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  • 1. Ragab, Adham Sensitivity Analysis of Casting Distortion and Residual Stress Prediction Through Simulation Modeling and Experimental Verification

    Doctor of Philosophy, The Ohio State University, 2003, Industrial and Systems Engineering

    The die casting process is one of the net shape manufacturing techniques and is widely used to produce high production castings with tight tolerances for many industries. An understanding of the stress distribution and the deformation pattern of parts produced by die casting will result in less deformed from the part design specification, a better die design and eventually to more productivity and cost savings. This dissertation presents a technique that can be used to simulate the die casting process in order to predict the deformation and stresses in the produced part. A coupled thermal-mechanical finite elements model was used to simulate the die casting process. The simulation models the effect of thermal and mechanical interaction between the casting and the die. It also includes the temperature dependant material properties of the casting. Based on a designed experiment, a sensitivity analysis was conducted on the model to investigate the effect of key factors. These factors include the casting material model, material properties and thermal interaction between casting and dies. To verify the casting distortion predictions, it was compared against the measured dimensions of produced parts. The comparison included dimensions along and across the parting plane and the flatness of one surface. In order to validate and verify the die casting machine model, experimental work was conducted. The contact forces between dies and platens, strain in tie bars and dies and die temperature were measured. The experiments were run on a 250 metric ton Buhler die casting machine available at the Ohio State University. A total of 68 sensors (35 load cells, 31 strain gauges and one thermocouple) were mounted on the machine. The readings from these sensors were compared to the similar simulation predictions.

    Committee: Richard Miller (Advisor) Subjects: Engineering, Industrial