Table of Contents
1. General Information for Creep Analysis Methods2. Analytical Creep Analysis Methods
3. Numerical Creep Analysis Methods
4. Experimental Creep Analysis Methods
5. Comprehensive Algorithm for Analyzing Elasticity and Plasticity Problems
6. Effects of Atomic Number and Atomic Weight on Creep
7. Simulation of Elasto-Plastic Deformation in Composite by Flow Rules
8. Obtaining the Viscosity of Solids Using Creep Semi-Theoretically
9. Creep Formulations and Diagrams
10. Application of Legendre Polynomials in Creep Analysis of Composites
11. Computational Modeling of Creep in Complex Plane for Composites
12. Solved Problems
Appendix
A. Some Mechanical Equations and Material Properties
B. Some Mathematical Relations
C. Useful Creep Data for Some Alloys References
Authors
Vahid Monfared Associate Professor, Department of Mechanical Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran and, Postdoctoral Research Fellow, Division of Engineering in Medicine, Brigham and Women's Hospital, Harvard Medical School, Cambridge, MA, USA.Vahid Monfared completed his PhD in Mechanical Engineering (Solid and Applied Mechanics), and works as a part-time lecturer/instructor at the University of Rhode Island/URI (USA), He also holds roles as an Associate Professor at Islamic Azad University of Zanjan, and as a Postdoctoral Research Fellow at Harvard Medical School (Harvard University) in the field of Machine Learning (AI) and Data Science to Medicine, Healthcare, and Engineering). Vahid also has practical experience as an engineer at Varian (a Siemens Healthineers company). In addition to the main filed in mechanical engineering (solid mechanics), his research interests include the application of Data Analytics and ML/AI in prediction of complex phenomena such as creep, mechanical deformations, and failure analysis. Along with working on AI and machine learning projects at Harvard Medical School. He furthered his knowledge of applied data analytics and machine learning at the Massachusetts Institute of Technology (MIT) and Boston University (BU) by completing a master's degree in applied data analytics.
