Engineering topics

Ziabari, Amir Koushyar

Publications and source records attributed to Ziabari, Amir Koushyar.

5 records

DOE OSTI2024

Creep deformation and cavitation in an additively manufactured Al-8.6Cu-0.4Mn-0.9Zr (wt%) alloy

Creep deformation and cavitation were investigated at 300 ºC in both tension and compression for an additively manufactured Al-8.6Cu-0.5Mn-0.9Zr (wt%) alloy in the as-fabricated state and after various aging treatments (aging at 300 °C/200 h or 350 °C/24 h and overaging at 400 °C/200 h). Creep mechanisms at 300 °C were determined by relating the measured creep response to corresponding microstructural and X-ray computed tomography observations. In compression, alloys in the as-fabricated and two aging conditions exhibited similarly high creep resistance. Overaging (400 °C/200 h) led to substantial coarsening of intragranular θ-Al2Cu precipitates and an expected drop in their Orowan strengthening contribution. In tension, minimum strain rates comparable to those in compression were obtained at any given stress; however, upon accumulation of some plastic strain in the matrix, creep cavities started to form, leading to accelerated tertiary stage creep deformation and rupture. Cavitation occurred exclusively along melt pool boundaries due to locally enhanced diffusion enabled by (i) large grain-boundary area in adjacent fine-grained zones and (ii) localization of creep strain in nearby heat-affected zones. Although cavity growth was initially diffusion-controlled, its rate was determined by matrix creep rate, consistent with constrained cavity growth mechanisms. This study reveals how microstructural complexities induced by the additive manufacturing process affect the creep and cavitation behavior of Al-Cu-Mn-Zr alloys. The underlying creep and cavitation mechanisms uncovered in this study point to pathways that improve the high-temperature properties of additively manufactured alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Reduction kinetics of hematite powder using argon/hydrogen plasma with prospects for near net shaping of sustainable iron

Direct reduction of iron ore using hydrogen plasma is being explored as a potential solution to decarbonize the iron and steel sector. The current state-of-the-art demonstrated reduction of hematite pellets via hydrogen plasma using Ar + 10% H 2 but had slow reduction kinetics, requiring 30 minutes of plasma exposure for complete reduction. Here we show that using hematite in a powder form, easily obtainable from beneficiated ore, results in 10× faster kinetics using plasma generated from Ar + 2% H 2 shielding gas compared to the current state-of-the art. Additionally, the increased kinetics using powders and a dilute hydrogen concentration can enable the use of advanced manufacturing techniques like blown powder directed energy deposition using a plasma tungsten arc welding torch to manufacture near net shape components directly from the ore concentrates. This ore to part approach will also reduce the emissions associated with downstream processes like rolling, forging, and machining, thereby further aiding in the sectorial decarbonization efforts.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A mesoscale 3D model of irradiated concrete informed via a 2.5 U-Net semantic segmentation

The concrete biological shield in light-water reactors is exposed to neutron and gamma irradiation, which deteriorates the concrete’s mechanical properties in the long term. To assess the irradiation-induced damage, predictive mechanical models are developed and used in parallel with the characterization of irradiated concrete samples. Realistic 3D simulation domains can drastically improve a model’s prediction. In this work, we utilized x-ray computed tomography (XCT) data of a concrete specimen to reconstruct its 3D microstructure. The XCT data shows low contrast between the concrete’s aggregates and cement paste, resulting in poor image segmentation when using traditional unsupervised techniques. To address this issue, we developed and trained a 2.5D U-Net model on only 24 pre-labeled XCT layers to segment 651 layers of the XCT data. The overall F1-score of the model is approximately 96%. Then, we created a 3D finite element (FE) mesh based on the stack of segmented images. The FE model contains radiation-induced expansion, damage, and creep. The constitutive equations are adapted to each phase (aggregates and cement paste). Here, we simulated the effects of neutron irradiation in the concrete specimen as well as the specimen’s mechanical response to uniaxial compression. Finally, model validation was performed using experimental data on similar concrete specimens in the literature.

2.5D U-Net↗

DOE OSTI2023

Digital polycrystalline microstructure generation using diffusion probabilistic models

Accurate micromechanical simulation of polycrystalline materials requires a realistic digital representation of the grain scale microstructure. Here, this work demonstrates the use of a generative diffusion probabilistic model for synthesizing single phase polycrystalline realizations. The model performs well and is capable of producing realistic microstructures consisting of not just simple equiaxed structures but also structures exhibiting more complex spatial arrangements. Masked microstructure generation reveals that the model is context aware of morphological descriptors which may be encoded in the latent space. Training on more diverse data sets, with scaled up architectures, may enable development of future models capable of synthesizing even more complex microstructural features.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗