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

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

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Zhongshu Ren, Jiayun Shao, Haolin Liu, Samuel J. Clark, Lin Gao, Lilly Balderson, Kyle Mumm, Kamel Fezzaa, Anthony D. Rollett, Levent Burak Kara, Tao Sun. Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning[J]. Materials Futures. doi: 10.1088/2752-5724/ad89e2
引用本文: Zhongshu Ren, Jiayun Shao, Haolin Liu, Samuel J. Clark, Lin Gao, Lilly Balderson, Kyle Mumm, Kamel Fezzaa, Anthony D. Rollett, Levent Burak Kara, Tao Sun. Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning[J]. Materials Futures. doi: 10.1088/2752-5724/ad89e2
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Zhongshu Ren, Jiayun Shao, Haolin Liu, Samuel J. Clark, Lin Gao, Lilly Balderson, Kyle Mumm, Kamel Fezzaa, Anthony D. Rollett, Levent Burak Kara, Tao Sun. Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning[J]. Materials Futures. doi: 10.1088/2752-5724/ad89e2
Citation: Zhongshu Ren, Jiayun Shao, Haolin Liu, Samuel J. Clark, Lin Gao, Lilly Balderson, Kyle Mumm, Kamel Fezzaa, Anthony D. Rollett, Levent Burak Kara, Tao Sun. Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning[J]. Materials Futures. doi: 10.1088/2752-5724/ad89e2
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OPEN ACCESS

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

doi: 10.1088/2752-5724/ad89e2

  • 1 Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208;

  • 2 Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904;

  • 3 Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213;

  • 4 X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439;

  • 5 Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA 15213

基金项目: 

This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science user facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. ADR and TS acknowledge partial support from a NASA Science Technology Research Institute under Grant Number 80NSSC23K1342.

详细信息
    通讯作者:

    Zhongshu Ren,E-mail:zren2@bnl.gov

    Tao Sun,E-mail:taosun@northwestern.edu

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

  • 1 Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208;

  • 2 Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904;

  • 3 Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213;

  • 4 X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439;

  • 5 Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA 15213

Funds: 

This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science user facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. ADR and TS acknowledge partial support from a NASA Science Technology Research Institute under Grant Number 80NSSC23K1342.

    摘要
  • 摘要: 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 100kHz and 20kHz 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.

     

    Abstract: 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 100kHz and 20kHz 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.

     

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