ARCTOS Technology Solutions, LLC — National Aeronautics and Space Administration SBIR Phase I: Z3

Description

<p style="margin-left:0in; margin-right:0in">This project aims to implement novel techniques for feedforward and feedback control&nbsp;that will allow&nbsp;better control, validation, and documentation of Selective Laser Melting (SLM) additive manufacturing (AM).&nbsp; Three complimentary key innovations will be realized&nbsp;in this project (two in Phase I and a third in Phase II) by combining and improving two current technologies.&nbsp; The first is the integration of Fringe Pattern Projection Profilometry (FPPP) into the SLM process.&nbsp; FPPP is the first profilometry technique that can capture high resolution dimensional measurements of the entire SLM build platform, <em>in situ </em>and nearly instantaneously.&nbsp; This facilitates direct dimensional measurement and validation of every single layer, and post-process 3D models (built from the measurements) for the digital twin.&nbsp; By capturing all dimensional information (including residual stress induced distortion) the FPPP sensor will&nbsp;provide&nbsp;a unique set of data for calibration of AM modelling software, which is the second key innovation.</p><p>The FPPP data will identify defects in layer morphologies that can be used to train unique integrated computational adaptive additive manufacturing (iCAAM) <em>feedforward</em> modeling tools (distortion is predicted and compensated for with the build strategy before the build starts).&nbsp; In most simulators, the layer thickness is assumed to be constant and perfect, but it is not.&nbsp;FPPP data will quantify the true variability present in layer thickness as the part is built.&nbsp; Access to this information will allow more accurate calibration&nbsp;of&nbsp;the model&nbsp;so final part distortion can be virtually eliminated.&nbsp; 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&nbsp;suboptimal layer morphologies that may arise, which is the third key innovation.&nbsp; The result will be a combination of hardware and software tools that eliminate distortion and capture critical information for the digital twin.</p>