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Deep learning accelerates the development of Ni-based single crystal superalloys: A physical-constrained neural network for ... 1 1 Introduction 1 2 Construction of the SaTNC model 2 2.1 Data collection and processing 2 2.2 Model architecture of SaTNC 3 3 Results 4 3.1 Prediction performance of SaTNC 4 3.2 Effectiveness of deep representation for superalloy, microstructural features and elemental properties 5 3.3 Sensitivities of SaTNC predictions on alloy compositions and service conditions 6 4 Discussion 9 4.1 Insights related to complex interactions among alloying elements 9 4.2 Materials information contained in elemental embedding 9 5 Composition design of new Ni-based SX superalloys with low density and high creep resistance at high temperature 9 6 Summary and conclusion 12 Declaration of Competing Interest 12 Data availability 12 Acknowledgements 12 Data and code availability 12 Appendix A Supplementary data 12 References 12
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Common subject areas: history, science, philosophy, social sciences.
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