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Critical Procedure Identification Method Considering the Key Quality Characteristics of the Product Manufacturing Process
| Content Provider | MDPI |
|---|---|
| Author | Gao, Zhenhua Xu, Fuqiang Zhou, Chunliu Zhang, Hongliang |
| Copyright Year | 2022 |
| Description | The product’s manufacturing process has an evident influence on product quality. In order to control the quality and identify the critical procedure of the product manufacturing process reasonably and effectively, a method combining genetic back-propagation (BP) neural network algorithm and grey relational analysis is proposed. Firstly, the genetic BP neural network algorithm is used to obtain the key quality characteristics (KQCs) in the product manufacturing process. At the same time, considering the three factors that have an essential impact on the quality of the procedures, the grey correlation analysis method is used to establish the correlation scoring matrix between the procedure and the KQCs to calculate the criticality of each procedure. Finally, taking the manufacturing process of the evaporator as a case, the application process of this method is introduced, and four critical procedures are identified. It provides a reference for the procedure quality control and improvement of enterprise in the future. |
| Starting Page | 1343 |
| e-ISSN | 22279717 |
| DOI | 10.3390/pr10071343 |
| Journal | Processes |
| Issue Number | 7 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-07-10 |
| Access Restriction | Open |
| Subject Keyword | Processes Manufacturing Engineering Manufacturing Process Procedures Quality Genetic Bp Neural Network Key Quality Characteristics Critical Procedure |
| Content Type | Text |
| Resource Type | Article |
