Year: 2026
Type: article
Abstract: This study proposes a method for predicting cutting tool flank wear using bolt-type piezo sensors (Piezobolts) and validates its performance through comparison with a three-axis dynamometer, the laboratory standard for cutting-force measurement. During machining, reaction forces transmitted through the workpiece clamping interface are measured by Piezobolts, converted into approximate three-axis cutting forces, and used to estimate tool wear. Results show that wear progression trends derived from Piezobolts closely match those obtained from the dynamometer throughout the full wear process. A support vector regression (SVR) model preserving temporal data order successfully predicts later-stage tool wear using only early-stage training data. Using features extracted solely from Piezobolt signals, the model captures both gradual wear progression and accelerated wear near the end of tool life with high consistency. These findings demonstrate the feasibility of Piezobolts as surrogate sensors for tool-wear monitoring without direct cutting-force measurement or machine-tool modification. Although validation was limited to a single tool, workpiece material, machining operation, and cutting condition, this study confirms experimental feasibility and provides a practical foundation for extending Piezobolt-based tool condition monitoring to broader machining environments.
Authors: Odong Kwon, Jae-Eun Kim, Kyunghoon Lee, Wonkyun Lee
Institution: Chungnam National University
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Awards: G2286842166, G7988061350
OpenAlex ID: W7143749963
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