A distributed model predictive control with machine learning for automated shot peening machine in remanufacturing processes

Abstract

In practical peening operation, the values of inlet air pressure and media ow rate are manually preset to acquire desired intensity requirements. The operator often needs to perform intensive experimental trials to determine a set of operational inputs for actual production. Obtaining these operational parameters is often time-consuming and labor-intensive. Thus, in this study, we propose an optimal distributed model predictive control for the multiple inputs / multiple outputs system to address the issues. In the newly developed system, control actions of inlet air pressure and voltage are optimally obtained with the anticipation of the predictive future states of the plant models, while reference values of air pressure at the nozzle and media owrate are determined using a proxy model. The dynamical plant models include an air pressure model and a media owrate model, which are developed based on measurement data and physics-based knowledge using the sparse identi cation of nonlinear dynamics algorithm. The proxy model is developed from the measurement data of the intensity, pressure, and media owrate using a deep machine-learning algorithm. The control performance is demonstrated using on-site controls at the physical machine for different operational scenarios. The obtained measurement results exhibit a favorable control performance in stability, robustness, and accuracy. The measurement intensity is consistent with the target setting value; the difference is smaller than the industrial threshold of ± 0.01mmA for all random tests. In another word, all target setting intensity can be achieved without the need of performing trials to determine the operational parameters. It also suggests that the developed control system can be deployed to the physical machine for actual production.

The effect of fine particle peening on aviation gear performance, part II: Rolling contact fatigue

“…Generally, FPP intensity is affected by the parameters such as flow rate, incidence distance, air pressure, shot material performance, and incidence time. 3,26 When the same particle is used, the incidence speed is approximately proportional to the strength. 3,27 Therefore, according to the fitting results of the paper, 22 the FPP intensity of 0.05 $ 0.15 mmN can correspond approximately to the incidence speed of 130 $ 170 mm/s in this study.…”

“…FPP process window (FPP intensity: 0.05 ~ 0.15 mmN; particle diameter: 0.05 ~ 0.10 mm; coverage rate: 200%) was focused. Generally, FPP intensity is affected by the parameters such as flow rate, incidence distance, air pressure, shot material performance, and incidence time 3,26 . When the same particle is used, the incidence speed is approximately proportional to the strength 3,27 .…”