Effect of the AWJM Method on the Machined Surface Layer of AZ91D Magnesium Alloy and Simulation of Roughness Parameters Using Neural Networks

Abstract

This paper investigates the effect of change of the abrasive flow rate and the jet feed on the effectiveness of machining of AZ91D casting magnesium alloy. The evaluation of the state of the workpiece surface was based on surface and area roughness parameters (2D and 3D), which provided data on: irregularities formed on the workpiece edge surface (water jet exit), the surface quality after cutting, the workpiece surface chamfering, microhardness of the machined surface, and of specimen cross-sections (along the water jet impact). The process was tested for two parameter settings: abrasive flow rate 50 at cutting speed vf = 5–140 mm/min, and abrasive flow rate 100% (0.5 kg/min) at vf = 5–180 mm/min. The results demonstrate a significant effect of the abrasive flow rate and the jet feed velocity on the quality of machined surface (surface roughness and irregularities). In addition, selected 2D surface roughness parameters were modelled using artificial neural networks (radial basis function and multi-layered perceptron). It has been shown that neural networks are a suitable tool for prediction of surface roughness parameters in abrasive water jet machining (AWJM).

Influence of Variable Radius of Cutting Head Trajectory on Quality of Cutting Kerf in the Abrasive Water Jet Process for Soda–Lime Glass

“…A wide range of two- and three-dimensional (2D and 3D) parameters can be used for a quantitative assessment of cut surface roughness. The most commonly used parameters for assessment of the surface texture in 2D, as presented in works by Löschner et al [ 32 ], Zagórski et al [ 8 ], Hreha et al [ 33 ], and Klichova and Klich [ 34 ] and defined in the ISO 4287:1997 standard [ 35 ], are the amplitude parameters Ra, Rq , and Rt. The 3D parameters can be divided into the following groups: amplitude, area and volume, spatial, hybrid, and functional; they are included in the ISO 25178-2:2012 standard [ 36 ] and the EUR 15178 EN report [ 37 ].…”

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

“…Therefore, an appropriate procedure is needed that can allow the surface roughness value to be assessed prior to the machining of the material, while being easy to use in industry and helping to minimize the time and cost of treatment. Moreover, such a procedure could be used to determine the appropriate cutting conditions to obtain the specific surface roughness [ 3 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

“…The ability to predict surface roughness before machining has attracted great interest from many scientists, being the main goals of many research studies. The prediction of surface roughness is currently determined by using various techniques such as theoretical models [ 3 , 32 , 33 , 34 , 35 ], response surface methodology (RSM) [ 3 , 6 , 9 , 36 , 37 , 38 ], the Taguchi procedure [ 3 , 6 , 28 , 38 , 39 , 40 , 41 , 42 , 43 ], multiple linear regression equations [ 44 ], the Monte Carlo (MC) method [ 7 , 24 , 33 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ], artificial intelligence through the use of the artificial neural networks (ANNs) [ 1 , 3 , 26 , 29 , 30 , 53 , 54 , 55 , 56 ], genetic algorithms (GAs) [ 3 , 57 ], fuzzy logic (FL) [ 3 , 36 , 54 , 58 , …”

Surface Quality in Milling of AZ91D Magnesium Alloy

“…plastic forming, to preliminary preparation of semi-finished products for further processing, e.g. using the AWJM method [18], and brush treatment (edge deburring) [8] there is a need for a broader description of the surface finish quality of workpieces. In the vast majority of the analysed literature, descriptions mainly pertain to the basic Ra roughness parameter; this seems to be highly insufficient given the utilitarian characteristics of magnesium alloy workpiece surfaces.…”