Control Model for an Elastomer Extrusion Process Obtained via a Comparative Analysis of Data Mining and Artificial Intelligence Techniques
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Universidad de La Rioja
info
ISSN: 0360-2559
Year of publication: 2010
Volume: 49
Issue: 8
Pages: 779-790
Type: Article
More publications in: Polymer - Plastics Technology and Engineering
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. © Taylor & Francis Group, LLC.