Application of ANN modelling for optimisation of surface quality and kerf taper angle in abrasive water jet machining of AISI 1018 steel

ABSTRACT

Abrasive water jet machining (AWJM) is the modern machining method used in various industrial applications. In this paper, an attempt has been made to determine optimum AWJM parameters using artificial neural network (ANN). Flow rate of abrasive, stand-off distance and traverse speed are used as input parameters for machining of AISI 1018 steel. Experimental data are used for training and testing of ANN. A feed forward ANN is established using the experimental data obtained during the AWJ machining of steel. Surface roughness (R_a) and kerf taper angle (ϴ) are estimated, on the basis of ANN performance and regression analysis. The regression plots of both responses show more than 97% of agreement among the estimated values and experimental data. Later on, novel approach of model optimisation is executed using univariate analysis on inputs to ANN to get the optimal AWJM process parameters. Optimal response solution is obtained as surface roughness (R_a) = 2.46 μm and taper angle (ϴ) = 1.25° at flow rate (A_f) = 450 g/min; stand-off distance (S_d) = 3 mm and traverse speed (T_v) = 85 mm/min. The study shows that the ANN is proficient tool for deciding the optimum AWJ machining parameters.

KEYWORDS:

• Abrasive water jet machining
• surface quality
• kerf taper angle
• artificial neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article or its supplementary materials.