research-article

The Core Industry Manufacturing Process of Electronics Assembly Based on Smart Manufacturing

Authors:

Xiaozhong Chen,

Jianxin LiAuthors Info & Claims

ACM Transactions on Management Information Systems, Volume 13, Issue 4

Article No.: 40, Pages 1 - 19

https://doi.org/10.1145/3529098

Published: 17 January 2023 Publication History

Abstract

This research takes a case study approach to show the development of a diverse adoption and product strategy distinct from the core manufacturing industry process. It explains the development status in all aspects of smart manufacturing, via the example of ceramic circuit board manufacturing and electronic assembly, and outlines future smart manufacturing plans and processes. The research proposed two experiments using artificial intelligence and deep learning to demonstrate the problems and solutions regarding methods in manufacturing and factory facilities, respectively. In the first experiment, a Bayesian network inference is used to find the cause of the problem of metal residues between electronic circuits through key process and quality correlations. In the second experiment, a convolutional neural network is used to identify false defects that were overinspected during automatic optical inspection. This improves the manufacturing process by enhancing the yield rate and reducing cost. The contributions of the study built in circuit board production. Smart manufacturing, with the application of a Bayesian network to an Internet of Things setup, has addressed the problem of residue and redundant conductors on the edge of the ceramic circuit board pattern, and has improved and prevented leakage and high-frequency interference. The convolutional neural network and deep learning were used to improve the accuracy of the automatic optical inspection system, reduce the current manual review ratio, save labor costs, and provide defect classification as a reference for preprocess improvement.

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Cited By

Tallat RHawbani AWang XAl-Dubai AZhao LLiu ZMin GZomaya AHamood Alsamhi S(2024)Navigating Industry 5.0: A Survey of Key Enabling Technologies, Trends, Challenges, and OpportunitiesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.332947226:2(1080-1126)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2023.3329472
Zeng YChen XJin R(2023)Ensemble Active Learning by Contextual Bandits for AI Incubation in ManufacturingACM Transactions on Intelligent Systems and Technology10.1145/362782115:1(1-26)Online publication date: 19-Dec-2023
https://dl.acm.org/doi/10.1145/3627821
Bretones Cassoli BMetternich JKhomh FChatterjee PMetzger A(2023)Challenges for Predictive Quality in Multi-stage Manufacturing: Insights from Literature ReviewProceedings of the 3rd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things10.1145/3617573.3618030(16-23)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3617573.3618030

Index Terms

The Core Industry Manufacturing Process of Electronics Assembly Based on Smart Manufacturing
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Cooperation and coordination

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Published In

cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 13, Issue 4

December 2022

255 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3555789

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 January 2023

Online AM: 09 August 2022

Accepted: 24 March 2022

Revised: 25 February 2022

Received: 12 October 2021

Published in TMIS Volume 13, Issue 4

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Refereed

Funding Sources

Scientific Research Fund of Dongguan Polytechnic
Dongguan Science and Technology of Social Development Programme, 2020
Special Fund for Science and Technology Innovation Strategy of Guangdong Province, 2021
Special Projects in Key Fields of Colleges and Universities in Guangdong Province
Special Projects in Key Areas of Ordinary Universities in Guangdong Province
Dongguan Polytechnic “Excellent Textbooks of Production and Operations Practice”
Dongguan Science and Technology of Social Development Program, 2021
Dongguan Polytechnic Intelligent Terminal and Intelligent Manufacturing Special Project, 2021
Special Project in Key Areas of Universities in Guangdong Province (New Generation of Information Technology): Research on Intelligent Library Mobile Vision Search Service Model and Its Technical Framework Based on Deep Learning

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Cited By

Tallat RHawbani AWang XAl-Dubai AZhao LLiu ZMin GZomaya AHamood Alsamhi S(2024)Navigating Industry 5.0: A Survey of Key Enabling Technologies, Trends, Challenges, and OpportunitiesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.332947226:2(1080-1126)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2023.3329472
Zeng YChen XJin R(2023)Ensemble Active Learning by Contextual Bandits for AI Incubation in ManufacturingACM Transactions on Intelligent Systems and Technology10.1145/362782115:1(1-26)Online publication date: 19-Dec-2023
https://dl.acm.org/doi/10.1145/3627821
Bretones Cassoli BMetternich JKhomh FChatterjee PMetzger A(2023)Challenges for Predictive Quality in Multi-stage Manufacturing: Insights from Literature ReviewProceedings of the 3rd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things10.1145/3617573.3618030(16-23)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3617573.3618030

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