In-Situ Fringe Pattern Profilometry for Feed-Forward Process Control, Phase I

Metadata Updated: February 28, 2019

This project aims to implement novel techniques for feedforward and feedback control[HTML_REMOVED]that will allow[HTML_REMOVED]better control, validation, and documentation of Selective Laser Melting (SLM) additive manufacturing (AM).[HTML_REMOVED] Three complimentary key innovations will be realized[HTML_REMOVED]in this project (two in Phase I and a third in Phase II) by combining and improving two current technologies.[HTML_REMOVED] The first is the integration of Fringe Pattern Projection Profilometry (FPPP) into the SLM process.[HTML_REMOVED] FPPP is the first profilometry technique that can capture high resolution dimensional measurements of the entire SLM build platform, in situ and nearly instantaneously.[HTML_REMOVED] This facilitates direct dimensional measurement and validation of every single layer, and post-process 3D models (built from the measurements) for the digital twin.[HTML_REMOVED] By capturing all dimensional information (including residual stress induced distortion) the FPPP sensor will[HTML_REMOVED]provide[HTML_REMOVED]a unique set of data for calibration of AM modelling software, which is the second key innovation.

The FPPP data will identify defects in layer morphologies that can be used to train unique integrated computational adaptive additive manufacturing (iCAAM) feedforward modeling tools (distortion is predicted and compensated for with the build strategy before the build starts).[HTML_REMOVED] In most simulators, the layer thickness is assumed to be constant and perfect, but it is not.[HTML_REMOVED]FPPP data will quantify the true variability present in layer thickness as the part is built.[HTML_REMOVED] Access to this information will allow more accurate calibration[HTML_REMOVED]of[HTML_REMOVED]the model[HTML_REMOVED]so final part distortion can be virtually eliminated.[HTML_REMOVED] In Phase II the model will also be inverted and turned into a fast-feedback lookup table for further tuning the build process to compensate for[HTML_REMOVED]suboptimal layer morphologies that may arise, which is the third key innovation.[HTML_REMOVED] The result will be a combination of hardware and software tools that eliminate distortion and capture critical information for the digital twin.

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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

Additional Metadata

Resource Type Dataset
Metadata Created Date February 28, 2019
Metadata Updated Date February 28, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_94627
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 307d01dd-10ff-492f-82d3-8ec198e77c01
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 aa0db333c880cb1b7905ac531da44c4380961724
Source Schema Version 1.1

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