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Recommendation System with Artificial Intelligence for Welding Quality Improvement

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Intelligence of Things: Technologies and Applications (ICIT 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 148))

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Abstract

This paper proposes the recommendation system (RS) to support the welding quality improvement system (WQIS) with artificial intelligence (AI). The proposed RS is applied to make suggestions for beginners and experienced workers about the welding quality predictions based on weld button size and the welding quality improvement by increasing welding current. The goal of the paper aims to develop an RS that has the ability to learn, analyze, predict and make these suggestions to humans through AI. Support Vector Machines (SVM) have been employed to predict the welding quality with impact parameters such as instantaneous (IHR), electrode tip diameter (De), and the status of welding current (\(I_w\)) during the operation of the resistance spot welding (RSW) machine. The practical experiments are set up with an RSW machine using an AC inverter on Galvanized (GI) steel to collect the dataset for the SVM model. Through the experimental result, the effectiveness of the utility application is validated. In addition, these experimental results should be helpful for developing the high-performance RSW machine with AI applications in practice.

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Author information

Authors and Affiliations

  1. FPT University, Da Nang, 550000, Vietnam

    Trung Tin Tran

  2. Dong-A University, Busan, 49315, South Korea

    Jang Hee-Dong

  3. Vietnam-Korea University of Information and Communication Technology, Da Nang, 550000, Vietnam

    Anh Quang Nguyen Vu

Authors
  1. Trung Tin Tran
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  2. Jang Hee-Dong
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  3. Anh Quang Nguyen Vu
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Corresponding author

Correspondence to Anh Quang Nguyen Vu .

Editor information

Editors and Affiliations

  1. Wroclaw University of Science and Technology, Wrocław, Poland

    Ngoc-Thanh Nguyen

  2. Sejong University, Seoul, Korea (Republic of)

    Nhu-Ngoc Dao

  3. Vietnam National University of Agriculture, Hanoi, Vietnam

    Quang-Dung Pham

  4. Hanoi University of Mining and Geology, Hanoi, Vietnam

    Hong Anh Le

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Tran, T.T., Hee-Dong, J., Vu, A.Q.N. (2022). Recommendation System with Artificial Intelligence for Welding Quality Improvement. In: Nguyen, NT., Dao, NN., Pham, QD., Le, H.A. (eds) Intelligence of Things: Technologies and Applications . ICIT 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 148. Springer, Cham. https://doi.org/10.1007/978-3-031-15063-0_21

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