Directed energy deposition (DED) stands as a pivotal additive manufacturing technique, revolutionizing the landscape of modern manufacturing. However, process-related defects hin-der its broad application across different sectors. In this paper, we propose a novel methodology for the in-situ prediction of defects in DED processes based on the thermal images of the melt pools. Initially, multiple features, summarizing the thermal and geometric characteristics of the melt pool, were extracted. Based on these features, an ensemble of unsupervised clustering models was constructed to distinguish anomalies - images with defects - from the defect-free images. Roughly 3% of the acquired images were predicted to include defects. Upon visual inspection, these images exhibited distinctive thermal distributions and geometric configurations compared to the remaining dataset. Furthermore, a 2D approximate visualization of the feature space revealed the clustering structure of the thermal images in their feature space. This visualization showed that the anomalies could be distinguished from the majority of normal images, which further validates the prediction model's effectiveness.

In this paper a neural network based modeling approach is proposed for localization and estimation of loads acting on aircraft wings from full field depth measurements. These measurements can be provided by a multitude of sensors such as depth cameras. Depth cameras have many advantages over other intensity sensors in that they can work in low light conditions and they are invariant to texture and color changes. First, an autoencoder is proposed to extract maximum informative data from the depth images and encode them at a much smaller dimension. Next, to develop the models for localization and estimation of loads, supervised multinomial classification and logistic regression networks are proposed, where the encoded depth features are utilized as input in both networks. The performance of the proposed method is validated on a composite wing subject to concentrated and distributed loads, during which the proposed methods for localization and estimation of loads achieved high accuracies of 90.6% and 90.5%, respectively.