基于彩色图像处理和人工神经网络的钻头磨损自动测量

U. Bopp, T. Sajima, H. Onikura
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引用次数: 4

摘要

目前的研究以孔质量为标准,对钻头的角磨损进行自动测量,以预测钻头的寿命终止。钻削试验表明,最大孔径的变化与孔表面粗糙度Ra随钻削寿命的变化有较强的相关性。该测量系统采用彩色图像处理和人工神经网络相结合的方法,能较准确地检测钻头的角磨损情况,并预测出钻孔的表面粗糙度Ra,平均误差为0.32μm,最大误差为-1.00μm。堆积边缘的存在不影响结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic drill wear measurement using colour image processing and artificial neural network
In the present research corner wear of drills is measured automatically in order to predict end of drill life, using hole quality as criterion. Drilling experiments show a strong correlation between the progress of maximum hole diameter and hole surface roughness Ra over drill life. The proposed measurement system, using colour image processing and an artificial neural network, can detect corner wear of a drill accurately and predict the surface roughness Ra of the hole to be drilled with mean and maximum errors of 0.32μm and -1.00μm, respectively. The presence of a built-up edge does not influence the results.
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