粉末冶金材料和工艺参数选择的神经网络方法

R.P. Cherian, L.N. Smith, P.S. Midha
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引用次数: 72

摘要

本文提出的人工神经网络(NN)方法用于粉末冶金零件的粉末和工艺参数的选择。这种方法不同于机械性能的统计建模,因为它不需要对与输入和输出变量相关的函数的形式做出假设。使用神经网络方法可以指定多个输入标准,并生成多个输出建议。输入包括PM材料所需的机械性能。系统在神经网络中使用这些数据,以推荐合适的金属粉末成分和工艺设置。预测和实验PM材料数据的比较证实了神经网络方法的准确性,用于预测实现所需工艺结果所需的材料和工艺设置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A neural network approach for selection of powder metallurgy materials and process parameters

The artificial neural network (NN) methodology presented in this paper has been developed for selection of powder and process parameters for Powder Metallurgy (PM) part manufacture. This methodology differs from the statistical modelling of mechanical properties in that it is not necessary to make assumptions regarding the form of the functions relating input and output variables. Employment of a NN approach allows specification of multiple input criterion, and generation of multiple output recommendations. The inputs comprise the required mechanical properties for the PM material. The system employs this data within the NN in order to recommend suitable metal powder compositions and process settings. Comparison of predicted and experimental PM materials data has confirmed the accuracy of the NN approach, for predicting the materials and process settings needed for attainment of required process outcomes.

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