SBIR-STTR Award

In this STTR effort, TDA and its team partner University of Louisville will focus on developing an innovative intelligent decision support tool using data-driven multi-scale multi-physics models (DDMM) to derive process-surface roughness relationships for selective laser melting (SLM). The proposed models account for both powder characteristics and AM processing/path planning, including powder size distribution, laser power, scanning speed, scanning strategy, geometry features, and build orientation. Critical experiments will be performed during the course of this research as part of verification and validation. The goal of this DDMM computational framework is to predict and optimize component-level surface roughness within a reasonable time. Proposed DDMM computational framework will address surface roughness caused by rippling marks, balling effect, staircase effect, and sintered powders. Solution for the DDMM framework will be obtained by discrete element method (DEM), computational fluid dynamics (CFD), finite element method (FEM) and data-driven modeling.

Benefit:
Technical Data Analysis Inc. (TDA) will deliver a novel DDMM technology along with an associated software package to predict and optimize surface roughness of metallic AM components. Our software will be the first-ever software of its kind using DDMM methodology for component-level surface roughness optimization. There are no tools currently available in the market to provide fast and reliable prediction of AM components surface roughness and to use these results to optimize the processing parameters, orientation, and scanning pattern, and powder selections thus minimizing the waste. Our research will first address how surface roughness is correlated with powder conditions and AM process settings. With critical testing and validation of the model, DDMM technique and envisioned software package associated with it will provide a toolkit to assess surface roughness for various materials processed on various AM machines.

Keywords:
Build Orientation, Build Orientation, Data-Driven Modeling, Powder Size Distribution, Computational Fluid Dynamics (CFD), Finite Element Method (FEM), Surface Roughness, Discrete Element Method (DEM), AM processing/path planning

In this STTR effort, TDA and its team partner University of Louisville will focus on developing an innovative intelligent decision support tool using data-driven multi-scale multi-physics (D2M2) models to derive process-surface roughness-fatigue relationships for selective laser melting (SLM). The proposed models account for both powder characteristics and AM processing/path planning, including powder size distribution, laser power, scanning speed, scanning strategy, geometry features, and build orientation. Critical experiments will be performed during the course of this research as part of verification and validation. The goal of this D2M2 computational framework is to predict and optimize component-level surface roughness within a reasonable time. Proposed D2M2 computational framework will address surface roughness caused by rippling marks, balling effect, staircase effect, and sintered powders. Solution for the D2M2 framework will be obtained by discrete element method (DEM), computational fluid dynamics (CFD), finite element method (FEM) and data-driven modeling.

Benefit:
Technical Data Analysis Inc. (TDA) will deliver a novel D2M2 technology along with an associated software package to predict and optimize surface roughness of metallic AM components. Our software will be the first-ever software of its kind using D2M2 methodology for component-level surface roughness optimization. There are no tools currently available in the market to provide fast and reliable prediction of AM components surface roughness and to use these results to optimize the processing parameters, orientation, and scanning pattern, and powder selections thus minimizing the waste. Our research will first address how surface roughness is correlated with powder conditions and AM process settings. With critical testing and validation of the model, D2M2 technique and envisioned software package associated with it will provide a toolkit to assess surface roughness for various materials processed on various AM machines.

Keywords:
Data-Driven Modeling, Build Orientation, Powder Size Distribution, Computational Fluid Dynamics (CFD), Surface Roughness, Finite Element Method (FEM), AM processing/path planning, Discrete Element Method (DEM)