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

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

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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.

• 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