增材制造部件的自动缺陷识别使用机器感知和视觉突出度
Jan Petrich1, Edward W Reutzel1
1Applied Research Laboratory, Pennsylvania State University, University Park, Pennsylvania, USA.
3D printing and additive manufacturing
|June 22, 2023
概括
本研究介绍了一种自动化的3DX射线计算机断层扫描 (CT) 方法,用于检测金属增材制造 (AM) 部件的内部缺陷. 该方法使用机器感知来识别异常,使得更快的质量评估,而不是事先的缺陷知识.
科学领域:
- 材料科学与工程 材料科学与工程
- 非破坏性测试是指非破坏性测试.
- 计算机视觉和图像分析
背景情况:
- 金属增材制造 (AM) 工艺可以引入影响零件性能的内部缺陷.
- 非破坏性检查 (NDI) 技术对于确保AM部件的质量和可靠性至关重要.
- 现有的方法往往需要对缺陷特征或特定部件数据的预先了解.
研究的目的:
- 开发一种全自动化的3D解决方案,用于使用X射线计算机断层扫描 (CT) 来识别金属AM部件的缺陷.
- 创建一个可以识别内部缺陷的算法,而不需要先验关于它们的外观,大小或形状的信息.
- 建立一个可靠的方法来评估和量化AM组件的构建质量.
主要方法:
- 一个机器感知框架使用多尺度,对称和可分离的3D卷积内核来识别异常的voxels (缺陷).
- 使用统计措施生成二进制面具,从背景对材料进行细分,容纳没有CAD或STL文件的任意部分几何形状.
- 邻近的异常声合在一起形成缺陷集群,提供大小,形态和方向的信息.
主要成果:
- 该算法使用硬件加速 (GPU支持) 实现了足够的处理时间,通常在几分钟内.
- 证明了非常低的假阳性率,特别是对于非常突出的和较大的缺陷.
- 开发的工具可以简化为2D图像分析.
结论:
- 拟议的3D自动缺陷识别系统为金属AM的质量控制提供了一个有前途的解决方案.
- 该方法仅依赖于CT扫描中的视觉线索,使其具有多功能性,并且独立于特定的缺陷知识或零件数据.
- 这项技术可以提高对AM构建质量评估的信心,并可能将缺陷特征与疲劳反应等机械性质联系起来.
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