Special Issue

Monitoring of Tool Wear and Delamination in Composite Machining: Challenges and Opportunities

Submission Deadline: January 31, 2027 (Open) Submit Now

Guest Editor

Krzysztof Szwajka, PhD

Assistant Professor, Division of Integrated Design Systems and Tribology, Head of the Integrated Design and Tribology Systems Department, Rzeszów University of Technology, Rzeszów, Poland

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Research Interests: Machinability, Cutting tool wear, Fuzzy logic, Welding, Neural networks, Wood composite, Friction, tribology

About This Topic

Fiber-reinforced polymers are increasingly used in demanding structures due to their mechanical properties. They typically outperform conventional materials. For this reason, fiber-reinforced polymers are widely used in the aerospace, military, defense, and automotive industries. Due to the inhomogeneous, anisotropic and abrasive structure of composites, deformations occur during processing, which cause damage to the material structure and deterioration of the surface quality.

Data-driven methods have provided unprecedented opportunities for smart manufacturing, facilitating the transition to Industry 4.0-based production. Machine learning and deep learning are playing a key role in the development of intelligent systems for descriptive, diagnostic, and predictive analytics for machine tools and process condition monitoring. This special issue provides an overview of the applications, opportunities, and challenges of data-driven approaches for smart machining and monitoring tool wear and surface quality.

We invite researchers to contribute to this Special Issue, “Monitoring of Tool Wear and Delamination in Composite Machining: Challenges and Opportunities”. The topics of interest include but are not limited to the following:

• The smart monitoring framework, which is the main steps towards developing a data-driven monitoring system;
• Machine learning and deep learning models applied to machining monitoring;
• The wear resistance of coatings;
• Modeling of wear of modified surface.

Keywords

Smart manufacturing, Tool condition monitoring, Data-driven manufacturing, Tool wear, Intelligent machining monitoring, Machine learning, Deep learning, Feature selection, Neural networks, Artificial intelligence

Manuscript Submission Information

Manuscripts should be submitted through the LIDSEN Submission System. Detailed information on manuscript preparation and submission is available in the Instructions for Authors. All submitted articles will be thoroughly refereed through a single-blind peer-review process and will be processed following the Editorial Process and Quality Control policy. Upon acceptance, the article will be immediately published in a regular issue of the journal and will be listed together on the special issue website, with a label that the article belongs to the Special Issue. LIDSEN distributes articles under the Creative Commons Attribution (CC BY 4.0) License in an open-access model. The authors own the copyright to the article, and the article can be free to access, distribute, and reuse provided that the original work is correctly cited.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). Research articles and review articles are highly invited. Authors are encouraged to send the tentative title and abstract of the planned paper to the Editorial Office (rpm@lidsen.com) for record. If you have any questions, please do not hesitate to contact the Editorial Office.

Welcome your submission!