Methods and systems for training a machine learning model with measurement data captured during manufacturing process

A system of training a machine learning model with measurement data captured during a manufacturing process, the system comprising:

• a plurality of sensors located at a plurality of manufacturing stations, each sensor configured to produce measurement data indicating a physical characteristic of a plurality of manufactured parts passing through a respective one of the manufacturing stations,
• at least one processor programmed to:
• execute a time-series dynamics machine learning model to encode the measurement data into a latent space having a plurality of nodes, each node associated with the measurement data of one of the manufactured parts as measured at one of the manufacturing stations,
• execute a prediction machine learning model to determine a predicted measurement of a first of the manufactured parts at a first of the manufacturing stations based on the latent space of at least some of the measurement data not including the measurement data corresponding to the first manufactured part at the first manufacturing station,
• compare the prediction measurement of the first manufactured part to the measurement data of the first manufactured part measured at the first manufacturing station,
• based on a difference between the prediction measurements and the actual measurement data, updating parameters of the machine learning model until convergence, and
• based upon the convergence, output a trained machine learning model with the updated parameters.

A computer-implemented method of training a machine learning model with measurement data captured during a manufacturing process, the method comprising:

• receiving measurement data regarding a physical characteristic of a plurality of manufactured parts as measured by a plurality of sensors at various manufacturing stations;
• via a time-series dynamics machine learning model, encoding the measurement data into a latent space having a plurality of nodes, each node associated with the measurement data of one of the manufactured parts as measured at one of the manufacturing stations;
• batching the measurement data to build a batch including a first plurality of nodes that are immediately connected to a first node via first edges and measured in time earlier than the first node, and a second plurality of nodes wherein each of the second plurality of nodes are immediately connected to, and measured in time earlier than, a respective one of the first plurality of nodes via second edges;
• and
• via a prediction machine learning model, determining a predicted measurement of a first of the manufactured parts at a first of the manufacturing stations based on the latent space of the batch of nodes.