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    <title language="eng">From Thermographic In-situ Monitoring to Porosity Detection – A Deep Learning Framework for Quality Control in Laser Powder Bed Fusion</title>
    <abstract language="eng">In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk.</abstract>
    <parentTitle language="eng">SMSI - Sensor and Measurement Science International - Proceedings</parentTitle>
    <identifier type="url">https://www.ama-science.org/proceedings/details/4404</identifier>
    <identifier type="doi">10.5162/SMSI2023/C5.4</identifier>
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    <author>Simon Oster</author>
    <author>Nils Scheuschner</author>
    <author>Keerthana Chand</author>
    <author>Philipp Peter Breese</author>
    <author>Tina Becker</author>
    <author>F. Heinrichsdorff</author>
    <author>Simon Altenburg</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Porosity</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser powder bed fusion</value>
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    <subject>
      <language>eng</language>
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      <value>In-situ monitoring</value>
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      <language>eng</language>
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      <value>Thermography</value>
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      <language>eng</language>
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    <title language="eng">In-situ defect detection via active laser thermographic testing for PBF-LB/M</title>
    <abstract language="eng">Great complexity characterizes Additive Manufacturing (AM) of metallic components via laser powder bed fusion (PBF-LB/M). Due to this, defects in the printed components (like cracks and pores) are still common. Monitoring methods are commercially used, but the relationship between process data and defect formation is not well understood yet. Furthermore, defects and deformations might develop with a temporal delay to the laser energy input. The component’s actual quality is consequently only determinable after the finished process.&#13;
&#13;
To overcome this drawback, thermographic in-situ testing is introduced. The defocused process laser is utilized for nondestructive testing performed layer by layer throughout the build process. The results of the defect detection via infrared cameras are shown for a research PBF-LB/M machine.&#13;
&#13;
This creates the basis for a shift from in-situ monitoring towards in-situ testing during the AM process. Defects are detected immediately inside the process chamber, and the actual component quality is determined.</abstract>
    <parentTitle language="eng">LiM 2023 Proceedings</parentTitle>
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    <author>Philipp Peter Breese</author>
    <author>Tina Becker</author>
    <author>Simon Oster</author>
    <author>C. Metz</author>
    <author>Simon Altenburg</author>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>Additive manufacturing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser powder bed fusion</value>
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    <subject>
      <language>eng</language>
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      <value>Nondestructive testing</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser thermography</value>
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    <subject>
      <language>eng</language>
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      <value>Defect detection</value>
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    <title language="eng">In-situ monitoring of the laser powder bed fusion process by thermography, optical tomography and melt pool monitoring for defect detection</title>
    <abstract language="eng">For the wide acceptance of the use of additive manufacturing (AM), it is required to provide reliable testing methods to ensure the safety of the additively manufactured parts. A possible solution could be the deployment of in-situ monitoring during the build process. However, for laser powder bed fusion using metal powders (PBF-LB/M) only a few in-situ monitoring techniques are commercially available (optical tomography, melt pool monitoring), which have not been researched to an extent that allows to guarantee the adherence to strict quality and safety standards.&#13;
In this contribution, we present results of a study of PBF-LB/M printed parts made of the nickel-based superalloy Haynes 282. The formation of defects was provoked by local variations of the process parameters and monitored by thermography, optical tomography and melt pool monitoring. Afterwards, the defects were characterized by computed tomography (CT) to identify the detection limits of the used in-situ techniques.</abstract>
    <parentTitle language="eng">Lasers in Manufacturing Conference 2023</parentTitle>
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    <author>Nils Scheuschner</author>
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    <author>E. Uhlmann</author>
    <author>J. Polte</author>
    <author>A. Gordei</author>
    <author>Kai Hilgenberg</author>
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      <value>Thermography</value>
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    <subject>
      <language>eng</language>
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      <value>Optical tomography</value>
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      <value>Melt-pool-monitoring</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser powder bed fusion</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Haynes 282</value>
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    <subject>
      <language>eng</language>
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      <value>Additive Manufacturing</value>
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    <collection role="themenfelder" number="">Additive Fertigung</collection>
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    <title language="eng">From Thermographic In-situ Monitoring to Porosity Detection – A Deep Learning Framework for Quality Control in Laser Powder Bed Fusion</title>
    <abstract language="eng">In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk.</abstract>
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    <author>Simon Oster</author>
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      <value>Porosity</value>
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    <title language="eng">Comparison of NIR and SWIR thermography for defect detection in Laser Powder Bed Fusion</title>
    <abstract language="eng">Since laser powder bed fusion (PBF-LB/M) is prone to the formation of defects during the building process, a fundamental requirement for widespread application is to find ways to assure safety and reliability of the additively manufactured parts. A possible solution for this problem lies in the usage of in-situ thermographic monitoring for defect detection. In this contribution we investigate possibilities and limitations of the VIS/NIR wavelength range for defect detection. A VIS/NIR camera can be based on conventional silicon-based sensors which typically have much higher spatial and temporal resolution in the same price range but are more limited in the detectable temperature range than infrared sensors designed for longer wavelengths. To investigate the influence, we compared the thermographic signatures during the creation of artificially provoked defects by local parameter variations in test specimens made of a nickel alloy (UNS N07208) for two different wavelength ranges (~980 nm and ~1600 nm).</abstract>
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