High-quality optical components have been widely used in various applications;thus,extremely high beam quality is required.Moreover,surface roughness is a key indicator of the surface quality.In this study,the angular...High-quality optical components have been widely used in various applications;thus,extremely high beam quality is required.Moreover,surface roughness is a key indicator of the surface quality.In this study,the angular distribution of light scattering field intensity was obtained for surfaces having different roughness profiles based on the finite difference time domain(FDTD)method,and the results were compared with those obtained using the generalized Harvey-Shack(GHS)theory.It was shown that the FDTD approach can be used for an accurate simulation of the scattered field of a rough surface,and the superposition of results obtained from many surfaces that have the same roughness level was in good agreement with the result given by the analytic GHS model.A light scattering matrix(LSM)method was proposed based on the FDTD simulation results that could obtain rich surface roughness information.The classification effect of LSM was compared with that of the single-incidence scattering distribution(SISD)based on a ResNet-50 deep learning network.The classification accuracy of the model trained with the LSM dataset was obtained as 95.74%,which was 23.40%higher than that trained using the SISD dataset.Moreover,the effects of different noise types and filtering methods on the classification performance were analyzed,and the LSM was also shown to improve the robustness and generalizability of the trained surface roughness classifier.Overall,the proposed LSM method has important implications for improving the data acquisition scheme of current light scattering measurement systems,and it also has the potential to be used for detection and characterization of surface defects of optical components.展开更多
The surface finish quality is critical to the service performance of a machined part,and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials.This study s...The surface finish quality is critical to the service performance of a machined part,and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials.This study studied the problem of predicting the surface roughness for titanium alloy workpieces in ultra-precision machining.Process data and surface roughness measurement results were obtained during end-face machining experiments.A deep learning neural network model was built based on the ResNet-50 architecture to predict surface roughness.We propose increasing prediction accuracy by using the energy ratio difference(ERD)as a stability feature that can be extracted using fast iterative variational mode decomposition(FI-VMD).The roughness value obtained with an analytic model was also used as an input feature of the prediction model.The prediction accuracy of the proposed approach was depicted to be improved by 8.7%with the two newly introduced roughness predictors.The influence of the tool parameters on the prediction accuracy was investigated,and the proposed hybrid-driven model exhibited higher robustness to errors of the tool parameters than the analytic roughness model.展开更多
基金supported by the National Key R&D Program of China(Grant No.2020YFB1710400)the Key R&D Project of Hubei Province(Grant No.2023BAB067)。
文摘High-quality optical components have been widely used in various applications;thus,extremely high beam quality is required.Moreover,surface roughness is a key indicator of the surface quality.In this study,the angular distribution of light scattering field intensity was obtained for surfaces having different roughness profiles based on the finite difference time domain(FDTD)method,and the results were compared with those obtained using the generalized Harvey-Shack(GHS)theory.It was shown that the FDTD approach can be used for an accurate simulation of the scattered field of a rough surface,and the superposition of results obtained from many surfaces that have the same roughness level was in good agreement with the result given by the analytic GHS model.A light scattering matrix(LSM)method was proposed based on the FDTD simulation results that could obtain rich surface roughness information.The classification effect of LSM was compared with that of the single-incidence scattering distribution(SISD)based on a ResNet-50 deep learning network.The classification accuracy of the model trained with the LSM dataset was obtained as 95.74%,which was 23.40%higher than that trained using the SISD dataset.Moreover,the effects of different noise types and filtering methods on the classification performance were analyzed,and the LSM was also shown to improve the robustness and generalizability of the trained surface roughness classifier.Overall,the proposed LSM method has important implications for improving the data acquisition scheme of current light scattering measurement systems,and it also has the potential to be used for detection and characterization of surface defects of optical components.
基金supported by the National Key Research and Development Project of China(Grant No.2020YFB1710400)the National Natural Science Foundation of China(Grant No.52005205)the National Science Fund for Distinguished Young Scholars(Grant No.52225506)。
文摘The surface finish quality is critical to the service performance of a machined part,and single-point diamond ultra-precision machining can achieve excellent surface quality for many engineering materials.This study studied the problem of predicting the surface roughness for titanium alloy workpieces in ultra-precision machining.Process data and surface roughness measurement results were obtained during end-face machining experiments.A deep learning neural network model was built based on the ResNet-50 architecture to predict surface roughness.We propose increasing prediction accuracy by using the energy ratio difference(ERD)as a stability feature that can be extracted using fast iterative variational mode decomposition(FI-VMD).The roughness value obtained with an analytic model was also used as an input feature of the prediction model.The prediction accuracy of the proposed approach was depicted to be improved by 8.7%with the two newly introduced roughness predictors.The influence of the tool parameters on the prediction accuracy was investigated,and the proposed hybrid-driven model exhibited higher robustness to errors of the tool parameters than the analytic roughness model.
