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Title: Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Abstract

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, suchmore » as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).« less

Authors:
 [1];  [2];  [2];  [3];  [1];  [3];  [2]
+ Show Author Affiliations
  1. University of Nebraska, Lincoln, NE (United States)
  2. Imperial College, London (United Kingdom)
  3. University of Nebraska, Lincoln, NE (United States); Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA (United States)
Publication Date:
Research Org.:
Univ. of Nebraska, Lincoln, NE (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation (NSF); Engineering and Physical Sciences Research Council (EPSRC); Atomic Weapons Establishment (AWE)
OSTI Identifier:
1977418
Grant/Contract Number:  
SC0021136; EP/K503733/1, EP/R513052/1; 30338995; OIA-1929172; CMMI-1920245; CMMI-173969; ECCS-2020246; PFI-TT 2044710; CMMI-1752069; CMMI-1719388
Resource Type:
Accepted Manuscript
Journal Name:
Materials & Design
Additional Journal Information:
Journal Volume: 221; Journal Issue: C; Journal ID: ISSN 0264-1275
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; laser powder bed fusion; laser defocus; thermal lensing; porosity; high-speed melt pool imaging; spatter; melt pool temperature; sensor data fusion

Citation Formats

Gaikwad, Aniruddha, Williams, Richard J., de Winton, Harry, Bevans, Benjamin D., Smoqi, Ziyad, Rao, Prahalada, and Hooper, Paul A. Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing. United States: N. p., 2022. Web. doi:10.1016/j.matdes.2022.110919.
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Gaikwad, Aniruddha, Williams, Richard J., de Winton, Harry, Bevans, Benjamin D., Smoqi, Ziyad, Rao, Prahalada, & Hooper, Paul A. Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing. United States. https://doi.org/10.1016/j.matdes.2022.110919
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Gaikwad, Aniruddha, Williams, Richard J., de Winton, Harry, Bevans, Benjamin D., Smoqi, Ziyad, Rao, Prahalada, and Hooper, Paul A. Mon . "Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing". United States. https://doi.org/10.1016/j.matdes.2022.110919. https://www.osti.gov/servlets/purl/1977418.
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@article{osti_1977418,
title = {Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing},
author = {Gaikwad, Aniruddha and Williams, Richard J. and de Winton, Harry and Bevans, Benjamin D. and Smoqi, Ziyad and Rao, Prahalada and Hooper, Paul A.},
abstractNote = {Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).},
doi = {10.1016/j.matdes.2022.110919},
journal = {Materials & Design},
number = C,
volume = 221,
place = {United States},
year = {Mon Jul 11 00:00:00 EDT 2022},
month = {Mon Jul 11 00:00:00 EDT 2022}
}
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https://doi.org/10.1016/j.matdes.2022.110919
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