Ultrasonic techniques are providing fast and nondestructive information for quality assurance of the composite and help to optimize process parameters. The Ultrasonic parameters are used to indicate the correlation between the acoustic properties and the microstructure of the material. To characterize the aluminium metals by knowing the aluminum and iron percentage present in the Aluminum metals so that it can be classified into the types of aluminium metals which are available. Grade of the aluminium samples help user in a position to decide its applications. In this paper an attempt is made to characterize the aluminium metals by ultrasonic non destructive techniques and signal processing technique. To develop the relationship between aluminum and iron percentage present in the aluminum metals and the various observed NDT parameters such as density, ultrasonic velocity, attenuation, compositions present in aluminium samples, peak amplitude of FFT, Time signal, Power Spectral Density etc IDASM Neural network is used. This Neural model calculates the percentage of aluminium and iron present in the aluminium samples and it is compare with the Experimental data. The impact of various variables on aluminum and iron percentage present in the aluminum samples is also discussed in this paper.

Keywords : Ultrasonic, Aluminium, , Characteristics, Neural Network.