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Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion

Additive manufacturing, in particular the powder bed fusion of metals using a laser beam, has a wide range of possible technical applications. Especially for safety-critical applications, a quality assurance of the components is indispensable. However, time-consuming and costly quality assurance mea...

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Autores principales: Harbig, Jana, Wenzler, David L., Baehr, Siegfried, Kick, Michael K., Merschroth, Holger, Wimmer, Andreas, Weigold, Matthias, Zaeh, Michael F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840304/
https://www.ncbi.nlm.nih.gov/pubmed/35161208
http://dx.doi.org/10.3390/ma15031265
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author Harbig, Jana
Wenzler, David L.
Baehr, Siegfried
Kick, Michael K.
Merschroth, Holger
Wimmer, Andreas
Weigold, Matthias
Zaeh, Michael F.
author_facet Harbig, Jana
Wenzler, David L.
Baehr, Siegfried
Kick, Michael K.
Merschroth, Holger
Wimmer, Andreas
Weigold, Matthias
Zaeh, Michael F.
author_sort Harbig, Jana
collection PubMed
description Additive manufacturing, in particular the powder bed fusion of metals using a laser beam, has a wide range of possible technical applications. Especially for safety-critical applications, a quality assurance of the components is indispensable. However, time-consuming and costly quality assurance measures, such as computer tomography, represent a barrier for further industrial spreading. For this reason, alternative methods for process anomaly detection using process monitoring systems have been developed. However, the defect detection quality of current methods is limited, as single monitoring systems only detect specific process anomalies. Therefore, a new methodology to evaluate the data of multiple monitoring systems is derived using sensor data fusion. Focus was placed on the causes and the appearance of defects in different monitoring systems (photodiodes, on- and off-axis high-speed cameras, and thermography). Based on this, indicators representing characteristics of the process were developed to reduce the data. Finally, deterministic models for the data fusion within a monitoring system and between the monitoring systems were developed. The result was a defect detection of up to 92% of the melt track defects. The methodology was thus able to determine process anomalies and to evaluate the suitability of a specific process monitoring system for the defect detection.
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spelling pubmed-88403042022-02-13 Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion Harbig, Jana Wenzler, David L. Baehr, Siegfried Kick, Michael K. Merschroth, Holger Wimmer, Andreas Weigold, Matthias Zaeh, Michael F. Materials (Basel) Article Additive manufacturing, in particular the powder bed fusion of metals using a laser beam, has a wide range of possible technical applications. Especially for safety-critical applications, a quality assurance of the components is indispensable. However, time-consuming and costly quality assurance measures, such as computer tomography, represent a barrier for further industrial spreading. For this reason, alternative methods for process anomaly detection using process monitoring systems have been developed. However, the defect detection quality of current methods is limited, as single monitoring systems only detect specific process anomalies. Therefore, a new methodology to evaluate the data of multiple monitoring systems is derived using sensor data fusion. Focus was placed on the causes and the appearance of defects in different monitoring systems (photodiodes, on- and off-axis high-speed cameras, and thermography). Based on this, indicators representing characteristics of the process were developed to reduce the data. Finally, deterministic models for the data fusion within a monitoring system and between the monitoring systems were developed. The result was a defect detection of up to 92% of the melt track defects. The methodology was thus able to determine process anomalies and to evaluate the suitability of a specific process monitoring system for the defect detection. MDPI 2022-02-08 /pmc/articles/PMC8840304/ /pubmed/35161208 http://dx.doi.org/10.3390/ma15031265 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Harbig, Jana
Wenzler, David L.
Baehr, Siegfried
Kick, Michael K.
Merschroth, Holger
Wimmer, Andreas
Weigold, Matthias
Zaeh, Michael F.
Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion
title Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion
title_full Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion
title_fullStr Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion
title_full_unstemmed Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion
title_short Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion
title_sort methodology to determine melt pool anomalies in powder bed fusion of metals using a laser beam by means of process monitoring and sensor data fusion
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840304/
https://www.ncbi.nlm.nih.gov/pubmed/35161208
http://dx.doi.org/10.3390/ma15031265
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