Data · dataset · 2025
Large-Gradient Wavefront Detection Method Based on Knife-Edge Scanning Filtering
Listed in ScienceDB
Wavefront error is a key indicator for evaluating the quality of high-power laser components.
Description
Accurate and efficient wavefront detection methods are crucial for the stable operation of high-power laser systems. Interference measurement is a commonly used measurement technique.
However, traditional interferometry methods are difficult to produce effective interference fringes when detecting wavefronts with large gradients. This article proposes a large gradient wavefront detection method based on knife edge scanning filtering. This method performs two scans in opposite directions using a blade in the spectral plane.
Read the rest (3 more)
By summing up the shadow maps from each scanning direction and calculating their differences, the wavefront phase gradient along the scanning direction can be extracted. The maximum detectable phase gradient of this method is positively correlated with the scanning range of the cutting edge, allowing for effective measurement of large gradient wavefronts through extended scanning. From wavefront gradient data, gradient root mean square (GRMS) and power spectral density (PSD) can be analyzed.
In addition, the Simpson integration method is used to complete wavefront reconstruction. The simulation results show that the proposed method has relative errors of 3.6% and 2.5% for peak valley (PV) and GRMS measurements, respectively, while the relative error of PSD characteristic peaks is less than 1%. In the experiment, PSD measurements of a 1 mm rectangular periodic sample showed that the relative error of the first three main peaks was within 7%.
This method demonstrates high accuracy in multiple wavefront error metrics, affirming its value as an effective tool for manufacturing and measuring optical components with large phase gradients.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.26129 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Simulation 75%
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.26129 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |