A multiscale approach of additive materials processing on the creep behavior analysis of functionally graded thick-walled cylinders

Document Type

Article

Publication Date

1-1-2024

Abstract

A multiscale model has been developed to investigate the creep behavior of functionally graded thick-walled cylinders under various thermal and mechanical boundary conditions. Finite element (FE) simulations are employed to evaluate the position-dependent parameters associated with creep constitutive law at the microscale. A macroscopic FE-model solves the non-linear boundary value problem to determine the time-varying creep stresses and strains. The framework proposed can predict the creep response of functionally graded pressure vessels based on the constitutive behavior of the creeping matrix and volume fraction profile. Effective creep properties have been computed using three different micromechanical models and the homogenized creep response along its effect on the macroscopic behavior are compared. Considering the computational expenses associated with the large 3D-finite element models, investigations show that simple 2D-axisymmetric model can “closely capture” the creep behavior in such multiscale methods. Radial variations of constituent volume fractions have significant effects on stress distributions and creep strains histories. A multi-objective “Particle-Swarm-Optimization (PSO)” algorithm is implemented to minimize the initial stress and final creep strain of functionally graded cylinder subjected to mechanical and thermal loads. Particle-Swarm-Optimization (PSO) is used to solve the multi-objective optimization problem, which is a heuristic search technique, inspired by the movement of a “flock-of-birds” aiming to find food. PSO has been found to be effective in engineering structural optimizations; it is a derivative free, population-based method that is easily parallelized, and has fewer parameters than other algorithms such as, GA (Genetic Algorithms, a stochastic optimization). In PSO, several particles “fly” through a multi-dimensional search space to explore for the best solution for the optimization problem. The models are beneficial to investigate the choice of material combinations and heterogeneity profiles, thereby reducing the cost of materials, fabrication, and testing associated with experimental trials.

Publication Source (Journal or Book title)

Comprehensive Materials Processing: Volume 1-13, Second edition

First Page

V6:54

Last Page

V6:72

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