基于机器学习的PCB缺陷检测与分类方法研究

Research on PCB defect detection and classification based on machine learning

印制电路板(PCB)在制造过程中难免会产生各种缺陷。为了提高生产效率和产品质量,针对PCB制造中常见的缺陷进行检测与分类。通过构建深度学习模型,采用图像处理技术,对PCB图像进行全面而高效的缺陷检测。通过大量的训练数据,模型能够学习各类缺陷的特征,包括但不限于短路、断路、焊接不良等。使用举例说明和推导论证等方法对PCB缺陷进行分类研究,在深度学习模型的巧妙构建和分类算法的优化应用相辅相成的应用基础上,为提高生产效率和产品质量提供了可行的解决方案,推动了PCB制造业智能化方向的发展。

Printed circuit board(PCB)in the manufacturing process will inevitably produce a variety of defects.In order to improve production efficiency and product quality,detection and classification should be done for the common defects in PCB manufacturing.The main purpose of this paper is to study how to construct a deep learning model and adopt image processing technology to carry out comprehensive and efficient defect detection on PCB images.Using a large amount of training data,the model can learn the characteristics of various defects,including but not limited to short circuit,open circuit,poor welding,etc.The classification of PCB defects is studied,and its methods are illustrated with examples,derivation and demonstration.On the basis of the ingenious construction of deep learning models and the optimization application of classification algorithms complementing each other,it provides feasible solutions are provided for improving production efficiency and product quality,promoting the development of PCB manufacturing industry in the direction of intelligence.