System and method for enhancing industrial final assembly quality

A system for detecting cross-station interactions that contribute to defects in a multi-station final assembly process, comprising:

• a plurality of sensors disposed at different assembly stations and configured to generate sensor data for individual products;
• a network configured to store the sensor data in association with product identifiers and assembly station identifiers;
• a computing device configured to execute a trained deep learning neural network that produces predictions related to defect occurrence based on the sensor data;
• and
• a cross-station interaction detection module implemented by the computing device;
• wherein the cross-station interaction detection module is configured to apply rule-extraction techniques to the trained deep learning neural network;
• and generate one or more human-readable rules that relate combinations of sensor deviations at different assembly stations to probabilities of defects in the products.

A method for enhancing industrial final assembly quality by integrating predictive warranty claims detection with cross-station interaction analysis, the method comprising:

• receiving, via a plurality of sensors disposed at a plurality of assembly stations sensor data indicative of assembly parameters for individual products;
• storing, via a network, the sensor data in association with product identifiers and assembly station identifiers;
• analyzing, by a computing device via a predictive warranty claims module, sequential sensor data using a deep learning neural network to detect early defect indicators;
• determining, before completion of a given product, a likelihood that the given product will lead to a warranty claim;
• applying, by the computing device via a cross-station interaction detection module, rule-extraction techniques to the deep learning neural network to generate human-readable rules identifying combinations of sensor deviations of the plurality of assembly stations that contribute to defects;
• and
• providing, via at least one user interface, real-time alerts and visual indications of both predicted warranty-risk levels for individual products and critical cross-station interactions, to support modification of at least one of assembly operations and sensor tolerance limits.