Prediction and Assessment of Tool Wear: Theory and Practical Applications

A special issue of Lubricants (ISSN 2075-4442).

Deadline for manuscript submissions: 1 October 2026 | Viewed by 139

Special Issue Editors


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Guest Editor
Production Servises Managemant Inc., A EWIE Group of Companies, Ann Arbor, MI 48176, USA
Interests: theory of metal cutting; tool wear mechanism; evaluation and optimization; cutting tool geometry and design; work material properties in machining; design of experiments

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Guest Editor
Center for Precision Metrology, Department of Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Interests: surface integrity; additive manufacturing process; advanced materials processing; digital twin
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Special Issue Information

Dear Colleagues,

Tool wear remains one of the most significant challenges in modern manufacturing. It directly compromises product quality, machining efficiency, and overall production costs. High-precision industries—such as aerospace, automotive, energy, and mold/die making—are particularly vulnerable, where even minor tool degradation can lead to substantial economic losses.

The situation has become especially urgent in recent years because of the dramatic surge in tool and material prices. These developments underscore the pressing need to advance our understanding and mitigation of tool wear to reduce tooling expenses, minimize downtime, enhance product quality, and boost productivity.

This Special Issue on “Tool Wear: Theory, Assessment, and Control” provides an excellent platform for researchers in experimental, theoretical, and applied fields to share cutting-edge work and contribute to the evolving knowledge base on tool wear phenomena and management.

We invite high-quality original contributions on topics including, but not limited to:

  • Theoretical and experimental studies exploring novel mechanisms, models, and methodologies for understanding and predicting tool wear;
  • Innovative research on tool wear mechanisms, especially when machining advanced, difficult-to-cut materials;
  • Recent progress in modeling, simulation, and optimization techniques, including finite element methods (FEMs), machine learning, deep learning, and hybrid approaches;
  • Advanced tribological investigations of tool–chip and tool–workpiece interfaces, including friction, adhesion, chemical interactions, and coating performance.

Dr. Viktor P. Astakhov
Dr. José Outeiro
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Lubricants 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 2600 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

  • tool wear theory
  • assessment
  • modeling and simulation
  • prediction and optimization
  • machine and deep learning
  • hybrid approaches

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Published Papers

This special issue is now open for submission.
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