A fuzzy-based reconstruction algorithm for estimating metal fill profile in lost foam casting

Lost foam casting (LFC) process is one of the most energy efficient casting methods in the industry. The metal-fill profile is an important factor that affects casting quality. Hence the characterization and the control of the metal fill, if possible, are essential in LFC. Electrical capacitive tomography (ECT) sensors, based on measuring the change in the coupling capacitance with the presence of grounded metal in its proximity, provide a simple non-intrusive visualization technique of acquiring the metal profile. The principle of ECT is to use a rugged and noninvasive array of capacitive electrodes mounted around a target area and measure the changes in inter-electrode capacitance caused by variations of the grounded metal inside the target area. Cross-sectional images of the metal distribution are then reconstructed from the measured data. Deducing the metal fill distribution from the capacitance measurements is a difficult problem. A novel technique for solving the inverse problem in the electrical capacitance tomography is introduced. The new technique is based on Fuzzy Inference Systems (FIS) to predict the molten metal profile from the capacitance measurements. The developed system is intended to be utilized on the foundry floor and hence a limited number of measurements are utilized. The proposed technique is able to detect the position of the metal by using just eight measurements from the sensors.