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Investigation into the optical emission of features for powder-bed fusion AM process monitoring

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Abstract

Process monitoring and control is an essential approach to improve additive manufacturing (AM) built quality. For the development of powder bed fusion (PBF) AM monitoring system, sensing process optical emission is a popular approach. This is because it provides rich information on melt pool condition which directly determines final part quality. However, the optical emission information is convoluted. And lack of full understanding of it limits the further development of an optimal monitoring system. Therefore, the aim of this study is to explore the correlations between the optical emission and the processing condition to help enhance PBF process monitoring. A high-speed camera was used to acquire the images of the optical emission in the waveband of 800–1,000 nm. Several typical features were extracted and analyzed with the increase of laser power. The K-means clustering method was used to identify the hidden patterns of these features. Five hidden patterns have been identified, and therefore the collected dataset was partitioned into five subsets. The extracted features in each subset were characterized. It is found that (1) plume area and plume orientation are the two most crucial features for processing condition monitoring; (2) number of spatters and spatter dispersion index are sensitive to some minor process vibrations which have little effect on built quality. Additionally, the SVM model was built for process quality identification. It is found that (3) the time sequence information of the features can help improve the quality identification performance.

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Acknowledgements

The authors would like to acknowledge the multiple supports from South China University of Technology (SCUT) and National University of Singapore (NUS).

Funding

The work is supported by the Basic and Applied Basic Research Programs of Guangzhou City (No. 202102020680) and the National Key R&D Program of China (grant No. 2021YFE0203500).

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Authors and Affiliations

  1. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China

    Yingjie Zhang, Xiaojun Peng, Zhangdong Chen & Zimeng Jiang

  2. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

    Wentao Yan

  3. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China

    Di Wang

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  1. Yingjie Zhang
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  2. Wentao Yan
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  3. Xiaojun Peng
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  4. Zhangdong Chen
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  5. Zimeng Jiang
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  6. Di Wang
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Contributions

Yingjie Zhang design the experiment, analyze the data, and write the paper. Wentao Yan helps improve the idea and experiment design. Xiaojun Peng helps collect and analyze the data. Zhangdong Chen and Zimeng Jiang help collect the data. Di Wang provides experiment equipment and helps improve the data analyses part.

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Correspondence to Di Wang.

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Zhang, Y., Yan, W., Peng, X. et al. Investigation into the optical emission of features for powder-bed fusion AM process monitoring. Int J Adv Manuf Technol 121, 2291–2303 (2022). https://doi.org/10.1007/s00170-022-09414-7

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  • DOI: https://doi.org/10.1007/s00170-022-09414-7

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