Intelligent Tool Wear Monitoring
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 1332
Special Issue Editor
Special Issue Information
Dear Colleagues,
Tool wear monitoring is a vital aspect of manufacturing systems, as it ensures production efficiency and product quality. This Special Issue primarily focuses on novel methodologies and frameworks that enable intelligent tool wear monitoring by integrating artificial intelligence (AI), machine vision, sensor fusion, physics-based models and human expertise. In addition, the deployment of such intelligent tool wear monitoring methodologies and frameworks exposes manufacturing systems to cyber vulnerabilities, such as adversarial attacks, data poisoning and evasion of AI models. Therefore, this Special Issue will also focus on the strategies used to prevent, detect, and mitigate these threats to ensure the integrity, resilience and trustworthiness of intelligent tool wear monitoring methodologies and frameworks. The specific topics of interest for the Special Issue include, but are not limited to, the following:
- Data acquisition systems for robust in-process tool wear monitoring;
- Data generation through sensor fusion, digital twins, and high-fidelity simulations;
- AI models for tool wear prediction, classification, and progression analysis;
- Hybrid modeling frameworks combining physics-informed and data-driven approaches;
- Frameworks integrating Large Language Models (LLMs), eXplainable AI (XAI), and Vision Transformers (VT);
- Human-centered AI systems to enhance decision making;
- Cybersecurity strategies for protecting manufacturing systems from adversarial threats;
- Edge and cloud-enabled architectures for scalable and real-time tool wear monitoring;
- Challenges and future directions in advancing intelligent tool wear monitoring;
- Case studies and industrial applications demonstrating intelligent, resilient and secure tool wear monitoring.
Dr. Ankit Agarwal
Guest Editor
Manuscript Submission Information
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Keywords
- tool wear
- digital twins
- real-time monitoring
- sensor fusion
- machine vision
- physics-based model
- artificial intelligence
- large language models (LLMs)
- eXplainable AI (XAI)
- vision transformers (VT)
- human-centered AI
- cybersecurity
- adversarial training
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