Browsing by Author "Seo, Junhyeon"

Now showing 1 - 4 of 4
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    Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks
    Seo, Junhyeon; Rao, Prahalada; Raeymaekers, Bart (2023-12-04)
    Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly measurements. However, existing algorithms to numerically generated surface topography are not well-suited to create the specific characteristics and geometric features of as-built surfaces that result from laser powder bed fusion (LPBF), such as partially melted metal particles, porosity, laser scan lines, and balling. Thus, we present a method to generate synthetic as-built LPBF surface topography maps using a progressively growing generative adversarial network. We qualitatively and quantitatively demonstrate good agreement between synthetic and experimental as-built LPBF surface topography maps using areal and deterministic surface topography parameters, radially averaged power spectral density, and material ratio curves. The ability to accurately generate synthetic as-built LPBF surface topography maps reduces the experimental burden of performing a large number of surface topography measurements. Furthermore, it facilitates combining experimental measurements with synthetic surface topography maps to create large data-sets that facilitate, e.g. relating as-built surface topography to LPBF process parameters, or implementing digital surface twins to monitor complex end-use LPBF parts, amongst other applications.
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    Lightweight Chassis Design of Hybrid Trucks Considering Multiple Road Conditions and Constraints
    De, Shuvodeep; Singh, Karanpreet; Seo, Junhyeon; Kapania, Rakesh K.; Ostergaard, Erik; Angelini, Nicholas; Aguero, Raymond (MDPI, 2020-12-28)
    The paper describes a fully automated process to generate a shell-based finite element model of a large hybrid truck chassis to perform mass optimization considering multiple load cases and multiple constraints. A truck chassis consists of different parts that could be optimized using shape and size optimization. The cross members are represented by beams, and other components of the truck (batteries, engine, fuel tanks, etc.) are represented by appropriate point masses and are attached to the rail using multiple point constraints to create a mathematical model. Medium-fidelity finite element models are developed for front and rear suspensions and they are attached to the chassis using multiple point constraints, hence creating the finite element model of the complete truck. In the optimization problem, a set of five load conditions, each of which corresponds to a road event, is considered, and constraints are imposed on maximum allowable von Mises stress and the first vertical bending frequency. The structure is optimized by implementing the particle swarm optimization algorithm using parallel processing. A mass reduction of about 13.25% with respect to the baseline model is achieved.
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    Machine Learning Applications in Structural Analysis and Design
    Seo, Junhyeon (Virginia Tech, 2022-10-05)
    Artificial intelligence (AI) has progressed significantly during the last several decades, along with the rapid advancements in computational power. This advanced technology is currently being employed in various engineering fields, not just in computer science. In aerospace engineering, AI and machine learning (ML), a major branch of AI, are now playing an important role in various applications, such as automated systems, unmanned aerial vehicles, aerospace optimum design structure, etc. This dissertation mainly focuses on structural engineering to employ AI to develop lighter and safer aircraft structures as well as challenges involving structural optimization and analysis. Therefore, various ML applications are studied in this research to provide novel frameworks for structural optimization, analysis, and design. First, the application of a deep-learning-based (DL) convolutional neural network (CNN) was studied to develop a surrogate model for providing optimum structural topology. Typically, conventional structural topology optimization requires a large number of computations due to the iterative finite element analyses (FEAs) needed to obtain optimal structural layouts under given load and boundary conditions. A proposed surrogate model in this study predicts the material density layout inputting the static analysis results using the initial geometry but without performing iterative FEAs. The developed surrogate models were validated with various example cases. Using the proposed method, the total calculation time was reduced by 98 % as compared to conventional topology optimization once the CNN had been trained. The predicted results have equal structural performance levels compared to the optimum structures derived by conventional topology optimization considered ``ground truths". Secondly, reinforcement learning (RL) is studied to create a stand-alone AI system that can design the structure from trial-and-error experiences. RL application is one of the major ML branches that mimic human behavior, specifically how human beings solve problems based on their experience. The main RL algorithm assumes that the human problem-solving process can be improved by earning positive and negative rewards from good and bad experiences, respectively. Therefore, this algorithm can be applied to solve structural design problems whereby engineers can improve the structural design by finding the weaknesses and enhancing them using a trial and error approach. To prove this concept, an AI system with the RL algorithm was implemented to drive the optimum truss structure using continuous and discrete cross-section choices under a set of given constraints. This study also proposed a unique reward function system to examine the constraints in structural design problems. As a result, the independent AI system can be developed from the experience-based training process, and this system can design the structure by itself without significant human intervention. Finally, this dissertation proposes an ML-based classification tool to categorize the vibrational mode shapes of tires. In general, tire vibration significantly affects driving quality, such as stability, ride comfort, noise performance, etc. Therefore, a comprehensive study for identifying the vibrational features is necessary to design the high-performance tire by considering the geometry, material, and operation conditions. Typically, the vibrational characteristics can be obtained from the modal test or numerical analysis. These identified modal characteristics can be used to categorize the tire mode shapes to determine the specific mode cause poorer driving performances. This study suggests a method to develop an ML-based classification tool that can efficiently categorize the mode shape using advanced feature recognition and classification algorithms. The best-performed classification tool can accurately predict the tire category without manual effort. Therefore, the proposed classification tool can be used to categorize the tire mode shapes for subsequent tire performance and improve the design process by reducing the time and resources for expensive calculations or experiments.
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    Topology optimization with advanced CNN using mapped physics-based data
    Seo, Junhyeon; Kapania, Rakesh K. (Springer, 2023-01)
    This research proposes a new framework to develop an accurate machine-learning-based surrogate model to predict the optimum topological structures using an advanced encoder-decoder network, Unet, and Unet++. The trained surrogate model predicts the optimum structural layout as output by inputting the results from the initial static analysis without any iterative optimization calculations. Input and output data are generated using the commercial finite element analysis package, Abaqus/Standard, and an optimization package, Abaqus/Tosca. We applied the data augmentation technique to increase the amount of data without actual calculations. Primarily, this research focused on overcoming the weaknesses of previous studies that the trained network is only applicable to limited geometry variations and requires an organized grid rectangular mesh. Therefore, this study suggests a mapping process to convert the analysis data on any type of mesh element to a tensor form, which enables training and employing the network. Also, to increase the prediction accuracy, we trained the network with the labeled optimum material data using a binary segmented output, representing the structure and void regions in the domain. Finally, the trained networks are evaluated using the intersection over union (IoU) scores representing the classification accuracy. The best-performing network provides highly accurate results, and this model provided the IoU scores for average, maximum, and standard deviation as 90.0%, 99.8%, and 7.1%, respectively. Also, we apply it to solve local-global structural optimization problems, and the overall calculation time is reduced by 98%.