AI Article Synopsis
- This research focuses on creating an adaptive control system for the injection molding process to ensure consistent product quality by reducing variations in mass.
- The system utilizes data from two sensors—the injection nozzle pressure and temperature sensors—to monitor and adjust the molding process.
- Preliminary experiments identified a master pressure curve linked to product quality, and a back propagation neural network was used to optimize the control system, achieving a minimal part weight variation of only 0.14%.
Article Abstract
This research developed an adaptive control system for injection molding process. The purpose of this control system is to adaptively maintain the consistency of product quality by minimize the mass variation of injection molded parts. The adaptive control system works with the information collected through two sensors installed in the machine only-the injection nozzle pressure sensor and the temperature sensor. In this research, preliminary experiments are purposed to find master pressure curve that relates to product quality. Viscosity index, peak pressure, and timing of the peak pressure are used to characterize the pressure curve. The correlation between product quality and parameters such as switchover position and injection speed were used to produce a training data for back propagation neural network (BPNN) to compute weight and bias which are applied on the adaptive control system. By using this system, the variation of part weight is maintained to be as low as 0.14%.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025472 | PMC |
| http://dx.doi.org/10.3390/polym14081607 | DOI Listing |
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