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

Sheng Hu,Wenhui Liu,Shuanjun Song

doi:10.1155/2020/7153841

Sheng Hu, Wenhui Liu + Show 1 more

Open Access

https://doi.org/10.1155/2020/7153841

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Abstract

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.

Highlights

Intelligent manufacturing can discover the potential laws of the production process through data mining technology, and the intelligent decision model can be built by using this rule to realize green and intelligent manufacturing [1]
Aiming at the problem of quality fluctuation modeling and monitoring in production process, this paper proposes a process quality-monitoring method based on data cloud model similarity. e method quantifies the degree of fluctuation of process quality by entropy and superentropy, which reflects the process fluctuation variability and the quality state randomness. e contributions are drawn as follows: (1) Using the digital feature of the cloud model to analyze the quality state fluctuations as a whole, it better integrates the ambiguity and randomness in the quantitative conversion of processing quality and quality state parameters, and the digital features of the constructed cloud model can reflect the fluctuation characteristic of the quality state
(2) Cloud model similarity technology is introduced to solve the similarity of quality cloud models with different fluctuation levels, in which it uses cloud model digital features to describe the similarity of the quality fluctuation state. e current quality volatility level is monitored based on the similarity calculation results. e monitoring results of the above cases show that the proposed cloud model similarity method can effectively capture the difference of quality state fluctuation information at different process stages

Summary

Introduction

Intelligent manufacturing can discover the potential laws of the production process through data mining technology, and the intelligent decision model can be built by using this rule to realize green and intelligent manufacturing [1]. Us, how to quantitative characterize the quality fluctuation and timely monitor the process state is the key link to guarantee the production process stability and product quality qualified, which will be of great significance to improve production efficiency and product quality, reduce energy and resource consumption, and realize the intelligent and green development in the production process To address this issue, this paper mainly focuses on the process fluctuation and proposes a data cloud model similarity-based quality-monitoring approach in the production process. In order to monitor the process fluctuation amplitude, the cloud model similarity theory is introduced to calculate the similarity degree, which is used to monitor the current process fluctuation and the degree of internal stability by judging the amount of similarity.

Related Works and Motivations

Experiments and Results

Conclusions and Future Works