Two methods are proposed for the control of part weight in injection molding. In the PT method, part weight control is achieved by controlling the temperature and pressure of the melt in the cavity at gate freeze time. This time is considered to occur when the cavity melt pressure starts to decrease from its peak value. For the purposes of control, the bulk melt temperature is estimated from measurements by surface thermocouples at strategic locations in the cavity. A cascade scheme is implemented for the control of bulk temperature from cycle to cycle. A self‐tuning algorithm, with an observer, is employed for controlling the cavity pressure‐time profile, to follow a set point trajectory during a cycle. In PWT control, the coolant temperature is controlled, while the peak cavity pressure is adjusted in a given cycle to compensate for bulk melt temperature deviations measured in the previous cycle. Both PT and PWT control reduce variance in part weight. PWT control appears to yield the best results.
A method to measure the melt temperature distribution in an injection mold cavity is developed. A thermocouple is used in the construction of a sensor with a tip that can be adjusted at different depths of a mold cavity, to measure temperature profiles at different positions in the mold. Polymer temperature distributions are measured and factors affecting temperatures and the key variables that influence distributions are determined. Measurements are compared with temperatures obtained from numerical simulation of the injection molding process using a three-dimensional heat transfer analysis. Although showing lower values and generally higher cooling rates, temperature data measured from the mold cavity indicate similar behavior to predicted transient temperature distributions.
A methodology is presented to estimate cavity melt temperatures and part weight in the injection molding of amorphous thermoplastics. The approach uses measurements of cavity pressure near the gate and surface temperatures at three sensor locations. The surface temperature data with a heat conduction model give estimated temperature profiles across the cavity thickness. These profiles are then used to estimate the average cavity melt temperature. Fitting the cycle-to-cycle values of average mold-cavity temperature and peak pressure to a Tait equation yields a model to estimate part weights. The estimated part weights agree with measured values for different injection-molding conditions. m
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