Ruoqi (Rosie) Gao

will propose a doctoral thesis entitled,

Texture-Aware Modeling and Experimental Investigation of Residual Stress and Microstructure Evolution in Laser Powder Bed Fusion

on

Thursday, October 9th at 2:30 PM

in

Love Manufacturing Building Room 210 and/or Virtually via Microsoft Teams

Meeting ID: 228 679 736 166 8

Passcode: Nu7bW2Cs

Committee:

Prof. Hamid Garmestani – School of Materials Science and Engineering (Advisor)

Prof. Steven Y. Liang – School of Mechanical Engineering

Prof. Preet Sigh – School of Materials Science and Engineering

Prof. Aaron Stebner – School of Mechanical Engineering

Prof. Saïd Ahzi – School of Materials Science and Engineering 
 

Abstract

Metal additive manufacturing (AM) has emerged as a transformative innovation, redefining modern manufacturing through its design freedom and ability to produce lightweight, high-performance, and complex parts beyond the limits of conventional processes. These advantages have enabled critical applications in aerospace, biomedical, automotive, and energy industries. However, the extreme processing conditions in metal AM – characterized by steep temperature gradients, rapid cooling rates (103 – 106 K/s), and repeated thermal cycling – often generate unique non-equilibrium microstructures, anisotropic materials properties, and significant residual stresses in printed parts. These characteristics pose challenges but also create opportunities for tailoring microstructure and materials performance through process parameter optimization and post-processing. Among metal AM technologies, laser powder bed fusion (LPBF) stands out for its high manufacturing precision and broad range of printable materials, yet persistent issues – particularly residual stresses, part defects, and limited process control – limit its broader adoption. 

This thesis addresses these challenges by developing a texture-aware modeling framework for process-structure-property-residual stress linkages in LPBF. An analytical thermo-mechanical model is developed to predict texture-induced residual stresses by explicitly incorporating anisotropic elastic properties informed by crystal plasticity. This modeling framework establishes a clear mechanistic pathway linking process parameters, melt pool geometry, crystallographic texture, anisotropic elasticity, and residual stress evolution in LPBF IN718. The novelty of this work lies in the explicit integration of crystallographic texture and property anisotropy – factors largely overlooked in previous residual stress studies – while maintaining the computational efficiency of analytical models. Planned experimental investigations of microstructure, residual stress, and defects in as-built LPBF samples will serve both to validate the modeling framework and to provide deeper insights into the underlying process physics.