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A blended empirical shot stream velocity model for improvement of shot peening production

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Abstract

Peening intensity and coverage are vital measurement outputs to quantify the quality of a peening process in the surface enhancement operation of metal parts. In practice, these parameters can only be measured offline upon process completion, which is not suitable for online tracking and operation. Instead, shot stream velocity can be used as a real-time monitoring parameter to bridge operational inputs to the outputs. As such, a robust and accurate shot stream velocity model is needed for real-time tracking. In this study, we propose a blended practical model for shot stream velocity to address the issues. The model is constructed using a regression algorithm based on the blended candidate functions, which are developed from the experimental data and nature of the particle-air flow inside the system. The obtained model is validated against the experimental data for ASR 70 media type for different operating conditions of the inlet airflow pressure and media flowrate. Calculated velocities are in good agreement with the measurements. In addition, the developed model is applied to predict the shot stream velocity for ASR 230 media type, as well as to evaluate the peening intensity and coverage for different media types under different operating conditions. The predicted results are comparable to the measurement data under the same operating conditions. The maximum relative error of the predicted shot stream velocity and measurement velocity is about 5%, while the maximum error in peening intensity is about ±0.0065 mmA. Furthermore, a single-input and single-output model-based control is developed based on the proposed shot stream velocity model. The developed control system is robust, accurate, and reliable. It implies that the developed model can be used to provide the necessary information, as well as to develop the optimal process control system to improve and accelerate the peening processes for cost and time reduction of actual production.

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Data Availability

The authors will make availability of any related data to this manuscript if requested (or data will available on request).

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Funding

This work is supported by the project entitled “Machine Learning Assisted Control of Shot Peening Process” under Grant number A1894a0032 at Institute of High Performance Computing (IHPC) and Advanced Remanufacturing and Technology Centre (ARTC), A*STAR Singapore.

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Authors and Affiliations

  1. Fluid Dynamics Department, Institute of High Performance Computing, A*STAR, Singapore, 138632, Singapore

    Van Bo Nguyen, Te Ba & Chang Wei Kang

  2. Temasek Laboratories, National University of Singapore, Singapore, 117411, Singapore

    Van Bo Nguyen

  3. Data-Driven Surface Enhancement Group, Advanced Remanufacturing and Technology Centre, A*STAR, Singapore, 637143, Singapore

    Augustine Teo, Ampara Aramcharoen & Kunal Ahluwalia

Authors
  1. Van Bo Nguyen
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  2. Augustine Teo
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  3. Te Ba
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  4. Ampara Aramcharoen
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  5. Kunal Ahluwalia
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  6. Chang Wei Kang
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Contributions

Van Bo Nguyen: Developed blended empirical model of shot stream velocity, designed and performed the predictions and control system, developed and performed in silico control scenarios, developed ideas for this paper, analyzed data, wrote original, and revised paper

Augustine Teo: Design experiments, performed experiments, analyzed data, and revised paper

Te Ba: Discussed, analyzed data, and revised paper

Kunal Ahluwalia: Provided some idea on experimental setup, analyzed data, and revised paper

Ampara Aramcharoen: Provide ideas on experimental trials and revised paper

Chang Wei Kang: Managed and provided direction for research development, provided ideas on control design, analyzed data, and revised paper

Corresponding author

Correspondence to Van Bo Nguyen.

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Nguyen, V.B., Teo, A., Ba, T. et al. A blended empirical shot stream velocity model for improvement of shot peening production. Int J Adv Manuf Technol 118, 801–815 (2022). https://doi.org/10.1007/s00170-021-07972-w

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  • DOI: https://doi.org/10.1007/s00170-021-07972-w

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