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Attention-based 3D – convolutional neural network model for mechanical property predictions using visible light images in metal additive manufacturing
Additive manufacturing (AM), while commonly used for rapid prototyping and creating components with complex geometries, has not been widely adopted for critical applications across the aerospace, automotive, defense, energy, and medical industries. This is, in part, due to the challenges of controlling flaws and uncertainty in the mechanical behavior of additively manufactured components. In recent years, there has been an increase in research aimed at predicting the final mechanical properties of additively manufactured components during the printing process. To address these issues, a 3D-CNN model was trained using low-cost in situ visible-light camera data, anomaly classifications, and the chosen process parameters to predict the ultimate tensile strength (UTS), yield strength (YS), total elongation (TE), and uniform elongation (UE). The 3D-CNN layers of the model employed attention mechanisms to prioritize features in the data, thereby improving prediction accuracy. Furthermore, the effect of each process parameter and anomaly class is investigated using attention-based dynamic sigmoid weighted gates to interpret the influence each class has on the final prediction. Different combinations of the in situ data were fed into the 3D-CNN, with varying amounts of image layers, to determine the ideal combination for predicting mechanical properties in situ. Here, the 3D-CNN model achieved mean absolute percentage errors (MAPE) below 5% for both UTS and YS while using only a single camera input and under half of the available image layers.
AC-Optimal Power Flow Solutions with Security Constraints from Deep Neural Network Models.
Abstract not provided.
Process structure-based recurrent neural network modeling for predictive control: A comparative study
Not Available
A tutorial review of neural network modeling approaches for model predictive control
Not Available
Attribution-Driven Explanation of the Deep Neural Network Model via Conditional Microstructure Image Synthesis
Not Available
Distilling physical origins of hardness in multi-principal element alloys directly from ensemble neural network models
Abstract Despite a plethora of data being generated on the mechanical behavior of multi-principal element alloys, a systematic assessment remains inaccessible via Edisonian approaches. We approach this challenge by considering the specific case of alloy hardness, and present a machine-learning framework that captures the essential physical features contributing to hardness and allows high-throughput exploration of multi-dimensional compositional space. The model, tested on diverse datasets, was used to explore and successfully predict hardness in Al x Ti y (CrFeNi) 1- x - y , Hf x Co y (CrFeNi) 1- x - y and Al x (TiZrHf) 1- x systems supported by data from density-functional theory predicted phase stability and ordering behavior. The experimental validation of hardness was done on TiZrHfAl x . The selected systems pose diverse challenges due to the presence of ordering and clustering pairs, as well as vacancy-stabilized novel structures. We also present a detailed model analysis that integrates local partial-dependencies with a compositional-stimulus and model-response study to derive material-specific insights from the decision-making process.
Deep neural network models of sound localization reveal how perception is adapted to real-world environments
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Signatures of a liquid–liquid transition in an ab initio deep neural network model for water
Significance Water is central across much of the physical and biological sciences and exhibits physical properties that are qualitatively distinct from those of most other liquids. Understanding the microscopic basis of water’s peculiar properties remains an active area of research. One intriguing hypothesis is that liquid water can separate into metastable high- and low-density liquid phases at low temperatures and high pressures, and the existence of this liquid–liquid transition could explain many of water’s anomalous properties. We used state-of-the-art approaches in computational quantum chemistry, statistical mechanics, and machine learning and obtained evidence consistent with a liquid–liquid transition, supporting the argument for the existence of this phenomenon in real water.
Global Deep Neural Network Modeling of Compton Form Factors Constrained from Local $\chi^2$ Maps Fits
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CAN: Concept-Aligned Neurons for Visual Comparison of Deep Neural Network Models
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Graph Convolutional Neural Network Modeling of Vacancy Formation for Materials Discovery in Solar Thermochemical Water Splitting.
Abstract not provided.
Graph Neural Network Modeling of Vacancy Formation for Materials Discovery in Solar Thermochemical Water Splitting.
Abstract not provided.
Optimizing Thermodynamic Properties of High Entropy Alloys and Hydrides with Lattice Graph Neural Network Models
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