data-driven techniques are used to predict the actuation strain (AS) of NiTiHfX shape memory alloy (SMA). A Machine Learning (ML) approach is used to overcome the high dimensional dependency of NiTiHfX AS on numerous ...
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data-driven techniques are used to predict the actuation strain (AS) of NiTiHfX shape memory alloy (SMA). A Machine Learning (ML) approach is used to overcome the high dimensional dependency of NiTiHfX AS on numerous factors, as well as the lack of fully known governing physics. Detailed data extraction on available experimental studies is performed to gather any related information about the actuation strain. The elemental composition, manufacturing approaches, thermal treatments, applied stress, and post-processing steps that are commonly used to process NiTiHfX and have an impact on the material AS are used as input parameters of the ML models. Since a broad data collection is performed the information for each input factor was sufficient for the use of the majority of the accessible information in the literature on NiTiHfX AS. Considering most of the regular NiTiHfX processing factors also enables the option of tuning additional characteristics of NiTiHfX in addition to the ASs. The work is unique as is the first to fully investigate the NiTiHfX actuation strain prediction. To forecast the NiTiHfX AS, a total of 901 data sets or 17,119 data points for eighteen inputs and one output were gathered, verified, and selected. Several machine-learning approaches were applied and joined to gather to guarantee robust modeling. The global model's overall determination factor (R-2) was 0.96, suggesting the viability of the proposed NN model. Such a model opens the possibility of intelligent material selection and processing to maximize the AS or shape memory effect of NiTiHf SMA.
Lithium-ion batteries have become integral to the energy storage industry, driving innovations like electric vehicles, renewable energy systems, and portable electronics. A critical aspect of enhancing LIB performance...
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Lithium-ion batteries have become integral to the energy storage industry, driving innovations like electric vehicles, renewable energy systems, and portable electronics. A critical aspect of enhancing LIB performance lies in developing anode materials, which directly influence the batteries' energy density, life cycle, and safety. In recent years, Machine Learning has emerged as a powerful tool in predicting, designing, and optimizing anode materials. This review explores the integration of ML techniques in advancing anode materials, including datadriven approaches to predicting electrochemical performance, optimizing synthesis processes, and discovering novel materials. Key ML methods such as supervised learning, unsupervised learning, and reinforcement learning are discussed in the context of improving material properties like capacity, conductivity, and stability. The paper also highlights current challenges, including the need for larger datasets, improved interpretability of ML models, and integrationof ML with experimental methods. The insights gained from this review provide a roadmap for future research on leveraging ML in developing next-generation anode materials for LIBs.
To understand the material paradigm, data-driven material design necessitates both microstructural input and output in the form of visual images. Therefore, generative adversarial networks (GAN)-based deep convolution...
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To understand the material paradigm, data-driven material design necessitates both microstructural input and output in the form of visual images. Therefore, generative adversarial networks (GAN)-based deep convolutional GAN, cycle-consistent GAN, and super-resolution GAN techniques are used to generate, translate, and improve the quality of microstructural images in this study. The reconstructed virtual microstructural images are realistic and indistinguishable from the real ones. Furthermore, using GAN techniques to reconstruct microstructural image suggests promising ways to design desired microstructures using parameterized descriptors and image augmentation, which are expected to advance data-drivenmaterials research.
data-driven techniques are used to predict the transformation temperatures (TTs) of NiTiHf shape memory alloy. A machine learning (ML) approach is used to overcome the high-dimensional dependency of NiTiHf TTs on nume...
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data-driven techniques are used to predict the transformation temperatures (TTs) of NiTiHf shape memory alloy. A machine learning (ML) approach is used to overcome the high-dimensional dependency of NiTiHf TTs on numerous factors, as well as the lack of fully known governing physics. The elemental composition, thermal treatments, and post-processing steps that are commonly used to process NiTiHf and have an impact on the material phase transitions are used as input parameters of the neural network model (NN) to design the TTs. Such a feature selection led to the use of most of the accessible information in the literature on NiTiHf TTs, as all processing features required to be fed into the NN model. Considering most of the regular NiTiHf processing factors also enables the option of tuning additional characteristics of NiTiHf in addition to the TTs. The work is unique as all the four main TTs and their associated peak transformation temperatures are predicted to have complete control over the material phase change thresholds. Since 1995, extensive experimental research has been conducted to design NiTiHf TTs with a large temperature range of around 800 degrees C, paving the path for the current work's ML algorithms to be fed. A thorough data collection is created using both unpublished data and available literature and then analyzed to select twenty input parameters to feed the NN model. To forecast the NiTiHf TTs, a total of 173 data points were gathered, verified, and selected. The model's overall determination factor (R-2) was 0.96, suggesting the viability of the proposed NN model in demonstrating the link between material composition and processing factors, as well as identifying the TTs of NiTiHf alloy. The effort additionally validates the generated results against existing data in the literature. The validation confirms the significance of the proposed model.
Employing Laser Powder Bed Fusion (LPBF) method to manufacture NiTiHf Shape Memory Alloy (SMA) is becoming more common. The major design property for NiTiHf is the transformation temperatures (TTs) which control the a...
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ISBN:
(纸本)9780791887233
Employing Laser Powder Bed Fusion (LPBF) method to manufacture NiTiHf Shape Memory Alloy (SMA) is becoming more common. The major design property for NiTiHf is the transformation temperatures (TTs) which control the activation threshold of the SMA material and enable it to create the shape change due to a microstructure phase transformation. Given the high number of fabrication factors, machine learning (ML) approaches provide a promising approach to the design of SMA to control the TTs. The main obstacle to using ML methods is the need for an established correlation between fabrication features and material properties. The presented work develops an ML approach to enable the prediction of the TTs for additively manufacturing NiTiHf. The work uses all available experimental data on additively and conventionally manufactured NiTiHf. Selected fabrication features included in the ML models consider the elemental compositions of NiTiHf, laser power, laser speed, hatch spacing, and almost all the processing steps historically used to manufacture, or heat treat the NiTiHf for SMA. Multiple models of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Neural Networks (NN) are developed to predict the TTs of LPBF-manufactured NiTiHf. The models successfully predict the TTs for various NiTiHf fabrication conditions.
In response to modern materials research, a data-driven properties-to-microstructure-to-processing inverse analysis is proposed for use in materialdesign. In the present work, machine learning optimization algorithms...
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In response to modern materials research, a data-driven properties-to-microstructure-to-processing inverse analysis is proposed for use in materialdesign. In the present work, machine learning optimization algorithms of Bayesian optimization, genetic algorithm, and particle swarm optimization are used to perform inverse analysis with a maximum property search. The use of machine learning algorithms readily involves careful tuning of learning parameters, which is often carried out by a trial-and-error method requiring expert experience or general guidelines, and the choices of such parameters can play a critical role in attaining good optimization performance. Thus, the influence of various parameters on the optimization performance of the aforementioned algorithms are systematically investigated to provide a protocol for selecting adequate algorithm parameters for a given optimization problem in data-driven material design.
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