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In the current study, we propose a bifractal stratified characterization method to evaluate the surface profile evolution of a plateau honing cylinder liner during the wear process. This method is suitable for the characterization of two-process and worn surface profiles with bifractal and stratified characteristics. We develop a bifractal truncated separation structure function method to calculate the characterization parameters of the proposed method. In particular, the upper- and lower-strata surface profiles are truncated and separated in order to calculate their fractal parameters. Experimental results demonstrate the ability of the proposed method to outperform the traditional fractal method in characterizing the evolution of the plateau honing cylinder liner surface profile during the wearing process.

This study employs Taguchi orthogonal design (L₉) to optimize the machining parameters of electro-discharge machining (EDM). The aluminum matrix composite (AMC) with 16wt.% titanium carbide (TiC) and 4wt.% graphite (Gr) specimen was prepared by stir casting process. This study involves three control parameters with three levels, namely pulse current, voltage and fluid pressure to predict the process response, such as material removal rate (MRR) and surface roughness (SR) of the worn surface. Maximum MRR of 0.1661g/min was attained for 10A, 500V and 15kgf/cm² fluid pressure with corresponding roughness of 11.43$\mu$m and the minimum value of 7.51$\mu$m was observed for 10A, 100V and fluid pressure of 5kgf/cm². A regression model was developed and the effect of control parameters on process responses were determined by analysis of variance (ANOVA). According to ANOVA outcome, the machining parameters which control the process response MRR were determined as voltage (47.94%), pulse current (33.19%) and fluid pressure (17.58%). Similarly, the SR was affected by machining parameters voltage (55.17%), pulse current (22.41%) and flushing pressure (21.47%). The optimum machining parameters were predicted and confirmed by conducting experiments with reasonable error of 2.49% and 2.02% for MRR and SR, respectively. Surface characteristics of the machined AMC was analyzed by scanning electron microscope (SEM) to observe the defects like craters, voids, glued debris and recast layers.

Digital image processing (DIP) becomes a common tool for analyzing engineering problems by fast, frequent and noncontact method of identification and measurement. An attempt has been made in the present investigation to use this method for automatically detecting the worn regions on the material surface and also its measurement. Brass material has been used for experimentation as it is used generally as a bearing material. A pin on disc dry sliding wear testing machine has been used for conducting the experiments by applying loads from 10 N to 50 N and by keeping sliding distance and sliding speed constant. After testing, images are acquired by using 1/2 inch interline transfer CCD image sensor with 795(H)$\ast$896(V) spatial resolution of 8.6$\mu$m (H)$\ast$8.3$\mu$m (V) unit cell. Denoising has been done to remove any possible noise followed by contrast stretching to enhance image for wear region extraction. Segmentation tool was used to divide the worn and unworn regions by identifying white regions greater than a threshold value with an objective of quantifying the worn surface for tested specimen. Canny edge detection and granulometry techniques have been used to quantify the wear region. The results revel that the specific wear rate increases with increase in applied load, at constant sliding speed and sliding distance. Similarly, the area of worn region as identified by DIP also increased from 42.7% to 69.97%. This is because of formation of deeper groves in the worn material.