Ann-Based Estimation of Diaphragm Parameters for Fabry-Perot Interferometer: An Application for Three Leaf Clover Diaphragm

In this study, an artificial neural network (ANN) based estimator is presented for static pressure response (d) and dynamic pressure response (f) analysis of TLC (three-leaf clover) diaphragms. The diaphragms used to train ANNs are designed with SOLIDWORKS and analyzed with ANSYS. A total of 1680 TLC diaphragms are simulated with eight diaphragm parameters (3 for SiO2 material, 4 for geometry, and 1 for pressure) to create a data pool for ANN’s training, validation, and testing processes. 80% of the data is used for training, 15% for validation, and the remaining for testing. The network models that estimate d and f values for all kinds of diaphragm materials are proposed, with a material-independently trained ANN structure. Thus, thanks to the proposed method, analyses of TLC diaphragms are quickly performed without the need for time-consuming and costly design and analysis programs.