Study on Methods and a System for Real-Time Monitoring of the Remaining Useful Life of a Milling Cutter

Applied Sciences 2026 Open access

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Authors: Shih-Ming Wang, Wan-Shing Tsou, Jian-Wei Huang, Shao-En Chen, Chia-Che Wu
DOI: 10.3390/app16020958
Cited by: 0

Abstract

Tool wear degrades sharpness and durability, causing poor surface quality, dimensional errors, and high costs. Precise RUL prediction optimizes production, reduces rework, and prevents downtime. Conventional replacement relies on experience and risks inaccuracy. Real-time monitoring enables optimal intervals. Predictive maintenance cuts tooling costs and ensures quality. Industry 4.0 integrates sensors for intelligent wear management. This study applies GRNN to predict RUL with minimal TMD. A C#-based system with intuitive HMI was validated in real machining.

Fields of Study & Subjects

Advanced machining processes and optimization Engineering Technology and Methodologies Reliability and Maintenance Optimization

Publication details

Journal / source
Applied Sciences
Volume
16
Issue
2
Pages
958–958
Publication date
16 January 2026
Language
EN