Quality Prediction of Discrete Manufacturing Process Based on CGAN&Catboost Hybrid Model. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Xu, Peng; Ren, Zhichen; Shen, Yili; Yu, Wendi; He, Lianghua
• Is Part Of: Journal of physics. Volume 1757:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: CGAN Module -- Hybrid Model -- Quality Prediction -- Industrial manufacturing; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1757/1/012072
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Accurate prediction of product quality can be used to control the online production process of workpieces, an appropriate prediction model can effectively improve the yield of industrial production. In order to obtain missing data, the classic method is to regress them one by one. However, the traditional regression prediction method destroys the correlation of the data and cannot use the quality inspection index efficiently. This paper presents an improved hybrid model. The essence lies in a novel easily pluggable CGAN module that refines the previous step of regression data. compared with the regression method, the CGAN module can learn the joint d istribution of data. The experiment of real data result shows that our method has high accuracy compared with the existing ones