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 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.

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Author:Philipp LechnerORCiDGND, Philipp Heinle, Christoph Hartmann, Constantin Bauer, Benedikt Kirchebner, Fabian Dobmeier, Wolfram Volk
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/109206
Parent Title (English):Applied Sciences
Publisher:MDPI AG
Date of first Publication:2021/11/12
Publishing Institution:Universität Augsburg
Release Date:2023/11/15
Tag:Fluid Flow and Transfer Processes; Computer Science Applications; Process Chemistry and Technology; General Engineering; Instrumentation; General Materials Science
First Page:10672
Institutes:Mathematisch-Naturwissenschaftlich-Technische Fakultät
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management / Juniorprofessur für Data-driven Materials Processing
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 50 Naturwissenschaften / 500 Naturwissenschaften und Mathematik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)