Engineering topics

Jacob D. Hochhalter

Publications and source records attributed to Jacob D. Hochhalter.

3 records

NASA NTRS2024

Exploring the Role of Type-II Residual Stresses in A Laser Powder Bed Fusion Nickel-Based Superalloy Using Measurement and Modeling

Far-field high-energy X-ray diffraction microscopy (ff-HEDM) and the crystal plasticity finite element method (CPFEM) are used to investigate the role of grain-scale (Type-II) residual stresses on the fatigue life of additively manufactured (AM) Inconel alloy 625 (IN-625). Grain-averaged orientations, centroids, and residual elastic strain tensors from ff-HEDM data are used to instantiate a crystal plasticity model to simulate the effect of residual stresses at the grain scale. Simulation results indicate that the presence of tensile residual strains increase stress localization and heterogeneity within grains, triggering an earlier onset of plasticity. A microscale fatigue indicator parameter (FIP) is computed to model the impact of these residual strains on the cycles to fatigue crack nucleation. The crack nucleation model, based on the computed FIPs, predicts a significant reduction in the number of cycles for fatigue crack nucleation for mid- and high-cycle fatigue due to the residual strain induced localization, while the residual strains have minimal impact on low-cycle fatigue life.

Inconel↗

NASA NTRS2023

Assessing Flow Formability of Aerospace Aluminum Alloys via DIC Tensile Testing

Over the past decade, NASA Langley Research Center (LaRC) has championed integrally stiffened cylinder (ISC) technology for single-piece, cryogenic tank barrels on launch vehicles. The current investigation aims to extend the hybrid spin/shear/flow forming process to aircraft fuselage structures, a damage tolerance critical application. The WF Maschinenbau VUD-600® vertical spin/flow forming facility recently established at LaRC represents a reasonable sub-scale facsimile of the ISC deformation process for research and development (R&D) purposes. The objective of this study is to explore whether tensile testing with digital image correlation (DIC) is an effective way to rank the formability of candidate aerospace Al alloys and expedite empirical forming trials. Specific tensile data, such as reduction of area, strain hardening exponent, and modulus of resilience, are used as formability metrics for a variety of alloy/temper/product form combinations. Results from high-strength aluminum alloys AA 2139, AA 2050, AA 2043 and AA 2219 are compared with the medium-strength, highly formable AA 6061 benchmark. Rolled, forged and cast preform materials in both the -O temper (fully annealed) and -T4 temper (solution-treated, quenched, and naturally aged) conditions are evaluated. AA 2139 plate in the -T4 temper emerges as the top-ranked material, based on the criteria selected. Starting with preforms in the -T4 temper will result in flow-formed material exhibiting mechanical properties closer to aircraft fuselage requirements. The optimum balance between strength and damage tolerance may also be achieved via post-forming procedures that avoid quenching and stretching.

Aluminum alloys↗

NASA NTRSDate not supplied

Bayesian Model Selection for Reducing Bloat and Overfitting in Genetic Programming for Symbolic Regression

When performing symbolic regression using genetic programming, overfitting and bloat can negatively impact generalizability and interpretability of the resulting equations as well as increase computation times. A Bayesian fitness metric is introduced and its impact on bloat and overfitting during population evolution is studied and compared to common alternatives in the literature. The proposed approach was found to be more robust to noise and data sparsity in numerical experiments, guiding evolution to a level of complexity appropriate to the dataset. Further evolution of the population resulted not in overfitting or bloat, but rather in slight simplifications in model form. The ability to identify an equation of complexity appropriate to the scale of noise in the training data was also demonstrated. In general, the Bayesian model selection algorithm was shown to be an effective means of regularization which resulted in less bloat and overfitting when any amount of noise was present in the training data.

Uncertainty quantification↗