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Integration of quality characteristics models as a software-based graphical interface for machining of AA6351 aluminum alloy using abrasive water jet process
Journal of the Brazilian Society of Mechanical Sciences and Engineering ( IF 2.2 ) Pub Date : 2020-05-24 , DOI: 10.1007/s40430-020-02410-6
Dinesh Singh , Rajkamal S. Shukla

In this paper, semi-empirical models of the performance characteristics, i.e., material removal rate, average surface roughness, kerf top width and taper angle in abrasive water jet machining (AWJM) of AA6351 aluminum alloy are reported. These models are developed using the dimensional analysis based on Buckingham ∏ theorem, and the proposed models for the considered performance parameters are analyzed both qualitatively and quantitatively using experimental data. Likewise, artificial feed forward neural network has been attempted to predict the considered performance parameters of AWJM process. For this purpose, different numbers of neurons have been tried at the hidden layer to obtain the best neural network. The validity of semi-empirical models and artificial neural network is verified using the experimental results. Furthermore, software prototype graphical user interface (GUI) is developed which allows user to interact with the models to predict the considered performance characteristics. The GUI is tested and validated through the experimental results. The results proved the functional capability and effectiveness of the GUI in predicting the performance characteristics.



中文翻译:

质量特性模型的集成,作为基于软件的图形界面,可使用磨料射流工艺加工A6353铝合金

本文报道了AA6351铝合金在磨料水射流加工(AWJM)中的性能特征的半经验模型,即材料去除率,平均表面粗糙度,切缝顶部宽度和锥角。这些模型是使用基于Buckingham ∏定理的尺寸分析开发的,并使用实验数据对所考虑的性能参数提出的模型进行了定性和定量分析。同样,已经尝试使用人工前馈神经网络来预测AWJM过程的性能参数。为此,已在隐藏层尝试了不同数量的神经元,以获得最佳的神经网络。实验结果验证了半经验模型和人工神经网络的有效性。此外,开发了软件原型图形用户界面(GUI),该界面允许用户与模型进行交互以预测所考虑的性能特征。通过实验结果对GUI进行了测试和验证。结果证明了GUI在预测性能特征方面的功能和有效性。

更新日期:2020-05-24
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