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Automatic high-frequency induction brazing through an ensembled detection with heterogenous sensor measurements

Author

Listed:
  • Joonhyeok Moon

    (Hanyang University)

  • Min-Gwan Kim

    (Hanyang University)

  • Ok Hyun Kang

    (LG Electronics)

  • Heejong Lee

    (LG Electronics)

  • Ki-Yong Oh

    (Hanyang University
    Hanyang University)

Abstract

This study proposes a new method to estimate the state of the high-frequency induction brazing by using the ensembled Rotational multi-pyramid-transformer tiny (RoMP-T2). The proposed method aims to identify the exact state of an induction brazing process because this information is effective to develop an automatic control system of an induction brazing machine. The proposed state estimation method features three characteristics. First, the method addresses a novel neural network for object detection titled the RoMP-T2. This neural network includes a rotational bounding box, multilevel and multiscale feature extraction module, and pyramid vision transformer, which effectively extract features highly correlated to an inducing brazing process from images. Second, the ensembled architecture of the RoMP-T2 is addressed to extract features from both optical and thermal images. Bayesian optimization was also addressed to optimize hyperparameters in the ensembled architecture of the RoMP-T2. Hence, the ensembled RoMP-T2 compensates features extracted from each optical and thermal images, accurately detecting an exact state and location of the filler material during an induction brazing process. Third, the proposed method addresses a cumulative alarm (CA) for determining the completion of the brazing process. The CA significantly reduces a false alarm rate, securing high safety and reliability when the proposed method is implemented to an automation process of the high-frequency induction brazing. An analysis on experiments with optical and thermal images reveals that the ensembled architecture secures the highest accuracy by compensating a limit of feature extraction from each optical and thermal image. The quantitative comparison of the RoMP-T2 with other base-line neural networks confirms that the proposed neural network outperforms other neutral networks in both accuracy and robustness perspectives. Furthermore, systematic analysis on experiments reveals that the CA significantly decreases a false alarm rate and thereby increases productivity. These experimental evidences confirm that the proposed framework would be effective to develop an active management system of an induction brazing process, which would be indispensable for manufacturing process automation in a smart factory.

Suggested Citation

  • Joonhyeok Moon & Min-Gwan Kim & Ok Hyun Kang & Heejong Lee & Ki-Yong Oh, 2025. "Automatic high-frequency induction brazing through an ensembled detection with heterogenous sensor measurements," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2439-2459, April.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02345-y
    DOI: 10.1007/s10845-024-02345-y
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    References listed on IDEAS

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    1. Fekri, Mohammad Navid & Patel, Harsh & Grolinger, Katarina & Sharma, Vinay, 2021. "Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network," Applied Energy, Elsevier, vol. 282(PA).
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