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Meeting Materials Science & Technology 2020
Symposium Additive Manufacturing: Qualification and Certification
Presentation Title Connecting Metal Powder Morphological Characteristics with Flowability Properties Using Machine Learning
Author(s) Srujana Rao Yarasi, Andrew Kitahara, Ryan Cohn, Elizabeth A Holm, Anthony D Rollett
On-Site Speaker (Planned) Srujana Rao Yarasi
Abstract Scope Computer vision and machine learning techniques are used to quantify the morphological characteristics of several different metal powders used in Powder Bed Fusion AM and connect them with their flowability. A framework is constructed to understand their differences in flowability based on their powder morphology distributions (PMDs), which includes powder sizes. These PMDs are obtained from SEM images of the powder particles. This is accomplished by using pre-trained convolutional neural networks to generate a feature vector for each powder particle, sets of which are then clustered according to morphological similarity, leading to a powder morphology distribution (PMD) for each powder system. Several machine learning algorithms are tested to correlate the PMDs to their flowability properties. Powder size metrics are also explored as a way to predict flowability behavior in powder bed fusion machines.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Comprehensive Digital Platform for Additive Manufacturing
A Multi-Sensor Comparative Study for Fatigue Prognosis of Additively Manufactured Metallic Specimens
Connecting Metal Powder Morphological Characteristics with Flowability Properties Using Machine Learning
CT Based Analysis of Generation and Characterization of Parameter- and Process-induced Defects in Powder Bed Fusion Additive Manufacturing
Effect of Sample Geometry and Orientation on Tensile Properties of Ti-6Al-4V Manufactured by Electron Beam Melting
Ensuring Build Quality thru Physics-based Support Design Optimization for Residual Stress
Influence of Printing Parameters within the Binder-powder Interaction
Introductory Comments: Additive Manufacturing: Qualification and Certification
Physics-based Qualification for Laser Powder Bed Fusion AM
Pore Formation in Laser Powder Bed Fusion Inconel 718 through Multiphysics Modeling
Post-build Heat Treatment of Wire-arc Additive Manufactured 410 SS for Hardness Tuning
Recyclability of Ti-6Al-4V Powders Used in Additive Manufacturing
Reducing Anisotropic Deformation of LPBF Inconel 718 for Applications in Extreme Conditions
Reducing Heat Buildup and Regularizing Melt Pool Dimensions in Laser Powder Bed Fusion through a “Powder Moat” Scan Strategy
Similarity Analysis and Clustering of Thermal History to Understand Process-structure Relationships
Simulation of the Effect of Texture on Anisotropy in SLM-Produced IN718 Microstructures
The Effects of Powder Particle Size Distribution on the Powder and Part Performance of Laser Powder Bed Fusion 17-4 PH Stainless Steel
Unveiling the Relationships between Powder Bed Conditions and Materials Quality during Selective Laser Melting

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