Formulation of Mathematical Modeling to Characterize The Aluminium Metals Using Ultrasonic Non-Destructive Techniques

Abstract

Predicting the type of aluminium metals and composition of elements present in the aluminium samples through Nondestructive testing (NDT) is a matter of very importance for aluminium Industry. The unique method to determine grade of the aluminium sample is required to characterize the aluminium metals. The Nondestructive Technique (NDT) and determination of characteristics and mechanical properties of aluminium metals are used to identify the grade of aluminium metals so that accordingly it can be used for the specific applications. Therefore a technique is required to predict the percentage of aluminium, Iron, Copper, Manganese of aluminium metals so as to categorize into different grades and applications. In Aluminium samples percentage of Aluminium plays very important role which may help to decide the grade of the aluminium metals hence its applications. The present work is focused on how the percentage of aluminium in aluminium samples can be calculated by adopting the mathematical modeling technique. There are various parameters which generally affect the percentage of aluminium in aluminium samples, and play a very major role. Therefore through this investigation an attempt is being made to formulate an approximate mathematical model which will certainly predict the percentage of aluminium in aluminium samples. In advent of this a dimensionless pie terms of various prominent parameters or variables have been taken to form a mathematical model. Some of these variables used to formulate this model are given as follows (i) physical properties of the aluminium samples like hardness, density, modulus of elasticity etc (ii) Signal analysis properties like Peak amplitude of Time signal, FFT, PSD and (iii) both the properties. The data of such types of variables have been recorded and calculated and thus the formulation of model is being done by multiple regression analysis. The model is then optimized and the reliability of the model has also been estimated. In fact this type of model will be helpful to estimate the aluminium percentage.