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Deep Learning Approach of Energy Estimation Model of Remote Laser Welding

Author

Listed:
  • Jumyung Um

    (Department of Industrial & Management System Engineering, Kyung Hee University, 1732 Deogyeong-daero, Yongin-si 17104, Korea)

  • Ian Anthony Stroud

    (SkAD Labs SA, Chemin de la Raye 13, 1024 Ecublens, Switzerland)

  • Yong-keun Park

    (Department of Industrial & Management System Engineering, Kyung Hee University, 1732 Deogyeong-daero, Yongin-si 17104, Korea)

Abstract

Due to concerns about energy use in production systems, energy-efficient processes have received much interest from the automotive industry recently. Remote laser welding is an innovative assembly process, but has a critical issue with the energy consumption. Robot companies provide only the average energy use in the technical specification, but process parameters such as robot movement, laser use, and welding path also affect the energy use. Existing literature focuses on measuring energy in standardized conditions in which the welding process is most frequently operated or on modularizing unified blocks in which energy can be estimated using simple calculations. In this paper, the authors propose an integrated approach considering both process variation and machine specification and multiple methods’ comparison. A deep learning approach is used for building the neural network integrated with the effects of process parameters and machine specification. The training dataset used is experimental data measured from a remote laser welding robot producing a car back door assembly. The proposed estimation model is compared with a linear regression approach and shows higher accuracy than other methods.

Suggested Citation

  • Jumyung Um & Ian Anthony Stroud & Yong-keun Park, 2019. "Deep Learning Approach of Energy Estimation Model of Remote Laser Welding," Energies, MDPI, vol. 12(9), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1799-:d:230372
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    References listed on IDEAS

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    1. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
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    Cited by:

    1. Konstantinos Salonitis, 2020. "Energy Efficiency of Manufacturing Processes and Systems—An Introduction," Energies, MDPI, vol. 13(11), pages 1-5, June.
    2. Jumyung Um & Taebyeong Park & Hae-Won Cho & Seung-Jun Shin, 2022. "Operation-Driven Power Analysis of Discrete Process in a Cyber-Physical System Based on a Modularized Factory," Sustainability, MDPI, vol. 14(7), pages 1-20, March.

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