A neural network-based approach for optimising rubber extrusion lines

Abstract

The current study shows how data mining and artificial intelligence techniques can be used to introduce improvements in the rubber extrusion production process. One of the keys for planning manufacturing values is prior knowledge of the properties of the material to be extruded. At present, such information is obtained through laboratory trials performed on samples taken off line after the elastomers have been manufactured, with the subsequent cost and delays. In view of these problems, the present study proposes a neural model capable of predicting the characteristics of rubber from the composition of the mixture and the mixing conditions, without having to wait for laboratory results, thus guaranteeing the traceability of the product in the process and the values according to their specific characteristics and also achieving a reduction in costs deriving from smaller amounts of discarded material during the performance of the tests, etc.

Keywords:

• Rubber extrusion lines
• Mixing conditions
• Cure characteristics
• Data mining
• Neural network

Acknowledgements

This work has been partially funded with a research scholarship granted by the State Secretary of Education and Universities of the Ministry of Education, Culture and Sport.

We would like to thank the CEUTIC (Interreg IIIA), the national funded programme DPI2004-07264-C02-01 and the ‘II Plan Riojano de I + D’ for their support and contribution to the diffusion of the results.

Finally, we sincerely thank the constant support provided by Metzeler Automotive Profile Systems Iberica, S.A.