Study on the Micro-Abrasion Wear Behavior of PVD Hard Coating under Different SiC Abrasive Particles/Distilled Water Ratios

Microscale abrasion has been intensively used to study the wear behavior o several hard coatings, enabling the observation of different wear mechanisms. Recently, a study arguing whether the surface texture of the ball could influence the dynamics of abrasive particles throughout the contact was presented. In this work, the influence of the abrasive particles concentration able to change the texture of the ball was studied to understand its influence on the wear modes-rolling or grooving. Thus, tests were carried out using samples with a thin coating of TiN, deposited using the Physical Vapor Deposition (PVD) technique, and AISI 52100 steel balls etched over 60 s to induce a change in their texture and roughness were used. Three abrasive slurries were prepared with black silicon carbide (SiC) particles (average particle size of 4 μm) with different concentrations, 0.25, 0.35, and 0.45 g/cm³. The rotation speed used in the tests was 80 rpm and the normal loads applied in the study were 0.2 N and 0.5 N, and 1 N. After the wear tests, the coated samples and tracks on the surface of the balls were observed by SEM and 3D microscopy to understand the abrasive particle dynamics, evaluating the wear mode transition as well as the function of both applied load and slurry concentration. The tracks in the balls showed particles embedded on their surface. A lower concentration of abrasion was conducted to higher specific wear rate. Moreover, a predominant two-body wear mechanism was induced when the abrasive concentration was increased. There was an increase in the roughness of the scar and the surface of the balls with an increase in the abrasive particles' concentration.

Optimization and Modeling of Material Removal Rate in Wire-EDM of Silicon Particle Reinforced Al6061 Composite

The mechanical, physical and interfacial properties of aluminum alloys are improved by reinforcing the silicon carbide particles (SiC_p). Machinability of such alloys by traditional methods is challenging due to higher tool wear and surface roughness. The objective of research is to investigate the machinability of SiC_p reinforced Al6061 composite by Wire-Electrical Discharge Machining (wire-EDM). The effect of wire-EDM parameters namely current (I), pulse-on time (T_on), wire-speed (W_s), voltage (I_v) and pulse-off time (T_off) on material removal rate (MRR) is investigated and their settings are optimized for achieving the high MRR. The experiments are designed by using Taguchi L₁₆ orthogonal arrays. The MRR obtained at different experiments are analyzed using statistical tools. It is observed that all the chosen process parameters showed significant influence of on the MRR with contribution of 27.39%, 22.08%, 21.32%, 15.76% and 12.94% by I, I_v, T_off, T_on and W_s, respectively. At optimum settings, the Wire-EDM resulted in MRR of 65.21 mg/min and 62.41 mg/min for samples with 4% and 8% SiC_p. The results also indicated reinforcing SiC_p upto 8% showed marginally low influence on MRR. Microstructural investigation of the cut surface revealed the presence of craters with wave pattern on its surface. The top surface of the crater is featured by the recast layers connecting adjacent craters. Further, the statistical model is developed using linear regression to predict the MRR (𝑅²—73.65%) and its predicting accuracy is verified by the confirmation trials. The statistical model is useful for predicting the MRR for different settings of the process parameters. The optimized settings can be used to improve the machining productivity by increasing the MRR while machining of Al6061-SiC_p (upto 8 wt. %) alloy by wire-EDM industries.

A stochastic microstructure model for particle reinforced aluminium matrix composites

Metal matrix composites are complex materials consisting of various phases which can display largely different mechanical properties. The deformation behaviour of these composites cannot be sufficiently modelled by averages or simple particle shapes due to the local stresses that occur on the particle edges. Therefore, a sophisticated model of the microstructure is needed. We introduce a method for stochastic modelling of a silicon carbide (SiC) particle reinforced aluminium matrix composite. The SiC particles are modelled by Laguerre polyhedra generated by densely packed spheres. The shape factors of the polyhedra have been fitted to the particle shapes observed in three-dimensional images. Particle elongation in extrusion direction and the observed log-normal volume distribution of the particles are included in the model by suitable scaling. An outlook is presented on how to model the grains of the polycrystalline aluminium matrix and intermetallic precipitates, which result from the strengthening mechanism of the matrix. LAY DESCRIPTION: Metal matrix composites are complex materials consisting of different phases which can display largely different mechanical properties. The deformation behaviour of these composites cannot be sufficiently modelled by averages or simple particle shapes due to the local stresses that occur on the particle edges. Therefore, a sophisticated model of the microstructure is needed. We introduce a method of stochastic modelling of a silicon carbide (SiC) particle reinforced aluminium matrix composite. The SiC particles are modelled by Laguerre polyhedra generated by densely packed spheres. The shape factors of the polyhedra have been fitted to the SiC shapes observed in three-dimensional images. Additionally, the polyhedra are scaled anisotropically to account for orientation anisotropy and to obtain a log-normal volume distribution. An outlook is presented on how to model the aluminium phase's grains and intermetallic precipitates, which result from the strengthening mechanism of the aluminium matrix alloy.