Revisiting optical scattering with machine learning (SPARKLE)

零部件的表面形貌会对部件的功能产生深远的影响。在摩擦学中，表面相互作用影响诸如摩擦、磨损和部件寿命等量。在流体动力学中，它是决定流体如何流动的表面，并且它影响诸如气动升力之类的特性，因此影响飞机的效率和燃料消耗。表面形貌与表面功能之间关系的例子几乎可以在传统和高科技的每一个制造部门找到。为了控制表面形貌，并因此控制部件的功能和/或性能，必须对其进行测量并从测量数据中提取有用的参数。有大量的仪器可以测量表面形貌，但由于需要在横向轴和/或垂直轴上扫描，其中许多仪器不能实际用于实时过程中的应用。在测量表面形貌的面积积分（散射）方法中已经有了一些发展，这些方法可以在制造过程中快速使用，但是这些方法在表面形貌的高度范围内受到限制。在传统的机械加工中，已经有了大量的研究工作来确定制造过程中加工零件的表面形貌。用于此的主导技术是机器视觉方法，其中确定纹理参数与来自强度传感器的测量场的方面之间的关系。这样的方法具有两个主要缺点：1.它们通常应用于具有在有限范围内的几何特征的表面;它们不具有测量过程的物理模型的优点，即它们是纯经验的。例如，增材制造零件的表面形貌的测量和表征仍然是一个重大挑战，特别是在测量速度可能是一个问题的情况下。典型的金属增材制造表面具有大范围的表面特征，其中主要特征通常是具有几百微米的典型波长和几十微米的振幅的焊接轨迹;这样的结构超出了用现有商业方法可以有效测量的范围。在拟议的项目中，我们的目标是证明，它是可以测量粗糙和结构化，加工或添加剂表面使用一个简单的，具有成本效益的实时测量系统。这将涉及开发一个完全严格的三维光学散射模型，该模型将与机器学习方法相结合，以挖掘光学散射数据，获得商业散射仪器无法提供的地形信息。所提出的系统可以安装到加工或添加操作中，而不会减慢过程，因此，降低了许多需要工程表面的先进产品的成本。为了证明项目产出的商业潜力，我们有几个先进的制造合作伙伴，他们将提供工业相关的案例研究，还有一个合作伙伴可以作为该仪器的商业开发路线。

The surface topography of a component part can have a profound effect on the function of the part. In tribology, it is the surface interactions that influence such quantities as friction, wear and the lifetime of a component. In fluid dynamics, it is the surface that determines how fluids flow and it affects such properties as aerodynamic lift, therefore, influencing efficiency and fuel consumption of aircraft. Examples of the relationships between the topography of a surface and how that surface functions in use can be found in almost every manufacturing sector, both traditional and high-tech. To control surface topography, and hence the function and/or performance of a component, it must be measured and useful parameters extracted from the measurement data. There are a large number instruments that can measure surface topography, but many of them cannot be used realistically for real-time in-process applications due to the need for scanning in either the lateral axes and/or the vertical axis. There have been developments in area-integrating (scattering) methods for measuring surface topography that can be fast enough to use during a manufacturing process, but these are limited in the height range of surface topography with which they can be used.In conventional machining, there has been a significant research effort to determine the surface topography of the machined parts during the manufacturing process. The dominant technology for this has been machine vision approaches, where a relationship between a texture parameter and an aspect of the measured field from an intensity sensor is determined. Such approaches have two major drawbacks: 1. they are usually applied to surfaces with geometrical features over a limited range and 2. they do not have the benefit of a physical model of the measurement process, i.e. they are purely empirical. As an example, the measurement and characterisation of the surface topography of additive manufactured parts remains a significant challenge, especially where measurement speed may be an issue. Typical metal additive manufactured surfaces have a large range of surface features, with the dominant features often being the weld tracks with typical wavelengths of a few hundred micrometres and amplitudes of a few tens of micrometres; such structures are beyond what can be measured effectively with existing commercial approaches. In the proposed project, we aim to demonstrate that it is possible to measure rough and structured, machined or additive surfaces using a simple, cost-effective real-time measurement system. This will involve the development of a fully rigorous three-dimensional optical scattering model, which will be combined with a machine learning approach to mine optical scattering data for topographic information that is not within the range of commercial scattering instruments. The proposed system could be mounted into a machining or additive operation without slowing down the process, therefore, reducing the cost of many advanced products that require engineered surfaces. To demonstrate the commercial potential of the project outputs, we have several advanced manufacturing partners who will supply industrially relevant case studies and one partner who could act as the commercial exploitation route for the instrument.