Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network

Hua Gong (), Wanning Xu, Congang Chen and Wenjuan Sun
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Hua Gong: School of Science, Shenyang Ligong University, Shenyang 110159, China
Wanning Xu: Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry, Shenyang 110159, China
Congang Chen: Faculty of Science and Technology, Beijing Normal University—Hong Kong Baptist University United International College, Zhuhai 519087, China
Wenjuan Sun: School of Science, Shenyang Ligong University, Shenyang 110159, China

Mathematics, 2024, vol. 12, issue 23, 1-26

Abstract: The electronic control module is an important part of a digital electronic detonator, which undergoes a complex production process that includes three electrical performance tests and three visual inspection procedures. In each inspection procedure, several different types of data are generated daily, including numerical and categorical data. To evaluate the production quality of electronic control modules, an algorithm based on a Deep Belief Network with Multi-mutation Differential Evolution (MDE-DBN) is designed in this study. First, key indicators are extracted to construct a production quality evaluation index system. A Multi-mutation Differential Evolution algorithm is designed to optimize the initial network weights of the Deep Belief Network (DBN) and integrate the production quality information into the pre-training phase. Subsequently, the preprocessed experimental data are input into the MDE-DBN algorithm to obtain the distributions of excellent, general, and unqualified production statuses, verifying the effectiveness of the algorithm. The experimental results show that the MDE-DBN algorithm has significant advantages in evaluation accuracy when compared with DBNs improved by other intelligent optimization algorithms and machine learning methods.

Keywords: production quality; deep belief network; multi-mutation differential evolution algorithm; quality evaluation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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