The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Outlier Detection and Data Cleaning in Multivariate Non-Normal Samples: The PAELLA Algorithm
Data Mining and Knowledge Discovery
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Computer Vision and Classification Techniques on the Surface Finish Control in Machining Processes
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
An intelligent supervision system for open loop controlled processes
Journal of Intelligent Manufacturing
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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.