Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks

The clogging of piezoelectric nozzles is a typical problem in various additive binder jetting processes, such as the manufacturing of casting molds. This work aims at print head monitoring in these binder jetting processes. The structure-born noise of piezoelectric print modules is analyzed with an...

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Autores principales:	Philipp Lechner, Philipp Heinle, Christoph Hartmann, Constantin Bauer, Benedikt Kirchebner, Fabian Dobmeier, Wolfram Volk
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	acoustic monitoring structure-born noise binder jetting core materials water-glass neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/af84aff0e17442789aae5ce8bc74a002
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id	oai:doaj.org-article:af84aff0e17442789aae5ce8bc74a002
record_format	dspace
spelling	oai:doaj.org-article:af84aff0e17442789aae5ce8bc74a0022021-11-25T16:34:55ZFeasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks10.3390/app1122106722076-3417https://doaj.org/article/af84aff0e17442789aae5ce8bc74a0022021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10672https://doaj.org/toc/2076-3417The clogging of piezoelectric nozzles is a typical problem in various additive binder jetting processes, such as the manufacturing of casting molds. This work aims at print head monitoring in these binder jetting processes. The structure-born noise of piezoelectric print modules is analyzed with an Artificial Neural Network to classify whether the nozzles are functional or clogged. The acoustic data are studied in the frequency domain and utilized as input for an Artificial Neural Network. We found that it is possible to successfully classify individual nozzles well enough to implement a print head monitoring, which automatically determines whether the print head needs maintenance.Philipp LechnerPhilipp HeinleChristoph HartmannConstantin BauerBenedikt KirchebnerFabian DobmeierWolfram VolkMDPI AGarticleacoustic monitoringstructure-born noisebinder jettingcore materialswater-glassneural networksTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10672, p 10672 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	acoustic monitoring structure-born noise binder jetting core materials water-glass neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	acoustic monitoring structure-born noise binder jetting core materials water-glass neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Philipp Lechner Philipp Heinle Christoph Hartmann Constantin Bauer Benedikt Kirchebner Fabian Dobmeier Wolfram Volk Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks
description	The clogging of piezoelectric nozzles is a typical problem in various additive binder jetting processes, such as the manufacturing of casting molds. This work aims at print head monitoring in these binder jetting processes. The structure-born noise of piezoelectric print modules is analyzed with an Artificial Neural Network to classify whether the nozzles are functional or clogged. The acoustic data are studied in the frequency domain and utilized as input for an Artificial Neural Network. We found that it is possible to successfully classify individual nozzles well enough to implement a print head monitoring, which automatically determines whether the print head needs maintenance.
format	article
author	Philipp Lechner Philipp Heinle Christoph Hartmann Constantin Bauer Benedikt Kirchebner Fabian Dobmeier Wolfram Volk
author_facet	Philipp Lechner Philipp Heinle Christoph Hartmann Constantin Bauer Benedikt Kirchebner Fabian Dobmeier Wolfram Volk
author_sort	Philipp Lechner
title	Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks
title_short	Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks
title_full	Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks
title_fullStr	Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks
title_full_unstemmed	Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks
title_sort	feasibility of acoustic print head monitoring for binder jetting processes with artificial neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/af84aff0e17442789aae5ce8bc74a002
work_keys_str_mv	AT philipplechner feasibilityofacousticprintheadmonitoringforbinderjettingprocesseswithartificialneuralnetworks AT philippheinle feasibilityofacousticprintheadmonitoringforbinderjettingprocesseswithartificialneuralnetworks AT christophhartmann feasibilityofacousticprintheadmonitoringforbinderjettingprocesseswithartificialneuralnetworks AT constantinbauer feasibilityofacousticprintheadmonitoringforbinderjettingprocesseswithartificialneuralnetworks AT benediktkirchebner feasibilityofacousticprintheadmonitoringforbinderjettingprocesseswithartificialneuralnetworks AT fabiandobmeier feasibilityofacousticprintheadmonitoringforbinderjettingprocesseswithartificialneuralnetworks AT wolframvolk feasibilityofacousticprintheadmonitoringforbinderjettingprocesseswithartificialneuralnetworks
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Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks

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