Tire rubber carbon black dispersity evaluation method and system based on neural network image processing

Abstract

The invention discloses a tire rubber carbon black dispersity evaluation method and a system based on neural network image processing. The method comprises the following steps: S1, rubber material images in a sample set are acquired, the rubber material images are pre-processed, and feature data are extracted; S2, feature data of a part of rubber material images in the sample set are acquired as training data, the training data are trained according to a BP network model and an RBF network model respectively, the optimal mixing coefficient is obtained through an adaptive genetic algorithm, the two network models are parallelly connected according to the optimal mixing coefficient, and a BP-RBF mixed neural network evaluation model is obtained; and S3, feature data of the other part of rubber material images in the sample set are acquired as evaluation data, the data are put in the BP-RBF mixed neural network evaluation model obtained through training, and the carbon black dispersity grade in the rubber material is acquired through output. The accuracy is high; parameters can be adjusted and optimized along with increasing of the sample number in use, and the robustness is strong.

Description

technical field [0001] The invention relates to the technical field of rubber quality detection, in particular to a method and system for evaluating the dispersion degree of tire rubber carbon black based on neural network image processing. Background technique [0002] Carbon black is the largest and most important reinforcing filler in the rubber mixing process, and its mixing uniformity with raw rubber directly affects the physical and mechanical properties of the rubber compound. In industrial production, the degree of uniform mixing is calibrated with ten grades from 1 to 10, that is, the degree of dispersion of carbon black, and the degree of dispersion is used as an important scale to measure the quality of the rubber compound and its rubber products. [0003] BP (Back Propagation) neural network was proposed by a team of scientists headed by Rumelhart and McCelland in 1986. It is a multi-layer feed-forward network trained by the error back propagation algorithm and i...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide a neural network-based method for accurately judging the dispersion of carbon black by means of image recognition in view of the difficult and inaccurate defect of measuring the dispersion of carbon black in the rubber mixing process in the prior art. Tire rubber carbon black dispersion evaluation method and system based on image processing

Images