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Master of Science in Mechanical Engineering (M.S.M.E.)
Year Degree Awarded
Month Degree Awarded
Wireless Sensor, Injection Molding, Optimization, Finite Element Analysis, Piezoelectric, FSI Analysis
Sensor technology has played an essential role in improving the observability in manufacturing processes and providing input to enabling more effective and efficient product and process design. To analyze an injection molding process, pressure and temperature variations have shown to be the most critical factors that affect quality in the molded parts. The state of sensing in the industry utilizes separate and wired sensors placed away from the mold cavity to measure these parameters, and holes have to be drilled through the mold steel to accommodate the wires. To minimize mold structural modification, which is time consuming and expensive, it is desired to design a miniaturized sensor module that can be structurally embedded into the molding cavity and simultaneously measures the two parameters (i.e. a dual-parameter sensor) in real time, during the molding process. This thesis presents the structural optimization of the sensor and development of a new Fluid-Structure algorithm to analyze the performance of the sensor as in an actual injection molding cycle. Thus, research involves three key tasks. Given a required mold steel thickness, an optimization problem was solved analytically with outer diameter, thickness and number of rings as variables under the maximum allowable pressure and minimum required energy constraints to achieve a minimum volume of the piezo stack. As it is infeasible to test the sensor with different dimensions under the flow to understand its behavior under high pressure and temperature polymer melt, the development of a numerical model is required. A mold-melt interaction algorithm is developed to have a mold-melt interface using finite element analysis, analogous to an injection molding process. The model showed the change in state of polymer melt and its effect on cavity due to change in viscosity with the change in temperature. The model validated the energy output of the optimized sensor when the temperature and pressure of polymer changes and the effect of these parameters on mold and sensor. The voltage output and temperature results were compared with analytical solution. The numerical results of voltage output matched within 0.1% and temperature results matched within 3% of the analytical solutions. Finally a test bed was fabricated to simulate and reconstruct the pressure profile obtained from the numerical model to study the actual output from a fabricated sensor. The aim of the test bed was to reconstruct pressure profiles obtained from numerical simulations to investigate the sensor output from the fabricated injection molding sensor. The test bed evaluated the output from sensor as can be observed in actual injection molding machine. Comparison of the injection molding sensor with a piezo-resistive sensor showed good agreement.
Robert X. Gao