A Framework of Cloud Model Similarity-Based Quality Control Method in Data-Driven Production Process

Considering the problem that the process quality state is difficult to analyze and monitor under manufacturing big data, this paper proposed a data cloud model similarity-based quality fluctuation monitoring method in data-driven production process. Firstly, the randomness of state fluctuation is ch...

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Bibliographic Details
Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-10
Main Authors: Hu, Sheng, Liu, Wenhui, Song, Shuanjun
Format: Article
Language:English
Subjects:
Algorithms
Continuous casting
Control charts
Data mining
Hypothesis testing
Manufacturing
Methods
Monitoring
Process controls
Product quality
Quality control
Similarity
Stability analysis
Theory
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Summary:Considering the problem that the process quality state is difficult to analyze and monitor under manufacturing big data, this paper proposed a data cloud model similarity-based quality fluctuation monitoring method in data-driven production process. Firstly, the randomness of state fluctuation is characterized by entropy and hyperentropy features. Then, the cloud pool drive model between quality fluctuation monitoring parameters is built. On this basis, cloud model similarity degree from the perspective of maximum fluctuation border is defined and calculated to realize the process state analysis and monitoring. Finally, the experiment is conducted to verify the adaptability and performance of the cloud model similarity-based quality control approach, and the results indicate that the proposed approach is a feasible and acceptable method to solve the process fluctuation monitoring and quality stability analysis in the production process.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/7153841