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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Main Authors: Eva Maria Scheideler*, Andrea Huxol
Format: Article
Language:English
Published: University North 2020-01-01
Series:Tehnički Glasnik
Subjects:
Online Access:https://hrcak.srce.hr/file/346965
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spelling 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
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