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Multiple response optimisation based on the ANN theory of complex injection moulding process

机译:基于复杂注塑工艺的ANN理论的多响应优化

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摘要

Multiple response optimisation design can effectively improve the quality of the products, and generate huge economic benefits. For multiple response problem in the process of industrial production, owing to the control variables, and because the relationship between the control variable is relatively complex, the traditional multiple response optimisation method will not be able to improve the fitting model. On the basis of the theory of ANN, this paper builds a multiple response optimisation model of the injection moulding process, using Principal Component Analysis (PCA) to deal with multiple correlation between response factor, through the TOPSIS method to obtain the optimal level of factor combination, combined with the enterprise product injection instances, and effectively solve the complicated multiple response optimisation problems in moulding process, with a certain referential significance.
机译:多重响应优化设计可以有效提高产品质量,并产生巨大的经济效益。对于工业生产过程中的多响应问题,由于控制变量的影响,并且由于控制变量之间的关系比较复杂,传统的多响应优化方法无法改进拟合模型。基于人工神经网络的理论,建立了注塑成型过程的多响应优化模型,利用主成分分析(PCA)处理响应因子之间的多重相关性,通过TOPSIS法获得最优的因子水平结合,结合企业产品注入实例,有效解决成型过程中复杂的多重响应优化问题,具有一定的参考意义。

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