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Lubricants
Special Issues
Monitoring and Remaining Useful Life (RUL) Technology of Tool Wear
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Monitoring and Remaining Useful Life (RUL) Technology of Tool Wear

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

Deadline for manuscript submissions: 18 February 2027 | Viewed by 1687

Special Issue Editors

Dr. Changfu Liu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Liaoning Petrochemical University, Fushun 113001, China
Interests: intelligent processing and fault diagnosis; prediction and monitoring of processing stability; monitoring and remaining useful life prediction of tool wear; monitoring and prediction of processing temperature
Dr. Boling Yan

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Interests: high-precision machining technology for complex components; milling stability and chatter suppression; machining process monitoring and intelligent diagnosis; cutting mechanism and machining performance optimization
Dr. Min Xia

E-Mail Website
Guest Editor
Department of Mechanical and Materials Engineering, Western University, London, ON N6A 5B9, Canada
Interests: scientific machine learning; intelligent machine condition monitoring; advanced manufacturing process monitoring and optimization; industrial data mining; smart clean energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Tool wear is a fundamental tribological phenomenon that directly affects manufacturing quality and efficiency. In smart manufacturing, real-time monitoring and accurate prediction of remaining useful life (RUL) are essential. This Special Issue brings together surface engineering, contact mechanics, and data science to address these challenges. We invite research that links the evolution of surface topography in machining processes with friction, wear, and lubrication to enable smarter predictions.

Key topics include:

Advanced sensing and modeling: Multi-sensor fusion and AI-driven models for wear state recognition and RUL prediction.

Physics-informed prediction: Integrating wear mechanisms with monitoring data.

Industrial applications: Robust strategies for real-world monitoring and implementation.

This issue aims to provide a critical reference for researchers in tribology, surface engineering, and smart manufacturing, fostering sustainable collaboration between academia and industry.

Dr. Changfu Liu
Dr. Boling Yan
Dr. Min Xia
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

  • wear
  • tribology
  • surface engineering
  • lubrication
  • contact mechanics
  • machining
  • surface topography
  • condition monitoring

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Published Papers (2 papers)

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Research

27 pages, 4383 KB  
Article
Classification of Tool Wear Condition During CNC Cutting Process from Spindle Motor Current Signal Monitoring
by Lloyd J. Augustine, Wani J. Morgan, Hsiao-Yeh Chu, Sheng-Jye Hwang and Hsin-Shu Peng
Lubricants 2026, 14(6), 227; https://doi.org/10.3390/lubricants14060227 - 31 May 2026
Viewed by 203
Abstract
Tool wear in CNC milling increases friction and torque demand at the tool-workpiece interface, which is reflected in spindle motor current. This study develops a non-intrusive tool wear condition classification method using spindle motor current monitoring during practical CNC milling of commercial medium-carbon [...] Read more.
Tool wear in CNC milling increases friction and torque demand at the tool-workpiece interface, which is reflected in spindle motor current. This study develops a non-intrusive tool wear condition classification method using spindle motor current monitoring during practical CNC milling of commercial medium-carbon steel workpieces (JIS S50C/AISI SAE 1050-equivalent; as-received and non-heat-treated; nominal laboratory hardness approximately 4.3 HRC). Experiments were performed on a Tongtai MDV-508 vertical machining center at fixed cutting conditions (3000 rpm spindle speed, 2 mm axial depth of cut, 5 mm cutting width, and 300 mm/min feed rate) using eight TiAlN-coated fine-grain WC–Co solid carbide end mills (10 mm diameter, four flutes; nominal Co binder approximately 10 wt%). An oil-based HS Highstart/HS-SSHS-BH10 cutting fluid was applied through the machine external coolant nozzle in flood mode at an estimated nominal flow rate of approximately 3 L/min and near-room coolant temperature (25 ± 2 °C), and was used as supplied without dilution. A clamp-type AC current sensor was installed on one phase line supplying the spindle motor, and current was acquired using an NI-9221 module at 20 kHz. Cutting intervals were isolated by envelope-based segmentation, concatenated, and divided into 1 s windows (0.5 s overlap) for feature extraction. Three feature sets were evaluated: time-domain statistics, frequency-domain statistics, and an FFT→PCA hybrid representation. Tool states (New, Mid-life, Old) were labeled using post-process surface roughness Ra thresholds supported by microscope observation. The PCA transformation was fitted only on training data and then applied to the held-out test data. A logistic regression classifier achieved 97.44% test accuracy (152/156 windows; 95% Wilson CI: 93.59–99.00%) with the PCA-hybrid features, outperforming time-domain (89.74%) and frequency-domain (94.87%) models. The results support spindle current monitoring as a low-cost approach for quality-aligned tool condition monitoring, while the external validity remains limited to the tested machine, material, tool, coolant, and cutting-parameter combination. Full article
(This article belongs to the Special Issue Monitoring and Remaining Useful Life (RUL) Technology of Tool Wear)
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15 pages, 3706 KB  
Article
RUL Prediction Method for Tools Based on Multi-Channel CNN and Cross-Modal Transformer
by Changfu Liu, Yubai Liu, Xiaoning Sun, Meng Wang, Siqi Feng, Yuelong Li and Jingjing Gao
Lubricants 2026, 14(3), 109; https://doi.org/10.3390/lubricants14030109 - 1 Mar 2026
Viewed by 841
Abstract
Excessive tool wear can compromise machining precision and increase costs, rendering accurate tool remaining useful life (RUL) prediction imperative in intelligent manufacturing. Traditional methods exhibit intrinsic limitations in cross-modal modeling accuracy and capturing temporal dependencies, failing to meet practical requirements. To transcend these [...] Read more.
Excessive tool wear can compromise machining precision and increase costs, rendering accurate tool remaining useful life (RUL) prediction imperative in intelligent manufacturing. Traditional methods exhibit intrinsic limitations in cross-modal modeling accuracy and capturing temporal dependencies, failing to meet practical requirements. To transcend these bottlenecks, this study proposes a robust tool RUL prediction framework that combines a multi-channel CNN and a Cross-Modal Transformer. The CNN performs convolution operations to extract local features from wear signals, while the Transformer adaptively synchronizes heterogeneous features (cutting force, vibration, and acoustic emission) to capture long-term degradation trends. Empirical evaluations conducted on the PHM2010 dataset demonstrate the model’s robustness and generalization capability: under the random shuffle–split protocol, the proposed method achieves an R2 of up to 0.99, with the RMSE and MAE reaching 2.51 and 1.98, respectively. To further evaluate the framework’s extrapolation ability under domain shifts, a cross-cutter validation protocol was implemented. Under this condition, the experimental results yield an R2 of 0.961, an RMSE of 6.92, and an MAE of 6.09. Additionally, the correlation between modality-specific attention weights and their corresponding physical interpretations is systematically investigated. These results confirm the model’s potential for cross-cutter life cycle management in smart manufacturing, providing stable and physically consistent wear estimation and remaining useful life prediction in noise-intensive environments. Full article
(This article belongs to the Special Issue Monitoring and Remaining Useful Life (RUL) Technology of Tool Wear)
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