Machine Learning, Dynamical Systems and Control

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Advanced Manufacturing. Manufacturing is a highly complex and dynamic process, involving the co-ordination and merging of several elaborate and precisely times stages. In a modern manufacturing environment, tremendous volumes of data are being generated, stored, and analyzed to improve process quality, reliability, and efficiency. For example, a Boeing 787 comprises 2.3 million parts that are sourced from around the globe and assembled in an extremely complex and intricate manufacturing process, resulting in vast multimodal data from supply chain logs, video feeds in the factory, inspection data, and hand-written engineering notes. After assembly, a single flight test will collect data from 200,000 multimodal sensors, including asynchronous signals from digital and analogue sensors, including strain, pressure, temperature, acceleration, and video. Thus, big data is presently a reality in modern aerospace engineering, and the field is ripe for AI/ML.

There are several opportunities to leverage ML to improve manufacturing processes. Several areas of high-priority include: part standardization; automation and robotics; streamlined assembly, including reduced measurements, processing, and inspection, towards just-in-time manufacturing; supply chain management; material design and fabrication; and non-destructive inspection. Working with our partners at Boeing, we have recently developed a sparse sensor algorithm to dramatically reduce measurements required for precision aircraft assembly, streamlining the manufacturing process. This technology is currently in production on the 777X and 787 aircraft with an estimated savings in the billions of dollars.