Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Ren, Zhongshu; Shao, Jiayun; Liu, Haolin; Clark, Samuel J.; Gao, Lin; Balderson, Lilly; Mumm, Kyle; Fezzaa, Kamel; Rollett, Anthony D.; Kara, Levent Burak; Sun, Tao

doi:10.1088/2752-5724/ad89e2

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Journal Article · Wed Nov 13 23:00:00 EST 2024 · Materials Futures

DOI:https://doi.org/10.1088/2752-5724/ad89e2· OSTI ID:2560442

Ren, Zhongshu ^[1]; ^[2]; Liu, Haolin ^[3]; Clark, Samuel J. ^[4]; Gao, Lin ^[5]; Balderson, Lilly ^[6]; Mumm, Kyle ^[1]; Fezzaa, Kamel ^[4]; Rollett, Anthony D. ^[3]; Kara, Levent Burak ^[3]; ^[1]

Northwestern Univ., Evanston, IL (United States); Univ. of Virginia, Charlottesville, VA (United States)
Northwestern Univ., Evanston, IL (United States)
Carnegie Mellon Univ., Pittsburgh, PA (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
Univ. of Virginia, Charlottesville, VA (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States)
Univ. of Virginia, Charlottesville, VA (United States)

Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100 kHz and 20 kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States). Advanced Photon Source (APS)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Aeronautics and Space Administration (NASA)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 2560442

Journal Information:: Materials Futures, Journal Name: Materials Futures Journal Issue: 4 Vol. 3; ISSN 2752-5724

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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