Neural network parametric modelling of abrasive waterjet cutting quality
by Lin Yang, Jun Song, Biaohui Hu
International Journal of Abrasive Technology (IJAT), Vol. 1, No. 2, 2007

Abstract: Abrasive Waterjet (AWJ) can provide very effective means for shape cutting of difficult-to-machine materials. One of the principal deficiencies of AWJ cutting process is the wavy striation on the generated cut surface in relatively thick workpieces or high traverse speed. It is therefore essential to predict the main AWJ processing parameters to achieve a desired cutting quality. But many aspects about this technology are neither fully understood nor have they been accurately modelled. In this paper, based on the experimental data, an Artificial Neural Network (ANN) parametric model of AWJ cutting process was developed and used in CNC machines. The predicted results indicated that the model could identify the cutting quality to a high desirable accuracy, and the ANN can be used as an appropriate method for prediction of cutting parameters in AWJ systems.

Online publication date: Fri, 12-Oct-2007

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