Dynamic image acquisition and particle recognition of laser-induced exit surface particle ejection in fused silica

Abstract

Particle ejection is an important process during laser-induced exit surface damage in fused silica. Huge quantities of ejected particles, large ejection velocity, and long ejection duration make this phenomenon difficult to be directly observed. An in situ two-frame shadowgraphy system combined with a digital particle recognition algorithm was employed to capture the transient ejecting images and obtain the particle parameters. The experimental system is based on the principle of polarization splitting and can capture two images at each damage event. By combining multiple similar damage events at different time delays, the timeline of ejecting evolution can be obtained. Particle recognition is achieved by an adaptively regularized kernel-based fuzzy C-means algorithm based on a grey wolf optimizer. This algorithm overcomes the shortcoming of the adaptively regularized kernel-based fuzzy C-means algorithm easily falling into the local optimum and can resist strong image noises, including diffraction pattern, laser speckle, and motion artifact. This system is able to capture particles ejected after 600 ns with a time resolution of 6 ns and spatial resolution better than 5 μm under the particle recognition accuracy of 100%.