Study on Methods and a System for Real-Time Monitoring of the Remaining Useful Life of a Milling Cutter
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Engineering Applied Research · Advanced machining processes and optimization · Digital Transformation in Industry
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, National Chung Hsing University, Taichung 40227, Taiwan
Funding
National Science and Technology Council, R.O.C.
References (13)
Show source-provided references
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