6 Feb 2024

Dynamic Tensile Behavior of Laser-Directed Energy Deposition and Additive Friction Stir-Deposited AerMet 100

Abstract: Quasi-static and high-rate tensile experiments were used to examine the strain rate sensitivity of laser-directed energy deposition (L-DED)- and additive friction stir deposition (AFSD)-formed AerMet 100 ultrahigh-strength steel-additive manufactured builds. Electron backscattered diffraction (EBSD) revealed similar as-deposited grain sizes between the two AM processes at approximately 24 µm and 17 µm for the L-DED and AFSD samples, respectively. The strain hardening rate, θ, revealed little change in the overall hardening observed in the L-DED and AFSD materials, with a consistent hardening in the quasi-static samples and three identifiable regions in that of the high-rate tested materials. The L-DED deposited materials displayed average ultimate tensile strength values of 1835 and 2902 MPa for the 0.001 s−1 and 2500 s−1 strain rates, respectively and the AFSD deposited materials displayed ultimate tensile strength values of 1928 and 3080 MPa for the 0.001 s−1 and 2500 s−1 strain rates, respectively. Overall, the strength for both processes displayed a positive strain rate sensitivity, with increases in strength of ~1000 MPa for both processes. Fractography revealed significant solidification voids in the laser DED material and poor layer adhesion in the AFSD material.

17 Jan 2023

State-of-Practice on the Mechanical Properties of Metals for Armor-Plating

Abstract: This report presents a review of quasi-static and dynamic properties of various iron, titanium, nickel, cobalt, and aluminum metals. The physical and mechanical properties of these materials are crucial for developing composite armoring systems vital for protecting critical bridges from terrorist attacks. When the wide range of properties these materials encompass is considered, it is possible to exploit the optimal properties of metal alloys though proper placement within the armoring system, governed by desired protective mechanism and environmental exposure conditions.

2 Jun 2022

Probabilistic Neural Networks that Predict Compressive Strength of High Strength Concrete in Mass Placements using Thermal History

Abstract: This study explored the use of artificial neural networks to predict UHPC compressive strengths given thermal history and key mix components. The model developed herein employs Bayesian variational inference using Monte Carlo dropout to convey prediction uncertainty using 735 datapoints on seven UHPC mixtures collected using a variety of techniques. Datapoints contained a measured compressive strength along with three curing inputs (specimen maturity, maximum temperature experienced during curing, time of maximum temperature) and five mixture inputs to distinguish each UHPC mixture (ce-ment type, silicon dioxide content, mix type, water to cementitious material ratio, and admixture dosage rate). Input analysis concluded that predictions were more sensitive to curing inputs than mixture inputs. On average, 8.2% of experimental results in the final model fell outside of the predicted range with 67.9%of these cases conservatively underpredicting. The results support that this model methodology is able to make sufficient probabilistic predictions within the scope of the provided dataset but is not for extrapo-lating beyond the training data. In addition, the model was vetted using various datasets obtained from literature to assess its versatility. Overall this model is a promising advancement towards predicting mechanical properties of high strength concrete with known uncertainties.