Highly efficient method for cutting position selection of an x-ray mono-capillary lens based on an improved SCA-CSA algorithm.

Abstract

In order to efficiently select the optimal cutting position of x-ray mono-capillary lenses, an improved sine cosine algorithm-crow search algorithm (SCA-CSA) algorithm is proposed, which combines the sine cosine algorithm with the crow search algorithm, with further enhancements. The fabricated capillary profile is measured using an optical profiler; then the surface figure error for interest regions of the mono-capillary can be evaluated using the improved SCA-CSA algorithm. The experimental results indicate that the surface figure error in the final capillary cut region is about 0.138µm, and the runtime is 2.284s. When compared with the traditional metaheuristic algorithm, the particle swarm optimization algorithm, the improved SCA-CSA algorithm, enhances the surface figure error metric by two orders of magnitude. Furthermore, the standard deviation index of the surface figure error metric for 30 runs also improves by more than 10 orders of magnitude, demonstrating the superior performance and robustness of the algorithm. The proposed method provides significant support for the development of precise cuttings of mono-capillaries.