Development of a resource-efficient real-time vibration-based tool condition monitoring system using PVDF accelerometers

Kodrič, Miha (author), Korbar, Jure (author), Pogačar, Miha (author), Čepon, Gregor (author)

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.

• Language: English
• Keywords: tool condition monitoring, surface roughness, tool vibration, turning, tool used life
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 8 str.
• Numbering: Vol. 251, art. 117183
• PID: 20.500.12556/RUL-168159
• UDC: 621
• ISSN on article: 1873-412X
• DOI: 10.1016/j.measurement.2025.117183
• COBISS.SI-ID: 230866179
• Publication date in RUL: 01.04.2025

Record is a part of a journal

• Title: Measurement
• Publisher: Elsevier
• ISSN: 1873-412X
• COBISS.SI-ID: 23272709

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: spremljanje stanja orodja, hrapavost površine, vibracije orodja, struženje, življenjska doba orodja

Projects

• Funder: EC - European Commission
• Funding programme: HE
• Project number: 101138182
• Name: Circularity and Remanufacturing-Enabling DIgital Twins
• Acronym: CREDIT