Multi-physics Predictive Modeling Platform for Qualification of Material Microstructure and Mechanical Performance of Aerospace Additive Manufacturing Parts

Abstract

Sentient has developed a predictive modeling tool for components built using AM to assess their performance, with rigorous consideration of the microstructural properties governing the nucleation and propagation of fatigue cracks. This tool, called DigitalClone® for Additive Manufacturing (DCAM), is an Integrated Computational Materials Engineering (ICME) tool that includes models of crack initiation and damage progression with the high-fidelity process and microstructure modeling approaches. The predictive model has three main modules: process modeling, microstructure modeling, and fatigue modeling. The feasibility and validation of our modeling tool is verified using experimental coupon testing. The predictive tool is able to account for temperature and microstructure variation as the function of process parameters and scanning strategies at various AM processes. The relationship of processmicrostructure in additive manufacturing is successfully linked implicitly in our tool. We simulate the AM build process considering the parameters (laser intensity, laser speed, hatching space, powder layer thickness, orientation of build, etc.) involved during the build process in order to generate the microstructure of AM part which is the outcome of the build process. There is a good agreement between our prediction and the experimental data. The physics-based computational modeling encompassed within DCAM provides an efficient capability to fully explore the design space across geometries and materials, leading to components that represent the optimal combination of performance, reliability, and durability.