Artificial Intelligence Techniques for Tool Wear Analysis in Material Processing Technologies
A special issue of Machines (ISSN 2075-1702).
Deadline for manuscript submissions: 31 March 2025 | Viewed by 164
Special Issue Editor
Interests: machine learning; deep learning; AI in cloud (AWS Amazon); programming: Python; Matlab; C# .NET Core
Special Issue Information
Dear Colleagues,
Tool wear analysis is a critical aspect of material processing technologies, impacting productivity, quality, and cost efficiency across various industrial applications. With the advent of artificial intelligence (AI), there has been a significant advancement in monitoring, predicting, and managing tool wear, enhancing the overall efficiency and lifespan of tools used in manufacturing processes.
This Special Issue aims to explore the latest and most significant developments in the application of AI techniques for tool wear analysis in material processing technologies. We invite original research articles that contribute to the numerical, theoretical, and experimental understanding of tool wear mechanisms and AI-based predictive models. Review articles that offer comprehensive insights into the state-of-the-art in this domain are also highly welcomed.
Potential topics include but are not limited to the following:
- AI-based modeling and simulation of tool wear;
- Failure mechanism analysis of tools;
- Development and application of intelligent sensors for tool condition monitoring;
- Integration of wireless sensors and sensor networks in material processing;
- Advanced signal processing theories and methods for tool wear detection;
- Data acquisition and innovative measurement methods;
- Machine learning algorithms for intelligent fault diagnosis of tools;
- Predictive maintenance strategies using AI for tool wear prediction;
- Big data analytics in tool wear management;
- Case studies showcasing the practical applications of AI in tool wear analysis;
- Deep learning techniques for tool wear classification and prediction;
- Reinforcement learning for optimizing tool usage and wear management;
- AI-driven optimization of cutting parameters for enhanced tool life;
- Real-time monitoring systems using AI for proactive tool wear management;
- Fuzzy logic systems and their application in tool wear prediction;
- Neural networks for multi-sensor data fusion in tool condition monitoring;
- AI-assisted development of novel tool materials and coatings to reduce wear;
- Genetic algorithms for optimizing tool wear prediction models;
- Hybrid AI models combining multiple AI techniques for improved accuracy;
- Autonomous systems and robotics in tool maintenance and replacement;
- AI-based adaptive control systems for real-time adjustment to tool wear;
- Cloud computing and IoT integration for scalable tool wear analysis solutions;
- Ethical and practical considerations in the deployment of AI for tool wear management.
We look forward to contributions that will drive the understanding and application of AI in enhancing the reliability and efficiency of material processing technologies.
Prof. Dr. Jaroslaw Kurek
Guest Editor
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Keywords
- artificial intelligence
- tool wear analysis
- machine learning
- predictive maintenance
- condition monitoring
- signal processing
- data analytics
- deep learning
- intelligent sensors
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