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摘要
基于神经网络,对聚四氟乙烯(PTFE)复合材料摩擦系数实时识别进行了研究,在此阶段中,可以对材料的物理性质进行实时监控,通过人工神经网络模型结构来对PTFE复合材料的摩擦系数进行实时识别,从而为后续复合材料的加工工艺提供实时监控的理论依据和支撑。采用BP算法训练的神经网络均可进行任意精度逼近,但在实际网络训练中,非线性作用函数类型、训练样本数量等均可能降低网络模型精度。采用非线性函数最小二乘曲线拟合的线性算法表明,该线性化处理方法是正确的,为已知材料性能时,快速识别摩擦系数提供了一种有效的手段。
Abstract
Based on neural network,the real-time identification of friction coefficient of PTFE composites was studied.Through on-line monitoring of physical parameters,the real-time identification of friction coefficient of PTFE can be realized by using artificial neural network model,which provided basic support for real-time control and real-time prediction of intelligent deep drawing.Neural networks trained by BP algorithm can be approximated with arbitrary accuracy,but in actual network training,the type of non-linear function and the number of training samples may reduce the accuracy of network model.The linear algorithm of least squares curve fitting with non-linear function shown that the linearization method was correct and provided an effective means for fast identification of friction coefficient when material properties were known.
关键词
摩擦系数
/
聚四氟乙烯复合材料
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BP算法
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实时识别
Key words
friction coefficient
/
PTFE composite material
/
BP algorithm
/
real-time identification
基于神经网络的聚四氟乙烯复合材料摩擦系数实时识别[J].
化工新型材料, 2019, 47(12): 130-134 DOI:
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基金资助
2018年度辽宁省普通高等学校本科教学改革研究项目(10841625)
