Machine-learning-based quality prediction of manufactured fiber optic cable

Abstract

According to an aspect, there is provided a method for monitoring quality of loose tube fiber optic cable during manufacture in a secondary coating line. Initially, a trained machine-learning algorithm for calculating expected values of one or more quality metrics of manufactured loose tube fiber optic cable based on values of the one or more production process parameters of the secondary coating line is maintained in a machine-learning database. A computing system monitors one or more values of the one or more production process parameters during miming of the secondary coating line and calculates, in real-time during the monitoring, one or more expected values of the one or more quality metrics using the trained machine-learning algorithm with the monitored values of the one or more production process parameters as input. The computing system outputs at least the one or more expected values of the one or more quality metrics to a user device.