Control Model for an Elastomer Extrusion Process Obtained via a Comparative Analysis of Data Mining and Artificial Intelligence Techniques

Abstract

Some elastomer profile extrusion processes in the automotive industry are still hard to control, generally because they are open loop systems with continual changes in manufacturing conditions. It is at the start-up stage that most time and raw materials are lost. This article describes the development of a dynamic extruder velocity control model that is capable of learning from good start-ups in earlier in manufacturing processes. The process of creating the model focuses on selecting the best technique from a set of data mining (DM) and artificial intelligence (AI) algorithms, which are put to the test with a database containing historical data on the start-up processes that have reached the steady state most quickly in the past. With the new models obtained, the process can be automated, and the time required for start-up in profile manufacturing can be reduced. This will result in increased output, higher quality, less faulty material and lower stress levels among production workers.

Keywords:

• Artificial intelligence
• Data mining
• Elastomer extrusion

ACKNOWLEDGMENTS

The authors thank the “Dirección General de Investigación” of the Spanish Ministry of Science and Innovation for the financial support of the projects DPI2006-03060, DPI2006-14784, DPI-2006-02454 and DPI2007-61090; and the European Union for the project RFS-PR-06035. Finally, the authors also thank the Autonomous Government of La Rioja for its support through the 3° Plan Riojano de I + D + i.

Notes

10 models were drawn up for each algorithm and configuration.

Ten models were drawn up for each algorithm and configuration.

Ten models were drawn up for each algorithm and configuration.