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A neural network-based assembly algorithm for chamferless parts mating

机译:基于神经网络的倒角零件配合算法

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This paper presents a neural network-based assembly algorithm for chamferless parts mating. The difficulties in assembly strategies result from the errors of assembly system as well as the complexity of the assembly process. To solve this problem, we propose an active-passive assembly algorithm by integrating a passive vibration method and an active force feedback signal, This method generates a vibration pattern by signs of moments, which is used to compensate the misalignment between mating parts. In this method, the relationship between the signs of moments and output patterns of the vibration is trained by neural network. The performance of the presented method is evaluated through a series of experiments. The experimental results show that the method can be applied to chamferless parts mating.
机译:本文介绍了一种基于神经网络的组装算法,用于倒角形零件配合。装配策略的困难是由装配系统的错误以及装配过程的复杂性导致的。为了解决这个问题,我们通过集成无源振动方法和有源力反馈信号来提出主动被动组装算法,该方法通过矩的迹象产生振动模式,其用于补偿配合部件之间的未对准。在该方法中,神经网络训练了振动的矩和输出模式的迹象与输出模式之间的关系。通过一系列实验评估所提出的方法的性能。实验结果表明,该方法可应用于倒角零件配合。

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