Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness

Jinrui Ye, Ya-ju Li, Zhao Zhang, Xinwei Wang, Kewei Tao, Qiang Zeng, Liangwen Chen, D. Qian, Shaofeng Zhang, Lei Yang, Xinwen Ma
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Abstract

A non-contact method for millimeter-scale inspection of materials’ surface flatness via Laser-Induced Breakdown Spectroscopy (LIBS) is investigated experimentally. The experiment is performed using a planished surface of an alloy steel sample to simulate its various flatness, ranging from 0 to 4.4 mm, by adjusting the laser focal plane to the surface distance with a step length of 0.2 mm. It is found that LIBS measurements are successful in inspecting the flatness differences among these simulated cases, implying that the method investigated here is feasible. It is also found that, for achieving the inspection of surface flatness within such a wide range, when univariate analysis is applied, a piecewise calibration model has to be constructed due to the complex dependence of plasma formation conditions on the surface flatness, which inevitably complicates the inspection procedure. To solve the problem, a multivariate analysis with the help of Back-Propagation Neural Network (BPNN) algorithms is applied to further construct the calibration model. By detailed analysis of the model performance, we demonstrate that a unified calibration model can be well established based on BPNN algorithms for unambiguous millimeter-scale range inspection of surface flatness with a resolution of about 0.2 mm.
激光诱导击穿光谱法作为毫米级检测表面平整度的一种方法
本实验研究了一种通过激光诱导击穿光谱(LIBS)进行毫米级材料表面平整度检测的非接触方法。实验使用合金钢样品的刨光表面,通过以 0.2 毫米的步长调整激光焦平面到表面距离,模拟其 0 至 4.4 毫米的不同平面度。结果发现,LIBS 测量能成功检测出这些模拟情况之间的平面度差异,这意味着本文研究的方法是可行的。研究还发现,要在如此大的范围内检测表面平整度,在应用单变量分析时,由于等离子体形成条件对表面平整度的依赖关系非常复杂,因此必须构建片断校准模型,这不可避免地使检测过程复杂化。为了解决这个问题,我们借助反向传播神经网络(BPNN)算法进行多变量分析,进一步构建校准模型。通过对模型性能的详细分析,我们证明了基于 BPNN 算法的统一校准模型可以很好地用于分辨率约为 0.2 毫米的毫米量程表面平整度的精确检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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