References
- Grzesik W, Żak K, Zawada-Tomkiewicz A. Analiza i modelowanie powierzchni wytwarzanych w obróbce ubytkowej. PWN Warszawa. 2024; 1–331.
- ISO 3685:1993 Tool-life testing with single-point turning tools.
- Abeni A, Metelli A, Attanasio A, Outeiro J, Poulachon G. A Predictive Method for Cumulative Tool Wear in Variable Cutting Speed Turning Operations. Procedia CIRP. 2025;133:454-459.
- Zhang X, Peng Z, Liu L, Zhang X. A Tool Life Prediction Model Based on Taylor’s Equation for High-Speed Ultrasonic Vibration Cutting Ti and Ni Alloys. Coatings. 2022;12(10):1553.
- Cheng Y, Gai X, Guan R, Jin Y, Lu M, Ding Y. Tool wear intelligent monitoring techniques in cutting: a review. Journal of Mechanical Science and Technology. 2023;37(1):289-303.
- Wang K, Wang A, Wu L, Xie G. Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion. Sensors. 2024;24: 2652.
- Zhou Y, Liu C, Yu X, Liu B, Quan Y. Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: A review. SN Applied Sciences. 2022;4:232.
- Ünal P, Deveci BU, Özbayoğlu AM. A review: Sensors used in tool wear monitoring and prediction. In: Awan I, Younas M, Poniszewska-Marańda A. (Eds.) Mobile Web and Intelligent Information Systems. MobiWIS. 2022. Lecture Notes in Computer Science. 2022;13475.
- Zhang C, Wang W, Li H. Tool wear prediction method based on symmetrized dot pattern and multi-covariance Gaussian process regression. Measurement. 2022;189:110466.
- Bombiński S, Kossakowska J, Jemielniak K. Detection of accelerated tool wear in turning. Mechanical Systems and Signal Processing. 2022;162:108021.
- Zhang X, Gao Y, Guo Z, Zhang W, Yin J, Zhao W. Physical model-based tool wear and breakage monitoring in milling process. Mechanical Systems and Signal Processing. 2023;184:109641.
- Sayyad S, Kumar S, Bongale A, Kotecha K, Abraham A. Remaining useful-life prediction of the milling cutting tool using time-frequency-based features and deep learning models. Sensors. 2023;23:5659.
- Gupta MK, Niesłony P, Sarikaya M, Korkmaz ME, Kuntoğlu M, Kró- lczyk GM. Studies on geometrical features of tool wear and other important machining characteristics in sustainable turning of aluminium alloys. International Journal of Precision Engineering and Manufacturing-Green Technology. 2023;10:943–957.
- Soori M, Arezoo B, Dastres R. Machine learning and artificial intelligence in CNC machine tools: A review. Sustainable Manufacturing and Service Economics. 2023;100009.
- Zawada-Tomkiewicz A, Tomkiewicz D. Monitoring System with a Vision Smart Sensor. In: Majewski M, Kacalak W. (eds) Innovations Induced by Research in Technical Systems. IIRTS 2019. Lecture Notes in Mechanical Engineering. 2020.
- Cheng M, Jiao L, Yan P, Jiang H, Wang R, Qiu T, Wang X. Intelligent tool wear monitoring and multi-step prediction based on deep learning model. Journal of Manufacturing Systems. 2022;62:286–300.
- https://www.alicona.com/en/technologies/focus-variation.