis
- materials performance optimisation
- process optimisation
- AI in manufacturing
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