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A non-conventional quality control system to detect surface faults in mechanical front seals

机译:一种非常规的质量控制系统,用于检测机械前密封件中的表面缺陷

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The Just in Time and the Total Quality policies have remarkably touched every field of modern industrial production. This context prompts companies to dedicate most of their efforts on researching and developing automatic systems of quality control to obtain the elevated standards of quality nowadays demanded by the market at every level of production. In fact the quantity of the exemplars allowed, which are not up to sample, is measured in parts per million in many sectors of production.rnMany methodologies have been proposed to yield high quality in the industrial production lines, in order to provide surface examination and classification.rnThis paper describes an alternative system for surface analysis based on artificial neural networks (ANNs), developed in collaboration with the Italian manufacturer "Meccanotecnica Umbra S.p.A". This system was implemented and tested in order to examine three particular surfaces of mechanical seals achieving good results in comparison with the deterministic system already implemented.
机译:准时制和全面质量政策已经触及了现代工业生产的各个领域。这种情况促使公司将大部分精力用于研究和开发自动质量控制系统,以获取当今市场各个生产水平所要求的更高质量标准。实际上,在许多生产部门中,所允许的样本数量(不超过样本)以百万分之几来衡量。为了提供表面检查和检测,已经提出了许多方法来提高工业生产线的质量。本文描述了与意大利制造商“ Meccanotecnica Umbra SpA”合作开发的基于人工神经网络(ANN)的表面分析的替代系统。该系统的实施和测试是为了检查机械密封件的三个特定表面,与已经实施的确定性系统相比,它们可以取得良好的结果。

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