Year: 2024
Type: article
Abstract: Tool wear management and real-time machining quality monitoring are pivotal components of realizing smart manufacturing objectives, as they directly influence machining precision and productivity. Traditionally, measuring and analyzing cutting force fluctuations in the time domain has been employed to diagnose tool wear effects. This study introduces a novel, indirect approach that leverages spindle-load current variations as a proxy for cutting force analysis. Compared to conventional methods relying on machining vibration or direct cutting force measurement, this technique provides a safer, simpler, and more cost-effective means of data aquisition, with reduced computational demands. Consequently, it is ideally suited for real-time monitoring and long-term analyses such as tool-life prediction and surface-roughness evolution induced by tool wear. An intelligent tool wear monitoring system was developed based on spindle-load current data. The system employs extensive cutting experiments to identify and analyze the correlation between tool wear and spindle-load current signal patterns. By establishing a tool wear near-end-of-life threshold, the system enables intelligent monitoring using C#. Experimental validation under both roughing and finishing conditions demonstrated the system’s exceptional diagnostic accuracy and reliability. The results demonstrate that the current ratio threshold value has good universality in different materials, indicating that monitoring the machining current ratio to estimate the degree of tool wear is a feasible research direction, and that the average error between the experimental surface-roughness measurement value and the predicted value was 10%.
DOI: 10.3390/jmmp8050194
Institution: National Chung Hsing University
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,046
Awards: Study of the Cyber-Physcal-Based Multi-Target Optimization Methods and System Development for Enhancement of Milling Accuracy and Machining Performance, Study of Technologies and Systems of Collaborative Smart-Manufacturing for a Production Line with Characteristics of Real-Time Optimization, Complementary Effectiveness, High Accuracy, High Robustness, and Smart Information Security Protection( II )
OpenAlex ID: W4402280241