Open AccessAutomated Detection of Printed Circuit Boards (PCB) Defects by Using Machine Learning in Electronic Manufacturing: Current Approaches
Author(s) -
Syed Muhammad Mamduh Syed Zakaria,
Amiza Amir,
Naimah Yaakob,
Shamsodin Nazemi
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/767/1/012064
Subject(s) - printed circuit board , automated optical inspection , line (geometry) , fault detection and isolation , computer science , assembly line , production line , quality (philosophy) , engineering drawing , engineering , manufacturing engineering , artificial intelligence , mechanical engineering , electrical engineering , philosophy , geometry , mathematics , epistemology , actuator
The manufacturing of a printed circuit board in the SMT assembly line goes through multiple phases of automatic handling. To ensure the quality of the board and reduce the number of defects, inspection tasks such as solder paste inspection and automatic optical inspection are conducted. The inspection tasks are carried out at various phases of the assembly line. The paper aims to answer the questions of how machine learning technology can contribute for better PCB fault detection in the assembly line and at which parts of the assembly line this technology has been applied. The paper discusses the PCB defect detection by using machine learning and other approaches. The current research shows that PCB defect detection using machine learning are miniscule. Early detection is still unexplored and experimented in the industry.