Methods and systems for performing root cause analysis with a trained machine learning model

A computer-implemented method of utilizing a pre-trained machine learning model to perform root cause analysis on a manufacturing process, the method comprising:

• providing a pre-trained machine learning model that is trained to predict measurements of non-faulty parts, wherein the pre-trained machine learning model is trained based on a first set of measurement data regarding physical characteristics of a first plurality of manufactured parts as measured by a plurality of sensors at a plurality of manufacturing stations;
• utilizing the pre-trained machine learning model to produce a set of predicted measurement data for each manufacturing station;
• receiving, from the plurality of sensors at the plurality of manufacturing stations, a second set of measurement data regarding physical characteristics of a second plurality of manufactured parts and an identification of the plurality of manufacturing stations;
• determining losses between the set of predicted measurement data and the second set of measurement data for each of the plurality of manufacturing stations;
• and
• identifying a root cause within the manufacturing process based on the losses.

A computer-implemented method of utilizing a pre-trained machine learning model to perform root cause analysis to determine a part type causing a fault within a manufacturing setting, the method comprising:

• providing a pre-trained machine learning model that is trained to predict measurements of non-faulty parts, wherein the pre-trained machine learning model is trained based on a first set of measurement data regarding physical characteristics of a first plurality of manufactured parts as measured by a plurality of sensors at a plurality of manufacturing stations;
• training a binary classification model using outputs of the pre-trained machine learning model to output predictions of whether at least one part type of a plurality of part type will be faulty;
• determining a magnitude of absolute gradients of the binary classification model;
• and
• identifying at least one of the plurality of part types as a root cause within the manufacturing setting based on the magnitude of absolute gradients.