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Machines
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Tool Wear Condition Monitoring in Smart Manufacturing: Sensors, Analytics, and...
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Tool Wear Condition Monitoring in Smart Manufacturing: Sensors, Analytics, and Decision-Making

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1072

Special Issue Editors

Dr. Saman Fattahi

E-Mail Website
Guest Editor
KSF—Institute for Advanced Manufacturing, Furtwangen University, Tuttlingen, Germany
Interests: data-driven manufacturing; big data analytics; machining; smart manufacturing; Industry 4.0; Industry 5.0; advanced manufacturing
Dr. Angkush Kumar Ghosh

E-Mail Website
Guest Editor
Division of Mechanical and Electrical Engineering, Kitami Institute of Technology, Kitami 090-8507, Japan
Interests: industry 4.0; smart manufacturing; semantic annotation; digital twin; machine learning; signal processing; process monitoring; system development; time latency; modeling; simulation; validation; process planning
Special Issues, Collections and Topics in MDPI journals
Dr. Armin Siahsarani

E-Mail Website
Guest Editor
KSF—Institute for Advanced Manufacturing, Furtwangen University, Tuttlingen, Germany
Interests: advanced manufacturing; additive manufacturing; precision machining; severe plastic deformation; shape memory materials

Special Issue Information

Dear Colleagues,

Tool wear condition monitoring is fundamental to quality, productivity, and sustainability in smart manufacturing and machining processes. With advances in high-frequency sensing (e.g., forces, acoustic emission, vibration, current/power, thermal/vision) and interoperable machine data (e.g., OPC UA/MTConnect), together with robust analytics from physics-informed models to uncertainty-aware AI, laboratory prototypes are rapidly evolving into explainable, real-time systems on the shop floor. In the context of Industry 4.0—and the human-centric goals of Industry 5.0—monitoring and decisions must be reliable, transparent, and operator-aware.

This Special Issue, entitled “Tool Wear Condition Monitoring in Smart Manufacturing: Sensors, Analytics and Decision-Making”, invites the submission of original research, reviews, data/benchmark papers, and industrial case studies that close the loop from sensing → analytics → action. Topics of interest include sensor design and fusion; signal processing and feature learning; physics-informed and hybrid modeling; remaining useful life (RUL) and uncertainty quantification; explainable AI; digital twins and cyber–physical systems for simulation-in-the-loop; and edge/embedded deployment for real-time control. Application domains span turning, milling, drilling/micro-drilling, grinding, and other machining operations.

We also welcome contributions focused on organizational and technical enablers—workforce competencies, data reliability and governance, system integration, and change management—as well as practical challenges such as scalability, interoperability, latency constraints, and validation in realistic industrial environments. Submissions that demonstrate actionable decisions (e.g., adaptive control, alarms, predictive maintenance, and scheduling) and measurable impact on throughput, quality, and sustainability are particularly encouraged.

We look forward to receiving your contributions.

Dr. Saman Fattahi
Dr. Angkush Kumar Ghosh
Dr. Armin Siahsarani
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. Machines 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 2400 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

  • data-driven decision-making
  • smart manufacturing
  • tool wear condition monitoring
  • acoustic emission/force/vibration sensing
  • machine data interoperability (OPC UA, MTConnect)
  • physics-informed and hybrid models
  • remaining useful life (RUL) prediction
  • explainable AI (XAI)
  • digital twin and cyber–physical systems
  • edge/embedded AI and real-time control
  • predictive maintenance

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Published Papers (1 paper)

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Research

26 pages, 7296 KB  
Article
AI-Driven Tool Wear Prediction Under Severe Data Scarcity with SHAP-Guided Feature Selection and Fold-Safe Augmentation: A Case Study of Titanium Microdrilling
by Saman Fattahi, Bahman Azarhoushang, Masih Paknejad and Heike Kitzig-Frank
Machines 2026, 14(2), 196; https://doi.org/10.3390/machines14020196 - 9 Feb 2026
Viewed by 677
Abstract
Microdrilling of titanium alloys suffers from rapid tool wear that degrades surface quality and dimensional accuracy, while industrial datasets are often too small for conventional data-hungry models. This work proposes a general, AI-driven modelling framework for tool wear prediction under severe data scarcity, [...] Read more.
Microdrilling of titanium alloys suffers from rapid tool wear that degrades surface quality and dimensional accuracy, while industrial datasets are often too small for conventional data-hungry models. This work proposes a general, AI-driven modelling framework for tool wear prediction under severe data scarcity, which is validated using a titanium microdrilling case study. The study focuses on maximum flank-wear prediction (VBmax) using 18 experimental observations (VBmax = 4–13 µm). Three regression models—support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost)—were benchmarked under multiple validation protocols, with leave-one-out cross-validation (LOOCV) used as the primary assessment due to the limited sample size. To improve reliability and transparency, feature selection was performed using SHapley Additive exPlanations (SHAP), yielding a compact, interpretable feature subset dominated by thrust-force descriptors. Robustness was further evaluated using hyperparameter tuning and a conservative, leakage-controlled (“fold-safe”) augmentation strategy applied strictly within training folds. After tuning and fold-safe augmentation, XGBoost achieved the best LOOCV performance (R2 = 0.89, MSE = 0.70 µm2, MAPE = 7.62%). External validation on two additional tools under identical cutting conditions using a frozen model configuration showed bounded prediction errors under geometry and coating shifts. Overall, the results indicate that combining systematic benchmarking, SHAP-guided explainable feature selection, and leakage-controlled augmentation can enable accurate and interpretable VBmax prediction in the investigated titanium microdrilling case study, while broader validation across additional tools and cutting conditions is required to confirm generalization. Full article
(This article belongs to the Special Issue Tool Wear Condition Monitoring in Smart Manufacturing: Sensors, Analytics, and Decision-Making)
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