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In-Situ Fringe Pattern Profilometry for Feed-Forward Process Control

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC18P2190
Agency Tracking Number: 183442
Amount: $124,990.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: Z3
Solicitation Number: SBIR_18_P1
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-07-27
Award End Date (Contract End Date): 2019-02-15
Small Business Information
1270 North Fairfield Road
Dayton, OH 45432-2600
United States
DUNS: 004475216
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 John Middendorf
 (937) 306-6707
 jmiddendorf@utcdayton.com
Business Contact
 Ronald Jacobsen
Phone: (937) 865-4046
Email: rjacobsen@utcdayton.com
Research Institution
N/A
Abstract

This project aims to implement novel techniques for feedforward and feedback control that will allow better control, validation, and documentation of Selective Laser Melting (SLM) additive manufacturing (AM).  Three complimentary key innovations will be realized in this project (two in Phase I and a third in Phase II) by combining and improving two current technologies.  The first is the integration of Fringe Pattern Projection Profilometry (FPPP) into the SLM process.  FPPP is the first profilometry technique that can capture high resolution dimensional measurements of the entire SLM build platform, in situ and nearly instantaneously.  This facilitates direct dimensional measurement and validation of every single layer, and post-process 3D models (built from the measurements) for the digital twin.  By capturing all dimensional information (including residual stress induced distortion) the FPPP sensor will provide 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).  In most simulators, the layer thickness is assumed to be constant and perfect, but it is not. FPPP data will quantify the true variability present in layer thickness as the part is built.  Access to this information will allow more accurate calibration of the model so final part distortion can be virtually eliminated.  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 suboptimal layer morphologies that may arise, which is the third key innovation.  The result will be a combination of hardware and software tools that eliminate distortion and capture critical information for the digital twin.

* Information listed above is at the time of submission. *

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