Jong-Hwa Ahn

Advisor: Dr. Christopher Muhlstein, Co- Advisor: Dr. Donggang Yao

will propose a doctoral thesis entitled,

Spatial Statistics-Driven Strain Field Analysis for Diagnostics of Adhesively Bonded Joints

On

Thursday, June 19 at 1:30 p.m.

 Virtually via MS Teams

https://teams.microsoft.com/l/meetup-join/19%3ameeting_YTRiN2Y4NGMtMGVhMi00NTcyLWIyZTAtZmNhZDc1MzdiZGZj%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2262c0e3f1-88f5-4227-b1ca-55c0b15b7ba8%22%7d

Committee

            Dr. Christopher Muhlstein – School of Materials Science and Engineering (advisor)

            Dr. Donggang Yao– School of Materials Science and Engineering (co-advisor)

            Dr. Shucong Li – School of Materials Science and Engineering

            Dr. Prett Singh– School of Materials Science and Engineering 

 Dr. Shuman Xia– George W. Woodruff School of Mechanical Engineering

Abstract

Adhesively bonded joints offer uniform load transfer, compatibility with dissimilar materials, and reductions in weight and manufacturing cost. Yet the qualification tests most widely used, specifically the ASTM D1002 single-lap-joint (SLJ) procedure, emphasize ultimate strength and routinely miss hidden defects that degrade performance long before catastrophic failure. To address these limitations, this work begins by intentionally introducing surface contamination to create defects in SLJ specimens. Comparisons between pristine and defective joints reveal that while contamination significantly lowers ultimate shear strength, it produces minimal changes in initial stiffness, illustrating the inability of global metrics to detect early-stage degradation.

Full-field Digital Image Correlation (DIC) is then employed to capture surface strain maps during loading. The SLJ’s variable thickness, overlap discontinuities, and composite microstructure naturally create heterogeneous strain fields that become even more complex in defective joints. Differentiating normal microstructural variation from defect-driven anomalies therefore demands a spatially aware analysis. A spatial-autocorrelation framework based on Local Moran’s I is introduced to identify statistically significant strain clusters, hotspots and cold spots, embedded within heterogenous strain field. These clusters provide objective, quantitative indicators of localized debonding or stress redistribution.

To interpret these spatial strain patterns in the context of underlying damage mechanisms, this proposal highlights the need for Mode I fracture toughness (G_Ic) experiments using Double Cantilever Beam (DCB) specimens. These tests will enable measurement of process-zone dimensions at the crack tip, offering a mechanistic length scale to evaluate whether observed strain clusters reflect natural material variation or true damage. A systematic design of experiments (DOE) will be implemented to study the effects of key factors, such as surface treatment, cure history, and substrate type, on both fracture behavior and spatial strain metrics.