Analytical modeling of effective depth of cut for ductile materials via abrasive waterjet machining

Abrasive waterjet (AWJ) cutting technology is widely used for nonconventional cold machining of ductile or brittle materials in various manufacturing fields. However, this technology has a significant limitation in terms of the effective depth of cut. As a solution, in this study, a model for the effective depth of cut is proposed based on the Gaussian distribution of the cut profile curve. The proposed model is used to investigate the dynamic evolution of the depth of cut and its influence mechanism. First, a prediction model for the effective depth of cut is established using the material removal mechanism and single particle erosion theory. In addition, an experimental analysis is conducted to improve and optimize the cutting performance of the AWJ considering the effect of machining parameters on (i) the effective depth of cut and (ii) cutting performance. The key factors that affect the geometric profile characteristics of the kerf are investigated, and the optimal parameters for achieving the effective depth of cut model are further determined. According to the AWJ cutting performance experiments conducted by using Ti-6Al-4 V, the prediction model is strongly correlated with the experimental data, and the average difference between the prediction model and experimental results is 6.46% of the effective depth of cut. Notably, this model can predict the effective depth of a cut for different cutting parameters. Furthermore, it exhibits significant industrial value by expanding the application fields of finishing machining using AWJ technology.

Automated summary

A model for the effective depth of cut in abrasive waterjet cutting technology is proposed based on the Gaussian distribution of the cut profile curve. The model is established using the material removal mechanism and single particle erosion theory. Experimental analysis shows that the model is strongly correlated with the experimental data, with an average difference of 6.46% for the effective depth of cut. This model can predict the effective depth of cut for different cutting parameters and has significant industrial value.