Machine Learning Models to Identify Anomalies in the Production of Flat Glass Using Machine Learning to Detect Anomalies in Flat Glass Production

Abstract

This work presents an innovative proposal for the prediction of defects in industrial glass refining processes. Although it is a complex process with several points that can cause defects, the current approach of specialists is only reactive, that is, they can only act after the damage has been caused. This study proposes the use of data collected from the industrial processes of a real company as a case study to create prediction models, in order to identify a possible failure before it occurs. The objective is to use multiple linear regression as a model and allow specialists to take preventive corrective measures, avoiding damage and reducing production costs.