INTRODUCTION
Material characterization denotes a complete description of the structure and composition of a particular material. It involves an analysis of the properties of the material, the fabrication of the material and the usage of the said material (Groves and Wachtman Jr., 1986). An analysis on the properties of the material used is in designing and engineering applications is very vital as disastrous engineering failures could be avoided. Hence, a research on material characterization is vital so as to have a better understanding on the performance of materials. Therefore, the materials can be used in a variety of ways to bring out their utmost potentials (Tan et al., 2011). A successful approach of this procedure relies upon:
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The information that is available on the analysis of material characterization using different methods 
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Availability of equipment to carry out a particular method of material characterization 
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In depth understanding of the process involved inthe application whereby only the characterization of materials which can be practically used in engineering materials are done 
A basic analysis and advancement in materials characterization can contribute to the creation of new materials.
A lot of methods have been experimented to determine the elasticity of materials which is very important in designing and engineering applications. Generally the methods can be categorized into static methods and dynamic methods (Bunshan, 1971).
Static method relies upon direct measurements of strain and stress using standard mechanical tests of compressive, flexural tensile and tensional. The samples used for testing must be of a particular size and shape and Young’s and Shear moduli can be ascertain from the slope of linear region of the stressstrain curve (Young and Budynas, 2002). Dynamic method is a constructive economic technique whereby the ease and straightforwardness in executing plus the accurateness of the stipulated findings caused it to be popular among the industries and researchers. Dynamic method can be divided into two techniques namely: Resonance technique means a sample is set in one or more mechanical vibration modes, at different frequency whereby the vibrations are at a maximum resonance (Nuawi et al., 2013). Excitation is made by drivers with uninterrupted variable frequencies outputs or by impact (Alfano and Pagnotta, 2007). Transducers are utilized to monitor the vibrations of the sample and analyse them so that the characteristic frequencies of the sample can be identified. The elasticity of the sample material can be calculated if the vibrational mode, frequency, dimension and mass of the sample is known (Radovic et al., 2004).
Pulse technique depends on transit time which is the time taken by the ultrasonic pulse to pass through the sample from the transmitting transducer to the receiving transducer.
This study highlights the dynamics of material characterization by measuring the vibration signals from an impulsive excitation test. It gives a detailed explanation on the setup and process involved.
METHODOLOGY
Experimental design and process: An experiment was carried out using four kinds of polymers namely Cast nylon (MC), Polycarbonate (PC), Polyvinylchloride (PVC) and Polyoxymethylene (POM). The shape and measurement of the sample is in accordance to the American Society for Test and Material ASTM C 125901 standard (ASTM C 125901, 2001). A disc shaped sample which was 120 mm in diameter and 20 mm thickness were used. Table 1 shows the specifications of sample materials tested in compliance with the Cambridge Engineering Selector software CES2011. Figure 1 shows the lay out of the experimental design which was made of a piezofilm sensor, an accelerometer sensor and an impact hammer and data acquisition device to measure force of impact and vibration signals. The experiment had to be carried out in a semi anechoic room. The accelerometer sensor and piezofilm sensor were placed in contact with the test sample. The centre of the sample was banged at specific range of applied force (200300, 300400, 400500, 500600, 600700, 700800, 800900 and 9001000 N) using an impact hammer. The resulting force of impact and vibration readings were noted concurrently.
Table 1:  Material properties 

Ikaz^{TM} analysis method: The statistical analysis of Integrated KurtosisBased Algorithm for ZFilter (Ikaz^{TM}) technique is constructed on the theory of scattering data to a central value (Nuawi et al., 2008). The time domain of a signal or any data of time series is divided into three frequency range as illustrated in Fig. 2.
Three frequencies ranges as set on the three axis of graphical representation of Ikaz are:
•  Xaxis: Represents Low Frequency (LF) range of 0  0.25 f_{max} 
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Yaxis: Represents High Frequency (HF) range of 0.25 f_{max}0.5 f_{max} 
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Zaxis: Represents very high frequency (VF) range of 0.5 f_{max}f_{max} 
f_{max} denotes the maximum frequency set in the signal measurement. Specifically, this value is half of the sampling frequency used during the process of recording a signal built on the sampling theorem (Figliola and Beasley, 2001). Equation 1 shows the degree of dispersion of the data measured using the variance parameters σ^{2}:
While Eq. 2 shows how the Ikaz coefficient, Z^{∞} is calculated:
N is the total number of data. x_{i}^{L}, x_{i}^{H}, x_{i}^{V} are the value of distinct data in low, high and very high frequency range, respectively at isample of time. μ_{L}, μ_{H} and μ_{V} are the corresponding mean value of distinct data at low, high and very high frequency ranges.
RESULTS AND DISCUSSION
The time domain of vibration signals collected from the accelerometer sensors and piezofilm are analysed using the Integrated KurtosisBased Algorithm for ZFilter (Ikaz^{TM}) technique by observing the information characteristic contained in the signal. In comparison to previous study which use frequencies of the vibrations to characterise types of material such as metal, stones (Yoshida et al., 2010; Dos Santos et al., 2013) this study shows the Ikaz capability to characterise other different type of material which is polymer). Figure 36 show the transient vibration signal obtained from Polycarbonate (PC), Polyoxymethylene (POM), Polyvinylchloride (PVC) and Cast nylon (MC), respectively.
The value of Ikaz coefficient calculated using Eq. 1, has a low value of 10^{4} to 10^{5}. All Ikaz coefficients are multiplied by the power of 10^{5}.
Table 2:  Ikaz coefficients of accelerometer and piezofilm sensors for PC, POM, PVC and MC 


Fig. 3(ad): 
Transient vibration signals of (a) Time domain Polycarbonate PC 987 N, (b) Polyoxymethylene POM 917 N, (c) Polyvinylchloride PVC 921 N and (d) Cast nylon MC 905 

Fig. 4: 
Ikaz coefficient captured by accelerometer sensor against impact force 

Fig. 5:  Ikaz coefficient of piezofilm sensor against impact force 

Fig. 6:  Bulk modulus vs. Ikaz quadratic coefficient (accelerometer) 
Table 2 display the values of Ikaz coefficient of vibration signal produced by the accelerometer sensor and piezofilm sensor for each type of polymer material and the corresponding impact force that had been used in the experiment.
Based on the findings, two important properties had been recognized. Firstly, the Ikaz coefficients of vibration signal which was captured by accelerometer and piezofilm sensors for all kinds of materials increased when the force of impact applied to the sample was increased. Secondly, the Ikaz coefficient of vibration signal for all types of materials at the same range of impact force were different from each other. This showed that the distribution of the vibration signals to the centroid was different for each type of material.
Figure 4 and 5 show the changes in the Ikaz coefficients of vibration signal captured by the accelerometer sensor and piezofilm sensor which were listed in Table 2 for four types of polymer materials.
A quadratic polynomial curve fitting was used for the calibration of Ikaz coefficients. All quadratic equations which resulted from quadratic polynomial curve fitting had a high value of correlation coefficients (R^{2}) that ranged between 0.974 and 0.998 which means a high precision of curve fitting. The quadratic equation and the value of its correlation coefficients (R^{2}) for accelerometer sensor is shown in Table 3 and for piezofilm sensor in Table 4.
The quadratic equation for each of the polymer material in Table 3 is for accelerometer sensor and Table 4 for piezofilm sensor. The difference between these quadratic equations (y = ax2+bx) can be characterized from its quadratic coefficient (a). The quadratic coefficient (a) denotes the degree of the curve curvature. The quadratic coefficient (a) of quadratic equations of Ikaz coefficients were examined in order to find out the relationship between the vibration signal of accelerometer sensor and piezofilm sensor and the mechanical properties of the materials. Hence, the quadratic coefficients of Ikaz curves were listed in ascending order. A comparison is made between quadratic coefficients (a) with the sequence arrangement of the material properties in Table 1. The sequence order of the quadratic coefficient of Ikaz of accelerometer sensor was similar to the sequence order of the bulk modulus of the materials. Apart from that, the sequence order of the quadratic coefficient of Ikaz of piezofilm sensor was similar to the sequence order of the hardnessVickers of the materials. Table 5 shows the quadratic coefficient (a) of Ikaz vibration for accelerometer sensor and bulk modulus for the four types of polymer materials. Table 6 shows the quadratic coefficient (a) of Ikaz vibration for piezofilm sensor and HardnessVickers for the four types of polymer materials.
Table 3:  Quadratic equation and quadratic coefficients (R^{2}) of accelerometer 

Table 4:  Quadratic equation and quadratic coefficients (R^{2}) of piezofilm 

Table 5:  Quadratic coefficients (α) and bulk modulus of materials for accelerometer 

Table 6:  Quadratic coefficients (α) and HardnessVickers of materials for piezofilm 

Based on the data of Table 5 and 6, it can be seen that there are a relationship between the vibration signal of the accelerometer sensor and the bulk modulus and the vibration signal of the piezofilm sensor and the hardnessVickers of the four tested of polymer materials. (In previous study, Ikaz characterise only young’s modulus of metallic material (medium carbon steel S50C, stainless steel AISI 304, brass and cast iron FCD 500) (Nuawi et al., 2014) while this study expand the capability of Ikaz statistical analysis method to characterise other different material properties (bulk modulus and hardnessVickers). The manner of the relationship is that the polymer which has highest bulk modulus and hardnessVickers, will have the highest quadratic coefficient of Ikaz of vibration signal of accelerometer and piezofilm sensors. To obtain a mathematical expression for correlation between quadratic coefficient of Ikaz and bulk modulus and hardnessVickers, the graph of the quadratic coefficient of Ikaz for four types of polymers, versus bulk modulus and hardnessVickers has been plotted as shown in Fig. 6 and 7.
Based on Fig. 6, the polynomial linear trendline is chosen to matching the data. Linear equation for the linear trendline is of the form of (y = ax + b). Linear trendline is chosen because its correlation coefficient (R^{2}) has a good value of 0.9768. The mathematical expression for correlation process is based on a resulting linear equation of Fig. 6.

Fig. 7:  HardnessVickers verse Ikaz quadratic coefficient (piezofilm) 
By using Eq. 3, it can be conclude that the mathematical expression for correlation process between the vibration signal of accelerometer sensor and the bulk modulus is as the Eq. 4:
While from Fig. 7, the linear equation (y = 16.293x10^{6}x+3.1378) obtained from the linear trendline has a good value of correlation coefficient (0.9867) that can be depended for calibrating data. Based on the resulting equation the mathematical expression for correlation can be expressed as Eq. 5 below:
Table 7 and 8 show the calculated bulk modulus and hardnessVickers for all tested material from the correlation Eq. 4 and 5, respectively and the percentage error of these calculated properties were compared with the average value of Bulk modulus and hardnessVickers obtained from the Cambridge Engineering Selector software CES2011.
Table 7:  Calculated bulk modulus and CES bulk modulus of tested materials 

Table 8:  Calculated Hardnessvickers and CES Hardnessvickers of materials 

In compared to other studies which use the frequencies of the vibration signals to determine Young’s modulus, shear modulus and Poisson’s ratio (Alfano and Pagnotta, 2007; ASTM Standard E1876, 2007; Dos Santos et al., 2013; Radovic et al., 2004) this study contribute to expand the functionality of the frequencies of the vibration signals of the materials to characterise the other type of material properties which are hardnessVickers and bulk modulus). The percentage error of the difference between the two values are ranged between (0.542.44%) which can be consider as an accepted value of error especially for engineering purposes.
CONCLUSION
The Ikaz coefficients for vibration signal that has been recorded in the experiment using the accelerometer and piezofilm sensors are obtained using Ikaz^{TM} method. It is found that the Ikaz coefficient for vibration signals increased when the specimen is subjected to higher impact and forms a quadratic curve yax2bx. Through the characterization of quadratic curves and the properties of tested materials, it is found that there is a relationship between Ikaz coefficient of vibration signals that recorded by the accelerometer sensor and the bulk modulus and Ikaz coefficient of vibration signals that recorded by the piezofilm sensor and the hardnessVickers of tested materials. However, the correlation expression in the form of a mathematical representation obtained for these two relationships. The mathematical expression for the correlation between the Ikaz of accelerometer vibration signal and the bulk modulus obtained is bulk modulus, B = 0.6842x106 (quadratic coefficient)+1.6963. While the mathematical expression for the correlation between the Ikaz of piezofilm vibration signal and the hardnessVickers obtained is hardnessVickers, H = 1.6388x106 (quadratic coefficient)+3.2889. The correlation equations of correlation processes can be used as standards for determining these properties through Ikaz^{TM }analysis of vibration signal.
ACKNOWLEDGEMENT
The authors wish to acknowledge ERGS/1/2013/TK01/UKM/02/2, for the research Grant.