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[加工表面的背散射特性及表面多参数反演]

[Backscattering Characteristics of Machining Surfaces and Retrieval of Surface Multi-Parameters].

作者信息

Tao Hui-rong, Zhang Fu-min, Qu Xing-hua

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2015 Jul;35(7):1986-91.

Abstract

For no cooperation target laser ranging, the backscattering properties of the long-range and real machined surfaces are uncertain which seriously affect the ranging accuracy. It is an important bottleneck restricting the development of no cooperation ranging technology. In this paper, the backscattering characteristics of three typical machining surfaces (vertidal milling processing method, horizontal milling processing method and plain grinding processing method) under the infrared laser irradiation with 1550 nm were measured. The relation between the surface nachining texture, incident azimuth, roughness and the backscattering distribution were analyzed and the reasons for different processing methods specific backscattering field formed were explored. The experimental results show that the distribution of backscattering spectra is greatly affected by the machined processing methods. Incident angle and roughness have regularity effect on the actual rough surface of each mode. To be able to get enough backscattering, knowing the surface texture direction and the roughness of machined metal is essential for the optimization of the non-contact measurement program in industry. On this basis, a method based on an artificial neural network (ANN) and genetic algorithm (GA), is proposed to retrieve the surface multi-parameters of the machined metal. The generalized regression neural network (GRNN) was investigated and used in this application for the backscattering modeling. A genetic algorithm was used to retrieve the multi-parameters of incident azimuth angle, roughness and processing methods of machined metal sur face. Another processing method of sample (planer processing method) was used to validate data. The final results demonstrated that the method presented was efficient in parameters retrieval tasks. This model can accurately distinguish processing methods and the relative error of incident azimuth and roughness is 1.21% and 1.03%, respectively. The inversion accuracy is high. It can reduce the impact of surface texture, the incident azimuth and incidence angle to the ranging scope. The experiments proved that the inversion of the surface parameters greatly broadened the ranging scope in no cooperation target laser ranging. Taking the Vertical milling sample with roughness Ra=6.3 microm for example, the measuring range can be increased by about 22 m when the incidence angle is increased in the incidence plane which is vertical to the surface texture. The study results of this paper have a certain reference value to the research of the backscattering of machined surface and its application in other areas.

摘要

对于非合作目标激光测距,远距离真实加工表面的后向散射特性不确定,严重影响测距精度。这是制约非合作测距技术发展的重要瓶颈。本文测量了1550nm红外激光辐照下三种典型加工表面(立式铣削加工方法、卧式铣削加工方法和平面磨削加工方法)的后向散射特性。分析了表面加工纹理、入射方位角、粗糙度与后向散射分布之间的关系,探讨了不同加工方法形成特定后向散射场的原因。实验结果表明,后向散射光谱的分布受加工方法影响较大。入射角和粗糙度对各模式实际粗糙表面有规律性影响。为了获得足够的后向散射,了解加工金属的表面纹理方向和粗糙度对于工业中非接触测量程序的优化至关重要。在此基础上,提出了一种基于人工神经网络(ANN)和遗传算法(GA)的方法来反演加工金属的表面多参数。研究了广义回归神经网络(GRNN)并将其用于该应用中的后向散射建模。采用遗传算法反演加工金属表面的入射方位角、粗糙度和加工方法等多参数。使用另一种样品加工方法(刨削加工方法)对数据进行验证。最终结果表明,所提出的方法在参数反演任务中是有效的。该模型能够准确区分加工方法,入射方位角和粗糙度的相对误差分别为1.21%和1.03%,反演精度高。它可以减少表面纹理、入射方位角和入射角对测距范围的影响。实验证明,表面参数反演大大拓宽了非合作目标激光测距的范围。以粗糙度Ra = 6.3微米的立式铣削样品为例,当在垂直于表面纹理的入射平面内增加入射角时,测量范围可增加约22米。本文的研究结果对加工表面后向散射的研究及其在其他领域的应用具有一定的参考价值。

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