AI-enhanced integrated surface metrology

人工智能增强的集成表面测量

• 批准号: EP/X031675/1
• 负责人: Richard Leach
• 金额: $ 272.78万
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX031675%2F1
• 关键词: AI; enhanced; integrated; surface; metrology

The world is experiencing the first stages of a digital industrial revolution: Industry 4.0. However, current digital quality control solutions are not delivering in terms of speed, capability, efficiency or futureproofing. An essential part of manufacturing is quality control, which is achieved through measurement. One of the most important measurands for quality control is the surface of the part; both shape and fine-scale topography are critical when considering tolerances, assembly and ultimately functionality. But current integrated surface measurement technologies are too slow and have little flexibility under variable processing conditions. Measurements are taken after manufacture or by slowing down the process - compromising the all-important throughput. To take surface measurement from lab to application can require speed increases of several orders of magnitude, and this is often beyond the capability of current technology. However, I have demonstrated that these challenges can be tackled using an emerging approach: information-rich metrology - the use of a priori information to enhance the measurement process by optimising what needs to be measured, so increasing the spatial bandwidth but decreasing the measurement time. Such optimisation generally requires complex physics models of the measurement; this is where a recent revolution comes to the rescue: machine learning, which I will use to combine newly developed physics models with a priori information to produce enhanced measurement systems that are an integral, real-time, and constantly learning part of the manufacturing process. This is not a proposal to make incremental developments; rather I seek to transform the field by combining the advances of three fields (basic physics, machine learning and metrology) - a binding energy approach that will be more than the sum of the parts. The proposed project will revolutionise digital quality, making measurement a seamless, yet constantly evolving part of manufacturing.

世界正在经历一场数字工业革命的第一阶段：工业4.0。然而，当前的数字质量控制解决方案在速度、能力、效率或面向未来方面并未交付。制造的一个重要部分是质量控制，这是通过测量来实现的。质量控制的最重要的衡量标准之一是零件的表面；在考虑公差、装配和最终功能时，形状和精细的表面都是至关重要的。但目前的集成表面测量技术速度太慢，在不同的加工条件下缺乏灵活性。测量是在制造后进行的，或者通过放慢工艺进行测量--这会影响至关重要的吞吐量。将表面测量从实验室应用到应用可能需要几个数量级的速度提升，而这往往超出了当前技术的能力。然而，我已经证明，这些挑战可以使用一种新兴的方法来解决：信息丰富的计量学--利用先验信息优化需要测量的内容，从而增强测量过程，从而增加空间带宽，但缩短测量时间。这种优化通常需要复杂的测量物理模型；这就是最近的一场革命的用武之地：机器学习，我将利用机器学习将新开发的物理模型与先验信息结合起来，产生增强的测量系统，它是制造过程中不可或缺的、实时的、不断学习的一部分。这不是一个循序渐进的发展建议；相反，我寻求通过结合三个领域(基础物理、机器学习和计量学)的进步来改变该领域--一种约束能量的方法，它将超过各个部分的总和。拟议中的项目将彻底改变数字质量，使测量成为制造业中无缝但不断发展的一部分。

项目成果

Improving the localisation of features for the calibration of cameras using EfficientNets.

使用 EfficientNets 改进相机校准的特征定位。

• DOI: 10.1364/oe.478934
• 发表时间: 2023
• 期刊: Optics express
• 影响因子: 3.8
• 作者: Eastwood J

New Standard for Metal Powder Bed Fusion Surface Texture Measurement and Characterisation

金属粉末床熔融表面纹理测量和表征的新标准

• DOI: 10.3390/metrology3020013
• 发表时间: 2023
• 期刊: Metrology
• 作者: Thompson A

Optimisation of Imaging Confocal Microscopy for Topography Measurements of Metal Additive Surfaces

用于金属增材表面形貌测量的成像共焦显微镜的优化

• DOI: 10.3390/metrology3020011
• 发表时间: 2023
• 期刊: Metrology
• 作者: Newton L

Evaluating parametric uncertainty using non-linear regression in fringe projection

• DOI: 10.1016/j.optlaseng.2022.107377
• 发表时间: 2023-03
• 期刊: Optics and Lasers in Engineering
• 影响因子: 4.6
• 作者: George Gayton;Mohammed A. Isa;R. Leach