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

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