System and method with sequence modeling of sensor data for manufacturing

Abstract

A computer-implemented system and method includes establishing a station sequence that a given part traverses. A first neural network generates a set of parameter data based on observed measurement data of the given part at each station of a station subsequence. The set of parameter data is associated with a latent variable subsequence corresponding to the station subsequence. A second neural network generates next parameter data based on history measurement data and the set of parameter data. The history measurement data relates to another part processed before the given part and is associated with each station of the station sequence. The next parameter data is associated with a next latent variable that follows the latent variable subsequence. The next latent variable corresponds to a next station that follows the station subsequence in the station sequence. The second neural network generates predicted measurement data of the given part at the next station based on the next latent variable and the next parameter data.