- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | YOU Zhichao, GAO Hongli, GUO Liang, CHEN Yucheng, LIU Yuekai. Equipment Deployment of Direct Tool-Condition Monitoring Based on Improved Information Entropy[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 160-167. doi: 10.3969/j.issn.0258-2724.20220025
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In-situ tool-condition monitoring system based on machine vision realizes tool wear measurement and condition assessment without removing the tool. However, the system deployment parameters that are closely related to the quality of the tool image are rarely studied. To this end, a polynomial regression model based on improved information entropy is constructed to realize the optimal deployment of the tool-condition monitoring system. First, the adaptive threshold method is used to remove the interference of background elements in the captured tool image, and the imaging quality of the tool wear area is evaluated by the information entropy metric. Then, a polynomial regression model with respect to the camera working distance, exposure time, and the proposed evaluation metric is constructed to describe the mapping relationship between the deployment parameters and the proposed evaluation metric. Finally, the least squares method is used to solve the coefficients of the polynomial model and obtain the optimal deployment parameters. Orthogonal experiments are designed to ensure that the factor levels of independent variables cover the optimal deployment parameters. The experimental results show that there is a main effect relationship between the proposed evaluation metric and deployment parameters, such as working distance and exposure time, which is in line with the changing rule of optical imaging systems. Compared with nonlinear regression prediction models such as support vector machine, decision tree and K-nearest neighbor (KNN), the cubic polynomial regression model has the smallest prediction error, with its mean absolute error, mean square error, and root mean square error being 0.022631, 0.00068, and 0.026069, respectively. The measurement accuracy of the tool image captured under the optimal deployment parameters reaches 96.76%, increased by 0.74%, demonstrating that it meets the accuracy requirements of tool condition monitoring.
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