PILOT CASE

Vidrala
Bottle manufacturing demonstrator

Glass container manufacturing is an extremely cost conscious process where a significant amount of products are defective due to limitations in materials involved, energy usage, and process steps driven by simplicity and cost-effectiveness more than by performance.

Thermal imaging of the finished bottle provides a significant amount of raw data, that well used can give insights into both process parameters (temperature) and finished product (thickness) – openZDM will use this information to improve the process both upstream (gob forming) and downstream (glass distribution).

Pilot Description

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 optimise. 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.

Expected Goals

Problem definition

The key problem is the uncertainty of the glass process, where “repeating” process conditions don’t deliver the same result. This is obviously due to a limited understanding of the relevant process conditions or the capacity to measure those conditions. Some of this can be solved by involving sound statistical knowledge in large amounts of process data. This is no different from how medical studies are done to assert the impact of given elements in human health. Results vary wildly due to the individual human condition and the difficulty to create identical case studies. But some conclusions are nevertheless possible with the right tools.

Identified challenges

Impact from openZDM technologies

The expected impact of openZDM project include reduction of defective products, early detection of problems and improvement on the quality levels. Besides, the involved technologies can help providing a better understanding of the manufacturing process itself.

The objective of the openZDM project is to work on the estimation of bottle wall thickness (WT) distribution using thermographic images at hot-end:

Being able to use hot-end data to predict (create early warnings) wall thickness issues.

Being able to optimize hot-end parameters (such as gob shape) to reduce the risk of having wall thickness issues.

Current status of the pilot:

NDIs and their corresponding modifications to gather all images and related data are deployed in 2 pilot lines (CR10 and EL11).

We are working on a predictive data-driven quality assessment module capable of predicting bottle thickness from infrared images.

This model has been trained with a dataset generated using a unitary laser marking deployed in EL11 line, in order to provide one-to-one input-output data relations.

Current preliminary results show good predictive capabilities for the short-term, but the performance of the model declines when using it with data a few days after the training period. This shows a need for a periodic retrain (or adjustment) of the model or trying a wider training dataset.

Several dashboards have been developed within the DT solution.

Leading company

Vidrala is a consumer packaging company that produces glass containers for food and beverages products and offers a wide range of packaging services, including logistic solutions and filling activities. Vidrala sells nearly 8.0 billion bottles and jars per year, among more than 1,600 customers. Vidrala is one of the main glass container manufacturers in Western Europe, through eight complementary sites located in five different countries.

Vidrala Bottle manufacturing demonstrator