Zachary S. Courtright
Advisor: Prof. Surya R. Kalidindi
will defend a doctoral thesis entitled,
Exploration of the Additive Manufacturing Process Development Space using High-throughput Mechanical Property Assays
On
Thursday, October 10 at 10:00 a.m.
CULC Room 141
And
Via Microsoft Teams
Committee
Prof. Surya R. Kalidindi - School of Materials Science and Engineering Georgia Institute of Technology (advisor)
Prof. David L. McDowell - School of Materials Science and Engineering Georgia Institute of Technology
Prof. Joshua P. Kacher - School of Materials Science and Engineering Georgia Institute of Technology
Prof. Richard W. Neu - School of Materials Science and Engineering Georgia Institute of Technology
Prof. Aaron Stebner - School of Materials Science and Engineering Georgia Institute of Technology
Prof. Naresh N. Thadhani - School of Materials Science and Engineering Georgia Institute of Technology
Dr. Brad L. Boyce - Sandia National Laboratory
Abstract
Additive Manufacturing (AM) of metals is a breakthrough technology that may fabricate parts more quickly and allow for the development of novel materials. Recent research has uncovered a variety of discrepancies in the development of AM processes which must be addressed. One such discrepancy is in the mechanical testing of AM samples so they may be optimized, qualified, and certified. This is mainly due to the highly anisotropic nature of AM builds and the costs associated with fabricating and testing mechanical test coupons. To mitigate this issue and find processing parameters that produce a sample with optimal mechanical properties, lower cost, High-Throughput (HT) mechanical testing protocols must be employed at multiple steps along the AM process development cycle.
In addition to the primary goal of developing and applying HT experimental protocols to better inform AM process development, it is critical to derive the greatest value from inherently limited experimental data. To address this challenge in part, collaborative research applied Machine Learning (ML) protocols informed by experimental data collected for this dissertation to both improve the throughput of mechanical test data analysis and generate Process-Structure-Property (PSP) linkages. This created a pathway toward the application of rapid experimental mechanical test data collection to inform ML models so Edisonian trial-and-error development methodologies may become less pertinent to the industrial infusion of new manufacturing technologies.
Indentation-type methods are ideal for mitigating the mechanical testing discrepancy in AM process or alloy development. Two primary benefits of indentation-type mechanical testing present themselves. 1). A significantly reduced sample volume compared to ASTM-E8 standard samples, and 2). A configuration that is highly conducive to HT automation. These two benefits lead to cost reductions concerning generating the relevant mechanical property data necessary to down-select AM process parameters, build orientations, alloys, and post-processing procedures. This cost reduction has the potential to convince AM developers to apply mechanical testing earlier in their development cycle, so they can reduce the number of builds or heat treatment cycles necessary to determine optimal processing windows.
The research described in this dissertation focuses on adding to the already growing amount of indentation-type test data from spherical microindentation and Small Punch Test (SPT). It centers specifically on additive manufactured alloys relevant to aeronautical and astronautical applications. Known issues with AM of aerospace relevant materials, such as microstructural and property heterogeneity, were addressed by performing tests in multiple planes with respect to the build orientation of samples. SPT was a significant focus in this dissertation due to its ability to predict plastic mechanical properties such as yield strength, ultimate strength, and uniform elongation. The data produced with indentation-type tests was validated, in part, by standard ASTM-E8 tensile tests. The two primary end goals of the research within this dissertation were to use HT experimental data to enable the utilizion of proven ML protocols to produce reliable correlations between indentation-type mechanical test results, processing parameters, and microstructural features in AM samples and to develop HT experimental and data analysis protocols for SPT.