AI-enhanced integrated surface metrology
Funding: ERC Advanced Grant
Duration: January 2023 – January 2028
Team: Richard Leach, Mohammed Isa, Helia Hooshmand, Afaf Remani, Ruidong Xue
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, in MMT we 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 we 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 project to make incremental developments; rather we 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 project will revolutionise digital quality, making measurement a seamless, yet constantly evolving part of manufacturing.
