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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 / / edited by Sharlotte L.B. Kramer, Emily Retzlaff, Piyush Thakre, Johan Hoefnagels, Marco Rossi, Attilio Lattanzi, François Hemez, Mostafa Mirshekari, Austin Downey



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Autore: Kramer Sharlotte L. B Visualizza persona
Titolo: 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 / / edited by Sharlotte L.B. Kramer, Emily Retzlaff, Piyush Thakre, Johan Hoefnagels, Marco Rossi, Attilio Lattanzi, François Hemez, Mostafa Mirshekari, Austin Downey Visualizza cluster
Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
Edizione: 1st ed. 2024.
Descrizione fisica: 1 online resource (101 pages)
Disciplina: 670
Soggetto topico: Industrial engineering
Production engineering
Machine learning
Artificial intelligence - Data processing
Materials - Analysis
Industrial and Production Engineering
Machine Learning
Data Science
Materials Characterization Technique
Altri autori: RetzlaffEmily  
ThakrePiyush  
HoefnagelsJohan  
RossiMarco  
LattanziAttilio  
HemezFrançois  
MirshekariMostafa  
DowneyAustin  
Nota di contenuto: 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.
Sommario/riassunto: Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 of the Proceedings of the 2023 SEM Annual Conference & Exposition on Experimental and Applied Mechanics, the fourth volume of five from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on a wide range of topics and includes papers in the following general technical research areas: AM Composites and Polymers Dynamic Behavior of Additively Manufactured Materials and Structures Joint Residual Stress and Additive Manufacturing ML for Material Model Identification Novel AM Structures Novel Processing and Testing of Additively Manufactured Materials Plasticity and Complex Material Behavior Virtual Fields Method.
Titolo autorizzato: Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4  Visualizza cluster
ISBN: 3-031-50474-7
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910838279403321
Lo trovi qui: Univ. Federico II
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Serie: Conference Proceedings of the Society for Experimental Mechanics Series, . 2191-5652