DOCTOR OF PHILOSOPHY

on Tuesday, December 13, 2022

12:00 PM

the

DISSERTATION PROPOSAL DEFENSE

will be held for

Alexander Caputo

via

Teams Meeting
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Meeting ID: 281 491 465 746
Passcode: YNdNny

or in-person in 4211 MRDC

"Investigation of Process Structure Property Relationships in Additively Manufactured IN718 Through Machine Learning"

Committee Members:

Prof. Rick Neu, Advisor, ME/MSE

Prof. Josh Kacher, MSE

Prof. Chris Saldaña, ME

Prof. Aaron Stebner, MSE/ME

Dr. Xuan Zhang, Argonne National Lab

ABSTRACT: Given its good fatigue strength at elevated temperatures, IN718 is an ideal material for hot gas path turbine components for energy and aviation. In both applications, additive manufacturing (AM) can be implemented to improve current turbine designs by increasing efficiency through light-weighting and implementation of cooling paths unattainable by conventional manufacturing methods. With its added benefits, AM comes with its own set of obstacles when it comes to applications that undergo cyclic loading and where fatigue is a critical property. Porosity created by the additive manufacturing process can be detrimental to these fatigue critical applications if not controlled and monitored properly. This work is being conducted to understand and model the effect of AM introduced porosity and microstructural anomalies on the fatigue behavior of IN718 through a combination of ex-situ characterization and machine learning methods.

In this work, using laser powder bed fusion (LPBF), a set of 10 walls were constructed on the same build plate. Each build wall was constructed using a different combination of laser scanning speed and power to explore the process regime space near the default IN718 process parameters set by the EOS manufacturer. The range of process parameters in this work were chosen to explore the different AM process regimes of weld pool morphologies: conduction (shallow), transition, and keyhole (deep). The build walls, after removal from the build plate, underwent a direct age heat treatment, 720 for 8 h with furnace cool to 620 for 8 h with air cool, to maintain as built microstructure while increasing strength to increase sensitivity to porosity. These build walls were sectioned into dogbone fatigue specimens, polished, characterized by XCT and tested in high cycle fatigue at 1000/ 538 with aof 690 MPa, a stress ratio of 0.1, and a cycling frequency of 20 Hz. Separate sections of the build walls were mounted and polished then characterized via SEM based EBSD. The XCT and EBSD data generated through these ex-situ characterizations and their relationships to the fatigue properties of AM IN718 will be analyzed using machine learning methods as a part of the proposed research.