Application for ANN Data Analysis

In this study, a new generalized regression neural network model for predicting the curing characteristics of rubber blends with different contents of carbon black filler cured at various temperatures has been proposed. The carbon black contents in the rubber blend and cure temperature have been used as input parameters, while the minimum and maximum elastic torque, scorch time, and optimal cure time, obtained from the analysis of 11 rheological cure curves registered at 10 various temperatures, have been considered as output parameters of the model. A special pre-processing procedure of the experimental input and target data, as well as the training algorithm has been described. Only a small part of the experimental data has been used in order to significantly reduce the total number of input and target data points needed for training the model. A satisfactory agreement between the predicted and experimental data, with a maximum error in the prediction not exceeding 5%, has been found. It has been concluded that the generalized regression neural network is a very powerful tool for intelligent modelling the curing process of rubber blends even in the case of small dataset, and it can find a wide practical application in the rubber industry.

本研究提出了一种新型广义回归神经网络（Generalized Regression Neural Network, GRNN）模型，用于预测不同炭黑填料掺量的胶料在不同硫化温度下的硫化特性。该模型以胶料的炭黑掺量与硫化温度作为输入参数；通过分析10种不同温度下测得的11条流变硫化曲线得到的最小弹性转矩、最大弹性转矩、焦烧时间以及最优硫化时间，则作为模型的输出参数。本文阐述了实验输入与目标数据的专用预处理流程，以及模型的训练算法。为大幅缩减模型训练所需的输入与目标数据点总数量，本研究仅使用了少量实验数据。研究结果显示，预测值与实验值吻合度良好，预测最大误差未超过5%。综上，广义回归神经网络即使在小样本数据集下，仍是用于胶料硫化过程智能建模的强有力工具，具备在橡胶工业中广泛推广应用的潜力。