Integration of quality characteristics models as a software-based graphical interface for machining of AA6351 aluminum alloy using abrasive water jet process

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.