Laser Powder Bed Fusion (LPBF) is an additive manufacturing process where laser power is applied to fuse the spread powder and fabricate industrial parts in a layer by layer fashion. Despite its great promise in fabrication flexibility, print quality has long been a major barrier for its widespread implementation. Traditional offline post-manufacturing inspections to detect the defects in finished products are expensive and time-consuming and thus cannot be applied in real-time monitoring and control. In-situ monitoring methods by relying on the in-process sensor data, on the other hand, can provide viable alternatives to aid with the online detection of anomalies during the process. Given the crucial importance of melt pool characteristics to the quality of final products, this paper provides a framework to process the melt pool images by a configuration of Convolutional Auto-Encoder (CAE) neural networks. The network’s corresponding bottleneck layer learns a deep yet low-dimensional representation from melt pools while preserving the spatial correlation and complex features intrinsic in the images. As opposed to the manual annotation of data by X-ray imaging or destructive tests, an agglomerative clustering algorithm is applied to these representations to automatically extract the anomalies and annotate the data accordingly. A control charting scheme based on Hotelling’s T² and S² statistics is then developed to monitor the process’s stability by keeping track of the learned representations and residuals obtained from the reconstruction of original images. Testing the proposed methodology on the collected data from an experimental build demonstrates that the method can extract a set of complex features that are inextricable otherwise by using hand-crafted feature engineering methods. Moreover, through extensive numerical studies, it is shown that the proposed feature extraction and statistical process monitoring scheme is capable of detecting the anomalies in real-time with accuracy and F₁ score of about 95% and 82%, respectively.

激光粉末床熔合（LPBF）是一种增材制造工艺，其中施加激光功率以熔合铺展的粉末并逐层制造工业零件。尽管在制造灵活性方面具有广阔的前景，但打印质量长期以来一直是其广泛实施的主要障碍。传统的离线制造后检查以检测成品中的缺陷是昂贵且费时的，因此不能应用于实时监视和控制中。另一方面，依靠过程中的传感器数据进行原位监视方法可以提供可行的替代方案，以帮助在过程中在线检测异常。鉴于熔池特性对最终产品的质量至关重要，本文提供了一个通过配置卷积自动编码器（CAE）神经网络来处理熔池图像的框架。网络的相应瓶颈层从熔池中学习到了深而低维的表示，同时保留了图像中固有的空间相关性和复杂特征。与通过X射线成像或破坏性测试手动注释数据相反，将聚集聚类算法应用于这些表示形式以自动提取异常并相应地注释数据。基于Hotelling's的控制图表方案 网络的相应瓶颈层从熔池中学习到了深而低维的表示，同时保留了图像中固有的空间相关性和复杂特征。与通过X射线成像或破坏性测试手动注释数据相反，将聚集聚类算法应用于这些表示形式以自动提取异常并相应地注释数据。基于Hotelling's的控制图表方案 网络的相应瓶颈层从熔池中学习到了深而低维的表示，同时保留了图像中固有的空间相关性和复杂特征。与通过X射线成像或破坏性测试手动注释数据不同，将聚集聚类算法应用于这些表示形式以自动提取异常并相应地注释数据。基于Hotelling's的控制图表方案然后，通过跟踪学习到的表示和从原始图像重建中获得的残差，来开发T ²和S ²统计量以监视过程的稳定性。在从实验构建中收集的数据上测试所提出的方法论，表明该方法可以提取一组复杂的特征，而这些复杂特征是可以通过手工制作的特征工程方法来实现的。此外，通过广泛的数值研究表明，所提出的特征提取和统计过程监视方案能够以大约95％和82％的准确度和F ₁分数实时检测异常。