Abstract
To increase the productivity of injection molding machines, we developed a smart injection part weight stability control system based on C++ programming and domain knowledge. The proposed system is meant to eliminate variability in the quality of injected parts by adjusting the changeover position. We developed a viscosity index based on melt pressure data related to guide the adjustment to the changeover position in accordance with material properties. This was achieved by mounting a pressure sensor on the nozzle of the injection molding machine to enable the on-line monitoring of pressure throughout the injection molding process. A series of experiments was conducted to characterize the relationship between viscosity index and injection-molded samples in order to validate the efficacy of the proposed injection stability system. Single-factor experiments were conducted with the changeover position and melt temperature as parameters. The quality of the molded samples obtained under different process parameters was evaluated in terms of weight. Experiment results revealed a correlation between changes in viscosity index and changes in the weight of the samples. The injection stability system can also be operated in self-adjusting mode, in which the changeover position is varied according to viscosity index. In experiments, abnormal machine operations prompted the adjustment of changeover position. Variation in the weight of parts was used to define an index to validate the efficacy of the proposed system.
Highlights
Labor shortages are a growing concern in many countries
Our objective in this experiment was to characterize the relationship between the viscosity index (VI) and the weight of the parts under different melt temperatures with the changeover position set at 8 mm
The results of single-factor experiments on changeover position and melt temperature revealed that the distribution between the VI and weight of parts conforms to the P-V-T relationship
Summary
Labor shortages are a growing concern in many countries. From the marketing standpoint, the life cycle of products is becoming shorter even as quality standards rise. Smart manufacturing can be used to improve product quality through adaptive process control (APC). Difficulties in measuring the specific volume in injection molding processes prompted us to develop the viscosity index (VI), proposed in literature[1,2], to represent the relationship between the weight of a molded item and its material properties. Huang[4] proposed the use of the Grey model to determine the changeover point instantaneously by monitoring the cavity pressure profile in each cycle. Process pressure was controlled by a cavity sensor rather than as a function of time. That system simulates control over pressure and velocity during the injection molding process In experiments, they considered overshoot, response time, and RMSD as indexes to evaluate the performance of the system. We adopted variations in the weight of molded items as an index to evaluate the efficacy of the proposed system
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