- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
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| Citation: | GAO Hongli, SUN Yi, GUO Liang, YOU Zhichao, LIU Yuekai, LI Shichao, LEI Yuncong. Research Status and Development Trend of Machining Quality Prediction[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 121-141. doi: 10.3969/j.issn.0258-2724.20220085
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The prediction of machining quality is a vital component of intelligent manufacturing and a prerequisite for achieving quality closed-loop control, playing an extremely important role in promoting the practical application of intelligent manufacturing systems. A brief review of the history of machining quality prediction reveals that scholars have mostly focused on the mechanism of the influence of a key component of the machine tool on machining quality, while research on the correlation between the coupling effects of machine components is rare. Based on the aforementioned challenges, firstly, seven types of factors that affect machining quality are analyzed, including tool geometry parameters, cutting parameters, cutting fluid type, thermal errors and deformations, degradation of CNC machine tool components, cutting chatter, and system characteristics. Subsequently, according to the different types of data and measurement methods for each factor, the monitoring and prediction methods of machining quality are divided into four categories, including machine vision measurement, power measurement, vibration measurement, and other measurement methods. The technical characteristics, limitations, and development trends of each method are then expounded. Finally, considering the deficiencies of various machining quality monitoring and prediction methods, this paper points out that research on material cutting mechanisms, data quality assessment methods, standards for constructing industry site databases, and intelligent representation and visualization of quality prediction information may be future development trends.
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