A principle-empirical model based on Bayesian network for quality improvement in mechanical products development

Mechanical product quality depends on many quality characteristics (QCs). There are many coupling relations between these QCs that are required to analyze these relations. A principle − empirical (P–E) model for quality improvement is proposed in this study. The architecture of the model is first introduced. The method of the P–E model structure learning is provided, and the QC relations are determined by empirical data. These discovered QC relations are validated by principal knowledge. The P–E model structure is built based on the validated QC relations. The maximum likelihood estimation (MLE) is used for parameter learning. Finally, a case study is given to demonstrate the P–E model. The results show that the learned structure based on the P–E model is superior to the K2 algorithm when the data size is small. The difference between the P–E model and the K2 algorithm is not significant when the data size is large.