Additive Manufacturing Fatigue and Fracture: Developing Predictive Capabilities: Poster Session
Sponsored by: TMS Structural Materials Division, TMS: Additive Manufacturing Committee, TMS: Mechanical Behavior of Materials Committee
Program Organizers: Nik Hrabe, National Institute of Standards and Technology; John Lewandowski, Case Western Reserve University; Nima Shamsaei, Auburn University; Mohsen Seifi, ASTM International/Case Western Reserve University; Steve Daniewicz, University of Alabama

Tuesday 5:30 PM
March 1, 2022
Room: Exhibit Hall C
Location: Anaheim Convention Center

Session Chair: Nik Hrabe, National Institute of Standards and Technology (NIST)

J-5: Evaluation of Heat-treated Microstructure and Mechanical Properties of Al-Zn-Mg-Cu Plates Repaired via Additive Friction Stir-deposition: Dustin Avery1; Conner Cleek1; Brandon Phillips1; M Y Rekha1; Ryan Kinser1; Harish Rao1; Paul Allison1; Luke Brewer1; James Jordon1; 1The University of Alabama
    Many aerospace alloys exhibit poor mechanical performance when subjected to fusion-based processing due to deleterious microstructural evolution during solidification. Additive Friction Stir Deposition (AFS-D), a relatively new solid-state additive manufacturing process, offers a potential solution for repairing these alloys. The present study elucidates the microstructural and mechanical properties of a damaged plate of aluminum alloy 7075-T651 (Al-Zn-Mg-Cu) additively repaired via AFS-D. Microstructural, tensile, and fatigue characteristics of wrought plate and heat-treated repairs were evaluated; notably, the heat-treated repair exhibited a yield and ultimate stress comparable to wrought, consistent with the recovery of the ƞ’ and ƞ strengthening phases observed via scanning and transmission electron microscopy, but exhibited a reduction in fatigue life attributed to fisheye defects stemming from carbon impurities introduced into the AFS-D process. Process-structure-property fatigue mechanisms were quantified by implementing a microstructure-sensitive fatigue life model for both the AA7075 wrought plate and heat-treated repair material.

J-7: Monitoring Additive Manufacturing Process Stability with Bayesian Changepoint Detection on High-Throughput Tensile Test Data: Stefan Colton1; Brad Boyce2; Aaron Stebner1; 1Georgia Institute of Technology; 2Sandia National Laboratories
    Additive Manufacturing (AM) allows one machine to produce diverse geometries, yet this creates a challenge for Statistical Process Control. Here, we apply ML techniques to optimally predict process changepoints solely from the tensile tests of miniaturized coupons. We analyze a dataset of over one year of high-throughput testing conducted by Sandia National Lab. Features are derived from the stress-strain curves using Principal Component Analysis (PCA) and common tensile properties; careful resampling is shown to be critical for PCA. Bayesian Changepoint Detection (BCD) is applied to determine changepoint probabilities. We find that using a subset of PCA features or tensile properties, both known changepoints can be identified with no false positives. Previously additional data sources were required for this result; thus, we demonstrate the potential of BCD to economically detect process changepoints in AM processes, and of PCA features as an alternative to considering a diverse set of tensile properties.