Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning

Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning

Abstract Advanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimat...

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Autores principales: Sung Wook Kim, Seong-Hoon Kang, Se-Jong Kim, Seungchul Lee
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/dcbc6a1fd009497aa6d565446bece42c
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spelling oai:doaj.org-article:dcbc6a1fd009497aa6d565446bece42c2021-12-02T13:17:42ZEstimating the phase volume fraction of multi-phase steel via unsupervised deep learning10.1038/s41598-021-85407-y2045-2322https://doaj.org/article/dcbc6a1fd009497aa6d565446bece42c2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85407-yhttps://doaj.org/toc/2045-2322Abstract Advanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network. This often comes as a challenge in many areas where obtaining data and labeling it is extremely labor-intensive. To overcome this challenge, an unsupervised way of learning DNN, which does not require any manual labeling, is proposed. Information maximizing generative adversarial network (InfoGAN) is used to learn the underlying probability distribution of each phase and generate realistic sample points with class labels. Then, the generated data is used for training an MLP classifier, which in turn predicts the labels for the original dataset. The result shows a mean relative error of 4.53% at most, while it can be as low as 0.73%, which implies the estimated phase fraction closely matches the true phase fraction. This presents the high feasibility of using the proposed methodology for fast and precise estimation of phase volume fraction in both industry and academia.Sung Wook KimSeong-Hoon KangSe-Jong KimSeungchul LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Sung Wook Kim
Seong-Hoon Kang
Se-Jong Kim
Seungchul Lee
Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning
description Abstract Advanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network. This often comes as a challenge in many areas where obtaining data and labeling it is extremely labor-intensive. To overcome this challenge, an unsupervised way of learning DNN, which does not require any manual labeling, is proposed. Information maximizing generative adversarial network (InfoGAN) is used to learn the underlying probability distribution of each phase and generate realistic sample points with class labels. Then, the generated data is used for training an MLP classifier, which in turn predicts the labels for the original dataset. The result shows a mean relative error of 4.53% at most, while it can be as low as 0.73%, which implies the estimated phase fraction closely matches the true phase fraction. This presents the high feasibility of using the proposed methodology for fast and precise estimation of phase volume fraction in both industry and academia.
format article
author Sung Wook Kim
Seong-Hoon Kang
Se-Jong Kim
Seungchul Lee
author_facet Sung Wook Kim
Seong-Hoon Kang
Se-Jong Kim
Seungchul Lee
author_sort Sung Wook Kim
title Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning
title_short Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning
title_full Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning
title_fullStr Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning
title_full_unstemmed Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning
title_sort estimating the phase volume fraction of multi-phase steel via unsupervised deep learning
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/dcbc6a1fd009497aa6d565446bece42c
work_keys_str_mv AT sungwookkim estimatingthephasevolumefractionofmultiphasesteelviaunsuperviseddeeplearning
AT seonghoonkang estimatingthephasevolumefractionofmultiphasesteelviaunsuperviseddeeplearning
AT sejongkim estimatingthephasevolumefractionofmultiphasesteelviaunsuperviseddeeplearning
AT seungchullee estimatingthephasevolumefractionofmultiphasesteelviaunsuperviseddeeplearning
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