About the journal

• Home page
• Contact information
• Aims and scope
• Indexing information
• Editorial policies
• ComSIS consortium
• Journal boards
• Managing board

For authors

• Information for contributors
• Paper submission
• Article submission through OJS
• Copyright transfer form
• Download section

For readers

• Forthcoming articles
• Current issue
• Archive

For reviewers

• View and review submissions

News

• Journal's Facebook page
• Calls for special issues
• New issue notification

Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of Things

Kai Zhang¹

1. School of Information, Guangdong Communication Polytechnic
   Guangzhou, 510650, China
   gdcpzk@qq.com

Abstract

Industry 4.0 is currently the goal of many factories, promoting manufacturing factories and sustainable operation. Automated Optical Inspection (AOI) is a part of automation. Products in the production line are usually inspected visually by operators. Due to human fatigue and inconsistent standards, product inspections still have defects. In this study, the sample component assembly printed circuit board (PCB), PCB provided by the company was tested for surface components. The types of defects on the surface of the PCB include missing parts, multiple parts, and wrong parts. At present, the company is still using visual inspection by operators, the PCB surface components are more complex. In order to reduce labor costs and save the development time required for different printed circuit boards. In the proposed method, we use digital image processing, positioning correction algorithm, and deep learning YOLO for identification, and use 450 images and 10500 components of the PCB samples. The result and contribution of this paper shows the total image recognition rate is 92% and the total component recognition rate reaches 99%, and they are effective. It could use on PCB for different light, different color backplanes, and different material numbers, and the detection compatibility reaches 98%.

Key words

Deep learning; Digital image processing; Printed Circuit Board (PCB); Automatic inspection system

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS220718020Z

Publication information

Volume 20, Issue 2 (April 2023)
Special Issue on Machine Learning-based Decision Support Systems in IoT systems
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

Available in PDF
Portable Document Format

How to cite

Zhang, K.: Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of Things. Computer Science and Information Systems, Vol. 20, No. 2, 723–741. (2023), https://doi.org/10.2298/CSIS220718020Z