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自动化PCBX射线CT图像的3D语义细分和网列提取的3D语义细分

Adrian Phoulady¹, Yara Suleiman¹, Hongbin Choi¹

¹University of Connecticut, Storrs, CT, USA.

Scientific reports

|January 17, 2025

概括

本研究介绍了一种自动化方法,用于使用X射线CT图像和机器学习的3D语义细分来重建印刷电路板 (PCB) 设计. 该方法提高了PCB设计重建的准确性和可扩展性.

科学领域:

工程工程师工程师工程师
计算机科学计算机科学
材料科学材料科学材料科学

背景情况:

印刷电路板 (PCB) 设计重建对于过时管理,知识产权恢复和质量保证至关重要.
现有的PCB设计提取方法在准确性,效率和可扩展性方面存在局限性.
需要自动化PCB设计重建来克服这些挑战.

研究的目的:

开发一种用于PCBX射线CT图像3D语义细分的自动化方法.
为了实现准确的网列提取,用于PCB设计重建.
为了改进传统的PCB设计提取技术.

主要方法:

利用图像处理和机器学习来对PCBX射线CT数据进行3D语义细分.
采用3D U-Net架构与ResNet-18骨干,在合成数据上进行训练.
集成的高分辨率3D成像,流域细分和网列提取.

主要成果:

实现了直接的3D语义细分,超过了以前的方法.
通过合成数据培训,消除了对广泛标记数据集的需求.
在合成和现实世界PCB数据集上都表现出高准确性和稳定性.
启用了曲PCB的细分,并减少了预处理工作.

结论:

开发的自动化方法为PCB设计重建提供了可扩展和准确的解决方案.
这种方法在PCB以外的各种3D成像领域都有潜在的应用.
该技术显著提高了PCB设计提取的易用性,通用性和适用性.

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