Despite its excellent qualities such as hardness, tensile, and yield strength, aluminum alloys are mostly used in aviation fins and car frames. However, wear resistance at maximum load is weak. This effort will now synthesize and investigate the tribological behavior of AA6063- (AlMg0.7Si-) AlN composites. The goal of this experiment is to determine the best wear rate and coefficient of friction for the AA6063-AlN with nanomagnesium composites developed. Weight percent, load (L), sliding velocity (SV), and sliding distance (SD) are the process factors studied, and the output responses are wear rate and friction coefficient. Bottom pouring type stir casting was used to create AA6063-AlN composites with various weight percentages. The various compositions are AA6063, AA6063-4 wt% AlN, AA6063-8 wt% AlN, and AA6063-12 wt% AlN. A pin-on-disc machine inspected the wear rate and friction coefficient of AA6063-AlN composites. Experimentation was done according to L16 orthogonal array (OA). Wear rate (WR) and coefficient of friction (COF) examinations were made to identify the optimum parameters to obtain minimum WR and COF for the AA6063-AlN composite via grey relational analysis (GRA). The contour plot analysis clear displays WR and COF with respect to wt% vs. L, wt% vs. SV and wt% vs. SD. The ANOVA outcomes revealed that wt% is the most vital parameter (85.55%) persuading WR and COF. The optimized parameters to achieve minor WR and COF was found as 12 wt% of AlN, L 20 N, SV 3 m/s, and SD 400 m. The worn surface was analyzed using scanning electron microscope and indicates that addition of AlN particles with matrix reduces the scratches. These articles offer a key for optimum parameters on wear rate and COF of AA6063-AlN composites via Taguchi grey relational analysis.

Laser cutting is a one of the efficient manufacturing processes in industry to cut the hard materials by vaporizing. Stainless steel (SS347) is the most popular material for many applications due its unique characteristics such as efficiency to retain good strength with no inter-granular corrosion even at elevated temperatures. However, the cutting or machining of this material is very difficult. On the other side, the machining cost of laser process is high when compared with other processes. In this work, GRA and TOPSIS techniques are used to study the laser cutting process parameters of SS347. The obtained results were compared with the data mining approach. The input parameters are power, speed, pressure and stand-off distance (SOD) and the output responses of surface roughness, machining time and HAZ are considered. The set of experiments were constructed by using the Taguchi’s L9 method. The predicted closeness value of TOPSIS is greater than the GRA technique and the predominant factor observed is SOD followed by pressure, speed and power. In this work, C4.5-decision tree algorithm is applied to find the most influential parameter. It also represents the low-level knowledge of data set into high level knowledge (If-Then rules form). This investigation reveals that both TOPSIS and data mining suggested the SOD as predominant factor. This result of the optimized process parameters supports the laser assisted manufacturing industries by providing optimized output. Better results were obtained using the optimized set of parameters with the machining time, HAZ and surface roughness being 7.83[Formula: see text]s, 0.09[Formula: see text]mm and 0.86[Formula: see text][Formula: see text]m, respectively. The results of this work would be very useful for automobiles and aircrafts industries where SS347 is highly employed.