Portable transient electromagnetic (TEM) systems can be well adapted to various terrains, including mountainous, woodland, and other complex terrains. They are widely used for the detection of unexploded ordnance (UXO). As the core component of the portable TEM system, the sensor is constructed with a transmitting coil and a receiving coil. Based on the primary field of the transmitting coil and internal noise of the receiving coil, the design and testing of such a sensor is described in detail. Results indicate that the primary field of the transmitting coil depends on the diameter, mass, and power of the coil. A higher mass–power product and a larger diameter causes a stronger primary field. Reducing the number of turns and increasing the clamp voltage reduces the switch-off time of the transmitting current effectively. Increasing the cross-section of the wire reduces the power consumption, but greatly increases the coil’s weight. The study of the receiving coil shows that the internal noise of the sensor is dominated by the thermal noise of the damping resistor. Reducing the bandwidth of the system and increasing the size of the coil reduces the internal noise effectively. The cross-sectional area and the distance between the sections of the coil have little effect on the internal noise. A less damped state can effectively reduce signal distortion. Finally, a portable TEM sensor with both a transmitting coil (constructed with a diameter, number of turns, and transmitting current of 0.5 m, 30, and 5 A, respectively) and a receiving coil (constructed with a length and resonant frequency of 5.6 cm and 50 kHz, respectively) was built. The agreement between experimental and calculated results confirms the theory used in the sensor design. The responses of an 82 mm mortar shell at different distances were measured and inverted by the differential evolution (DE) algorithm to verify system performance. Results show that the sensor designed in this study can not only detect the 82 mm mortar shell within 1.2 m effectively but also locate the target precisely.
Fatigue life prediction of electronic devices is of great importance in both research and industry. Traditionally, fatigue tests and finite element modeling (FEM) are the two main methods. This paper presents a new hybrid approach (FEM combined with artificial neural network, (ANN)) for fatigue life prediction. Finite element models on wafer-level chip scale packages (WLCSP) with different chip thickness, PCB thickness, and solder joint pitches were created to evaluate the effect of structure parameters on the increase in maximum creep strain under thermal fatigue load. Modified Coffin–Manson equation was then employed to estimate the corresponding fatigue life. ANNs were built, and then trained, tested, and optimized with the datasets from modeling to predict increase in maximum creep strain and fatigue life. For the ANN built for strain prediction, prediction accuracy of the optimal network was 97% in accuracy tests and 93% in generalization tests. Accuracy of the other ANN for predicting fatigue life was 94.2% in accuracy tests and 88% in generalization tests. This hybrid method shows very promising application in fatigue life estimation of electronic devices which requires much less time and lower cost.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.