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In situ monitoring plasma arc additive manufacturing process with a fully convolutional network

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Abstract

Plasma arc additive manufacturing (PAM) is an additive manufacturing technology that has been widely used in the past and has practical applications in many fields. The non-destructive testing methodology of PAM workpiece quality monitoring requires high level of accuracy and real-time capability. The characteristics of the melt pool and plasma arc are the keys to characterizing the dynamic manufacturing process during the powder feed PAM process, allowing for process prediction and real-time feedback control. This paper describes a new image recognition system that uses a fully convolutional network (FCN) to acquire melt pool and plasma arc morphologies simultaneously. Its image segmentation performance is compared with four conventional methods and three artificial intelligence methods. Results show that the FCN method can extract melt pool and plasma arc quickly and accurately, even in a complex manufacturing environment. The image segmentation-based FCN’s accuracy is 95.1%, and the average processing time is only 84 ms, according to the results. The performance is far superior to the existing seven methods. The relationship between average captured areas (melt pool and plasma arc) and parameters of the plasma arc (current intensity and scanning speed) is then analyzed. Finally, the quality of products in terms of sample surface roughness is measured, and its relationship with the average areas of the melt pool and plasma arc is clarified.

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Funding

This work was supported by the Key-Area Research and Development Program of Guangdong Province, China (2018B090905001); the Key Research and Development Program of Sichuan Province, China (2020YFSY0054); and the Key Research and Development Program of Hubei province, China (2020BAB045).

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Authors and Affiliations

  1. The Institute of Technological Sciences, Wuhan University, Wuhan, 430074, China

    Yikai Zhang, Jiqian Mi, Hui Li & Shengnan Shen

  2. School of Power and Mechanical Engineering, Wuhan University, Wuhan, 430072, China

    Hui Li & Shengnan Shen

  3. Research Institute of Wuhan University, Shenzhen, Shenzhen, 518057, China

    Hui Li & Shengnan Shen

  4. School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, 510641, China

    Yongqiang Yang & Changhui Song

  5. Science and Technology On Plasma Dynamics Laboratory, Air Force Engineering University, Xi’an, 710038, China

    Xin Zhou

Authors
  1. Yikai Zhang
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  2. Jiqian Mi
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  3. Hui Li
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  4. Shengnan Shen
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  5. Yongqiang Yang
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  6. Changhui Song
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  7. Xin Zhou
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Contributions

Yikai Zhang: Conceptualization, Methodology, Formal analysis. Jiqian Mi: Data Curation, Visualization. Hui Li: Writing-Review and Editing, Supervision. Shengnan Shen: Writing-Review and Editing. Yongqiang Yang: Writing-Review and Editing, Supervision. Changhui Song: Writing-Review and Editing. Xin Zhou: Writing-Review and Editing.

Corresponding authors

Correspondence to Hui Li or Yongqiang Yang.

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Zhang, Y., Mi, J., Li, H. et al. In situ monitoring plasma arc additive manufacturing process with a fully convolutional network. Int J Adv Manuf Technol 120, 2247–2257 (2022). https://doi.org/10.1007/s00170-022-08929-3

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  • DOI: https://doi.org/10.1007/s00170-022-08929-3

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