Sentient Science - A Multiscale Modeling Suite for Process and Microstructure Prediction in Metal Additive Manufacturing, Phase I

Metadata Updated: February 28, 2019

In response to NASA[HTML_REMOVED]s topic T12.02 of [HTML_REMOVED]Extensible Modeling of Metallurgical Additive Manufacturing Processes[HTML_REMOVED], Sentient proposes to incorporate its DigitalClone technique to develop a multiscale and multiphysics computational modeling suite to predict comprehensive outcomes from AM building processes, including geometrical accuracy, and resulting microstructure and defects. Figure 1 shows the proposed framework for the multiscale modeling suite. The process model will first predict the microscale thermal evolution in respect of various parameters. The temperature results will feed a subsequent macroscale model for prediction of stress and distortion at part scale. Moreover, the predicted thermal history and distribution will feed subsequent microstructure model to further predict the micro-scale features including grain morphology and porosity. The proposed computational modeling framework allows a comprehensive prediction and understanding of the metal AM process at multiple levels.

In Phase I, Sentient will upgrade and demonstrate DigitalClone[HTML_REMOVED]s capability to integrate process-microstructure simulation for metal AM process. Specifically, selective laser melting of IN 718 alloy will be used for development and demonstration purposes in Phase I. AM coupons with different geometries will be fabricated by Selective Laser Melting (SLM) at different parameters. DigitalClone will be used to simulate all different scenarios of coupons made from IN718 alloys, and predict temperature, stress, part distortion, and grain structure. Materials characterization will be performed on the coupons to examine geometrical accuracy, microstructure, residual stress, all of which will be used to validate the DigitalClone model. In Phase II, different materials and AM platforms and more complex geometrical components will be tested for model validation. Additionally, close-loop optimization framework will be explored for improving geometrical design and microstructure features.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date February 28, 2019
Metadata Updated Date February 28, 2019

Metadata Source

Harvested from NASA Data.json

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Resource Type Dataset
Metadata Created Date February 28, 2019
Metadata Updated Date February 28, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_94745
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 5b5b0dab-ecec-4d3f-b1e8-d7f2d3197585
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2019-01-01
Homepage URL
Data Last Modified 2018-09-07
Program Code 026:027
Source Datajson Identifier True
Source Hash c1f061b002795149694e013791325c4b94386f2c
Source Schema Version 1.1

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