5 October 2017 Development of Geometry Normalized Electromagnetic System (GNES) instrument for metal defect detection
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Abstract
It has been already made, calibrated and tested a geometry normalized electromagnetic system (GNES) for metal defect examination. The GNES has an automatic data acquisition system which supporting the efficiency and accuracy of the measurement. The data will be displayed on the computer monitor as a graphic display then saved automatically in the Microsoft Excel format. The transmitter will transmit the frequency pair (FP) signals i.e. 112.5 Hz and 337.5 Hz; 112.5 Hz and 1012.5 Hz; 112.5 Hz and 3037.5 Hz; 337.5 Hz and 1012.5 Hz; 337.5 Hz and 3037.5 Hz. Simultaneous transmissions of two electromagnetic waves without distortions by the transmitter will induce an eddy current in the metal. This current, in turn, will produce secondary electromagnetic fields which are measured by the receiver together with the primary fields. Measurement of percent change of a vertical component of the fields will give the percent response caused by the metal or the defect. The response examinations were performed by the models with various type of defect for the master curves. The materials of samples as a plate were using Aluminum, Brass, and Copper. The more of the defects is the more reduction of the eddy current response. The defect contrasts were tended to decrease when the more depth of the defect position. The magnitude and phase of the eddy currents will affect the loading on the coil thus its impedance. The defect must interrupt the surface eddy current flow to be detected. Defect lying parallel to the current path will not cause any significant interruption and may not be detected. The main factors which affect the eddy current response are metal conductivity, permeability, frequency, and geometry.
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Zakaria Zakaria, Muhammad Syukri Surbakti, Saumi Syahreza, Mohd. Zubir Mat Jafri, Kok Chooi Tan, "Development of Geometry Normalized Electromagnetic System (GNES) instrument for metal defect detection ", Proc. SPIE 10430, High-Performance Computing in Geoscience and Remote Sensing VII, 104300J (5 October 2017); doi: 10.1117/12.2278163; https://doi.org/10.1117/12.2278163
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