Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4

Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 portes grátis

Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4

Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics

Downey, Austin; Kramer, Sharlotte L.B.; Lattanzi, Attilio; Retzlaff, Emily; Mirshekari, Mostafa; Hemez, Francois; Thakre, Piyush; Hoefnagels, Johan; Rossi, Marco

Springer International Publishing AG

02/2024

102

Dura

Inglês

9783031504730

15 a 20 dias

Descrição não disponível.
Chapter 1. Quantifying residual stresses generated by laser powder bed fusion of metallic samples.- Chapter 2. Loading-Unloading Compressive Response and Energy Dissipation of Liquid Crystal Elastomers and Their 3D Printed Lattice Structures at Low and Intermediate Strain Rates.- Chapter 3. Residual Stress Induced in Thin Plates During Additive Manufacturing.- Chapter 4. Investigating the Effects of Acetone Vapor Treatment and Post Drying Conditions on Tensile and Fatigue behavior of 3D Printed ABS Components.- Chapter 5. Mechanics of Novel Double-Rounded-V Hierarchical Auxetic Structure - Finite Element Analysis and Experiments Using Three-dimensional Digital Image Correlation.- Chapter 6. Repeatability of Residual Stress in Replicate Additively Manufactured 316L Stainless Steel Samples.- Chapter 7. Acoustic nondestructive characterization of metal pantographs for material and defect identification.- Chapter 8. Rapid prototyping of a micro-scale spectroscopic system by two-photondirect laser writing.- Chapter 9. Bioinspired Interfaces for Improved Interlaminar Shear Strength in 3D Printed Multi-Material Polymer Composites.- Chapter 10. Thermo-mechanical Characterization of High-strength Steel through Inverse Methods.- Chapter 11. A multi-testing approach for the full calibration of 3D anisotropic plasticity models via inverse methods.- Chapter 12. Finite Element Based Material Property Identification Utilizing Full-Field Deformation Measurements.- Chapter 13. Data-driven material models for engineering materials subjected to arbitrary loading paths: influence of the dimension of the dataset.- Chapter 14. Data-driven methodology to extract stress fields in materials subjected to dynamic loading.
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data science;additive manufacturing;machine learning;advanced manufacturing;Materials Characterization;Conference Proceedings