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A framework for a knowledge based cold spray repairing system

Author

Listed:
  • Hongjian Wu

    (ICB-PMDM-LERMPS UMR 6303, CNRS, UTBM, Université de Bourgogne Franche-Comté)

  • Shaowu Liu

    (ICB-PMDM-LERMPS UMR 6303, CNRS, UTBM, Université de Bourgogne Franche-Comté)

  • Xinliang Xie

    (ICB-COMM UMR 6303, CNRS, UTBM, Université de Bourgogne Franche-Comté)

  • Yicha Zhang

    (ICB-COMM UMR 6303, CNRS, UTBM, Université de Bourgogne Franche-Comté)

  • Hanlin Liao

    (ICB-PMDM-LERMPS UMR 6303, CNRS, UTBM, Université de Bourgogne Franche-Comté)

  • Sihao Deng

    (ICB-PMDM-LERMPS UMR 6303, CNRS, UTBM, Université de Bourgogne Franche-Comté)

Abstract

Restoring damaged components is a very promising and high-value project, which enable to save a lot of production time and cost, and thus has already attracted wide attention, especially in the aviation industry. In the past few years, cold spray (CS) had been widely adopted in restoration and repair applications due to its unique advantages, such as no thermal influence, high efficiency, flexibility, etc. Nowadays, speeding up the product lifecycle as well as improving the accuracy and reliability of CS based repairing require an advance strategy with higher efficiency and more agility. To respond to this need, in this article, a concept on the development of a knowledge based intelligent CS repairing framework is presented. The framework includes a 3D scanning system for providing the information needed on the partially damaged part to repair, a dynamic defect repairing knowledge base for providing related standard defect geometry repairing strategy, including matching the standardized defect geometry, machining pre-treatment, CS toolpath, CS parameter setting, etc., and a CS additive repair system involving robotic repair trajectory programming, simulation and material deposition. Based on the proposed framework, the design of intelligent CS additive repair system and its flow are explained. The novel repair strategy and method proposed in this article can become a model for the metal repair industry in the future.

Suggested Citation

  • Hongjian Wu & Shaowu Liu & Xinliang Xie & Yicha Zhang & Hanlin Liao & Sihao Deng, 2022. "A framework for a knowledge based cold spray repairing system," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1639-1647, August.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-021-01770-7
    DOI: 10.1007/s10845-021-01770-7
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    References listed on IDEAS

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    1. A. Garg & Jasmine Siu Lee Lam & M. M. Savalani, 2018. "Laser power based surface characteristics models for 3-D printing process," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1191-1202, August.
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