This paper discusses the wear and friction with the 2 W% Al2O3 nanocomposite content of pure Mg and AZ91D Mg alloys. Sliding speeds of 0.5 and 1.5 m/s in cast materials with normal stress conditions have been used in sliding distances up to 2000 m/s (0.5, 1.0, and 1 MPa). In order to evaluate the work hardness of the materials measured on temperature similar to the contact surface, we used hardness patterns and hot-compression flow curves. Mg and AZ91D magnesium alloy pure monolithic Mg are low wear resistant due to an increase in contact temperature due to the adjustment of working conditions, but the wear rate was significantly lower in composite materials, mainly because of nanoparticle strength improvements. Although wear generally contributes to grain refining, increased wear capacity, and greater durability, wear resilience due to dislocation resistance and nanoparticles is seen as the primary wear mechanism in the existing nanocomposites.
As a known fact, energy usage and demand exponentially rises year after year, hence forth power based companies are apparently looking out for a forecasting approach with better approximations. Based on the usage history at the customer level with the emergence of machine learning, and its association with various prediction and decision making fields. This paper aims to use a machine learning algorithm to predict the cost levied on the customer proportional to the usage. The efficacy of this model is compared to the results obtained with the mathematical computations. It is evident that the accuracy is 95% with reference to the multilinear regression algorithm
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