Continuous dynamic measurement of frequency scanning interferometry based on motion phase synchronization compensation and calibration.

We present a continuous dynamic frequency scanning interferometry (DFSI) measurement method based on motion phase synchronization compensation and calibration. By introducing heterodyne interferometry (HI) synchronization measurement and frequency scanning interferometry (FSI) motion phase compensation, dynamic continuous measurement is achieved and effectively suppresses the distance error introduced by the Doppler effect (DE). Based on this, the influence of the initial optical frequency deviation (OFD) of the tunable laser and the OFD of the HI laser on the dynamic absolute distance measurement (DADM) is analyzed; the relationships between the error of DADM with the variation of the OFD and the target motion parameters are investigated; and the residual DE introduced by the OFD is shown as the fundamental cause of the degradation of the accuracy of DFSI. We propose an online optical frequency measurement method based on HI combined with H13C14N gas absorption cells to resolve this problem. High-precision motion phase compensation is achieved by calibrating the optical frequency (fixed frequency) of the measured HI laser and the initial frequency of the tunable laser online during measurement and then performing motion phase calibration. To verify the effectiveness of our method, an optical frequency calibration experiment, a continuous DADM experiment, and a precision evaluation experiment were conducted, and a highly accurate continuous DADM was achieved.

Dynamic nonlinearity errors in laser Doppler vibrometer measurements induced by environmental vibration and error correction.

Laser Doppler vibrometers (LDVs) are widely used for vibration testing in various fields. Nonlinearity errors are the key factor affecting the measurement accuracy of LDVs. The conventional Heydemann method cannot correct nonlinearity errors produced by noisy environments. Thus, we establish a novel model to describe dynamic nonlinearity errors produced in noisy environments and propose a compensation method to mitigate signal distortion. The performance of the proposed method is assessed by performing both simulations and experiments. The results of experiments carried out in a noisy environment indicate that the proposed method suppresses the nonlinearity to 30 nm compared to 737 nm using the conventional Heydemann correction. The proposed method can improve the accuracy of LDV measurements in industrial environments.

Laser energy prediction with ensemble neural networks for high-power laser facility.

The energy accuracy of laser beams is an essential property of the inertial confinement fusion (ICF) facility. However, the energy gain is difficult to control precisely by traditional Frantz-Nodvik equations due to the dramatically-increasing complexity of the huge optical system. A novel method based on ensemble deep neural networks is proposed to predict the laser output energy of the main amplifier. The artificial neural network counts in 39 more related factors that the physical model neglected, and an ensemble method is exploited to obtain robust and stable predictions. The sensitivity of each factor is analyzed by saliency after training to find out the factors which should be controlled strictly. The identification of factor sensitivities reduces relatively unimportant factors, simplifying the neural network model with little effect on the prediction results. The predictive accuracy is benchmarked against the measured energy and the proposed method obtains a relative deviation of 1.59% in prediction, which has a 2.5 times improvement in accuracy over the conventional method.