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首页> 外文期刊>International journal of design engineering >Concurrent tolerance allocation using an artificial neural network and continuous ant colony optimisation
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Concurrent tolerance allocation using an artificial neural network and continuous ant colony optimisation

机译：使用人工神经网络和连续蚁群优化的同时公差分配

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摘要

The allocation of tolerances for the components of a mechanical assembly strongly influences manufacturing cost and functional performance. In order to get a reliable tolerances and costs, it is necessary to obtain manufacturing cost-tolerance models. Traditionally, these models are established by various curve-fitting techniques using empirical experimental data. Existing empirical models, however, have considerably large model fitting error, inconsistent modelling accuracy over the tolerance range of typical manufacturing processes. Using these mathematical models will introduce a considerably large error in optimal design of component tolerances.rnThis work presented in this paper uses an artificial neural network (ANN), to overcome above limitations, for establishing manufacturing cost-tolerance models for various manufacturing processes. Having built the ANNrncost-tolerance models, continuous ant colony optimisation (CACO) algorithm is used to obtain optimum combination of tolerances for minimum manufacturing cost. A typical tolerance design example is used to illustrate the effectiveness and reliability of the proposed approach.
机译：机械组件的零件公差分配严重影响制造成本和功能性能。为了获得可靠的公差和成本，必须获得制造成本公差模型。传统上，这些模型是使用经验数据通过各种曲线拟合技术建立的。然而，现有的经验模型具有相当大的模型拟合误差，在典型制造过程的公差范围内，建模精度不一致。使用这些数学模型将在零件公差的最佳设计中引入相当大的误差。本文中提出的这项工作使用人工神经网络（ANN）来克服上述限制，从而为各种制造过程建立制造成本公差模型。建立了ANNrncost公差模型后，连续蚁群优化（CACO）算法可用于获得公差的最佳组合，从而将制造成本降至最低。一个典型的公差设计实例用于说明所提出方法的有效性和可靠性。

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