Thermal imaging, the door to Zero Defects - VIDRALA

Vidrala manufactures glass containers through a continuous process involving the melting of glass. This molten glass is brought through some forehearths to a spout, where some glass gobs are cut. These glass gobs will be formed into a bottle or jar each, without any addition or subtraction of mass. The conditions in which these gobs are formed depend on the temperature and homogeneity of the glass and will affect the regularity of the thickness of the bottle’s walls.

This relationship is not obvious. Some experience is held inside the factories, and some shapes are regarded as better than others, but this knowledge is qualitative and very difficult to optimize. In the first use case of Vidrala, the usage of modern data science tools should define the actual limits for the gob shape and the impacts of those limits.

For the second use case, the focus is set up downstream, where the bottle is already cold. The thickness of the bottles is measured between 45 and 60 minutes after it has been manufactured. The late measurement has an impact on the amount of defective produced until the measurement is made. The objective is to use data science to predict the thickness problem with the information available in the hot end, saving time and having a big impact on the performance of the factories.