Search NASA⌕ Search

DOE OSTI · 1490321

A robust process-structure model for predicting the joint interface structure in impact welding

Abstract

The impact welding is gaining popularity in joining of dissimilar metals as any intermetallics are often very thin or discontinous with little to no heat affected zone resulting from the process. This work focuses on a recently developed impact welding technique, known as the Vaporizing Foil Actuator Welding (VFAW), which uses the pressure created from the electrically driven rapid vaporization of a thin metallic conductor to drive the flyer. A typical characteristic of an impact-welded joint is the wavy weld interface which is attributed to the complex interfacial kinematics resulting from the high-speed oblique impact of the metal plates. A numerical simulation framework based on the finite element method (FEM) with Eulerian formalism is developed to model the high-speed impact between the metal plates. The model accounts for the thermomechanical interactions in the process and captures the complex interfacial deformations. A thorough validation of the model is achieved by comparing the wave characteristics obtained from numerical simulations with the experimental results from vaporizing foil actuator welding. The amplitude and wavelength of the interfacial waves and the resulting joint microstructure is shown to be strongly dependent on the process conditions and the specific material system, which is well captured by the model.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gupta, Varun (ORCID:0000000243222143), Lee, Taeseon, Vivek, Anupam (ORCID:0000000216770099), Choi, Kyoo Sil, Mao, Yu, Sun, Xin, Daehn, Glenn. 2019-02-01. A robust process-structure model for predicting the joint interface structure in impact welding. https://doi.org/10.1016/j.jmatprotec.2018.08.047

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗