Application of Neural Networks to Testing Printed Circuit Boards Using Data from a X-ray 3D Microtomograph

IF 0.9 4区材料科学 Q4 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Russian Journal of Nondestructive Testing Pub Date : 2025-04-21 DOI:10.1134/S1061830924602848

V. I. Syryamkin, F. A. Klassen, A. N. Bertsun

引用次数: 0

Abstract

A method for defect recognition in printed circuit boards using neural networks is discussed. An analysis of various neural network architectures is performed to identify the most effective one. An approach to data filtering simulating the operation of a microtomograph using convolutional autoencoders is also presented. The quality of the proposed approaches was evaluated using the mean Average Precision (mAP) metric for YOLOv8 and Faster R-CNN models.

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神经网络在利用x射线三维显微层析仪数据测试印刷电路板中的应用

讨论了一种基于神经网络的印刷电路板缺陷识别方法。对各种神经网络结构进行了分析，以确定最有效的神经网络结构。本文还提出了一种利用卷积自编码器模拟显微层析成像操作的数据滤波方法。使用YOLOv8和Faster R-CNN模型的平均平均精度（mAP）度量来评估所提出方法的质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Russian Journal of Nondestructive Testing 工程技术-材料科学：表征与测试

CiteScore

1.60

自引率

44.40%

发文量

审稿时长

6-12 weeks

期刊介绍： Russian Journal of Nondestructive Testing, a translation of Defectoskopiya, is a publication of the Russian Academy of Sciences. This publication offers current Russian research on the theory and technology of nondestructive testing of materials and components. It describes laboratory and industrial investigations of devices and instrumentation and provides reviews of new equipment developed for series manufacture. Articles cover all physical methods of nondestructive testing, including magnetic and electrical; ultrasonic; X-ray and Y-ray; capillary; liquid (color luminescence), and radio (for materials of low conductivity).