Erosion behavior of Al-SiC and Al-WC MMCs via powder metallurgy under solid particle impingement.

This study investigates the erosion behavior of Al-SiC and Al-WC metal-based composites in a solid particle impingement environment developed through powder metallurgy. Micro-sized powders of SiC and WC were utilized in different weight proportions (0-10 wt%) for fabricating the composite samples. The angle of impingements of 30-90° and impact velocity of 30-90 m/s were used for solid particle erosion testing.

A hybrid deep leaning model for prediction and parametric sensitivity analysis of noise annoyance.

Noise annoyance is recognized as an expression of physiological and psychological strain in acoustical environment. The studies on prediction of noise annoyance and parametric sensitivity analysis of factors affecting it have been rarely reported in India. A hybrid ConvLSTM technique was developed in the study to predict traffic-induced noise annoyance in 484 people based on ambient noise levels, as well as survey information.

Experimental Investigation of Different Fineness and Firing Temperatures on Pellets Properties of Different Iron Ore fines from Indian Mines.

In India, during mining and ore processing, ore fine generation is a common phenomenon, in which more than 60% of process ore becomes discarded material. To explore the alternative of high-grade ores, mutual replacement with the utility of dump ore fines is the best way. With this perspective, Kiruburu iron ore mine (Iron Ore No.

Selection of efficient degradation features for rolling element bearing prognosis using Gaussian Process Regression method.

Rolling Element Bearings are one of the most ubiquitous machine elements used in various machineries in the manufacturing industry. Prognosis and estimation of residual life of rolling element bearing are very important for efficient implementation of health monitoring and condition-based maintenance. In this paper, a rolling element bearing fault or degradation trend prediction is modeled using Gaussian Process Regression (GPR) method.