Showing papers by "Gun Jin Yun published in 2023"

Microstructure Design of Multifunctional Particulate Composite Materials using Conditional Diffusion Models

Abstract: This paper presents a novel modeling framework to generate an optimal microstructure having ultimate multifunctionality using a diffusion-based generative model. In computational material science, generating microstructure is a crucial step in understanding the relationship between the microstructure and properties. However, using finite element (FE)-based direct numerical simulation (DNS) of microstructure for multiscale analysis is extremely resource-intensive, particularly in iterative calculations. To address this time-consuming issue, this study employs a diffusion-based generative model as a replacement for computational analysis in design optimization. The model learns the geometry of microstructure and corresponding stress contours, allowing for the prediction of microstructural behavior based solely on geometry, without the need for additional analysis. The focus on this work is on mechanoluminescence (ML) particulate composites made with europium ions and dysprosium ions ( SrAl 2 O 4 : Eu 2+ , Dy 3+ , SAOED). Multi-objective optimization is conducted based on the generative diffusion model to improve light sensitivity and fracture toughness. The results show multiple candidates of microstructure that meet the design requirements. Furthermore, the designed microstructure is not present in the training data but generates new morphology following the characteristics of particulate composites. The proposed approach provides a new way to characterize a performance-based microstructure of composite materials.

A defect detection framework using three-dimensional convolutional neural network (3D-CNN) with in-situ monitoring data in laser powder bed fusion process

Abstract: Laser powder bed fusion (L-PBF) is a promising additive manufacturing (AM) technology for manufacturing complex-shaped metallic parts with high density. Since L-PBF has been rapidly developed and applied in various industries, the quality assurance of the printed part using the process became a topic of primary importance. In this respect, in-situ monitoring techniques have received increased attention in recent years for aiding the quality control of L-PBF process and the certification of the products. This study proposes a novel defect detection framework with a three-dimensional convolutional neural network (3D-CNN) and in-situ monitoring of light intensity for detecting both the lack-of-fusion and keyhole-induced defects. The proposed 3D-CNN model works with a 3D moving window to perform the local inspection using the measured light intensities in three-dimensional space to classify the type of defect. Furthermore, the model predicts the local volume fraction to provide insights into the degree of defect. To perform the classification and regression with a single 3D-CNN, the joint classification and regression approach was adopted to train the model using the results obtained with micro-computed tomography as the ground truth. In order to build the training dataset, the samples with artificial defects were fabricated in different process regimes with energy densities ranging from 19.84J/mm3 to 110.12J/mm3. After the training process, the proposed model was evaluated with the test specimens which contain randomized defects generated due to the excessively low and high energy input. The results showed that the proposed 3D-CNN-based defect detection framework can detect pores greater than 80μm induced by both lack-of-fusion and keyhole mode melting. The sensitivity of the proposed framework was evaluated, showing the true positive rate of 75.69% and 70.47% for lack-of-fusion and keyhole defects with a void volume larger than 0.0003mm3. The prediction of local volume fraction with R2 score of 0.91 was also achieved with the proposed 3D-CNN approach.

3D Printing of Covalent Adaptable Networks: Overview, Applications and Future Prospects

Abstract: 3D printing, a rapidly growing material processing technique has found its broad applications in construction, automobiles, robotics, domestic usage and in biomedical sectors due to its ability to fabricate the desirable objects from scratch. However, due to the non-recyclable and non-reprocessable nature of most printed structures, the discarded 3D printed objects generate wastes after damage or use. Covalent Adaptable Networks (CANs) are polymeric networks those can change their network topology by exchanging their functionalities under external stimuli, thus, rendering the printed objects recyclable, therefore helpful in terms of reducing waste. The fabricated objects may also be endowed with properties such as self-healing, shape-memory, enhanced mechanical strength, degradability, and reprintability. The present article covers different methods utilized for 3D printing of the polymers having CANs, including a detailed insight to present trends and technologies in the field. In addition, their applications, particularly in soft robotics and biomedical fields have been discussed. Future perspectives regarding the challenges, new potential applications as well as importance of continuous advancements in the field of 3D printing of CANs have also been discussed.

Birdstrike Analysis of the Flap Driving Parts Considering Effects of Bird Geometry and SPH(Smoothed Particle Hydrodynamics) Parameters

Abstract: 이 논문에서는 조류충돌로 인한 항공기 플랩 구동부의 안정성 분석 방법을 소개한다. 정확한 해석을 위해 실제 조류 형상을 이용한 SPH(Smoothed Particle Hydrodynamics) 모델링과 SPH 매개변수의 최적화를 진행하였고, 이어서 최대 변형률 에너지 기반으로 플랩의 구동부에 가장 취약한 상황을 조사하였다. 일반적으로 간단한 조류 형상을 이용한 조류충돌을 위한 SPH 해석은 실제 실험 결과와 비교할 때 정확성에 한계가 있었다. 본 연구에서는 충돌 부위의 변위 및 응력뿐 아니라 보다 실제적인 조류 형상 SPH 모델을 사용하여 플랩 구동부의 응력 및 소성 변형도 분석하였다.

Durability and Performance Analysis of Polymer Electrolyte Membranes for Hydrogen Fuel Cells by a Coupled Chemo-mechanical Constitutive Model and Experimental Validation

Abstract: In this paper, a chemo-mechanically coupled behavior of Nafion 212 is investigated through predictive multiphysics modeling and experimental validation. Fuel cell performance and durability are critically determined by the mechanical and chemical degradation of a perfluorosulfonic acid (PFSA) membrane. However, how the degree of chemical decomposition affects the material constitutive behavior has not been clearly defined. To estimate the degradation level quantitatively, fluoride release is measured. The PFSA membrane in tensile testing shows nonlinear behavior, which is modeled by J2 plasticity-based material modeling. The material parameters, which contain hardening parameters and Young’s modulus, are characterized in terms of fluoride release levels by inverse analysis. In the sequel, membrane modeling is performed to investigate the life prediction due to humidity cycling. A continuum-based pinhole growth model is adopted in response to mechanical stress. As a result, validation is conducted in comparison with the accelerated stress test (AST) by correlating the size of the pinhole with the gas crossover generated in the membrane. This work provides a dataset of degraded membranes for performance and suggests the quantitative understanding and prediction of fuel cell durability with computational simulation.

A Data-Driven Framework for Designing Microstructure of Multifunctional Composites with Deep-Learned Diffusion-Based Generative Models

Abstract: This paper puts forward an integrated microstructure design methodology that replaces the common existing design approaches: 1) reconstruction of microstructures, 2) analyzing and quantifying material properties, and 3) inverse design of materials using deep-learned generative and surrogate models. The long-standing issue of microstructure reconstruction is well addressed in this study using a new class of state-of-the-art generative model, the diffusion-based generative model (DGM). Moreover, the conditional formulation of DGM for guidance to the embedded desired material properties with a transformer-based attention mechanism enables the inverse design of multifunctional composites. A convolutional neural network (CNN)-based surrogate model is utilized to analyze the nonlinear material behavior to facilitate the prediction of material properties for building microstructure-property linkages. Combined, these generative and surrogate models enable large data processing and database construction that is often not affordable with resource-intensive finite element method (FEM)-based direct numerical simulation (DNS) and iterative reconstruction methods. An example case is presented to demonstrate the effectiveness of the proposed approach, which is designing mechanoluminescence (ML) particulate composites made of europium and dysprosium ions. The results show that the inversely-designed multiple ML microstructure candidates with the proposed generative and surrogate models meet the multiple design requirements (e.g., volume fraction, elastic constant, and light sensitivity). The evaluation of the generated samples' quality and the surrogate models' performance using appropriate metrics are also included. This assessment demonstrates that the proposed integrated methodology offers an end-to-end solution for practical material design applications.