Revisiting optical scattering with machine learning (SPARKLE)

通过机器学习重新审视光学散射 (SPARKLE)

• 批准号: EP/R028826/1
• 负责人: Richard Leach
• 金额: $ 40.98万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR028826%2F1
• 关键词: Revisiting; optical; scattering; machine; learning

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.

零件的表面形貌会对零件的性能产生深远的影响。在摩擦学中，影响摩擦、磨损和部件寿命等数量的是表面相互作用。在流体动力学中，是表面决定了流体的流动方式，并影响了空气动力升力等特性，从而影响了飞机的效率和燃油消耗。表面的地形和表面在使用中的功能之间的关系的例子可以在几乎每个制造部门中找到，无论是传统的还是高科技的。为了控制表面形貌，从而控制组件的功能和/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/或垂直轴上进行扫描，许多仪器不能实际用于实时过程中的应用。测量表面形貌的面积积分（散射）方法已经有了发展，可以在制造过程中足够快地使用，但是这些方法在表面形貌的高度范围内是有限的。在传统的机械加工中，在制造过程中确定被加工零件的表面形貌一直是一个重要的研究工作。这方面的主要技术是机器视觉方法，其中确定纹理参数与强度传感器测量场的一个方面之间的关系。这种方法有两个主要缺点：1。它们通常应用于在有限范围内具有几何特征的表面。它们没有测量过程的物理模型的好处，也就是说，它们纯粹是经验的。例如，增材制造零件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地方。典型的金属增材制造表面具有大范围的表面特征，主要特征通常是典型波长为几百微米、振幅为几十微米的焊缝轨迹；这种结构超出了现有商业方法所能有效测量的范围。在提议的项目中，我们的目标是证明使用简单，具有成本效益的实时测量系统可以测量粗糙和结构化，机械加工或附加表面。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，以挖掘不在商业散射仪器范围内的地形信息的光学散射数据。所提出的系统可以安装到机械加工或添加剂操作中，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。为了展示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。

项目成果

Geometrical metrology for metal additive manufacturing

• DOI: 10.1016/j.cirp.2019.05.004
• 发表时间: 2019-01-01
• 期刊: CIRP ANNALS-MANUFACTURING TECHNOLOGY
• 影响因子: 4.1
• 作者: Leach, R. K.;Bourell, D.;Dewulf, W.
• 通讯作者: Dewulf, W.

Quantifying the validity conditions of the Beckmann-Kirchhoff scattering model

• DOI: 10.1117/12.2639003
• 发表时间: 2022-10
• 作者: Helia Hooshmand;Mingyu Liu;R. Leach;S. Piano

Intelligent quality monitoring for additive manufactured surfaces by machine learning and light scattering

通过机器学习和光散射对增材制造表面进行智能质量监控

• DOI: 10.1117/12.2592554
• 发表时间: 2021
• 作者: Liu M

Measurement of laser powder bed fusion surfaces with light scattering and unsupervised machine learning

• DOI: 10.1088/1361-6501/ac6569
• 发表时间: 2022-04
• 期刊: Measurement Science and Technology
• 影响因子: 2.4
• 作者: Ming-Yu Liu;N. Senin;Rong Su;R. Leach

Cascaded machine learning model for reconstruction of surface topography from light scattering

• DOI: 10.1117/12.2555035
• 发表时间: 2020-04
• 期刊: Optics and Photonics for Advanced Dimensional Metrology
• 作者: Mingyu Liu;N. Senin;Rong Su;R. Leach