28 research outputs found

    Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Data

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    The product design process in manufacturing involves iterative design modeling and analysis to achieve the target engineering performance, but such an iterative process is time consuming and computationally expensive. Recently, deep learning-based engineering performance prediction models have been proposed to accelerate design optimization. However, they only guarantee predictions on training data and may be inaccurate when applied to new domain data. In particular, 3D design data have complex features, which means domains with various distributions exist. Thus, the utilization of deep learning has limitations due to the heavy data collection and training burdens. We propose a bi-weighted unsupervised domain adaptation approach that considers the geometry features and engineering performance of 3D design data. It is specialized for deep learning-based engineering performance predictions. Domain-invariant features can be extracted through an adversarial training strategy by using hypothesis discrepancy, and a multi-output regression task can be performed with the extracted features to predict the engineering performance. In particular, we present a source instance weighting method suitable for 3D design data to avoid negative transfers. The developed bi-weighting strategy based on the geometry features and engineering performance of engineering structures is incorporated into the training process. The proposed model is tested on a wheel impact analysis problem to predict the magnitude of the maximum von Mises stress and the corresponding location of 3D road wheels. This mechanism can reduce the target risk for unlabeled target domains on the basis of weighted multi-source domain knowledge and can efficiently replace conventional finite element analysis

    Topology Optimization via Machine Learning and Deep Learning: A Review

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    Topology optimization (TO) is a method of deriving an optimal design that satisfies a given load and boundary conditions within a design domain. This method enables effective design without initial design, but has been limited in use due to high computational costs. At the same time, machine learning (ML) methodology including deep learning has made great progress in the 21st century, and accordingly, many studies have been conducted to enable effective and rapid optimization by applying ML to TO. Therefore, this study reviews and analyzes previous research on ML-based TO (MLTO). Two different perspectives of MLTO are used to review studies: (1) TO and (2) ML perspectives. The TO perspective addresses "why" to use ML for TO, while the ML perspective addresses "how" to apply ML to TO. In addition, the limitations of current MLTO research and future research directions are examined

    Variable optical elements for fast focus control

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    In this Review, we survey recent developments in the emerging field of high-speed variable-z-focus optical elements, which are driving important innovations in advanced imaging and materials processing applications. Three-dimensional biomedical imaging, high-throughput industrial inspection, advanced spectroscopies, and other optical characterization and materials modification methods have made great strides forward in recent years due to precise and rapid axial control of light. Three state-of-the-art key optical technologies that enable fast z-focus modulation are reviewed, along with a discussion of the implications of the new developments in variable optical elements and their impact on technologically relevant applications

    Performance Comparison of Design Optimization and Deep Learning-based Inverse Design

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    Surrogate model-based optimization has been increasingly used in the field of engineering design. It involves creating a surrogate model with objective functions or constraints based on the data obtained from simulations or real-world experiments, and then finding the optimal solution from the model using numerical optimization methods. Recent advancements in deep learning-based inverse design methods have made it possible to generate real-time optimal solutions for engineering design problems, eliminating the requirement for iterative optimization processes. Nevertheless, no comprehensive study has yet closely examined the specific advantages and disadvantages of this novel approach compared to the traditional design optimization method. The objective of this paper is to compare the performance of traditional design optimization methods with deep learning-based inverse design methods by employing benchmark problems across various scenarios. Based on the findings of this study, we provide guidelines that can be taken into account for the future utilization of deep learning-based inverse design. It is anticipated that these guidelines will enhance the practical applicability of this approach to real engineering design problems

    Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress

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    The impact performance of the wheel during wheel development must be ensured through a wheel impact test for vehicle safety. However, manufacturing and testing a real wheel take a significant amount of time and money because developing an optimal wheel design requires numerous iterative processes of modifying the wheel design and verifying the safety performance. Accordingly, the actual wheel impact test has been replaced by computer simulations, such as Finite Element Analysis (FEA), but it still requires high computational costs for modeling and analysis. Moreover, FEA experts are needed. This study presents an aluminum road wheel impact performance prediction model based on deep learning that replaces the computationally expensive and time-consuming 3D FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and barrier mass value used for wheel impact test are utilized as the inputs to predict the magnitude of maximum von Mises stress, corresponding location, and the stress distribution of 2D disk-view. The wheel impact performance prediction model can replace the impact test in the early wheel development stage by predicting the impact performance in real time and can be used without domain knowledge. The time required for the wheel development process can be shortened through this mechanism

    Shape-shifting nanoparticles on a perovskite oxide for highly stable and active heterogeneous catalysis

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    Controlling the geometric shape of nano-catalysts plays a key role in maximizing unique properties of the materials. Although shape control of nanoparticles is well known by various preparation methods, still there is no clear case for exsolution. Here we show that the shape of embedded Ni nanoparticles can be changed on exsolution process, by controlling reduction temperature and time. To elucidate and generalize the shape-shifting, we develop a model which describes the equilibrium shape of nanoparticles on support thermodynamically. Our results suggest that there is a thermodynamic driving force for the exsolved nanoparticle to be stabilized into faceted shape with low surface/interface energy, during the particle growth. Through catalytic activity testing, the improved durability of shape-shifted Ni catalysts is confirmed on dry-reforming condition over 390 h, resulting from enhanced interface stability and cocking resistance. This provides theoretical and experimental framework for the shape control of exsolved particle on oxide support, but also for the design of unique catalyst with high stability and reactivity

    Multiplicity of Advanced T Category–Tumors Is a Risk Factor for Survival in Patients with Colorectal Carcinoma

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    Background Previous studies on synchronous colorectal carcinoma (SCRC) have reported inconsistent results about its clinicopathologic and molecular features and prognostic significance. Methods Forty-six patients with multiple advanced tumors (T2 or higher category) who did not receive neoadjuvant chemotherapy and/or radiotherapy and who are not associated with familial adenomatous polyposis were selected and 99 tumors from them were subjected to clinicopathologic and molecular analysis. Ninety-two cases of solitary colorectal carcinoma (CRC) were selected as a control considering the distributions of types of surgeries performed on patients with SCRC and T categories of individual tumors from SCRC. Results SCRC with multiple advanced tumors was significantly associated with more frequent nodal metastasis (p = .003) and distant metastasis (p = .001) than solitary CRC. KRAS mutation, microsatellite instability, and CpG island methylator phenotype statuses were not different between SCRC and solitary CRC groups. In univariate survival analysis, overall and recurrence-free survival were significantly lower in patients with SCRC than in patients with solitary CRC, even after adjusting for the extensiveness of surgical procedure, adjuvant chemotherapy, or staging. Multivariate Cox regression analysis revealed that tumor multiplicity was an independent prognostic factor for overall survival (hazard ratio, 4.618; 95% confidence interval, 2.126 to 10.030; p < .001), but not for recurrence-free survival (p = .151). Conclusions Findings suggested that multiplicity of advanced T category–tumors might be associated with an increased risk of nodal metastasis and a risk factor for poor survival, which raises a concern about the guideline of American Joint Committee on Cancer’s tumor-node-metastasis staging that T staging of an index tumor determines T staging of SCRC
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