Developing a Predictive Wear Model for Intelligent Tool Change Systems

1. Grzesik W, Żak K, Zawada-Tomkiewicz A. Analiza i modelowanie powierzchni wytwarzanych w obróbce ubytkowej. PWN Warszawa. 2024; 1–331. Grzesik W Żak K Zawada-Tomkiewicz A. Analiza i modelowanie powierzchni wytwarzanych w obróbce ubytkowej . PWN Warszawa . 2024 ; 1 – 331 . Search in Google Scholar

2. ISO 3685:1993 Tool-life testing with single-point turning tools. ISO 3685:1993 Tool-life testing with single-point turning tools . Search in Google Scholar

3. 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. 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 . Search in Google Scholar

4. 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. 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 . Search in Google Scholar

5. 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. 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 . Search in Google Scholar

6. Wang K, Wang A, Wu L, Xie G. Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion. Sensors. 2024;24: 2652. Wang K Wang A Wu L Xie G. Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion . Sensors . 2024 ; 24 : 2652 . Search in Google Scholar

7. 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. 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 . Search in Google Scholar

8. 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. 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 . Search in Google Scholar

9. 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. 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 . Search in Google Scholar

10. Bombiński S, Kossakowska J, Jemielniak K. Detection of accelerated tool wear in turning. Mechanical Systems and Signal Processing. 2022;162:108021. Bombiński S Kossakowska J Jemielniak K. Detection of accelerated tool wear in turning . Mechanical Systems and Signal Processing . 2022 ; 162 : 108021 . Search in Google Scholar

11. 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. 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 . Search in Google Scholar

12. 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. 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 . Search in Google Scholar

13. 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. 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 . Search in Google Scholar

14. Soori M, Arezoo B, Dastres R. Machine learning and artificial intelligence in CNC machine tools: A review. Sustainable Manufacturing and Service Economics. 2023;100009. Soori M Arezoo B Dastres R. Machine learning and artificial intelligence in CNC machine tools: A review . Sustainable Manufacturing and Service Economics . 2023 ; 100009 . Search in Google Scholar

15. 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. 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 . Search in Google Scholar

16. 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. 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 . Search in Google Scholar

17. https://www.alicona.com/en/technologies/focus-variation. https://www.alicona.com/en/technologies/focus-variation . Search in Google Scholar