基于PCA融合神经网络的低碳钢钻削磨损预测

S. S. Panda, S. Mahapatra
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引用次数: 5

摘要

本文介绍了主成分在钻头磨损预测中的应用。并对基于大传感器的钻头磨损预测技术进行了对比分析。为了减少网络的冗余,将主成分与人工神经网络(ANN)相融合,用于钻头磨损预测。利用数据采集系统进行了大量的实验,采集了传感器信号。切削力、扭矩、振动以及其他工艺参数,如主轴转速、进给速度、钻头直径、切屑厚度和表面粗糙度,已被用作表征钻头渐进磨损的指示参数。然后推导了这些输入参数的主成分,并将其用于bp神经网络预测翼面磨损
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PCA fused NN approach for drill wear prediction in drilling mild steel specimen
The present paper describes use of principal components for drill wear prediction. It also makes a comparative analysis in using large sensor based technique in predicting drill wear. In order to reduce the redundancy of the network, principal component has been fused with artificial neural network (ANN) for prediction of drill wear. Large numbers of experiments have been conducted and sensor signals have been acquired using data acquisition system. Cutting force, torque, vibrations along with other process parameters such as spindle speed, feed rate, drill diameter, chip thickness and surface roughness have been used as indicative parameters for characterizing the progressive wear of drill. Principal component of these input parameters has been derived thereafter and has been used to predict the flank wear using BPNN
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