arXiv subjects

Hayoung Chung

Publications and source records attributed to Hayoung Chung.

5 recordsLinked to original sources

arXiv2026

Self-Refining Topology Optimization via an LLM-Based Multi-Agent Framework

Topology optimization is a widely used design method that produces optimized material distributions for prescribed objectives and constraints through well-established numerical algorithms. Throughout the workflow, engineers make a series of decisions ranging from setting and adjusting numerical parameters to assessing whether the converged design meets considerations beyond those explicitly included in the optimization problem, such as physical feasibility. These decisions, which draw on domain expertise, interfere with the autonomous design process. To address this difficulty, this study presents TopOptAgents, a multi-agent system for automating not only the design process but also decision-making during the key stages of the topology optimization process. TopOptAgents consists of six LLM-based agents collaborating through iterative self-refinement cycles spanning problem formulation, validation, code generation and execution, and quality assessment of the optimized structure. This process enables error correction and progressive improvement of both the optimization setup and resulting design. The framework is demonstrated on optimization problems selected to cover a range of settings that differ in their literature coverage and numerical characteristics The benefits of iterative self-refinement are found to be particularly pronounced for problem classes where the pretrained language model has limited prior exposure, such as formulations whose literature and open-source implementations are comparatively sparse. In such cases, the proposed framework reliably produces converged designs where a single state-of-the-art LLM struggles, suggesting that self-refinement broadens the range of topology optimization problems that LLM-based automation can reliably address.

cs.MA↗

arXiv2025

Structural characterization and bonding energy analysis for plasma-activated bonding of SiCN films: A reactive molecular dynamics study

Plasma-activated bonding of SiCN films offers high bonding strength at the hybrid-bonding interface, thereby enhancing mechanical reliability. Although experimental studies have shown that the interfacial bonding properties of SiCN films vary with SiCN composition and plasma treatment parameters, a clear correlation between these parameters and the resulting bonding properties has not yet been established. This study presents an atomistic investigation of SiCN-SiCN plasma-activated bonding with controlled SiCN composition and plasma fluence, which performs O2 plasma surface activation, surface hydroxylation, direct bonding, post-bonding annealing, and debonding using reactive molecular dynamics. The structural characterization of the plasma-activated SiCN surface, including density of various covalent bonds and surface roughness, exhibits composition- and plasma fluence-dependent chemical and morphological modification. Bonding energy evaluated from atomic traction-separation responses in cohesive zone volume elements (CZVE) during debonding simulations shows a positive correlation with the interfacial Si-O-Si density. Since the interfacial Si-O-Si density reflects the combined effects of these chemical and morphological modifications, the dependence of bonding energy on composition and plasma fluence is successfully elucidated by the structural characterization. These results establish an atomic-level material-process-property relationship and offer practical guidance for optimizing SiCN composition and plasma treatment parameters for SiCN-SiCN plasma-activated bonding.

cond-mat.mtrl-sci↗

arXiv2025

Topology Optimization for Multi-Axis Additive Manufacturing Considering Overhang and Anisotropy

Topology optimization produces designs with intricate geometries and complex topologies that require advanced manufacturing techniques such as additive manufacturing (AM). However, insufficient consideration of manufacturability during the optimization process often results in design modifications that compromise the optimality of the design. While multi-axis AM enhances manufacturability by enabling flexible material deposition in multiple orientations, challenges remain in addressing overhang structures, potential collisions, and material anisotropy caused by varying build orientations. To overcome these limitations, this study proposes a novel space-time topology optimization framework for multi-axis AM. The framework employs a pseudo-time field as a design variable to represent the fabrication sequence, simultaneously optimizing the density distribution and build orientations. This approach ensures that the overhang angles remain within manufacturable limits while also mitigating collisions. Moreover, by incorporating material anisotropy induced by diverse build orientations into the design process, the framework can take the scan path-dependent structural behaviors into account during the design optimization. Numerical examples demonstrate that the proposed framework effectively derives feasible and optimal designs that account for the manufacturing characteristics of multi-axis AM.

cs.CE↗

arXiv2022

AI Augmented Digital Metal Component

The aim of this work is to propose a new paradigm that imparts intelligence to metal parts with the fusion of metal additive manufacturing and artificial intelligence (AI). Our digital metal part classifies the status with real time data processing with convolutional neural network (CNN). The training data for the CNN is collected from a strain gauge embedded in metal parts by laser powder bed fusion process. We implement this approach using additive manufacturing, demonstrate a self-cognitive metal part recognizing partial screw loosening, malfunctioning, and external impacting object. The results indicate that metal part can recognize subtle change of multiple fixation state under repetitive compression with 89.1% accuracy with test sets. The proposed strategy showed promising potential in contributing to the hyper-connectivity for next generation of digital metal based mechanical systems

eess.SP↗

arXiv2019

Level-set topology optimization considering nonlinear thermoelasticity

At elevated temperature environments, elastic structures experience a change of the stress-free state of the body that can strongly influence the optimal topology of the structure. This work presents level-set based topology optimization of structures undergoing large deformations due to thermal and mechanical loads. The nonlinear analysis model is constructed by multiplicatively decomposing thermal and mechanical effects and introducing an intermediate stress-free state between the undeformed and deformed coordinates. By incorporating the thermoelastic nonlinearity into the level-set topology optimization scheme, wider design spaces can be explored with the consideration of both mechanical and thermal loads. Four numerical examples are presented that demonstrate how temperature changes affect the optimal design of large-deforming structures. In particular, we show how optimization can manipulate the material layout in order to create a counteracting effect between thermal and mechanical loads, even up to a degree that buckling and snap-through are suppressed. Hence the consideration of large deformations in conjunction with thermoelasticity opens many new possibilities for controlling and manipulating the thermo-mechanical response via topology optimization.

cs.CE↗