The objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

中文翻译：

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使用与遗传算法集成的神经网络进行制造成本容差建模和优化的显式方法

本文的目的是开发一种新方法来模拟制造成本公差，并优化公差值及其制造成本。成本-容差关系在它们之间具有复杂的非线性相关性。神经网络的特性使得对复杂相关性的建模成为可能，遗传算法（GA）与最佳神经网络模型相结合以优化容差值。所提出的方法使用了三种类型的神经网络模型（多层感知器、反向传播网络和径向基函数）。这些网络模型是为棱柱形和旋转部件分别开发的。对于网络模型的构建，零件尺寸和公差值被用作输入神经元。参考制造成本被指定为输出神经元。定性生产数据集在车间收集，并分为三个文件，分别用于培训、测试和验证。基于最佳回归系数和均方根误差值确定网络模型的架构。将最佳网络模型集成到遗传算法中，并研究了遗传算子的作用。最后，展示了来自文献的两个案例研究，以验证所提出的方法。基于神经网络模型的新方法使设计和工艺规划工程师能够提出明智的决策，而无需考虑他们的经验。基于最佳回归系数和均方根误差值确定网络模型的架构。将最佳网络模型集成到遗传算法中，并研究了遗传算子的作用。最后，展示了来自文献的两个案例研究，以验证所提出的方法。基于神经网络模型的新方法使设计和工艺规划工程师能够提出明智的决策，而无需考虑他们的经验。基于最佳回归系数和均方根误差值确定网络模型的架构。将最佳网络模型集成到遗传算法中，并研究了遗传算子的作用。最后，展示了来自文献的两个案例研究，以验证所提出的方法。基于神经网络模型的新方法使设计和工艺规划工程师能够提出明智的决策，而无需考虑他们的经验。