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The purpose of this paper is to address the determination of crack location and depth of multiple transverse cracks by monitoring natural frequency and its prediction using Artificial Neural Networks (ANN). An alternative to the existing NDTs is suggested.

Modal analysis is performed to extract the natural frequency. Analysis is performed for two cases of cracks. In first case, both cracks are perpendicular to axis. In second case, both cracks are inclined to vertical plane and also inclined with each other. Finite element method (FEM) is performed using ANSYSTM software which is theoretical basis. Experimentation is performed using Fast Fourier Transform (FFT) analyzer on simply supported stepped rotor shaft and cantilever circular beam with two cracks each.

The results of FEM and experimentation are validated and are in good agreement. The error in crack detection by FEM is in the range of 3-15 percent while 5-20 percent by experimentation. The database obtained by modal analysis is used to train the network of ANN which predicts crack characteristics. Validity of method is investigated by comparing the predictions of ANN with FEM and experimentation. The results are in good agreement with error of 7-16 percent between ANN and FEM while 9-21 percent between ANN and experimental analysis.

It envisages that the method is capable. It is an effective as well as an alternate method of fault detection in beam/rotating element to the existing methods.

Aluminium matrix composites are subjected to wear as well as higher temperature applications such as pistons, cylinder heads and blocks for car engines. Therefore, it is important to evaluate the performance of aluminium metal matrix composite at elevated temperature.

In the present work wear performance of Al-TiC composite with 7.5% reinforcement of TiC powder is carried out at elevated temperature. The composite specimens are prepared with the help of centrifugal casting method to get the large segregation of reinforcement on the outer layer of the composite which is subjected to wear. Taguchi method is used for preparing design of experiments.

The wear test is performed on DUCOM pin on disc setup having the heating chamber facility. The results of wear test are analysed with the help of MINITAB 19 software. The results show that temperature has dominant effect on the wear rate. The mathematical model through regression is predicted for wear rate and coefficient of friction. The study of worn-out surface is performed with the help of scanning electron microscope. The micrographs show that the type of wear is changes from abrasive to severe wear and some delamination.

The experiments are conducted as per ASTM standards. The results give the mathematical equation for wear rate and coefficient of friction at elevated temperatures.

The purpose of this paper is to examine the mechanical characteristics and optimization of wear parameters of hybrid (TiO₂ + Y₂O₃) nanoparticles with Al matrix using squeeze casting technique.

The hybrid aluminium matrix nanocomposites (HAMNCs) were fabricated with varying concentrations of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃), from 2.5 to 10 Wt.% in 2.5 Wt.% increments. Dry sliding wear test variables were optimized using the Taguchi method.

The introduction of hybrid nanoparticles in the aluminium (Al) matrix was evenly distributed in contrast to the base matrix. HAMNC6 (5 Wt.% TiO₂ + 5 Wt.% Y₂O₃) reported the maximum enhancement in mechanical properties (tensile strength, flexural strength, impact strength and density) and decrease in porosity% and elongation% among other HAMNCs. The results showed that the optimal combination of parameters to achieve the lowest wear rate was A3B3C1, or 15 N load, 1.5 m/s sliding velocity and 200 m sliding distance. The sliding distance showed the greatest effect on the dry sliding wear rate of HAMNC6 followed by applied load and sliding velocity. The fractured surfaces of the tensile sample showed traces of cracking as well as substantial craters with fine dimples and the wear worn surfaces were caused by abrasion, cracks and delamination of HAMNC6.

Squeeze-cast Al-reinforced hybrid (TiO₂+Y₂O₃) nanoparticles have been investigated for their impact on mechanical properties and optimization of wear parameters.