Impact Echo Array Development for Concrete Inspection
An array of impact-echo array technique for the two dimensional imaging of defects in thick structures such as concrete. A Finite Difference Time Difference Model was employed for the forward model of the impact-echo method. The use of absorbing boundary conditions allow for efficient modeling of the technique. The signals predicted using the FDTD models from a linear array of impact-echo transmitter-receiver system in a multiplexed configuration was used for 2-D image reconstruction of the cross-sectional region of the structure. The reconstruction algorithm uses time-shifting of the signals based point-source assumption for the impact sources. The images are represented in a typical “B-scan” representation. The data reconstruction algorithm uses phased addition in the time domain to reconstruct the position of reflectors present in the structure such as the back wall, defects, voids. The pixel based approach is used to perform phased addition reconstruction in the domain of interest. The scanning area is sampled in Cartesian co-ordinates and the phased addition is done point by point in this domain. Wherever there are defects or other features like the back wall etc., constructive addition takes place and the signal is reinforced. However when there is no particular feature in the point considered, then a very weak resultant is produced as a result of both constructive and destructive interference taking place. Thus, the signal at the defect location is reinforced and the relative noise level is reduced. The phased reconstruction algorithm can be both applied to the single transmitter case as well as the multiple transmitter case.
The B-scan images were successfully reconstructed from the surface displacements along the points on the surface using a phased addition reconstruction algorithm. The algorithm was tested for different pulse-width of the impact and it was found, that smaller the pulse width, greater is the resolution of the reconstructed image. The algorithm was also tested for different types of defects, laminations, and crack. A parameter study, involving (a) different spacing of the transmitters and receivers, and (b) different number of transducers, was also conducted. From the results so obtained, an optimal combination of different parameters in the experimental study was taken, to obtain effective results from the real specimen.
Sridhar, C, A. Muralidharan, K. Balasubramaniam, and C.V. Krishnamurthy, “An Impact Echo Array Technique- Simulation Studies” Nondestr. Test. Eval 21(3) 123-40 (2006)
Phase Addition Algorithm Domain with Transmitters and Receivers. Phase Addition Reconstructed Results on a concrete block with a defect.
The prototype impact array prototype with a typical signal.
Brake Pad Inspection using Impact Echo Method
Damages like cracks, delaminations, etc., in composite parts have traditionally been evaluated using manual methods like acoustic impact (using measurements in the audio frequencies). This technique is currently used during manufacturing for product quality testing and later for maintenance and assurance of structural integrity.
The automation of this technique will significantly improve the reliability of inspection. The signals obtained from the composites are analyzed using signal-processing techniques in the time-frequency domain to build a robust algorithm for detection and identification of defects. A feature vector is constructed using these techniques and then applied to a neural network for defect identification. Comparative studies are conducted to search for the best and most comprehensive feature vector. Results using different signal processing techniques are presented. Similarly comparative results are presented between two different kinds of neural networks (namely Radial Basis functions and MLP) and various architectures in each kind.
A low cost data acquisition system has also been developed for acquiring audio signals using the sound card and the microphone in a multi-media PC.
Frequency extraction is done on the time domain signal and the frequency spectral data was used as input to a neural network to detects the defects or delaminations in the composites at very high production rates (2-5 parts per second).
The mode shapes for different impact locations
Srivatsan V., Krishnan Balasubramaniam, and N. V. Nair, “Artificial Neural Network Based Algorithm for Acoustic Impact Based Nondestructive
Process Monitoring of Composite Products” AIP Conf. Proc. 657, 1651 (2003)
Brake Pad Inspection Station
ANN based lab view software
Frequency response from different locations in the pad.