In this work, process monitoring data, including layerwise imagery, multi-spectral emissions, and laser scan vector data, were collected during laser-based powder bed fusion additive manufacturing and correlated to fatigue performance. All parts were X-ray CT scanned post-build, and internal flaws were identified via an automated defect recognition software. Convolutional neural networks were trained to discriminate flaws from nominal build conditions using in situ data modalities only. Trained classifiers were then tested against a previously unseen data set collected from an independent build, and classification performance and metrics for information content provided by each individual modality were formally established. Correlations were drawn between the detected flaw populations and the corresponding fatigue properties, demonstrating that fatigue critical lack-of-fusion flaws can be detected via machine learning of in situ sensor data. The present results also show that, at least from a classification accuracy perspective, flaw detection via ML on process monitoring data is a viable path forward for real-time flaw detection and automated, interlayer repair strategies. However, strategies for extracting and analyzing sensor data in real-time without incurring excessive increases in build time must first be developed. These developments represent necessary components to draw direct correlations between in situ data modalities, internal part quality, and fatigue performance.

在这项工作中，在基于激光的粉末床融合增材制造过程中收集了过程监控数据，包括分层图像、多光谱发射和激光扫描矢量数据，并与疲劳性能相关联。所有部件都在构建后进行 X 射线 CT 扫描，并通过自动缺陷识别软件识别内部缺陷。训练卷积神经网络以仅使用原位数据模式从名义构建条件中区分缺陷。然后针对从独立构建中收集的先前未见过的数据集测试经过训练的分类器，并正式建立分类性能和每个单独模态提供的信息内容的度量。在检测到的缺陷种群和相应的疲劳特性之间绘制了相关性，证明可以通过原位传感器数据的机器学习来检测疲劳临界缺乏融合缺陷。目前的结果还表明，至少从分类准确性的角度来看，通过机器学习对过程监控数据进行缺陷检测是实时缺陷检测和自动化层间修复策略的可行途径。然而，必须首先开发实时提取和分析传感器数据而又不会导致构建时间过度增加的策略。这些发展代表了在现场数据模式、内部零件质量和疲劳性能之间建立直接关联的必要组成部分。通过机器学习对过程监控数据进行缺陷检测是实时缺陷检测和自动化层间修复策略的可行途径。然而，必须首先开发实时提取和分析传感器数据而又不会导致构建时间过度增加的策略。这些发展代表了在现场数据模式、内部零件质量和疲劳性能之间建立直接关联的必要组成部分。通过机器学习对过程监控数据进行缺陷检测是实时缺陷检测和自动化层间修复策略的可行途径。然而，必须首先开发实时提取和分析传感器数据而又不会导致构建时间过度增加的策略。这些发展代表了在现场数据模式、内部零件质量和疲劳性能之间建立直接关联的必要组成部分。