Surface Topography Analysis of Mg-Based Composites with Different Nanoparticle Contents Disintegrated Using Abrasive Water Jet

Abstract

This study investigated the effect of abrasive water jet kinematic parameters, such as jet traverse speed and water pressure, on the surface of magnesium-based metal matrix nanocomposites (Mg-MMNCs) reinforced with 50 nm (average particle size) Al2O3 particles at concentrations of 0.66 and 1.11 wt.%. The extent of grooving caused by abrasive particles and irregularities in the abrasive waterjet machined surface with respect to traverse speed (20, 40, 250 and 500 mm/min), abrasive flow rate (200 and 300 g/min) and water pressure (100 and 400 MPa) was investigated using surface topography measurements. The results helped to identify the mode of material disintegration during the process. The nanoindentation results show that material softening was decreased in nanocomposites with higher reinforcement content due to the presence of a sufficient amount of nanoparticles (1.11 wt.%), which protected the surface from damage. The values of selected surface roughness profile parameters—average roughness (Ra), maximum height of peak (Rp) and maximum depth of valleys (Rv)—reveal a comparatively smooth surface finish in composites reinforced with 1.11 wt.% at a traverse speed of 500 mm/min. Moreover, abrasive waterjet machining at high water pressure (400 MPa) produced better surface quality due to sufficient material removal and effective cleaning of debris from the machining zone as compared to a low water pressure (100 MPa), low traverse speed (5 mm/min) and low abrasive mass flow rate (200 g/min).

Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance

“…Substituting the number of input layer nodes and the number of output layer nodes into Equation (17), the interval of the number of hidden layer nodes is [4,12]. In order to obtain the optimal value of the number of hidden layer nodes, each value of the interval is substituted into the MATLAB prediction program to calculate.…”

“…Braun et al used the dial test method to characterize the friction and wear behavior of steel sliding pairs with a diameter of 15–800 µm under mixed lubrication conditions, stated that at a certain sliding speed, using the best texture diameter can reduce friction by 80% and reduce wear [ 4 ]; Tillmann W et al used the micro-milling method to prepare surfaces with honeycomb and dimple topographies using high-speed steel materials, and focused on the analysis of the impact of the surface topography on its friction and wear properties [ 5 ]; Conradi et al studied the different morphologies of the Ti6Al4V surface: linear, cross-corrosion and dimples, and analyzed the effect of titanium alloy surface topography, weave density, and orientation (parallel, perpendicular and at 45°) on frictional wear under dry and lubricated conditions [ 6 ]; Razfa et al pointed out that the micro topography of the machined surface has a great influence on the surface wear performance of the parts, and the analysis and research of the surface topography is of great significance to the wear resistance of the product [ 7 ]; Wiciak-Pikuła used machine learning algorithms to predict the surface morphology parameters Ra and Rz of composite materials, and the effectiveness of the prediction model was verified by experiments [ 8 ]; Feng et al found that the fabric surface with high entanglement and nano-structured particles could obtain high abrasion resistance [ 9 ]; Daymi et al designed an experiment for ball-end milling Ti-6Al-4V titanium alloy, and explored the influence law of the machining inclination on the milling surface at the same time, a prediction model of milled surface roughness with cutting speed, feed, and radial depth of cut as variables was also established [ 10 ]; Taoheed et al studied the influence of different spindle speeds and feed rates on the surface topography of aluminum-based alloys. The test results showed that the surface roughness will decrease as the spindle speed decreases [ 11 ]; Mardi et al studied the influence of kinematic parameters on the surface morphology of nanocomposites, and expressed the results through topography parameters [ 12 ]; Maher et al established an adaptive neuro-fuzzy system of machining parameters and surface roughness by studying and analyzing the correlation between machining parameters (spindle speed, feed per tooth, depth of cut), milling forces, and surface roughness [ 13 ]; Vishwas et al investigated the effect of process parameters such as cutting speed, feed, and depth of cut on the surface topography of martensitic stainless steel by means of turning machining [ 14 ].…”

Effect of Waterjet Cleaning Parameters During Paint Removal Operation on Automotive Steel Components

“…In order to define the process output for the WJ application, the waterjet input parameter is adjusted. Due to the influence of its input settings, the use of a waterjet to remove paint causes a variation in the surface's topography of the material being machined [6] [7]. A crucial component of the cleaning process is the pressurized water of the water jet; this water's energy is transformed into kinetic energy as it passes through the orifice and creates a high-velocity jet [8] [9].…”