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、效率或未来方面都不尽如人意。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。质量控制的最重要的措施之一是零件的表面；在考虑公差、装配和最终功能时，形状和细尺寸地形都至关重要。但目前的集成表面测量技术速度太慢，且在多变的加工条件下缺乏灵活性。测量是在制造后进行的，或者是通过减慢过程进行的，从而影响了最重要的吞吐量。从实验室到实际应用的表面测量需要提高几个数量级的速度，这通常超出了当前技术的能力。然而，我已经证明，这些挑战可以通过一种新兴的方法来解决：信息丰富的计量学-使用先验信息来优化需要测量的东西来增强测量过程，从而增加空间带宽，减少测量时间。这种优化通常需要复杂的测量物理模型；这就是最近的一场革命：机器学习，我将使用它将新开发的物理模型与先验信息相结合，以产生增强的测量系统，这些测量系统是制造过程中一个完整的、实时的、不断学习的部分。这不是一个渐进发展的建议；相反，我寻求通过结合三个领域（基础物理学、机器学习和计量学）的进步来改变这个领域——一种结合能方法，它将超越部分的总和。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.