20.500.12556/RUL-168159 Development of a resource-efficient real-time vibration-based tool condition monitoring system using PVDF accelerometers Sustainable machining demands efficient tool condition monitoring (TCM) to maximize tool utilization and reduce environmental impact. Existing TCM solutions range from high-cost multi-sensor systems to ultra-low-cost alternatives with limited accuracy. This research bridges the gap with a resource-efficient, standalone TCM system for on-site tool wear estimation. The system integrates a PVDF-based accelerometer, Raspberry Pi 4, and a data acquisition card. A multi-level software architecture is designed to fully leverage this hardware, optimizing real-time signal processing while supporting both machine learning model training and inference. The proposed method employs two elementary models: k-means clustering for machining phase segmentation and ridge regression for tool wear estimation. A case study on an industrial lathe established a linear correlation between tool wear and surface roughness. Data from three tool inserts over their lifetimes proved sufficient for training machine learning models, achieving promising prediction accuracy. This research advances standalone TCM solutions tailored for manufacturing sectors seeking a balance between affordability and performance. tool condition monitoring surface roughness tool vibration turning tool used life spremljanje stanja orodja hrapavost površine vibracije orodja struženje življenjska doba orodja true false true Angleški jezik Slovenski jezik Članek v reviji 2025-04-01 09:14:26 2025-04-01 09:14:32 2025-04-02 03:54:40 0000-00-00 00:00:00 2025 0 0 8 str. art. 117183 Vol. 251 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621 1873-412X 10.1016/j.measurement.2025.117183 230866179 1-s2.0-S0263224125005421-main.pdf 1-s2.0-S0263224125005421-main.pdf 1 5649AC25990FFF1D9A156F449E65906C 95884bf8b99079a29dfeca5e8680cd75ca995d8b2b61489d2222b7b1fa0bfa36 4c6518f3-0ec8-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=201229 https://www.sciencedirect.com/science/article/pii/S0263224125005421 0 21377c09-0ec8-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=201228 Fakulteta za strojništvo 0 0 0