Shape parameterisation for identification and characterisation of polymer surface features

虽然我们可能会认为普通物体的表面是光滑或平坦的，但当在微观水平上进行研究时，总是有一个相当复杂的景观。会有山丘（“凹凸不平”）和山谷，这些几何对象的大小和形状可能由于各种原因而很重要。在聚合物的微模塑中，表面特性可能是控制产品功能的关键特征，例如由非常小的透镜阵列组成的微透镜。因此，需要控制产品的表面，为了自动做到这一点，我们需要对表面进行数学建模。在布拉德福德的聚合物IRC实验室里，我们有可以测量表面并产生数字图像的仪器。每个图像都由三维空间中的数千个点组成，操作起来很麻烦。在长图像序列的情况下，例如在大规模生产过程的监控中提出的情况下，将有许多这些图像文件将占用过多的计算机存储量。为了将这些文件作为过程控制和优化方案的一部分进行常规处理，需要使用少量参数表征表面的方法。三维表面表征的方法，PDE方法，正在信息学院开发中。偏微分方程的解用于在三维空间中形成近似的表面。可以生成由与PDE解相关联的少量参数控制的高度多样化的形状集，并且这些形状集可以拟合到真实的三维对象。最近的应用是为了识别目的而拟合扫描的人脸。我们建议使用这种方法来处理聚合物表面。这将是一个非常重要的进步，通过表面粗糙度，它使用一个单一的数字，平均粗糙半径表征表面的传统手段。丰富和现实的近似表面将成为可能，并创建理想的表面。然后，机器人控制的测量和实时计算将被用于对生产的表面进行优化，将它们与理想的表面进行比较，从而优化微成型工艺。

While we may perceive the surfaces of commonplace objects as smooth or flat, when investigated at a microscopic level there is always quite a complicated landscape. There will be hills ('asperities') and valleys, and the sizes and shapes of these geometrical objects may be important for a variety of reasons. In micromoulding of polymers, the surface characteristics may be the crucial feature that controls the function of a product, as is the case (for instance) with a microlens consisting of an array of very small lenses. There is therefore a need to control the surface of the product, and to do this automatically we need to characterise the surface mathematically. Within the polymer IRC laboratories at Bradford, we have instruments that can measure surfaces and produce digital images of them. Each image consists of many thousands of points in three-dimensional space, and is cumbersome to manipulate. In the case of a long sequence of images, such as is proposed in the monitoring of a mass production process, there will be many of these image files that will occupy a prohibitively large amount of computer storage. To handle these files routinely as part of a control and optimisation scheme for a process, a means of characterising the surfaces using a small number of parameters is required.A method of characterisation of three-dimensional surfaces, the PDE method, is under development in the School of Informatics. Solutions of partial differential equations are used to form surfaces in three-dimensional space that approximate. A highly diverse set of shapes can be generated that are controlled by the small number of parameters that are associated with the PDE solutions, and these can be fitted to real three-dimensional objects. The most recent application has been in fitting to scanned human faces for recognition purposes. We propose to use this method to characterise polymer surfaces. This will be a highly significance advance over the conventional means of characterising surfaces via the surface roughness, which uses a single number, the mean asperity radius. Rich and realistic approximations of surfaces will be made possible, and ideal surfaces created. Then, robotically controlled measurement and real-time computing will be used to characterise the produced surfaces, compare them with the ideal and thus optimise the micromoulding process.