Application of machine learning in automatic discrimination of product quality of deep drawn steel

XU Gang; LI Min; XU Jin-wu

doi:10.13374/j.issn2095-9389.2021.05.08.002

Volume 44 Issue 6

May 2022

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XU Gang, LI Min, XU Jin-wu. Application of machine learning in automatic discrimination of product quality of deep drawn steel[J]. Chinese Journal of Engineering, 2022, 44(6): 1062-1071. doi: 10.13374/j.issn2095-9389.2021.05.08.002

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XU Gang, LI Min, XU Jin-wu. Application of machine learning in automatic discrimination of product quality of deep drawn steel[J]. Chinese Journal of Engineering, 2022, 44(6): 1062-1071. doi: 10.13374/j.issn2095-9389.2021.05.08.002

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Application of machine learning in automatic discrimination of product quality of deep drawn steel

doi: 10.13374/j.issn2095-9389.2021.05.08.002

XU Gang^{1, 2
,
,},
LI Min¹,
XU Jin-wu¹

1.
Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
2.
Suzhou Baolian Heavy Industry Co., Ltd., Suzhou 215131, China

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Corresponding author: E-mail: watermoon999@126.com
Received Date: 2021-05-08
Available Online: 2021-10-15
Publish Date: 2022-06-25

Abstract

Abstract

In process industries, the discrimination of final product quality must be implemented in the manufacturing process. At present, the primary method is “after spot test ward,” but there is no other way to realize online automatic discrimination for all products, which frequently leads to customer return purchases and complaints about the product quality, and annual economic loss of 10 billion Yuan in Chinese steel enterprises. This paper proposed online product quality automatic discrimination method based on machine learning to realize online automatic discrimination for all products. First, multidimensional process parameters were mapped into a low-dimensional data set using nonlinear multidimensional parity scaling (MDPS), and the data set is clustered. The distribution feature in the data set was analyzed. The quality index values were then transformed into a low-dimensional map with the class labels determined by process parameter clustering, and the diverse class margins were determined using a support vector machine (SVM) with L2-soft margins. The kernel method set was used to reduce the number of support vectors to simplify the class boundary, and the reduced set determined the actual class margins. Finally, the quality indexes were predicted using machine learning algorithms, such as back-propagation network (BPN), long short-team memory (LSTM), kernel partial least squares (KPLS), and k-nearest neighbors (KNN), including the online automatic discrimination of product quality was realized using the determined class margins and the predicted values of quality indexes. The accuracy of the online automatic discrimination of steel types is up to 97% in the training stage and up to 96% in the testing stage based on industrial production data of interstitial-free (IF) steel.
- machine learning,
- cyber-physical system,
- deep drawn steel,
- product quality,
- automatically discrimination

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References(31)

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