Engineer design process assisted by explainable deep learning network

Abstract Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important...

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Autores principales:	Chia-Wei Hsu, An-Cheng Yang, Pei-Ching Kung, Nien-Ti Tsou, Nan-Yow Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2231ad4cc8fb4f7dbf4e1587f08fd1fb
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spelling	oai:doaj.org-article:2231ad4cc8fb4f7dbf4e1587f08fd1fb2021-11-21T12:23:37ZEngineer design process assisted by explainable deep learning network10.1038/s41598-021-01937-52045-2322https://doaj.org/article/2231ad4cc8fb4f7dbf4e1587f08fd1fb2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01937-5https://doaj.org/toc/2045-2322Abstract Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design. In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process. The process is based on a complex, time-dependent, multi-physical biomechanical theory, known as mechano-regulatory method. It has been used to evaluate the performance of dental implants and to assess the tissue recovery after the oral surgery procedures. We provided a deep learning network (DLN) with calibrated simulation data that came from different simulation conditions with experimental verification. The DLN achieves nearly exact result of simulated bone healing history around implants. The correlation of the predicted essential physical properties of surrounding bones (e.g. strain and fluid velocity) and performance indexes of implants (e.g. bone area and bone-implant contact) were greater than 0.980 and 0.947, respectively. The testing AUC values for the classification of each tissue phenotype were ranging from 0.90 to 0.99. The DLN reduced hours of simulation time to seconds. Moreover, our DLN is explainable via Deep Taylor decomposition, suggesting that the transverse fluid velocity, upper and lower parts of dental implants are the keys that influence bone healing and the distribution of tissue phenotypes the most. Many examples of commercial dental implants with designs which follow these design strategies can be found. This work demonstrates that DLN with proper network design is capable to replace complex, time-dependent, multi-physical models/theories, as well as to reveal the underlying features without prior professional knowledge.Chia-Wei HsuAn-Cheng YangPei-Ching KungNien-Ti TsouNan-Yow ChenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chia-Wei Hsu An-Cheng Yang Pei-Ching Kung Nien-Ti Tsou Nan-Yow Chen Engineer design process assisted by explainable deep learning network
description	Abstract Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design. In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process. The process is based on a complex, time-dependent, multi-physical biomechanical theory, known as mechano-regulatory method. It has been used to evaluate the performance of dental implants and to assess the tissue recovery after the oral surgery procedures. We provided a deep learning network (DLN) with calibrated simulation data that came from different simulation conditions with experimental verification. The DLN achieves nearly exact result of simulated bone healing history around implants. The correlation of the predicted essential physical properties of surrounding bones (e.g. strain and fluid velocity) and performance indexes of implants (e.g. bone area and bone-implant contact) were greater than 0.980 and 0.947, respectively. The testing AUC values for the classification of each tissue phenotype were ranging from 0.90 to 0.99. The DLN reduced hours of simulation time to seconds. Moreover, our DLN is explainable via Deep Taylor decomposition, suggesting that the transverse fluid velocity, upper and lower parts of dental implants are the keys that influence bone healing and the distribution of tissue phenotypes the most. Many examples of commercial dental implants with designs which follow these design strategies can be found. This work demonstrates that DLN with proper network design is capable to replace complex, time-dependent, multi-physical models/theories, as well as to reveal the underlying features without prior professional knowledge.
format	article
author	Chia-Wei Hsu An-Cheng Yang Pei-Ching Kung Nien-Ti Tsou Nan-Yow Chen
author_facet	Chia-Wei Hsu An-Cheng Yang Pei-Ching Kung Nien-Ti Tsou Nan-Yow Chen
author_sort	Chia-Wei Hsu
title	Engineer design process assisted by explainable deep learning network
title_short	Engineer design process assisted by explainable deep learning network
title_full	Engineer design process assisted by explainable deep learning network
title_fullStr	Engineer design process assisted by explainable deep learning network
title_full_unstemmed	Engineer design process assisted by explainable deep learning network
title_sort	engineer design process assisted by explainable deep learning network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2231ad4cc8fb4f7dbf4e1587f08fd1fb
work_keys_str_mv	AT chiaweihsu engineerdesignprocessassistedbyexplainabledeeplearningnetwork AT anchengyang engineerdesignprocessassistedbyexplainabledeeplearningnetwork AT peichingkung engineerdesignprocessassistedbyexplainabledeeplearningnetwork AT nientitsou engineerdesignprocessassistedbyexplainabledeeplearningnetwork AT nanyowchen engineerdesignprocessassistedbyexplainabledeeplearningnetwork
_version_	1718419051487690752

Engineer design process assisted by explainable deep learning network

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