Audible sound, the most suitable signals for online monitoring of tool wear condition?

In general, tool failure contributes about 7% to the down time of machine centers. And more severely, tool failure will reduce the machining quality of parts, and even damage the machine. Therefore, Tool wear condition monitoring (TWCM) is an important part of intelligent manufacturing, and has been a research hotspot since 1968. TWCM can be divided into two methods according to the sensor types: direct method and indirect method. The direct method employs vision or laser sensors. Despite its superior precision and reliability, achieving online real-time monitoring is challenging, as the sensors are easy to be interfered by cutting fluid, chips and other elements inherent to the processing environment. Conversely, the indirect method holds promise for online real-time monitoring capabilities, yet its practical application remains limited despite an abundant of academic publications. A key obstacle is the selection of suitable monitoring sensors, such as those that measure force, vibration, temperature, and acoustic emissions, which can inevitably disrupt the natural processing conditions. The microphone sensor, which captures audible sound signals, emerges as a promising strategy. Its potential is contingent upon enhancing its accuracy through initiative denoising system and the development of interpretable decision-making algorithms.

Recently, a team of intelligent manufacturing scientists led by Guochao Li from Jiangsu University of Science and Technology in China has conducted the first systematic review of the status and trend of audible sound-based tool wear monitoring. This comprehensive work not only serves as a valuable resource for researchers and manufacturers by summarizing recent trends, but also highlights four promising research directions: the development of datasets, initiative denoising system, specialized feature extraction techniques, and the creation of interpretable decision-making algorithms.

The team published their work in Journal of Advanced Manufacturing Science and Technology on July 12, 2024.

“In this report, we've conducted a review of the current state and future direction of tool wear monitoring focused on audible sound, spanning a decade of scholarly publications," said Guochao Li, Associate Professor at the School of Mechanical Engineering at Jiangsu University of Science and Technology in China, who is a seasoned expert in the field of tool wear condition monitoring. "This includes an in-depth analysis of the physical properties and characteristics of machining audible sound, the generation mechanisms of milling audible sound, and advancements in key technologies such as signal acquisition, noise reduction, feature extraction, and decision-making algorithms, along with potential areas for future research."

"We believe that our work could significantly promote the practical application of tool wear monitoring ", noted Li Sun, a Lecturer at the same institution, who also specializes in machining process monitoring. "The use of microphone sensors to collect audible sound signals presents a promising strategy due to their close correlation with tool wear, eliminating the need for additional sensors, ease of installation, adaptability in measurement, and non-interference with the processing environment. However, the accuracy of tool wear condition monitoring based on audible sound signals is currently insufficient. Our study offers valuable insights and the latest trends to researchers and manufacturers, potentially facilitating the broader application of tool wear monitoring in practical scenarios."

The research team also includes notable contributions from Xinhang Shang, Lei Yang, and Honggen Zhou from the School of Mechanical Engineering at Jiangsu University of Science and Technology in Zhenjiang, China, as well as Bofeng Fu from Shanxi Diesel Engine Heavy Industry Co., Ltd. in Xingping, China.

This work was supported by the National Natural Science Foundation of China (No. 62203193 and 51605207).

About Author

Guochao Li earned his Ph.D. in Mechanical Engineering from Shandong University in 2015. He currently holds the position of Associate Professor at Jiangsu University of Science and Technology in Zhenjiang, China. Between 2017 and 2019, Li expanded his expertise with a postdoctoral fellowship at the Institute of Advanced Manufacturing Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences. As an accomplished scholar, Li has authored four monographs and contributed over fifty papers to academic journals. His areas of expertise encompass cutting tool design and the critical field of tool wear monitoring.

Li Sun was awarded her Ph.D. from Nanjing University of Science and Technology in 2019. In 2017, she was funded by the China Scholarship Council to pursue joint training at McMaster University in Canada, where she studied under the esteemed Professor N. Balakrishnan, a Distinguished Professor in the Department of Mathematics and Statistics and a Fellow of the Royal Society of Canada. She is currently a lecturer at Jiangsu University of Science and Technology in China. To date, she has published two monographs and over ten academic papers. Her research interests include health monitoring and reliability assessment of machining processes.

About Journal of Advanced Manufacturing Science and Technology

Journal of Advanced Manufacturing Science and Technology (JAMST) is an open-access and peer-reviewed journal that was launched by Dalian University of Technology and Engineering Research Center of Advanced Manufacturing Technology for Aero Engine, Ministry of Education, Northwestern Polytechnical University in 2021. The journal is published by Tsinghua University Presss.

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