Wang Lixiang, Dai Wei, Sun Dongmei, Zhao Yu
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
No. 208 Research Institute of China Ordnance Industries, Beijing 100191, China.
Entropy (Basel). 2020 Jun 23;22(6):699. doi: 10.3390/e22060699.
The quality of a manufacturing process can be represented by the complex coupling relationship between quality characteristics, which is defined by the directed weighted network to evaluate the risk of the manufacturing process. A multistage manufacturing process model is established to extract the quality information, and the quality characteristics of each process are mapped to nodes of the network. The mixed embedded partial conditional mutual information (PMIME) is used to analyze the causal effect between quality characteristics, wherein the causal relationships are mapped as the directed edges, while the magnitudes of the causal effects are defined as the weight of edges. The node centrality is measured based on information entropy theory, and the influence of a node is divided into two parts, which are local and indirect effects. Moreover, the entropy value of the directed weighted network is determined according to the weighted average of the centrality of the nodes, and this value is defined as the risk of the manufacturing process. Finally, the method is verified through a public dataset.
制造过程的质量可以通过质量特性之间的复杂耦合关系来表示，这种关系由有向加权网络定义，以评估制造过程的风险。建立了一个多阶段制造过程模型来提取质量信息，并将每个过程的质量特性映射到网络的节点上。使用混合嵌入部分条件互信息（PMIME）来分析质量特性之间的因果效应，其中因果关系被映射为有向边，而因果效应的大小被定义为边的权重。基于信息熵理论测量节点中心性，节点的影响分为局部和间接效应两部分。此外，根据节点中心性的加权平均值确定有向加权网络的熵值，该值被定义为制造过程的风险。最后，通过一个公共数据集对该方法进行了验证。
