LD-pumped high-power continuous-wave Pr3+:YLF deep red lasers at 718.5 and 720.8 nm

We demonstrated 22 W LD-pumped high-power continuous-wave (CW) deep red laser operations at 718.5 and 720.8 nm based on an a-cut Pr3+:YLF crystal. The output power of both polarized directions reached the watt-level without output power saturation. A single wavelength laser operated at 720.8 nm in the π-polarized direction was achieved, with a high output power of 4.5 W and high slope efficiency of approximately 41.5%. To the best of our knowledge, under LD-pumped conditions, the laser output power and slope efficiency are the highest at 721 nm. By using a compact optical glass plate as an intracavity etalon, we suppressed the π-polarized 720.8 nm laser emission. And σ-polarized single-wavelength laser emission at 718.5 nm was achieved, with a maximum output power of 1.45 W and a slope efficiency of approximately 17.8%. This is the first time that we have achieved the σ-polarized laser emission at 718.5 nm generated by Pr3+:YLF lasers.

Optical Design of a Quantitative Microvolume Nucleic Acid Spectrophotometer with Non-Optical Fiber and All Radiation-Hardened Lens Elements

The purity of the nucleic acid samples obtained by extraction/precipitation or adsorption chromatography must be verified with microvolume spectrophotometry to ensure a high success rate of the subsequent nucleic acid sequencing while exploring the trace rare nucleic acids in space exploration with in-situ life detection. This paper reports an optical design for a radiation-hardened quantitative microvolume spectrophotometer with all radiation-hardened lens elements for space exploration instruments by using a non-optical fiber optical path with radiation-hardened optical glass elements. The results showed that the mean absolute error rate of the measured standard ribonucleic acid samples at concentrations between 50 ng/μL and 2300 ng/μL was within 2% when compared with a LINKO LKU–6000 ultraviolet–visible spectrophotometer.

Material removal predictions in the robot glass polishing process using machine learning

Robot polishing is increasingly being used in the production of high-end glass workpieces such as astronomy mirrors, lithography lenses, laser gyroscopes or high-precision coordinate measuring machines. The quality of optical components such as lenses or mirrors can be described by shape errors and surface roughness. Whilst the trend towards sub nanometre level surfaces finishes and features progresses, matching both form and finish coherently in complex parts remains a major challenge. With increasing optic sizes, the stability of the polishing process becomes more and more important. If not empirically known, the optical surface must be measured after each polishing step. One approach is to mount sensors on the polishing head in order to measure process-relevant quantities. On the basis of these data, machine learning algorithms can be applied for surface value prediction. Due to the modification of the polishing head by the installation of sensors and the resulting process influences, the first machine learning model could only make removal predictions with insufficient accuracy. The aim of this work is to show a polishing head optimised for the sensors, which is coupled with a machine learning model in order to predict the material removal and failure of the polishing head during robot polishing. The artificial neural network is developed in the Python programming language using the Keras deep learning library. It starts with a simple network architecture and common training parameters. The model will then be optimised step-by-step using different methods and optimised in different steps. The data collected by a design of experiments with the sensor-integrated glass polishing head are used to train the machine learning model and to validate the results. The neural network achieves a prediction accuracy of the material removal of 99.22%. Article highlights First machine learning model application for robot polishing of optical glass ceramics The polishing process is influenced by a large number of different process parameters. Machine learning can be used to adjust any process parameter and predict the change in material removal with a certain probability. For a trained model,empirical experiments are no longer necessary Equipping a polishing head with sensors, which provides the possibility for 100% control

Prediction of Surface Profile Evolution of Workpiece and Lapping plate in Lapping Process

Lapping has a history of hundreds of years, yet it still relies on the experience of workers. To improve the automaticity and controllability of the lapping process, a modeling method of friction and wear is developed to predict the surface profile evolution of the workpiece and lapping plate in the lapping process. In the proposed method, by solving the balance equations of resultant force and moment, the inclination angles of the workpiece can be calculated, thus more accurate contact pressure distribution of the workpiece/lapping plate interface can be calculated. Combined with the material removal rate model, the continuous evolution process of the workpiece and lapping plate can be predicted in the lapping process. The modeling method was validated by a lapping test of a flat optical glass (Φ 100 mm) with a composite copper plate. The results show that the proposed method can predict the evolution of the surface profile of the workpiece and lapping plate with high accuracy. Consequently, the lapping plate can be dressed at the right time point. Benefit from this, in the validation test the PV value of the workpiece (with 5 mm edge exclusion) was reduced from 5.279 μm to 0.267 μm in 30 min. The proposed surface profile evolution modeling method not only improves the lapping efficiency but also provides an opportunity to understand the lapping process.