Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks
| Autor: | Christoph Hartmann, Wolfram Volk, Philipp Heinle, Benedikt Kirchebner, Fabian Dobmeier, Constantin Bauer, Philipp Lechner |
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| Přispěvatelé: | Publica |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Computer science QH301-705.5 QC1-999 Nozzle Mechanical engineering General Materials Science binder jetting Biology (General) Instrumentation QD1-999 Fluid Flow and Transfer Processes Artificial neural network Process Chemistry and Technology Physics General Engineering neural networks Engineering (General). Civil engineering (General) structure-born noise Computer Science Applications core materials Noise Chemistry Casting (metalworking) Frequency domain Head (vessel) acoustic monitoring TA1-2040 water-glass |
| Zdroj: | Applied Sciences, Vol 11, Iss 10672, p 10672 (2021) Applied Sciences Volume 11 Issue 22 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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