THESIS
1998
xv, 122 leaves : ill. (some col.) ; 30 cm
Abstract
A process analysis is carried out systematically to quantify how the melt temperature at the nozzle exit is affected by the operating conditions during the plastication, dwell and injection phases in a thermoplastics injection molding process. The following process variables are considered in this study: rotation speed, back pressure, barrel heater temperatures, nozzle heater temperature, dwell time and injection velocity profile. The three consecutive phases, plastication, dwell, and injection are studied and modeled separately. A set of artificial neural networks (ANNs) has been developed to predict the effect of the operating conditions on the melt temperature during plastication. One dimensional heat conduction model is used to simulate the dwell period. A free boundary model for th...[
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A process analysis is carried out systematically to quantify how the melt temperature at the nozzle exit is affected by the operating conditions during the plastication, dwell and injection phases in a thermoplastics injection molding process. The following process variables are considered in this study: rotation speed, back pressure, barrel heater temperatures, nozzle heater temperature, dwell time and injection velocity profile. The three consecutive phases, plastication, dwell, and injection are studied and modeled separately. A set of artificial neural networks (ANNs) has been developed to predict the effect of the operating conditions on the melt temperature during plastication. One dimensional heat conduction model is used to simulate the dwell period. A free boundary model for the injection phase is developed to simulate the melt flow pushed by the forward movement of the screw, and the finite element method (FEM) is used to solve this model. The over-all prediction of the nozzle melt temperature is in good agreement with the experimental measurements. This work enhances the understanding of the injection molding process and provides a basis for future work on the optimization and advanced control of the process.
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