Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting

As one of the most promising metal additive manufacturing (AM) technologies, the selective laser melting (SLM) process has high expectations ofr its use in aerospace, medical, and other fields. However, various defects such as spatter, crack, and porosity seriously hinder the applications of the SLM...

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Autores principales:	Shuyang Luo, Xiuquan Ma, Jie Xu, Menglei Li, Longchao Cao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	selective laser melting process acoustic signal deep learning Spatter monitoring Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/8d655a9aa96e49fb8af97659bf163073
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id	oai:doaj.org-article:8d655a9aa96e49fb8af97659bf163073
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spelling	oai:doaj.org-article:8d655a9aa96e49fb8af97659bf1630732021-11-11T19:10:10ZDeep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting10.3390/s212171791424-8220https://doaj.org/article/8d655a9aa96e49fb8af97659bf1630732021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7179https://doaj.org/toc/1424-8220As one of the most promising metal additive manufacturing (AM) technologies, the selective laser melting (SLM) process has high expectations ofr its use in aerospace, medical, and other fields. However, various defects such as spatter, crack, and porosity seriously hinder the applications of the SLM process. In situ monitoring is a vital technique to detect the defects in advance, which is expected to reduce the defects. This work proposed a method that combined acoustic signals with a deep learning algorithm to monitor the spatter behaviors. The acoustic signals were recorded by a microphone and the spatter information was collected by a coaxial high-speed camera simultaneously. The signals were divided into two types according to the number and intensity of spatter during the SLM process with different combinations of processing parameters. Deep learning models, one-dimensional Convolutional Neural Network (1D-CNN), two-dimensional Convolutional Neural Network (2D-CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were trained to establish the relationships between the acoustic signals and characteristics of spatter. After K-fold verification, the highest classification confidence of models is 85.08%. This work demonstrates that it is feasible to use acoustic signals in monitoring the spatter defect during the SLM process. It is possible to use cheap and simple microphones instead of expensive and complicated high-speed cameras for monitoring spatter behaviors.Shuyang LuoXiuquan MaJie XuMenglei LiLongchao CaoMDPI AGarticleselective laser melting processacoustic signaldeep learningSpattermonitoringChemical technologyTP1-1185ENSensors, Vol 21, Iss 7179, p 7179 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	selective laser melting process acoustic signal deep learning Spatter monitoring Chemical technology TP1-1185
spellingShingle	selective laser melting process acoustic signal deep learning Spatter monitoring Chemical technology TP1-1185 Shuyang Luo Xiuquan Ma Jie Xu Menglei Li Longchao Cao Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting
description	As one of the most promising metal additive manufacturing (AM) technologies, the selective laser melting (SLM) process has high expectations ofr its use in aerospace, medical, and other fields. However, various defects such as spatter, crack, and porosity seriously hinder the applications of the SLM process. In situ monitoring is a vital technique to detect the defects in advance, which is expected to reduce the defects. This work proposed a method that combined acoustic signals with a deep learning algorithm to monitor the spatter behaviors. The acoustic signals were recorded by a microphone and the spatter information was collected by a coaxial high-speed camera simultaneously. The signals were divided into two types according to the number and intensity of spatter during the SLM process with different combinations of processing parameters. Deep learning models, one-dimensional Convolutional Neural Network (1D-CNN), two-dimensional Convolutional Neural Network (2D-CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were trained to establish the relationships between the acoustic signals and characteristics of spatter. After K-fold verification, the highest classification confidence of models is 85.08%. This work demonstrates that it is feasible to use acoustic signals in monitoring the spatter defect during the SLM process. It is possible to use cheap and simple microphones instead of expensive and complicated high-speed cameras for monitoring spatter behaviors.
format	article
author	Shuyang Luo Xiuquan Ma Jie Xu Menglei Li Longchao Cao
author_facet	Shuyang Luo Xiuquan Ma Jie Xu Menglei Li Longchao Cao
author_sort	Shuyang Luo
title	Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting
title_short	Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting
title_full	Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting
title_fullStr	Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting
title_full_unstemmed	Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting
title_sort	deep learning based monitoring of spatter behavior by the acoustic signal in selective laser melting
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8d655a9aa96e49fb8af97659bf163073
work_keys_str_mv	AT shuyangluo deeplearningbasedmonitoringofspatterbehaviorbytheacousticsignalinselectivelasermelting AT xiuquanma deeplearningbasedmonitoringofspatterbehaviorbytheacousticsignalinselectivelasermelting AT jiexu deeplearningbasedmonitoringofspatterbehaviorbytheacousticsignalinselectivelasermelting AT mengleili deeplearningbasedmonitoringofspatterbehaviorbytheacousticsignalinselectivelasermelting AT longchaocao deeplearningbasedmonitoringofspatterbehaviorbytheacousticsignalinselectivelasermelting
_version_	1718431597442629632

Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting

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