Abstract
Real-time evaluation quality fluctuation is an important way to ensure product quality in manufacturing process. This study proposes a multidimensional data flow driven-based quality fluctuation evaluation. Firstly, the multidimensional decomposition and transition mode analysis is carried out to reveal the essence of quality fluctuation. Then, the technique uses hierarchical network architecture to model the process, and quality fluctuation network is built to track fluctuation evolution trend; then, the node relative entropy measure is defined to statistically analyze the change of net-node state fluctuation. Finally, a case study is used to verify the proposed quality fluctuation evaluation approach.
Highlights
Intelligent manufacturing integrates the smart machine and human intelligence to complete the whole process of product manufacturing [1]
Once the stability of fluctuation state is not timely evaluated, it will lead the abnormal state to continue to transfer, accumulate, and grow to result in the process abnormalities [6, 7]. us, how to dynamically characterize the quality evolution and timely measure the fluctuation degree of process state is the key link to guarantee the production process’s stability and qualification of product quality, which will be of great value for analysis of the root causes of quality problems, as well as developing preventive measures to improve product’s quality in manufacturing process
This paper mainly focuses on the process state fluctuation and proposes a multidimensional data flow driven-based quality fluctuation evaluation approach; the idea of process dynamic network modeling, node relative entropy analysis, and fluctuation evaluation are adopted
Summary
Intelligent manufacturing integrates the smart machine and human intelligence to complete the whole process of product manufacturing [1]. Us, how to dynamically characterize the quality evolution and timely measure the fluctuation degree of process state is the key link to guarantee the production process’s stability and qualification of product quality, which will be of great value for analysis of the root causes of quality problems, as well as developing preventive measures to improve product’s quality in manufacturing process To address this issue, this paper mainly focuses on the process state fluctuation and proposes a multidimensional data flow driven-based quality fluctuation evaluation approach; the idea of process dynamic network modeling, node relative entropy analysis, and fluctuation evaluation are adopted. A great waste of manufacturing resources appears and the monitor effect lacks the ability to analyze the process’s stability dynamically
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