PS/PMMA复合材料的光散射

对以聚苯乙烯(PS)和自行合成的纳米类双亲PMMAPS为光散射剂,以聚甲基丙烯酸甲酯(PMMA)为基体的复合光散射材料进行了实验研究.测试并分析了样品的雾度及透光率与添加量之间的关系,目的在于通过调节散射剂添加量来调控以PMMA为基体的高聚物材料的光散射性能.实验结果表明,添加少量PS到PMMA中即可制备出光散射材料,PS添加量为1%时,复合光散射材料的透光率为80%;不添加PMMAPS时雾度为50%,添加了PMMAPS时雾度达到80%,PMMAPS可以改善PMMA和PS之间的相容性,提高样品的雾度值.因此通过调节散射剂PS和PMMAPS添加量可实现光散射高雾度和高透光率的双高要求.     

受激布里渊散射中Stokes光的反Stokes散射与慢光效应

为研究受激布里渊散射(SBS)慢光效应的物理机制,研究了Stokes光与布里渊声场之间的反Stokes散射,以及它与SBS慢光之间的内在联系。理论上证明了在SBS中除了泵浦光外,Stokes散射光与布里渊声场之间也满足散射条件,可发生反Stokes散射。该散射过程是泵浦光的Stokes散射的逆过程,因此泵浦光和Stokes光在布里渊声场中经历往返式散射。在此物理前提下建立了SBS慢光模型,其中Stokes光经历多次散射而光程变大,从而产生了慢光。模型所给出的Stokes脉冲的慢光延迟量与前人得到的结果吻合,表明SBS中存在Stokes光的反Stokes散射,多次散射导致的光程变大是产生SBS慢光效应的物理根源。     

控制光散射的液晶元件

对用不同结构的液晶来控制光散射的电气效应作了研讨。介绍了在成像光学系统中运用控制散射液晶元件的特点；确定了描述角分量和正常分量光散射的液晶装置函数。     

光散射法测量表面微粗糙度

本文阐述了基于是电偶极子辐射模型的矢量散射理论及以光散射法测量表面微粗糙度的基本原理；介绍了基于该理论研制而成的光散射空间角分布测量系统的原理与结构；最后给出了两个在此测量系统上完成的实验及结果。     

激光光散射仪及应用

介绍激光光散射仪的测定原理,常用测定仪器、测定方法以及在聚合物研究中的实际应用。     

一种新型侧光式白光LED背光源的设计

本文分析了现有LED背光源的优缺点,提出一种新型的侧光式白光LED背光源的设计方法,在该设计方案中,传统的CCFL线光源被LED光源及微细导光管所取代,LED光源经导光管转化为线光源.所设计的侧光式LED背光源应用在大尺寸液晶产品中,可以减少光源的使用数量、并保留CCFL背光源导光板成熟的设计方法.模拟实验表明:微细导...     

基于光散射原理的尘埃粒子检测仪

介绍了一种自行研制的尘埃粒子检测仪的测量原理和工作系统,该仪器用于检测洁净室中空气的尘埃粒子数和监测洁净度级别。尘埃粒子检测仪基于光散射原理,工作系统主要分为传感器、电路、气路和中央控制四部分。选用了直角散射光学传感器接收信号光,经光电转换和电路处理,可获得环境的尘埃粒子数目和尺寸信息。气路部分中采用了先进的微桥气流质量传感器,能够精确控制气体的采样量,通过单片机反馈控制,进一步提高了仪器的稳定性和精度。试验数据证实了该尘埃粒子检测仪具有市场上同类检测仪器的检测能力。     

基于米氏散射理论的激光粒度仪的介绍

微小颗粒的柱径测量是一个十分有意义的研究领域。在依据不同测量原理开发的各种测量仪器中，本文主要介绍了目前最通用的基于米氏散射理论的激光拉度仪的原理、发展历史以及发展现状，列举了国内外众多生产厂家开发的相关产品。     

用光透消光法测定颗粒物质的折射率

基于Ｍｉｅ光散射理论及Ｌａｍｂｅｒｔ—Ｂｅｅｒ光透射定律，本文提出一种测定颗粒状物质的折射率的有效方法──光透消光法。本文给出了测量原理、方法及对几种标准颗粒折射率的实测结果     

激光粒度分析仪在水煤浆中的应用

本文介绍了激光粒度分析仪的工作原理和在水煤浆应用中存在的问题,并对此提出了改进算法以及改进后的实验效果.     

离轴三反空间光学望远系统的杂散光抑制

以点源透过率(PST)为评价标准,对一个离轴三反空间望远系统的杂散光进行了分析,并给出了分析结果。通过建立系统的实体模型,确定了一次、二次散射路径,采用改进的蒙特卡洛法,对20°之间各离轴角分别进行光线追迹。对模型的分析结果表明,系统产生杂散光的主要原因为一次散射,与光学系统结构密切相关。离轴角为±0.1°时,PST分别等于3.56和4.02,离轴角为±20°时,PST分别为6.63×10-5和4.58×10-5。实验表明,通过加入遮光罩及减小镜面散射率可减少杂散光。与离轴两反望远系统相比,三反系统的杂散光水平在大离轴角时偏大1到2个数量级,但可满足使用要求。     

Practical neural networks for industrial inspection: A case study using web-process inspection

Neural networks have been successful at pattern recognition and discovery of hidden relationships amongst parameters and as such are likely supplements to the sensory systems employed in industrial applications. This paper examines four resulting issues imposed upon any industrial inspection system using a neural network: the feature set which the sensory system must provide, the accuracy of neural-network-based inspection, the robustness required of the sensory system for accurate inspection, and the computational burden imposed by accuracy requirements. This is accomplished in the context of web-process inspection, which requires rapid examination of vast amounts of data for on-line detection of faults in the sheet material. Each of the four crucial issues is addressed:

1. (i) Feature vectors with nine or 17 dimensions, created by a simulated segmented photodetector using measurement of the angular distribution over a 25° cone angle of the scattering were evaluated for inspection CrO₂-coated sheet steel samples. The scattered coherent light from the surface of the material being processed could be directly conditioned by a photodetector so as to produce this small set of features which are then examined by a neural network trained to find and categorize unsatisfactory surface conditions. Details are presented to show how a modified feature set was developed and tested after an examination of feature space. This new, smaller set proved to be more accurate than the larger set.

2. (ii) Classification by fault or no fault categorized 133 samples correctly out of 135, while there were seven errors in one attempt at classification into the various common surface faults out of the same number of test samples and nine in another. It is shown that a bit of insight in feature selection can improve the capability of the network to recognize faults.

3. (iii) The robustness issue is important since the inspection system must function in the industrial environment, where maintaining an exact alignment of the optics is not feasible. Tests are described where it is shown that fault classification using the proposed system is reasonably robust to slight variation of the angle between the laser beam and camera.

4. (iv) The computational issue is discussed in the context of the data handling requirements of the inspection system.

Microtopographic analysis of turned surfaces by model-based scatterometry

Turned surface profiles can be divided into a periodic and a random component. The periodic component is a function of the tool shape and the feed rate. The random component is caused by machine vibrations. To monitor the turning process, the periodic component is the essential global microtopographic feature which reacts sensitively to tool wear and tool scars. A measuring method evaluating the periodic component of turned surfaces is developed based upon the model-based scatterometry. Using an optimized scattering geometry, the model-based scatterometry leads to an intensified mapping of the microtopographic surface features in the angular distribution of scattered light. The profile parameters measured with the introduced scattering technique agree well with the values obtained by stylus measurements. Hence, in the range of precision engineering (R_a≤1.5 μm) the mean profile of the dominant periodic component can be measured optically without scanning and without resorting to comparator standards.     

光散射在线分析系统

光散射在线分析系统是以斯坦教授的小角光散射法为理论基础，主要用于高分子材料内部结构的在线分析测试与研究。它是由硬件──光散射在线分析仪和软件──数据采集处理系统两大部分组成。该系统首创了高聚物内部结构的在线分析，并且成功地将数字图象处理技术应用于光散射图象的分析处理；由于计算机的优势和潜力，使得整个计算处理准确、快速，形象、直观，而且易于统一标准，从而提高了计算结果的可比性和可信度。