Mechanical & Aerospace Engineering

Biography

Biography: Steven Y. Liang

Abstract

Recognized as a milestone technology, additive manufacturing (AM) has promised unparalleled part complexity and small-batch cost effectiveness.  However the control of AM throughput and build quality has been challenged by the deficiency in process mechanics understanding to support systematic prediction, monitoring, and optimization.  a fair amount of experimental observations and numerical FEM studies have been pursued and documented, but they unfortunately suffer from the need of trial-and-errors and the lack of knowledge extendibility.  Aiming at a scientific scope and engineering applicability way beyond experimentation and FEM, physics-based analytical modeling flanked on computational mechanics of materials is developed at Georgia Tech and presented herein to quantify the thermodynamics, heat-transfer, and materials thermos-physical behaviors in powder bed and powder feed metal AM.  Closed-form solutions have been established for temperature distributions.  Subsequently the corresponding thermal stresses, residual stresses, microstructure, build distortion, and mechanical properties are expressed as explicit and algebraic functions of process parameters and powder properties, factoring in the effects of scan strategy, and powder packing.  Bounded-medium solutions have been established by folding boundary thermal balance conditions into the traditional semi-infinite medium solutions to compute material responses near build edges without iterations.  Extensive experimental validations are also presented.  The solutions deliver more penetrating physics of the metal AM process, showing much higher accuracy, and costing less than 1% time of commercial FEM’s, thus promising effective prediction and optimization for first-and-every-print-correct AM.