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A cellular neural networks approach for non-destructive control of mechanical parts

机译:一种蜂窝神经网络方法,用于机械部件的非破坏性控制

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摘要

In this paper an approach is proposed using Cellular Neural Networks applied image processing, for the detection and characterisation of superficial faults in mechanical parts. There are above all two advantages deriving from an application of the proposed methodologies: the automization of a procedure, that of non-destructive tests (NDT), which is today carried out manually, and the possibility to reduce to a negligible amount the time spent on checking operations, at present estimated to be in the order of a number of hours for each separate mechanical part.
机译:本文采用蜂窝神经网络施加图像处理提出一种方法,用于检测和表征机械部件中的浅层故障。最重要的是从拟议方法的应用中产生的所有两个优点:程序的自动化,无损检测(NDT),这是今天进行手动进行的,并且可能降低到所花费的时间忽略量的可能性在检查操作时,目前估计每个单独的机械部件的数小时数。

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