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Abstract In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

Metal lattices are an important class of cellular materials that offer great advantages by providing high-strength and lightweight structures as compared to bulk materials. Progress in additive manufacturing techniques has led to increased complexity in design and shape of produced objects and is greatly beneficial for the development of metallic lattice structures. However additive manufacturing of lattices suffers from unpredictable defect creation that can compromise its mechanical integrity. Although post-build inspection techniques can provide quality assurance of the process, accurate assessment can be technically challenging, time consuming and costly. In this work, we investigate the use of high-speed measurements of thermal emission from the melt pool to identify defective individual struts formed with a missing bottom half in an otherwise fully built lattice structure produced with laser powder bed fusion. Surprisingly, results indicate lower photodiode signal, suggesting colder melt pool surface temperature, when printing struts with missing bottom half as compared to nominal struts. Additional thermographic imaging and multi-physics simulations reveal that the low photodiode signal is accompanied by presence of hot spatters carrying heat away from detection and continuous avalanche of powder on the melt pool. Based on these observations, a method was developed to identify defective individual struts with missing bottom half in full built lattices. This prediction approach provides valuable insights about part quality which are important for process qualification and illustrates the utility of melt pool thermal emission monitoring for identifying specific defects introduced by laser powder bed fusion.

Understanding and quantifying migration phenology of commercially harvested Pacific salmon (Oncorhynchus spp.) is a cornerstone for managing sustainable populations. Here, we use a multidecadal data time series together with a hypothesis-driven framework to evaluate migration phenology in adult fall and winter ecotype chum salmon (O. keta) in a poorly studied but highly managed system--the South Puget Sound (SPS) of Washington State, USA. Using generalized additive mixed models that accounted for temporal autoregressive dynamics, we examined the effect of commercial harvest, climate variation, intra-specific density dependence, and predator buffering on migration timing and run duration. SPS chum salmon are migrating earlier over time, especially the winter ecotype that showed the strongest temporal shift from historical timing. Migration timing shifts were closely associated with regional marine climate regimes, local scale freshwater availability, and increasing pinniped abundance. We conclude that there is potential for the winter ecotype migration converging with that of the fall ecotype and that directional change in migration phenology may be driven by a unique combination of ecosystem factors. La comprehension et la quantification de la phenologie des migrations des saumons du Pacifique (Oncorhynchus spp.) faisant l'objet d'une exploitation commerciale constituent un des elements essentiels de la gestion de populations perennes. Nous utilisons une serie de donnees multidecennale jumelee a un cadre module par differentes hypotheses pour evaluer la phenologie de la migration de saumons ketas (O. keta) adultes des ecotypes automnal et hivernal dans un systeme peu etudie, mais faisant l'objet d'une gestion intense, le sud du Puget Sound (SPS, Etat de Washington, Etats-Unis). En utilisant des modeles mixtes additifs generalises qui tiennent compte de la dynamique autoregressive temporelle, nous examinons les effets de l'exploitation commerciale, des variations climatiques, de la dependance intraspecifique sur la densite et de la modulation par les predateurs sur le moment et la duree des migrations. Les saumons ketas du SPS migrent de plus en plus tot, en particulier ceux de l'ecotype hivernal, qui presentent le plus grand decalage temporel par rapport aux observations passees. Les changements du moment de la migration sont etroitement associes aux regimes climatiques marins regionaux, a la disponibilite locale d'eau douce et a l'augmentation de l'abondance de pinnipedes. En conclusion, il est possible que la migration de l'ecotype hivernal converge avec celle de l'ecotype automnal et que les changements directionnels de la phenologie des migrations soient modules par une combinaison unique de facteurs ecosystemiques. [Traduit par la Redaction], Introduction Predicting migratory behavior of commercially harvested salmonids is important for managing and maintaining sustainable populations. However, evaluating the underlying biotic and abiotic mechanisms that drive migration phenology remains complex, [...]

A common cause of traumatic brain injuries (TBIs) is the head’s kinematic response to rapid movement, which can be reduced with dynamic neck strengthening. This study aimed to determine the most effective neck-strengthening program by comparing a traditional and nontraditional program. Isometric neck strength was assessed in 32 subjects randomly assigned to one of the traditional and nontraditional neck-strengthening programs. The nontraditional program used a novel neck-strengthening device. After weeks 6 and 10 of training, isometric neck strength was reassessed. With the collected data, linear mixed models were established to compare the changes in neck strength between the 2 groups during the 10-week training period. Statistical analysis results suggest that, for both cervical extension (CE) and cervical flexion (CF), subjects in the novel neck-strengthening device group had a significantly higher gain of strength during the 10 weeks than the traditional group. With test statistics of 22.691 and 23.203 and corresponding 2-sided p-value of 0.01289 and 0.003889, respectively, we conclude that there is a statistically significant difference in the linear slopes of increase for both CE and CF between the 2 groups. As to left cervical lateral flexion and right cervical lateral flexion, the novel neck strength group had increased strength gains compared with the traditional group. However, the increase was not enough to demonstrate significant findings. Results were considered significant at p, 0.05. The results of this study show that the novel neck-strengthening device may be an effective mechanism for preventing mild TBIs. [ABSTRACT FROM AUTHOR]

Laser-matter interactions during laser powder bed fusion additive manufacturing remain poorly understood. Here, the authors combine in situ X-ray imaging and finite element simulations to show how detrimental pores form under printing conditions and develop a strategy to suppress them.

Gaussian laser intensity profiles are standard in laser-based metal additive manufacturing, although recent work in single-layer melt tracks showed that beam shaping could offer a feasible route towards microstructural control. Since thermal cycling and grain orientation templating in multilayer builds can alter microstructures, we compare three-dimensional 316 L stainless steel cubes built using Gaussian and elliptical laser intensity profiles. Microstructural characterization confirms that elliptical beams result in a modified and improved microstructure compared to Gaussian beams. This assessment favoring the elliptical beam is based on: (1) the observed refinement of the columnar and equiaxed grains; (2) more importantly, the volume fraction occupied by equiaxed grains increases dramatically such that the average grain area is reduced by nearly 50%; (3) reduced texture in cubes built using an elliptical beam. The random orientation of small equiaxed grains in samples built using an elliptical beam also suggests a higher nucleation frequency. High-fidelity finite element simulations that deliver accurate thermal profiles by incorporating laser ray tracing and fluid dynamics were performed. Using a time-dependent solidification map based on local thermal gradients (G) and growth rates (R), our simulation results confirm the experimentally observed trend that an elliptical beam results in a favorable thermal profile.

Optical coherence tomography (OCT) is explored as a method to image laser-damage sites located on the surface of large aperture fused silica optics during postprocessing via C[O.sub.2] laser ablation. The signal analysis for image acquisition was adapted to meet the sensitivity requirements for this application. A long-working-distance geometry was employed to allow imaging through the opposite surface of the 5 cm thick optic. The experimental results demonstrate the potential of OCT for remote monitoring of transparent material processing applications. OCIS codes: 180.1655, 120.4630, 140.3380.