In-Situ Process Monitoring in Additive Manufacturing Using Statistics and Pre-Process Data
Long computation times are a major obstacle for the application of in-situ monitoring in additive manufacturing. This paper presents rapid in-situ monitoring, which returns a control value within typical build times. Observing powder bed fusion processes reveals that unsuitable parameter settings in...
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Online Access: | https://hrcak.srce.hr/file/346965 |
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doaj-ba6e78979071410692cb4767b39858b02020-11-25T02:39:56ZengUniversity NorthTehnički Glasnik1846-61681848-55882020-01-01142180185In-Situ Process Monitoring in Additive Manufacturing Using Statistics and Pre-Process DataEva Maria Scheideler*0Andrea Huxol1Department of Production Engineering, TH-OWL University of Applied Sciences and Arts, Campusallee 12, D 32657 Lemgo, GermanyQuality Assurance, Hora Holter Regelarmaturen GmbH & Co. KG, Helleforthstraße 58-60, D 33758 Schloß Holte-Stukenbrock, GermanyLong computation times are a major obstacle for the application of in-situ monitoring in additive manufacturing. This paper presents rapid in-situ monitoring, which returns a control value within typical build times. Observing powder bed fusion processes reveals that unsuitable parameter settings influence the appearance of the molten surface and the surrounding powder bed. The presented research approach evaluates the changing appearance of the exposed layers, in combination with the information from the pre-process about the position and geometry of the components in each layer. Grayscale images are captured with the build envelope camera and examined regarding the grayscale distribution in the critical areas surrounding the component boundaries. The grayscale distribution is then used to predict product quality by using standard statistical methods. The combination of the pre-process data and the fast analysis of the grayscale distribution allows promptly calculating a performance indicator for required process intervention and control.https://hrcak.srce.hr/file/346965energy densityimage processinglayer checkpart qualitySelective Laser Melting |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Eva Maria Scheideler* Andrea Huxol |
spellingShingle |
Eva Maria Scheideler* Andrea Huxol In-Situ Process Monitoring in Additive Manufacturing Using Statistics and Pre-Process Data Tehnički Glasnik energy density image processing layer check part quality Selective Laser Melting |
author_facet |
Eva Maria Scheideler* Andrea Huxol |
author_sort |
Eva Maria Scheideler* |
title |
In-Situ Process Monitoring in Additive Manufacturing Using Statistics and Pre-Process Data |
title_short |
In-Situ Process Monitoring in Additive Manufacturing Using Statistics and Pre-Process Data |
title_full |
In-Situ Process Monitoring in Additive Manufacturing Using Statistics and Pre-Process Data |
title_fullStr |
In-Situ Process Monitoring in Additive Manufacturing Using Statistics and Pre-Process Data |
title_full_unstemmed |
In-Situ Process Monitoring in Additive Manufacturing Using Statistics and Pre-Process Data |
title_sort |
in-situ process monitoring in additive manufacturing using statistics and pre-process data |
publisher |
University North |
series |
Tehnički Glasnik |
issn |
1846-6168 1848-5588 |
publishDate |
2020-01-01 |
description |
Long computation times are a major obstacle for the application of in-situ monitoring in additive manufacturing. This paper presents rapid in-situ monitoring, which returns a control value within typical build times. Observing powder bed fusion processes reveals that unsuitable parameter settings influence the appearance of the molten surface and the surrounding powder bed. The presented research approach evaluates the changing appearance of the exposed layers, in combination with the information from the pre-process about the position and geometry of the components in each layer. Grayscale images are captured with the build envelope camera and examined regarding the grayscale distribution in the critical areas surrounding the component boundaries. The grayscale distribution is then used to predict product quality by using standard statistical methods. The combination of the pre-process data and the fast analysis of the grayscale distribution allows promptly calculating a performance indicator for required process intervention and control. |
topic |
energy density image processing layer check part quality Selective Laser Melting |
url |
https://hrcak.srce.hr/file/346965 |
work_keys_str_mv |
AT evamariascheideler insituprocessmonitoringinadditivemanufacturingusingstatisticsandpreprocessdata AT andreahuxol insituprocessmonitoringinadditivemanufacturingusingstatisticsandpreprocessdata |
_version_ |
1724783944741683200 |