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

Journal Article · Mon Jul 11 00:00:00 EDT 2022 · Materials & Design

DOI:https://doi.org/10.1016/j.matdes.2022.110919· OSTI ID:1977418

Gaikwad, Aniruddha ^[1]; Williams, Richard J. ^[2]; de Winton, Harry ^[2]; Bevans, Benjamin D. ^[3]; Smoqi, Ziyad ^[4]; Rao, Prahalada ^[3]; Hooper, Paul A. ^[2]

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1. University of Nebraska, Lincoln, NE (United States); University of Nebraska-Lincoln, Lincoln, NE, USA
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)
4. University of Nebraska, Lincoln, NE (United States)

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).

• Research Organization: University of Nebraska, Lincoln, NE (United States)
• Sponsoring Organization: Atomic Weapons Establishment (AWE); Engineering and Physical Sciences Research Council (EPSRC); National Science Foundation (NSF); USDOE Office of Science (SC)
• Grant/Contract Number: SC0021136
• OSTI ID: 1977418
• Journal Information: Materials & Design, Journal Name: Materials & Design Journal Issue: C Vol. 221; ISSN 0264-1275
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
high-speed melt pool imaging
laser defocus
laser powder bed fusion
melt pool temperature
porosity
sensor data fusion
spatter
thermal lensing