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Anti-vibration wavefront interferometry by physics-informed U-Net
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AI
DOI：10.1016/j.engappai.2026.114875.png)
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Abstract
En 中文
High-precision wavefront interferometry is extremely sensitive to environmental vibrations, which limits the industrial deployment of conventional phase-shifting interferometers. In this paper, an anti-vibration wavefront interferometry (AVWI) framework using only one or two interferograms is proposed. AVWI reduces the reliance on vibration-isolated environments and high-precision phase shifters by explicitly decoupling vibration-induced errors from the test wavefront. This is enabled by a vibration-detection model based on a physics-informed U-Net (AVWI-PIUN). The network learns vibration information from interferograms and enforces cross-frame consistency among phase-shifted interferograms. A physics-informed loss, denoted as LPhy, is further introduced using partial-derivative relationships between phase-shifted interferograms, which significantly reduces the required training data and accelerates convergence. Experiments under vibrating conditions demonstrate that AVWI achieves superior accuracy, repeatability, and efficiency compared with a temporal phase-shifting method. Specifically, AVWI achieves a Q peak-to-valley (PVq) figure error of 13.750 nm (nm), a root mean square (RMS) figure error of 1.784 nm, and an RMS repeatability of 0.411 nm, corresponding to reductions of 50.1%, 76.0%, and 92.2%, respectively. In addition, AVWI tolerates phase-shifting errors up to ±9π/20. These results indicate that AVWI provides a practical route toward nanometer-level surface figure metrology in vibration-prone industrial and in-situ machining environments.
Keywords:
wavefront interferometry
vibration compensation
physics-informed neural network
phase-shifting interferometry
nanometer-level metrology
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