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Lubricants
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Advances in Tool Wear Monitoring 2024
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Advances in Tool Wear Monitoring 2024

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2159

Special Issue Editors

Prof. Dr. Alisson Rocha Machado

E-Mail Website
Guest Editor
1. Graduate Program in Mechanical Engineering, Pontifícia Universidade Católica do Paraná—PUC-PR, R. Imaculada Conceição, 1155, Bairro Prado Velho, Curitiba 80215-901, Brazil
2. School of Mechanical Engineering, Federal University of Uberlandia, Av. João Naves de Ávila, 2121, Bloco 1M, Uberlândia 38400-902, Brazil
Interests: machining; cutting fluids; chip formation; tool wear; surface integrity; tribology
Special Issues, Collections and Topics in MDPI journals
Dr. Gustavo Henrique Nazareno Fernandes

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, Federal University of Uberlandia, Av. João Naves de Ávila, 2121, Uberlândia 38400-902, Brazil
Interests: materials; manufacturing sciences; advanced manufacturing; eco-friendly machining; machining cooling techniques; innovation; sustainability; coatings; tribology; wear analysis; lubrication; friction

Special Issue Information

Dear Colleagues,

Tool wear is unavoidable and a crucial aspect of modern manufacturing processes, impacting productivity, quality, and cost-effectiveness. With the rapid advancements in sensor technology, data analytics, and machine learning algorithms, the field of tool wear monitoring is witnessing transformative changes. To explore and showcase these innovations, we are pleased to announce a Special Issue entitled "Advances in Tool Wear Monitoring 2024".

This Special Issue aims to compile cutting-edge research, innovative methodologies, and practical applications in the realm of tool wear monitoring. We invite contributions addressing various aspects of the topic, including but not limited to, the following:

  1. Novel sensor technologies for real-time tool condition monitoring.
  2. Development of predictive models for tool wear estimation and prognostics.
  3. Integration of machine learning and artificial intelligence techniques for predictive tool wear.
  4. In situ and non-destructive monitoring techniques for tool wear assessment.
  5. Implementation of IoT (Internet of Things) and Industry 4.0 concepts in tool wear monitoring.
  6. Case studies and applications demonstrating the effectiveness of tool wear monitoring in diverse manufacturing environments.
  7. Challenges and future directions in advancing tool wear monitoring systems.
  8. Eco-friendly devices addressing advanced monitoring wear issues.

Contributors are invited to submit original research articles, reviews, or short communications relevant to the theme of the Special Issue. All submissions will undergo a rigorous peer-review process to ensure the quality and relevance of the accepted manuscripts. Manuscripts should adhere to the formatting and submission guidelines of the journal. Detailed instructions for authors can be found on the journal's website.

Prof. Dr. Alisson Rocha Machado
Dr. Gustavo Henrique Nazareno Fernandes
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. 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 monitoring
  • tool wear mechanisms
  • real-time monitoring
  • wear rate analysis
  • sensor technology
  • machine learning
  • data analytics
  • predictive models
  • prognostics
  • Internet of Things (IoT)
  • industry 4.0
  • in situ monitoring
  • wear estimation
  • tool life prediction
  • fault detection
  • diagnostic techniques
  • smart manufacturing

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

16 pages, 1946 KiB  
Article
Multi-Objective Optimization of Friction Stir Processing Tool with Composite Material Parameters
by Aniket Nargundkar, Satish Kumar and Arunkumar Bongale
Lubricants 2024, 12(12), 428; https://doi.org/10.3390/lubricants12120428 - 2 Dec 2024
Viewed by 555
Abstract
Compared to base aluminum alloys, the surface composites of aluminum alloys are more widely used in the automotive, aerospace, and other industries. The ability to yield enhanced physical properties and a smoother microstructure has made friction stir processing (FSP) the method of choice [...] Read more.
Compared to base aluminum alloys, the surface composites of aluminum alloys are more widely used in the automotive, aerospace, and other industries. The ability to yield enhanced physical properties and a smoother microstructure has made friction stir processing (FSP) the method of choice for developing aluminum-based surface composites in recent times. In this work, the Goal Programming (GP) approach is adopted for the Multi-Objective Optimization of FSP processes with three Artificial Intelligence (AI)-based metaheuristics, viz., Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Teaching–Learning-Based Optimization (TLBO). Three parameters, copper percentage (Cu%), graphite percentage (Gr%), and number of passes, are considered, and multi-factor non-linear regression prediction models are developed for the three responses, Tool Vibrations, Power Consumption, and Cutting Force. The TLBO algorithm outperformed the ABC and PSO algorithms in terms of solution quality and robustness, yielding significant improvements in tool life. The results with TLBO were improved by 20% and 14% compared to the PSO and ABC algorithms, respectively. This proves that the TLBO algorithm performed better compared with the ABC and PSO algorithms. However, the computation time required for the TLBO algorithm is higher compared to the ABC and PSO algorithms. This work has opened new avenues towards applying the GP approach for the Multi-Objective Optimization of FSP tools with composite parameters. This is a significant step towards toll life improvement for the FSP of composite alloys, contributing to sustainable manufacturing. Full article
(This article belongs to the Special Issue Advances in Tool Wear Monitoring 2024)
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23 pages, 10026 KiB  
Article
Enhancing Machining Efficiency: Real-Time Monitoring of Tool Wear with Acoustic Emission and STFT Techniques
by Luís Henrique Andrade Maia, Alexandre Mendes Abrão, Wander Luiz Vasconcelos, Jánes Landre Júnior, Gustavo Henrique Nazareno Fernandes and Álisson Rocha Machado
Lubricants 2024, 12(11), 380; https://doi.org/10.3390/lubricants12110380 - 31 Oct 2024
Viewed by 1077
Abstract
Tool wear in machining is inevitable, and determining the precise moment to change the tool is challenging, as the tool transitions from the steady wear phase to the rapid wear phase, where wear accelerates significantly. If the tool is not replaced correctly, it [...] Read more.
Tool wear in machining is inevitable, and determining the precise moment to change the tool is challenging, as the tool transitions from the steady wear phase to the rapid wear phase, where wear accelerates significantly. If the tool is not replaced correctly, it can result in poor machining performance. On the other hand, changing the tool too early can lead to unnecessary downtime and increased tooling costs. This makes it critical to closely monitor tool wear and utilize predictive maintenance strategies, such as tool condition monitoring systems, to optimize tool life and maintain machining efficiency. Acoustic emission (AE) is a widely used technique for indirect monitoring. This study investigated the use of Short-Time Fourier Transform (STFT) for real-time monitoring of tool wear in machining AISI 4340 steel using carbide tools. The research aimed to identify specific wear mechanisms, such as abrasive and adhesive ones, through AE signals, providing deeper insights into the temporal evolution of these phenomena. Machining tests were conducted at various cutting speeds, feed rates, and depths of cut, utilizing uncoated and AlCrN-coated carbide tools. AE signals were acquired and analyzed using STFT to isolate wear-related signals from those associated with material deformation. The results showed that STFT effectively identified key frequencies related to wear, such as abrasive between 200 and 1000 kHz and crack propagation between 350 and 550 kHz, enabling a precise characterization of wear mechanisms. Comparative analysis of uncoated and coated tools revealed that AlCrN coatings reduced tool wear extending tool life, demonstrating superior performance in severe cutting conditions. The findings highlight the potential of STFT as a robust tool for monitoring tool wear in machining operations, offering valuable information to optimize tool maintenance and enhance machining efficiency. Full article
(This article belongs to the Special Issue Advances in Tool Wear Monitoring 2024)
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