AI-enhanced integrated surface metrology

世界正在经历数字工业革命的第一阶段：工业4.0。然而，目前的数字质量控制解决方案在速度、能力、效率或未来方面并不理想。制造业的一个重要组成部分是质量控制，这是通过测量来实现的。质量控制中最重要的被测对象之一是零件的表面;在考虑公差、装配和最终功能时，形状和精细尺寸的形貌都至关重要。但目前的集成表面测量技术速度太慢，在可变的加工条件下没有灵活性。测量是在生产后进行的，或者通过减慢过程来进行-这会影响至关重要的吞吐量。从实验室到应用进行表面测量可能需要几个数量级的速度增加，这通常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-使用先验信息，通过优化需要测量的内容来增强测量过程，从而增加空间带宽，但减少测量时间。这种优化通常需要复杂的测量物理模型;这就是最近的革命：机器学习，我将使用联合收割机将新开发的物理模型与先验信息结合起来，以产生增强的测量系统，这是制造过程中不可或缺的，实时的，不断学习的一部分。这不是一个渐进式发展的建议;相反，我试图通过结合三个领域（基础物理学，机器学习和计量学）的进步来改变这个领域-结合能方法将超过部分的总和。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。

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.