학술논문

Classification of Solder Quality in Through-Hole Devices by Convolutional Neural Networks
Document Type
Conference
Source
2022 Innovations in Intelligent Systems and Applications Conference (ASYU) Intelligent Systems and Applications Conference (ASYU), 2022 Innovations in. :1-4 Sep, 2022
Subject
Computing and Processing
Engineering Profession
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Soldering
Deep learning
Convolutional neural networks
Production
Image recognition
Computer vision
Visualization
electronics production
automated optical inspection
soldering
ResNet
Inception-v4
Inception-ResNet
Language
ISSN
2770-7946
Abstract
Solder inspection has an important place in electronic board production. Deep learning methods can be used to control the quality of solders. In this paper, a comparative study on the performance of deep learning methods for the classification of solder quality of through-hole devices is presented. The dataset used contains 7320 pieces of data. Soldering quality will be classified using three different networks based on ResNet, Inception-v4 and Inception-ResNet, which were created in this study. The test results obtained will be compared.