Dynamic Response Testing Based on Pulsed Laser and WOA-BP Neural Network

Abstract

Laser heating has become a common means of dynamic response testing, but accurate measurement of the dynamic response time poses a challenge, mainly due to the interference from the thermophysical parameters of K-type thin-film thermocouple, temperature measurement environments, and stray signals. To address this issue, this article designs a dynamic response testing platform for thermocouples based on lasers and constructs a WOA-BP algorithm model. This model aims to accurately predict the dynamic response time and output peak voltage of the thermocouple, providing guidance for parameter optimization during the experimental process and ensuring efficient capture of the thermocouple’s dynamic response signals. Meanwhile, this article compared the WOA-BP algorithmic model with back propagation (BP) and other optimized BP models, evaluated by RMSE, MAE, and R2. The results demonstrate that with the guidance of parameter optimization by the WOA-BP algorithm model, the dynamic testing system is capable of accurately performing dynamic performance tests on thermocouples. Besides, the dynamic response time is inversely proportional to laser power and directly proportional to laser pulsewidth, but independent of repetition frequency. The output peak voltage increases with the increase of laser power and pulsewidth, but is also independent of laser repetition frequency. And the WOA-BP algorithm model can predict dynamic response time and output peak voltage accurately, whose R2 values of dynamic response time and output peak voltage are 0.9954 and 0.9982, the RMSE values are 0.5766 and 0.2981, and the MAE values are 0.4152 and 0.2700, respectively, being the best compared with BP, PSO-BP, GA-BP, MFO-BP, and GWO-BP prediction models.