Signatures Of Processes Research Articles

High-intensity physiological activation disrupts the neural signatures of conflict processing

Physiological activation fluctuates throughout the day. Previous studies have shown that during periods of reduced activation, cognitive control remains resilient due to neural compensatory mechanisms. In this study, we investigate the effects of high physiological activation on both behavioural and neural markers of cognitive control. We hypothesize that while behavioural measures of cognitive control would remain intact during periods of high activation, there would be observable changes in neural correlates. In our electroencephalography study, we manipulate levels of physiological activation through physical exercise. Although we observe no significant impact on behavioural measures of cognitive conflict, both univariate and multivariate time-frequency markers prove unreliable under conditions of high activation. Moreover, we observe no modulation of whole-brain connectivity measures by physiological activation. We suggest that this dissociation between behavioural and neural measures indicates that the human cognitive control system remains resilient even at high activation, possibly due to underlying neural compensatory mechanisms.

Imaging Transcriptomics of Brain Functional Alterations in MS and Neuromyelitis Optica Spectrum Disorder.

The underlying transcriptomic signatures driving brain functional alterations in MS and neuromyelitis optica spectrum disorder (NMOSD) are still unclear. Regional fractional amplitude of low-frequency fluctuation (fALFF) values were obtained and compared among 209 patients with MS, 90 patients with antiaquaporin-4 antibody (AQP4)+ NMOSD, 49 with AQP4- NMOSD, and 228 healthy controls from a discovery cohort. We used partial least squares (PLS) regression to identify the gene transcriptomic signatures associated with disease-related fALFF alterations. The biologic process and cell type-specific signature of the identified PLS genes were explored by enrichment analysis. The correlation between PLS genes and clinical variables was explored. A prospective independent cohort was used to validate the brain fALFF alterations and the repeatability of identified genes. MS, AQP4+ NMOSD, and AQP4- NMOSD showed decreased fALFF in cognition-related regions and deep gray matter, while NMOSD (both AQP4+ and AQP4-) additionally demonstrated lower fALFF in the visual region. The overlapping PLS1- genes (indicating that the genes were overexpressed as regional fALFF decreased) were enriched in response to regulation of the immune response in all diseases, and the PLS1- genes were specifically enriched in the epigenetics profile in MS, membrane disruption and cell adhesion in AQP4+ NMOSD, and leukocyte activation in AQP4- NMOSD. For the cell type transcriptional signature, microglia and astrocytes accounted for the decreased fALFF. The fALFF-associated PLS1- genes directly correlated with Expanded Disability Status Scale of MS and disease duration across disorders. We revealed the functional activity alterations and their underlying shared and specific gene transcriptional signatures in MS, AQP4+ NMOSD, and AQP4- NMOSD.

Beyond the Positivity Bias: The Processing and Integration of Self-Relevant Feedback Is Driven by Its Alignment With Pre-Existing Self-Views.

Our self-concept is constantly faced with self-relevant information. Prevailing research suggests that information's valence plays a central role in shaping our self-views. However, the need for stability within the self-concept structure and the inherent alignment of positive feedback with the pre-existing self-views of healthy individuals might mask valence and congruence effects. In this study (N = 30, undergraduates), we orthogonalized feedback valence and self-congruence effects to examine the behavioral and electrophysiological signatures of self-relevant feedback processing and self-concept updating. We found that participants had a preference for integrating self-congruent and dismissing self-incongruent feedback, regardless of its valence. Consistently, electroencephalography results revealed that feedback congruence, but not feedback valence, is rapidly detected during early processing stages. Our findings diverge from the accepted notion that self-concept updating is based on the selective incorporation of positive information. These findings offer novel insights into self-concept dynamics, with implications for the understanding of psychopathological conditions.

Ancient Channel-Mouth Bifurcation Angles on Earth and Mars

Channel mouth bifurcation angles on modern river deltas are remarkably consistent with a theoretical prediction of 72°. However, the persistence of this angle through channel evolution and preservation into the stratigraphic record remains untested. Ancient channel mouth bifurcations were measured using stratal slices from 3D seismic volumes as well as outcropping delta deposits in Mars orbital imagery. We find that channel mouth bifurcations interpreted from terrestrial strata exhibit a mean angle of 71.9° ± 3.8° (95% confidence interval), consistent with modern deltas as well as theoretical and numerical predictions. Angles from martian strata preserved as inverted topography exhibit a mean angle of 80.1° ± 4.8°. A larger angle on Mars may be biased by measurements on eroding outcrops, or possibly the signature of altered sediment transport processes on Mars. Expanding channel network analysis into the stratigraphic archive for the first time provides a new mechanism interpreting paleohydraulics on Earth and Mars.

Adaptive learning rate in dynamical binary environments: the signature of adaptive information processing

Adaptive learning rate in dynamical binary environments: the signature of adaptive information processing

Electrophysiological signature of interoceptive attention: a spectral and source localization analysis.

The ability to attend to and consciously process interoceptive signals is deemed critical for the development of minimal self, adaptive self-regulation and affective experience, and optimal expression of both instrumental and executive cognitive functions. Yet, notwithstanding the richness of theoretical proposals concerning inferential accounts of interoception, empirical evidence is still scarce. Building on such premises, this study was designed to investigate the electrophysiological signature of cognitive processes leading to aware coding of interoceptive signals via EEG source localization. Thirty-six healthy participants completed an interoceptive accuracy task, i.e., the Heartbeat Counting Task (HCT), while we collected task-related and resting state electrophysiological activity. eLORETA modelling and statistical nonparametric mapping were used to estimate intracortical current density and link such estimates to participants' performance at the task. Source analysis highlighted higher current density estimates for alpha frequencies during HCT with respect to rest, with the primary cortical generator in the right parahippocampal gyrus. Also, a set of medial cortical structures-primarily represented by the cingulate gyrus-showed significant relation between task-related changes in current density estimates for beta oscillations and HCT scores. Findings suggest the informativity of EEG task-related measures of neural activation when used to assess interoceptive skills, as well as of the potential of metrics and analysis based on source localization in the quest to improve our understanding of interoceptive accuracy and neurofunctional correlates of related active inferences.

A comprehensive review of livestock development: insights into domestication, phylogenetics, diversity, and genomic advances.

Livestock plays an essential role in sustaining human livelihoods, offering a diverse range of species integral to food security, economic stability, and cultural traditions. The domestication of livestock, which began over 10,000years ago, has driven significant genetic changes in species such as cattle, buffaloes, sheep, goats, and pigs. Recent advancements in genomic technologies, including next-generation sequencing (NGS), genome-wide association studies (GWAS), and genomic selection, have dramatically enhanced our understanding of these genetic developments. This review brings together key research on the domestication process, phylogenetics, genetic diversity, and selection signatures within major livestock species. It emphasizes the importance of admixture studies and evolutionary forces like natural selection, genetic drift, and gene flow in shaping livestock populations. Additionally, the integration of machine learning with genomic data offers new perspectives on the functional roles of genes in adaptation and evolution. By exploring these genomic advancements, this review provides insights into genetic variation and evolutionary processes that could inform future approaches to improving livestock management and adaptation to environmental challenges, including climate change.

Provenance of Paleogene Strata at Slim Buttes, South Dakota: Implications for post-Laramide Evolution of Western Laurentia

Slim Buttes is a 30 km long by 10 km wide set of buttes containing Paleogene strata in northwest South Dakota. At Reva Gap in northern Slim Buttes, Eocene-Oligocene terrestrial strata of Chadron and Brule Formations of the White River Group unconformably overlie the Paleocene Fort Union Formation. An angular unconformity separates the White River Group from overlying Oligocene and Miocene strata of the Arikaree Group. Using detrital zircon U-Pb ages, we determine the provenance of these rocks as part of a broader synthesis of post-Laramide sedimentation in the Rocky Mountains and western Great Plains. The Chadron Formation age spectrum is dominated by Cretaceous and Proterozoic grains that are interpreted to be locally recycled from the underlying Cretaceous and Paleocene strata. The Brule Formation has a maximum depositional age of ~34 Ma; Paleogene zircons dominate the age spectrum, and a wide variety of older zircons are also present. The Oligocene zircons are interpreted to have been sourced from volcanic systems in the Great Basin to the southwest, while the subsequent proportions of the zircons were derived from a variety of source areas in the Nevadaplano and Rocky Mountain areas to the southwest. Sparse amounts of Archean zircons are thought to represent the burial of Laramide uplifts throughout Wyoming at the time of Brule deposition, making for a regional paleotopography with little relief across the western interior of the United States. The Miocene-age Arikaree Group sand has a maximum depositional age of ~26 Ma and a multimodal detrital zircon age spectrum. The Arikaree Group provenance likely represents continued sourcing in the Great Basin volcanic systems and Nevadaplano, the beginnings of the re-exhumation of Laramide basement uplifts, and subsequent sediment evacuation out of the western interior and into the Gulf of Mexico to the southeast. Our findings indicate that the transport process and detrital zircon provenance signatures of these strata are decoupled, and each have their own independent evolution. The volcanic signature is primarily transported via aeolian processes (i.e. volcanic ash), and the recycled detrital zircon signature is primarily transported via fluvial processes.

Macroscale Roughness Reveals the Complex History of Asteroids Didymos and Dimorphos

Morphological mapping is a fundamental step in studying the processes that shaped an asteroid surface. However, it is challenging and often requires multiple independent assessments by trained experts. Here we present fast methods to detect and characterize meaningful terrains from the topographic roughness: entropy of information, and local mean surface orientation. We apply our techniques to Didymos and Dimorphos, the target asteroids of NASA's Double Asteroid Redirection Test mission—the first attempt to deflect an asteroid. Our methods reliably identify morphological units at multiple scales. The comparative study reveals various terrain types, signatures of processes that transformed Didymos and Dimorphos. Didymos shows the most heterogeneity and morphology that indicate recent resurfacing events. Dimorphos is comparatively rougher than Didymos, which may result from the formation process of the binary pair and past interaction between the two bodies. Our methods can be readily applied to other bodies and data sets.

CAPP-GPT: A computer-aided process planning-generative pretrained transformer framework for smart manufacturing

Smart manufacturing (SM) constitutes the backbone of Industry 4.0 (I4.0), allowing for heightened autonomy of the various interacting cyber-physical systems, making the various entities on the production floor. Connectivity, a vital enabler, plays a crucial role through state-of-the-art Digital Twinning (DT) technologies driven by underlying innovations like the industrial Internet of Things, Cloud Computing, and advancements in sensory devices. DT, which plays a vital role in the various planning functions under the production and operations management umbrella, is being used in the developed combined CAPP-GPT (Computer-Aided Process Planning-Generative Pretrained Transformer) and production scheduling approach to address disruptions on the shopfloor and in self-healing of the manufacturing processes at a micro-CAPP level by optimally adapting the process parameters and the developed toolpath on the fly based on online process signature measurements. In a leap commensurate with that which has taken place in Natural Language Processing-Large Language Models (Chat-GPT), similar efforts are currently being undertaken to parse CAD data structures and blueprints, fusing operations research and predictive analytics algorithms to carry out setup planning as well as sequencing and grouping manufacturing sub-operations. A hybridized Optimization and Machine Learning (ML) approach is employed where Logical Analysis of Data is used to solve the problem heuristically, exploiting various generative and variant methods at heart. Another extension of this macro-CAPP problem is being tackled by integrating the problem with delayed product differentiation, lot-sizing, and transfer line balance for futuristic batch-production shops employing Hybrid Manufacturing (HM) and Smart Assembly. At the micro-CAPP level, HM process parameters are optimized using a comprehensive approach employing the Taguchi loss function to assess surface roughness, internal failure costs, and other criteria, including greenhouse gas emissions and expended energy. Online measurements of the process signatures are also employed to adapt the initial set of process parameters using different automatic control schemes. ML is used to identify the process parameters carrying simulations on Simulink before the system is deployed.

Molecular and spatial signatures of human and rat corpus cavernosum physiopathological processes at single-cell resolution

The composition and cellular heterogeneity of the corpus cavernosum (CC) microenvironment have been characterized, but the spatial heterogeneity at the molecular level remains unexplored. In this study, we integrate single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing to comprehensively chart the spatial cellular landscape of the human and rat CC under normal and disease conditions. We observe differences in the proportions of cell subtypes and marker genes between humans and rats. Based on the analysis of the fibroblast (FB) niche, we also find that the enrichment scores of mechanical force signaling vary across different regions and correlate with the spatial distribution of FB subtypes. Invitro, the soft and hard extracellular matrix (ECM) induces the differentiation of FBs into apolipoprotein (APO)+ FBs and cartilage oligomeric matrix protein (COMP)+ FBs, respectively. In summary, our study provides a cross-species and physiopathological transcriptomic atlas of the CC, contributing to a further understanding of the molecular anatomy and regulation of penile erection.

Is That You I Hear? Speaker Familiarity Modulates Neural Signatures of Lexical-semantic Activation in 18-month-old Infants.

Developmental language studies have shown that lexical-semantic organization develops between 18 and 24 months of age in monolingual infants. In the present study, we aimed to examine whether voice familiarity facilitates lexical-semantic activation in the infant brain. We recorded the brain activity of 18-month-old, French-learning infants using EEG while they listened to taxonomically related and unrelated spoken word pairs by one voice with which they were familiarized with before the experiment, and one voice with which they were not familiarized. The ERPs were measured in response to related and unrelated target words. Our results showed an N400 effect (greater amplitudes for unrelated as opposed to related target words) over the left hemisphere, only for the familiar voice, suggesting that the voice familiarity facilitated lexical-semantic activation. For unfamiliar voices, we observed an earlier congruence effect (greater amplitudes for related than for unrelated target words). This suggests that although 18-month-olds process lexical-semantic information from unfamiliar speakers, their neural signatures of lexical-semantic processing are less mature. Our results show that even in the absence of personal relation with a speaker, familiarity with a voice augments infant lexical-semantic processing. This supports the idea that extralinguistic information plays a role in infant lexical-semantic activation.

Do fungi look like macroparasites? Quantifying the patterns and mechanisms of aggregation for host-fungal parasite relationships.

Most hosts contain few parasites, whereas few hosts contain many. This pattern, known as aggregation, is well-documented in macroparasites where parasite intensity distribution among hosts affects host-parasite dynamics. Infection intensity also drives fungal disease dynamics, but we lack a basic understanding of host-fungal aggregation patterns, how they compare to macroparasites, and if they reflect biological processes. To address these gaps, we characterized aggregation of the fungal pathogen Batrachochytrium dendrobatidis (Bd) in amphibian hosts. Utilizing the slope of Taylor's Power Law, we found Bd intensity distributions were more aggregated than macroparasites, conforming closely to lognormal distributions. We observed that Bd aggregation patterns are strongly correlated with known biological processes operating in amphibian populations, such as epizoological phase-invasion, post-invasion, and enzootic-and intensity-dependent disease mortality. Using intensity-dependent mathematical models, we found evidence of evolution of host resistance based on aggregation shifts in systems persisting with Bd following disease-induced declines. Our results show that Bd aggregation is highly conserved across disparate systems and is distinct from aggregation patterns in macroparasites, and contains signatures of potential biological processes of amphibian-Bd systems. Our work lays a foundation to unite host-fungal dynamics under a common theoretical framework and inform future modeling approaches that may elucidate host-fungus interactions.

(Invited) Disentangling the Nonequilibrium Dynamics of Excitations in 2D Material Interfaces Under Bias: Leveraging Theory, Electrochemistry, and Time-Resolved Spectroscopy

A fundamental challenge in elucidating, controlling, and exploiting nonequilibrium relaxation in condensed phase systems is the ability to find and employ physically transparent models. Through such models, one can develop a compelling assignment and interpretation of the spectral signatures of processes spanning charge and energy transfer, quasiparticle formation, and even chemical reactivity. Two-dimensional materials, such as transition metal dichalcogenides (TMDs), offer one such challenge, especially under applied fields. In this talk, I will describe our recent work combining theory, electrochemical measurements, and ultrafast spectroscopy to demonstrate hot carrier extraction in photoelectrochemical cells composed of monolayer MoS2 on an ITO electrode exposed to electrolyte solution. In addition, I will discuss our critical assessment of the ability of a physically intuitive Hamiltonian, consisting of an infinitely heavy exciton immersed in a fermi sea of conduction band electrons, to capture and offer an interpretation of the spectral features in the linear and transient absorption optical signals of this material under an applied bias. Importantly, we leverage our analysis to identify, physically, how trion formation moves, broadens, and resizes the “A exciton” absorption of our working device. We further unify the interpretation over various sources of such TMD spectral shifts: applied bias, fluence, and (TA) delay time. Our work thus demonstrates hot carrier extraction in 2D photoelectrochemical cells, outlines a path to exploit this phenomenon in semiconductor devices, delineates open questions that are only now becoming possible to address with theory and experiment, and identifies the underlying physical process responsible for previously misidentified spectral features in 2D materials that changed with applied voltage, fluence, and time.

Prediction of spatter-related defects in metal laser powder bed fusion by analytical and machine learning modelling applied to off-axis long-exposure monitoring

Laser powder bed fusion of metals is increasingly used for fabricating complex parts requiring good mechanical properties. Simultaneously, researchers in the field are intensifying the efforts to reduce defects, such as internal porosities, which hinder a wider industrial adoption of this technology, urging process monitoring to a pivotal role in defect identification and mitigation. Therefore, understanding the correlation between in-process monitoring signals and post-process actual defects is fundamental to taking informed decisions and potential corrective actions during the process. This work focuses on developing models to predict spatter-related defects from specific process signatures detected through off-axis long-exposure imaging. Layer-wise images were properly aligned with corresponding cross-sections from tomographic reconstructions to investigate the relationship between spatter-related signatures and actual defects measured by X-ray computed tomography. This relationship was used as a knowledge basis to develop an analytical image-processing approach and a machine learning-based methodology, which were then compared in terms of their correlation performances. The advantages and limitations of both methods are discussed in the paper. Both approaches led to promising results in the prediction of lack-of-fusion defects caused by spatters, with the machine learning approach showing a prediction accuracy in the order of 90 % for defects with equivalent diameter above 90 µm, while the analytical model needed equivalent diameters larger than 130 µm to reach a prediction accuracy in the order of 80 %. Furthermore, the machine learning method led to strong results regarding early defect detection, with most of the investigated defects properly predicted by analysing two consecutive layers after the signature detection.

Stimulus-related oscillatory brain activity discriminates hoarding disorder from OCD and healthy controls

Hoarding disorder (HD) and obsessive-compulsive disorder (OCD) are highly comorbid and genetically related, but their similarities and differences at the neural level are not well characterized. The present study examined the time-frequency information contained in stimulus-related EEG data as participants worked on a visual flanker task. Three groups were included: participants diagnosed with HD (N = 33), OCD (N = 26), and healthy controls (N = 35). Permutation-controlled mass-univariate analyses found no differences between groups in terms of the magnitude of the oscillatory responses. Differences between groups were found selectively for phase-based measures (phase-locking across trials and across sensors) in time ranges well after those consistent with initial visuocortical processes, in the alpha (10 Hz) as well as theta and beta frequency bands, centered around 6 Hz and 15 Hz, respectively. Specifically, HD showed attenuated phase locking in theta and alpha compared to OCD and HC, while OCD showed heightened inter-site phase locking in alpha/beta. Including age as a covariate attenuated, but did not eliminate, the group differences. These findings point to signatures of cortical dynamics and cortical communication task processing that are unique to HD, and which are specifically present during higher-order visual cognition such as stimulus-response mapping, response selection, and action monitoring.

N1 and LNC reveal the differential electrophysiological signatures of functional and hedonic images processing

In the process of creative sentence or phrase utilization, novel and appropriate evaluations cause the different brain responses observed in event-related potentials: the N400 reflects the novelty evaluation, whereas a late negative component marks appropriate processing. Do we have similar brain reactions in image perception when we rapidly browse pictures of objects with different novelty, functional/appropriate, and hedonic value? To explore this question, participants were presented with four novel object images with high or low functional and hedonic properties, as well as the ordinary product images, with the instruction to attentively observe and understand each image. We found a clear dissociation between processing of novelty and functional value: novelty objects produced negative deflections in the N2–N400 time window relative to the ordinary object images, whereas images with high functional value elicited a larger N2 and late negative waves (LNC) resembling the late component found for the appropriate phrases. Object images with high hedonic value, however, were associated with earlier aesthetic preference reflected in smaller N1 amplitudes, but failed to elicit a LNC effect. We therefore conclude that the processing of novelty, functional, and hedonic value are dissociation, and the perception of hedonic value is earlier (N1) than the novelty processing (N400) and the verification of functional value (LNC).

P-011 Maternal gestational methyl donor deficiency affects reproductive development and fertility of mice offspring

Abstract Study question Is maternal methyl donor deficiency during gestation affects offspring’s reproductive health at adulthood? Summary answer Maternal gestational methyl donor deficiency affects reproductive development and fertility of F1 offspring and also leads to increased post implantation loss What is known already Reproductive disorders and their exact mechanisms are scarce in “The developmental origins of health and disease” (DOHaD) hypothesis which well explains the metabolic complications such as obesity, type 2 diabetes and hypertension that may take root during early development. The maternal nutritional environment during early development has important implications on offspring’s health later in life including reproductive health. The gestational period is of critical importance during which major epigenetic reprogramming occurs. The maternal methyl donor deficiency during gestational period may affect this process and epigenetic signatures of developing gametes and the fertility potential Study design, size, duration Pregnant female OCT4-GFP mice, fed on control chow diet (CCD) & 40% methyl-donors (Folate, Methionine & Vitamin B12) deficient diet (MDD) from gestation day (GD) 5 to GD 20. The gonadal development of F1 offsprings was studied on gestation day GD 18, post-natal day (PND) 22 and PND 60. Fertility study was done by mating adult F1 male offspring with adult female mice fed on standard diet Participants/materials, setting, methods Homocysteine levels in dams (F0) at GD18 were estimated by ELISA. Testosterone levels in F1 offsprings at PND 60 were estimated by ELISA. Furthermore, Serum glucose levels in F1 offsprings were measured at PND 60 and PND 75 by glucometer. Testicular histology, quantitative histomorphometry, immunofluorescence and TUNEL assay was performed at PND 60. Main results and the role of chance Fetal weight was reduced at GD13 and GD18 but postnatal body weight at PND 22 and 60 was not significantly affected in MDD group. Teratological defects were observed in pups of 40% MDD group as compared to CDD. Testosterone levels were significantly reduced in F1 offsprings of 40% MDD group. Glucose levels were similar in both the groups at PND 60 but at PND 75 it is significantly higher in case of F1 offsprings of 40% MDD. Maternal serum homocysteine levels and global DNA methylation levels of fetal gonads were affected. Testicular histology was significantly impaired in 40% MDD group at GD18, PND22 and PND60. Quantitative histomorphometry and immunofluorescence study showed the loss of spermatogonia and spermatocytes with significant increase in germ cells sloughing and seminiferous tubules atrophy. Apoptosis is also observed in seminiferous tubules. Copulation index, sperm count and motility was significantly reduced in case of 40% MDD group at adulthood. The F1 offspring of this group were sub-fertile with significant post implantation loss. Limitations, reasons for caution Although mice share ∼90% genome with humans, they may not resemble all aspect of physiology or pathology in humans hence it could limit the findings to extrapolate to humans. Wider implications of the findings The knowledge gained from this work could be helpful in highlighting the need of gestational micronutrient interventions specially methyl donors, which could help improving maternal and child health as well as reducing the future disease burden of reproductive disorders Trial registration number not applicable

BRIDGING DATA GAPS: A FEDERATED LEARNING APPROACH TO HEAT EMISSION PREDICTION IN LASER POWDER BED FUSION

Abstract Deep learning has impacted defect prediction in Additive Manufacturing (AM), which is important to ensure process stability and part quality. However, its success depends on extensive training, requiring large, homogenous datasets–remaining a challenge for the AM industry, particularly for small- and medium-sized enterprises (SMEs). The unique and varied characteristics of AM parts, along with the limited resources of SMEs, hampers data collection, posing difficulties in the independent training of deep learning models. Addressing these concerns requires enabling knowledge sharing from the similarities in the physics of the AM process and defect formation mechanisms while carefully handling privacy concerns. Federated learning (FL) offers a solution to allow collaborative model training across multiple entities without sharing local data. This paper introduces an FL framework to predict section-wise heat emission during Laser Powder Bed Fusion (LPBF), a vital process signature. It incorporates a customized Long Short-Term Memory (LSTM) model for each client, capturing the dynamic AM process's time series properties without sharing sensitive information. Three advanced FL algorithms are integrated–FedAvg, FedProx, and FedAvgM–to aggregate model weights rather than raw datasets. Experiments demonstrate that the FL framework ensures convergence and maintains prediction performance comparable to individually trained models. This work demonstrates the potential of FL-enabled AM modeling and prediction where SMEs can improve their product quality without compromising data privacy.

A Pragmatic Approach for Rapid, Non-Destructive Assessment of Defect Types in Laser Powder Bed Fusion Based on Melt Pool Monitoring Data.

Process monitoring systems, e.g., systems based on photodiodes, could be used in laser-based powder bed fusion (PBF-LB/M) to measure various process parameters and process signatures to eventually allow for a local, detailed analysis of the produced parts. Here, simple statements only concerning the occurrence of defects in parts are sufficient in many cases, especially with respect to industrial application. Therefore, a pragmatic approach to rapidly infer the occurrence of defects and their types based on in situ data obtained by commercially available process monitoring systems is introduced. In this approach, a color distribution in form of a histogram is determined for each produced part using layer-wise screenshots of the visualized data provided by the monitoring software. Assessment of the histograms of AlSi10Mg samples, which were processed with different parameter combinations, revealed characteristics depending on the prevailing defect types. These characteristics enable the prediction of the occurring defect types without the necessity to apply conventional downstream testing methods, and thus, a straightforward separation of parts with good quality from defective components. Since the approach presented uses the data visualization of the monitoring software, it can be used even when direct access to the raw data is not provided by the machine manufacturer.