A Blended Empirical Shot Stream Velocity Model for Improvement of Shot Peening Production

Abstract Peening intensity and coverage are vital measurement outputs to quantify the quality of a peening process in surface enhancement operation of metal parts. In practice, these parameters can only be measured offline upon process completion, which are 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 regression algorithm based on the blended candidate functions, which are developed from experimental data and nature of the particle-air flow inside the system. Obtained model is validated against experimental data for different conditions. Calculated velocities are in good agreement with the measurements. In addition, applications of the model in predicting the shot stream velocity for different peen types, different peening intensities and coverages. The obtained results are comparable to the measurement data. Furthermore, a single-input and single-output model-based control is developed from 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 necessary information as well as in optimal process control development to improve and accelerate the peening processes to reduce cost and time of actual productions.