|About this Abstract
||2018 TMS Annual Meeting & Exhibition
||Additive Manufacturing of Metals: Establishing Location Specific, Processing-Microstructure-Property-Relationships III
||Porosity Analysis of AM Powder Based on Machine Learning Approach and In-situ Annealing Technique for Observation of Property Evolution of AM Material
||He Liu, Yufeng Shen, Ross Cunningham, R.M Suter, A. D. Rollett
|On-Site Speaker (Planned)
The properties of porosity within AM powder will affect the quality of final product. A 3-D segmentation and analysis method based on machine learning approach was developed to analyze the porosity and size distribution of AM powders. Powders and pores are segmented and recognized in three dimensional volume data to increase accuracy. Valuable statistical information can be extracted to evaluate the quality of AM powder. This will give a quantitative guide for the selection of AM powder for manufacturing. We are also developing the capability to perform in-situ annealing of AM (and other) materials at the Advanced Photon Source during high-energy X-ray diffraction microscopy (HEDM) measurements. This will allow measurement of the evolution of micro-structure of AM materials. First results from this technique will be presented.
||Planned: Supplemental Proceedings volume