TY - JOUR
T1 - Advances in subsurface defect detection techniques for fused silica optical components
T2 - A literature review
AU - Cao, Hongbing
AU - Peng, Xing
AU - Shi, Feng
AU - Tian, Ye
AU - Kong, Lingbao
AU - Chen, Menglu
AU - Hao, Qun
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/3/1
Y1 - 2025/3/1
N2 - Fused silica is widely used in high-power laser systems, astronomy and military fields due to its excellent optical and physical properties. However, Subsurface defects(SSDs), such as microcracks, scratches, plastic deformation, and pits are often formed during mechanical processing, which can seriously reduce the optical performance and durability of fused silica. This paper first discusses the formation mechanism of SSDs in fused silica due to mechanical stress, thermal effects, etc. during mechanical processing such as cutting, grinding, and polishing. Then, the commonly used destructive and non-destructive detection methods are reviewed, and the principles and latest progress of each technique are discussed. The potential of deep learning algorithms for SSD detection is also examined. Finally, future directions for research and development are proposed to provide a valuable reference for researchers in the field.
AB - Fused silica is widely used in high-power laser systems, astronomy and military fields due to its excellent optical and physical properties. However, Subsurface defects(SSDs), such as microcracks, scratches, plastic deformation, and pits are often formed during mechanical processing, which can seriously reduce the optical performance and durability of fused silica. This paper first discusses the formation mechanism of SSDs in fused silica due to mechanical stress, thermal effects, etc. during mechanical processing such as cutting, grinding, and polishing. Then, the commonly used destructive and non-destructive detection methods are reviewed, and the principles and latest progress of each technique are discussed. The potential of deep learning algorithms for SSD detection is also examined. Finally, future directions for research and development are proposed to provide a valuable reference for researchers in the field.
KW - Deep learning
KW - Defect detection
KW - Fused silica
KW - Subsurface damage
UR - http://www.scopus.com/inward/record.url?scp=85214919095&partnerID=8YFLogxK
U2 - 10.1016/j.jmrt.2025.01.045
DO - 10.1016/j.jmrt.2025.01.045
M3 - Article
AN - SCOPUS:85214919095
SN - 2238-7854
VL - 35
SP - 809
EP - 835
JO - Journal of Materials Research and Technology
JF - Journal of Materials Research and Technology
ER -