SAP-Net: Deep learning to predict sound absorption performance of metaporous materials

Airborne sound absorption coefficient is the premise for investigating the sound absorption performance or mechanism of metaporous materials. The common numerical evaluation approach is FEM which is relatively computationally costly particularly when processing complex structures or a large batch of...

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Autores principales:	Hongjia Zhang, Yang Wang, Keyu Lu, Honggang Zhao, Dianlong Yu, Jihong Wen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Sound Absorption Coefficient Prediction Convolutional Neural Networks Metaporous Materials Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/72781e6ab04d40d79fcc56046e85e2e1
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id	oai:doaj.org-article:72781e6ab04d40d79fcc56046e85e2e1
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spelling	oai:doaj.org-article:72781e6ab04d40d79fcc56046e85e2e12021-11-04T04:25:55ZSAP-Net: Deep learning to predict sound absorption performance of metaporous materials0264-127510.1016/j.matdes.2021.110156https://doaj.org/article/72781e6ab04d40d79fcc56046e85e2e12021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0264127521007115https://doaj.org/toc/0264-1275Airborne sound absorption coefficient is the premise for investigating the sound absorption performance or mechanism of metaporous materials. The common numerical evaluation approach is FEM which is relatively computationally costly particularly when processing complex structures or a large batch of data. Rapidly developing deep learning algorithms, on the other hand, show a promising trend in the data-driven manner to learn and predict material parameters efficiently and precisely. We propose SAP-net based on deep convolutional neural network to predict the sound absorption coefficient at a specific frequency of an input image representing the topological structure of metaporous materials. Trained with FEM-prepared data for six frequency points, SAP-net demonstrates outstanding evaluation speed of 0.007 s/image and brilliant prediction accuracy with mean absolute errors all smaller than 0.019 (the smallest 0.008 at f = 1000 Hz). Meanwhile, the fact that SAP-net remains accurate when predicting for images that are essentially different from those in the training data shows its capability of learning and capturing the underlying physical mechanism linking the topological structure to the sound absorption performance. In conclusion, SAP-net provides an extraordinarily fast and accurate approach for the investigation of sound absorption performance, which is expected to accelerate the examination and design process of materials.Hongjia ZhangYang WangKeyu LuHonggang ZhaoDianlong YuJihong WenElsevierarticleSound Absorption Coefficient PredictionConvolutional Neural NetworksMetaporous MaterialsMaterials of engineering and construction. Mechanics of materialsTA401-492ENMaterials & Design, Vol 212, Iss , Pp 110156- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Sound Absorption Coefficient Prediction Convolutional Neural Networks Metaporous Materials Materials of engineering and construction. Mechanics of materials TA401-492
spellingShingle	Sound Absorption Coefficient Prediction Convolutional Neural Networks Metaporous Materials Materials of engineering and construction. Mechanics of materials TA401-492 Hongjia Zhang Yang Wang Keyu Lu Honggang Zhao Dianlong Yu Jihong Wen SAP-Net: Deep learning to predict sound absorption performance of metaporous materials
description	Airborne sound absorption coefficient is the premise for investigating the sound absorption performance or mechanism of metaporous materials. The common numerical evaluation approach is FEM which is relatively computationally costly particularly when processing complex structures or a large batch of data. Rapidly developing deep learning algorithms, on the other hand, show a promising trend in the data-driven manner to learn and predict material parameters efficiently and precisely. We propose SAP-net based on deep convolutional neural network to predict the sound absorption coefficient at a specific frequency of an input image representing the topological structure of metaporous materials. Trained with FEM-prepared data for six frequency points, SAP-net demonstrates outstanding evaluation speed of 0.007 s/image and brilliant prediction accuracy with mean absolute errors all smaller than 0.019 (the smallest 0.008 at f = 1000 Hz). Meanwhile, the fact that SAP-net remains accurate when predicting for images that are essentially different from those in the training data shows its capability of learning and capturing the underlying physical mechanism linking the topological structure to the sound absorption performance. In conclusion, SAP-net provides an extraordinarily fast and accurate approach for the investigation of sound absorption performance, which is expected to accelerate the examination and design process of materials.
format	article
author	Hongjia Zhang Yang Wang Keyu Lu Honggang Zhao Dianlong Yu Jihong Wen
author_facet	Hongjia Zhang Yang Wang Keyu Lu Honggang Zhao Dianlong Yu Jihong Wen
author_sort	Hongjia Zhang
title	SAP-Net: Deep learning to predict sound absorption performance of metaporous materials
title_short	SAP-Net: Deep learning to predict sound absorption performance of metaporous materials
title_full	SAP-Net: Deep learning to predict sound absorption performance of metaporous materials
title_fullStr	SAP-Net: Deep learning to predict sound absorption performance of metaporous materials
title_full_unstemmed	SAP-Net: Deep learning to predict sound absorption performance of metaporous materials
title_sort	sap-net: deep learning to predict sound absorption performance of metaporous materials
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/72781e6ab04d40d79fcc56046e85e2e1
work_keys_str_mv	AT hongjiazhang sapnetdeeplearningtopredictsoundabsorptionperformanceofmetaporousmaterials AT yangwang sapnetdeeplearningtopredictsoundabsorptionperformanceofmetaporousmaterials AT keyulu sapnetdeeplearningtopredictsoundabsorptionperformanceofmetaporousmaterials AT honggangzhao sapnetdeeplearningtopredictsoundabsorptionperformanceofmetaporousmaterials AT dianlongyu sapnetdeeplearningtopredictsoundabsorptionperformanceofmetaporousmaterials AT jihongwen sapnetdeeplearningtopredictsoundabsorptionperformanceofmetaporousmaterials
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SAP-Net: Deep learning to predict sound absorption performance of metaporous materials

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