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Impedance plane is one of the most important ways for presenting results in Eddy current testing, which includes major data for evaluation of results. In this study, the impedance plane was drawn for carburized steel with different surface carbon content. The influences of temperature, fill factor, and edge effect on impedance plane were investigated. The ability of Eddy current testing for determination of surface carbon content using normalized impedance was also shown. Results demonstrate a strong relationship between normalized impedance and surface carbon content (R 2 = 0.82). Besides the effects of temperature, fill factor, and edge effect on determination of surface carbon content were investigated. The fill factor and temperature have the largest and the least effect on correlation coefficient between surface carbon content and impedance plane, respectively.

Abstract. In several magnetic Non-Destructive Testing (NDT) methods, the local measurement of the magnetic field inside the material is required. Moreover, looking at difficult part geometries, magnetic field sensors have to be small enough in order to reach the measuring position. The most-used magnetic field sensors are coils, Hall-effect sensors, flux gates and magnetoresistive sensors. However, regarding the industrial application, those sensors are often packaged and cannot be placed close enough to the measuring position. As part of an ongoing research project funded by the German Ministry of Economics and Technology (BMWi), a new kind of magnetic field sensor was developed and used in order to measure the strength of remanent magnetic field spots. This so-called 'Point Probe' is based upon a needle-shaped ferromagnetic core having a primary coil as a magnetic field source and a secondary coil as an inductive pick-up. This contribution describes the details of the sensor design and its operating principle. The sensitivity of the measured signals for local magnetic fields is described. Finally, a method for nondestructive hardness estimation of materials by using the Point Probe is presented. The results show a high correlation between hardness and a new coercivity-dependent testing parameter.

Application of Co (III)/Al 2 O 3 catalyst in Fischer-Tropsch synthesis (FTS) was studied in a wide range of synthesis gas conversions and compared with ANN Simulation results. Present study applies Neural Network model to predict composition of CH 4 , CO 2 and CO of the Fischer-Tropsch Process of Natural Gas, while the input vector was 4-dimension vector including four variables from operating pressure, operating temperature, time and ratio of CO/H 2 of 70 different experiments and the output were composition of CO 2 , CO and CH 4 . The MLP algorithm has been applied for the training and the test set was applied to evaluate the performance of the system including R2, MAE, MSE and RMSE. The results exposed that the predicted values from the model were in good agreement with the experimental data. The paper indicates how Neural Network, as a promising predicting technique, would be effectively used for FTS.