Abstract
Quality prediction is one of the key links of quality control. Benefitting from the development of digital manufacturing, manufacturing process data have grown rapidly, which allows product quality predictions to be made based on a real-time manufacturing process. A real-time quality control system (RTQCS) based on manufacturing process data is presented in this paper. In this study, the relationship between the product real-time quality status and processing task process was established by analyzing the relationship between the product manufacturing resources and the quality status. The key quality characteristics of the product were identified by analyzing the similarity of the product quality characteristic variations in the manufacturing process based on the big data technology, and a quality-resource matrix was constructed. Based on the quality-resource matrix, the RTQCS was established by introducing an association-rule incremental-update algorithm. Finally, the RTQCS was applied in actual production, and the performance of RTQCS was verified by experiments. The experiments showed that the RTQCS can effectively guarantee the quality of product manufacturing and improve the manufacturing efficiency during production.
Highlights
In recent years, the manufacturing industry has become more and more personalized and automated [1], [2], which has created opportunities for the use of quality control measures for manufacturing processes
Quality diagnosis and prediction models based on historical data have difficulty adapting to the requirements of current production
In the real-time quality control system (RTQCS), the quality prediction model is updated dynamically as the manufacturing process progresses by combining historical data information with measured data flow information
Summary
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China This work was supported in part by the National Numerical Control (NC) Machine Project of China (No 2018ZX04008001), and in part by the Project of Intelligent Manufacturing of Ministry of Industry and Information Technology, China (No NM2016720BH001), and in part by the Project of Lifecycle quality data resources planning and application of Ministry of Industry and Information Technology, China (No 2016204A001).
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