On modeling of tool wear using sensor fusion and polynomial classifiers

Ibrahim Deiab; Khaled Assaleh; Firas Hammad

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On modeling of tool wear using sensor fusion and polynomial classifiers

机译：使用传感器融合和多项式分类器的工具磨损建模

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摘要

With increased global competition, the manufacturing sector is vigorously working on enhancing the efficiency of manufacturing processes in terms of cost, quality, and environmental impact. This work presents a novel approach to model and predict cutting tool wear using statistical signal analysis, pattern recognition, and sensor fusion. The data are acquired from two sources: an acoustic emission sensor (AE) and a tool post dynamometer. The pattern recognition used here is based on two methods: Artificial Neural Networks (ANN) and Polynomial Classifiers (PC). Cutting tool wear values predicted by neural network (ANN) and polynomial classifiers (PC) are compared. For the case study presented, PC proved to significantly reduce the required training time compared to that required by an ANN without compromising the prediction accuracy. The predicted results compared well with the measured tool wear values.

机译：随着全球竞争的加剧，制造业在成本，质量和环境影响方面正在积极致力于提高制造过程的效率。这项工作提出了一种使用统计信号分析，模式识别和传感器融合来建模和预测切削刀具磨损的新颖方法。数据从两个来源获取：声发射传感器（AE）和刀架测功机。此处使用的模式识别基于两种方法：人工神经网络（ANN）和多项式分类器（PC）。比较了由神经网络（ANN）和多项式分类器（PC）预测的刀具磨损值。对于所展示的案例研究，事实证明，与ANN相比，PC可以显着减少所需的训练时间，而不会影响预测准确性。预测结果与测得的工具磨损值进行了很好的比较。

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来源
《Mechanical systems and signal processing》 |2009年第5期|1719-1729|共11页
作者
Ibrahim Deiab; Khaled Assaleh; Firas Hammad;
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作者单位

Mechanical Engineering Department, American University of Sharjah, P.O. Box 26666 Sharjah, United Arab Emirates;

Electrical Engineering Department, American University of Sharjah, P.O. Box 26666 Sharjah, United Arab Emirates;

Mechanical Engineering Department, American University of Sharjah, P.O. Box 26666 Sharjah, United Arab Emirates;

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原文格式 PDF
正文语种 eng
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关键词
tool wear; feature extraction; neural networks; polynomial classifiers;

机译：工具磨损;特征提取;神经网络;多项式分类器;

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On modeling of tool wear using sensor fusion and polynomial classifiers

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