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

Abstract

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.

Prediction of Abrasive Waterjet Machining Parameters of Military-Grade Armor Steel by Semi-Empirical and Regression Models

“…Shanmugam and Masood [24] created a semi-empirical model for predicting the kerf taper and verified it using an average percentage deviation and standard deviation. Singh and Shukla [25] studied AWJM, where semi-empirical dimensional analysis models had been developed and effectively used to estimate the kerf angle and Ra, respectively. Pahuja and Ramulu [26] created a semi-empirical model to determine the depth of cut in the AWJM of Ti6Al4V, CFRP, and layered Ti/CFR.…”