Virtual microstructure design for steels using generative adversarial networks

Lee, Jin Woong; Goo, Nam Hoon; Park, Woon Bae; Pyo, Myungho; Sohn, Kee‐Sun

doi:10.1002/eng2.12274

Cited by 27 publications

(13 citation statements)

References 46 publications

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“…Their cGAN uses an augmented loss based on the matching of perimeter, volume, and Euler characteristics. DGM studies in microstructure design have also used other advanced image translation methods introduced in computer vision such as super-resolution [59], CycleGAN [68,8], and Pix2Pix [68,127].…”

Section: Microstructure Nanostructure and Metamaterialsmentioning

confidence: 99%

Deep Generative Models in Engineering Design: A Review

Regenwetter¹,

Nobari²,

Ahmed³

2021

Preprint

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Automated design synthesis has the potential to revolutionize the modern human design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to design engineering may be the key to such automated design synthesis and is a research subject of great importance. We present a review and analysis of Deep Generative Learning models in engineering design. Deep Generative Models (DGMs) typically leverage deep networks to learn from an input dataset and learn to synthesize new designs. Recently, DGMs such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), feedforward Neural Networks (NNs) and certain Deep Reinforcement Learning (DRL) frameworks have shown promising results in design applications like structural optimization, materials design, and shape synthesis. The prevalence of DGMs in Engineering Design has skyrocketed since 2016. Anticipating continued growth, we conduct a review of recent advances with the hope of benefitting researchers interested in DGMs for design. We structure our review as an exposition of the algorithms, datasets, representation methods, and applications commonly used in the current literature. In particular, we discuss key works that have introduced new techniques and methods in DGMs, successfully applied DGMs to a design-related domain, or directly supported development of DGMs through datasets or auxiliary methods. We further identify key challenges and limitations currently seen in DGMs across design fields, such as design creativity, handling complex constraints and objectives, and modeling both form and functional performance simultaneously. In our discussion we identify possible solution pathways as key areas on which to target future work.

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Section: Microstructure Nanostructure and Metamaterialsmentioning

confidence: 99%

Deep Generative Models in Engineering Design: A Review

Regenwetter¹,

Nobari²,

Ahmed³

2021

Preprint

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“…Lee et al [94] employed DCGAN [134], CycleGAN [197] and Pix2Pix [70] to generate realistic virtual microstructural graph images. KL-divergence, a similarity metric that is considerably below 0.1, confirmed the similarity between the GAN-generated and ground truth images.…”

Section: Dan Et Al Proposed Matganmentioning

confidence: 99%

“…The quality of the GAN generated designs was confirmed by FEM simulation and experimental evaluation, demonstrating that GANs are capable of generating metaporous material designs with satisfactory broadband absorption performance. Applications in Material Science GAN models Micro and crystal structure generation and design Hybrid (WGAN-GP [52]+GIN) [148], GAN+GP-Hedge Bayesian optimization framework [181], CrystalGAN [123], Composition-Conditioned Crystal GAN [85] Designing complex architectured materials GAN-based model [112] Inorganic materials design MatGAN [27], WGAN [7] based model [65] Virtual microstructure design (DCGAN [134], CycleGAN [197] and Pix2Pix [70]) [94] Topological design of metaporous materials for sound absorption GAN based model [188] 4.8 Finance Financial data modeling is a challenging problem as there are complex statistical properties and dynamic stochastic factors behind the process. Many financial data are time-series data, such as real property price and stock market index.…”

Section: Dan Et Al Proposed Matganmentioning

confidence: 99%

A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing

Dash¹,

Ye²,

Wang³

2021

Preprint

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We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another using zero-sum game theory, allowing them to create much crisper and discrete outputs. GANs can be used to perform image processing, video generation and prediction, among other computer vision applications.GANs can also be utilised for a variety of science-related activities, including protein engineering, astronomical data processing, remote sensing image dehazing, and crystal structure synthesis. Other notable fields where GANs have made gains include finance, marketing, fashion design, sports, and music. Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines. We first cover the theory supporting GAN, GAN variants, and the metrics to evaluate GANs.Then we present how GAN and its variants can be applied in twelve domains, ranging from STEM fields, such as astronomy and biology, to business fields, such as marketing and finance, and to arts, such as music. As a result, researchers from other fields may grasp how GANs work and apply them to their own study. To the best of our knowledge, this article provides the most comprehensive survey of GAN's applications in different field. CCS Concepts: • General and reference → Surveys and overviews; • Computing methodologies → Computer vision; Neural networks; • Theory of computation → Machine learning theory.

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“…Iyer et al [16] demonstrated that microstructure can be generated from process conditions using an auxiliary classifier Wasserstein generative adversarial network (WGAN). Lee et al [17] used scanning electron microscope (SEM) and optical microscope (OM) images of various metal surfaces for GAN algorithm learning to create realistic virtual microstructures, which can be used to study the relationship between microstructures and physical properties. For flow stress prediction, Shouwu et al [18] reported that flow stress can be accurately predicted through an artificial neural network (ANN) by comparing the accuracy of the ANN model and the Arrhenius model.…”

Section: Introductionmentioning

confidence: 99%

Predicting High Temperature Flow Stress of Nickel Alloy A230 Based on an Artificial Neural Network

Moon

Jeong

Lee

et al. 2022

Metals

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The high-temperature deformation behavior of metals and alloys undergoes complex mechanisms depending on the deformation conditions. The microstructure and mechanical properties after deformation are important factors that determine the strength and durability of the final product. Therefore, many studies to predict the microstructure and mechanical properties have been conducted. In this regard, numerous mathematical approaches for predicting microstructure and flow stress have been proposed over the past half century. Accordingly, many advances have been made in the field of material science. Nevertheless, there are limitations in the mathematical modeling method as there is a complex relationship between the deformation conditions and the mechanical properties. Therefore, in this study, flow stress prediction was performed by applying conventional constitutive equation and artificial intelligence technology, which is known to be effective in modeling complex relationships. As a result, it was confirmed that the flow stresses modeled by the artificial neural network showed a higher accuracy than the flow stresses modeled by the conventional Arrhenius hyperbolic sine equation.

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Virtual microstructure design for steels using generative adversarial networks

Cited by 27 publications

References 46 publications

Deep Generative Models in Engineering Design: A Review

Deep Generative Models in Engineering Design: A Review

A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing

Predicting High Temperature Flow Stress of Nickel Alloy A230 Based on an Artificial Neural Network

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