Session: 07-01: NDE for Additive Manufacturing / 03-01: Electromagnetic NDE Techniques

Paper Number: 135272

135272 - Determination Of Strut Quality Factors In Additively Manufactured Lattices Using In-Situ Compression Testing µ-CT 

Abstract: 

In response to the need for an automated, commercial method to qualify additively manufactured lattice components, an experiment was conducted to evaluate the effects of defective lattice ligaments on compression yield strength.  Lattice samples with known defective or missing ligaments were compressed using a DebenCT5000 and imaged using Micro-CT.  Each defect was designed to resemble defects observed in truss-style lattice structures.  Deviations of 25%, 50%, and 75% were designed and printed to replicate thinned, wavy, and cracked defects and used to train the machine learning algorithms.  The force and CT results were compared to defect free standards.  Local and global information about each ligament in the lattice are extracted using LatticeJ, a proprietary software package developed at SRNL, and fed into COMSOL to perform finite element analysis and determine quality factors.  The verification of the local and global effects of defective struts are used to train machine learning classification algorithms to assign each ligament with a quality factor and identify the number of defective ligaments in the lattice. Each ligament can then be replaced with a beam element matching the relative strength of the defective beam, drastically reducing the computing time for FEA or other analysis.  The proposed methodology can be used to qualify future parts using X-ray CT.

Presenting Author: vincent dinova Savannah River National Laboratory

Presenting Author Biography: Dr. Vincent Dinova is a Senior Engineer in the Instrumentation, Robotics, and Imaging Systems group at Savannah River National Laboratory (SRNL). Vincent completed a BS in Nuclear Engineering from North Carolina State in 2009 immediately followed by a MS in 2011. After a 3-year stint working for Bechtel constructing the Watts Bar Unit 2 Nuclear Power Station, Vincent returned to North Carolina State University and received his PhD. in Nuclear Engineering in 2019. Vincent joined SRNL in 2020 as a postdoc working on improving X-Ray imaging and on qualification processes for additively manufactured components. He has presented his research on X-Ray Computed Tomography at technical conferences and government symposiums while also serving on several government radiography working groups.

Authors:

Vincent DiNova Savannah River National Laboratory
H. B. Flynn Savannah River National Laboratory
Paul Korinko Savannah River National Laboratory
David Immel Savannah River National Laboratory