Faster prediction with AI: Using process simulations and production data to develop fast-running inference models for manufacturing

A talk by Vic Castillo
Senior Research Scientist, Livermore National Lab

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About this talk

In the manufacturing environment there are many control parameters that affect the final product quality. Navigating this high-dimensional control space with computer simulations is a significant improvement over doing so with physical experiments but can still be very time consuming. Deep learning methods are often used to develop fast-running surrogate models of more computationally expensive simulations in order to get near-real-time predictions and to optimize complex systems such as manufacturing processes, energy systems, and even fusion reactors. In this talk, I will discuss an application of machine learning to develop a fast-running surrogate model that captures the dynamics of industrial multiphase fluid flows. I will also discuss a method for incorporating sparse manufacturing data to elevate the model to better predict ground-truth production quality. Results from demonstration problems and from real-world manufacturing systems will be presented.

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