Evaluation of gas pressure dynamics for gas-assisted injection molding process

1999 ◽  
Vol 26 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Sher-Meng Chao ◽  
Shih-Ming Wang ◽  
Shia-Chung Chen ◽  
Furong Gao
Keyword(s):  
Injection Molding ◽  
Gas Pressure ◽  
Injection Molding Process ◽  
Molding Process ◽  
Pressure Dynamics

Determination of Optimum Process Parameters in Gas-Assisted Injection Molding

2011 ◽  
Vol 143-144 ◽  
pp. 494-498
Author(s):  
Ke Ming Zi ◽  
Li Heng Chen
Keyword(s):  
Injection Molding ◽  
Delay Time ◽  
Optimization Design ◽  
Gas Pressure ◽  
Optimum Process ◽  
Injection Molding Process ◽  
Technological Parameters ◽  
Molding Process ◽  
Element Analysis ◽  
Melt Temperature

With finite element analysis software Moldflow, numerical simulation and studies about FM truck roof handle were conducted on gas-assisted injection molding process. The influences of melt pre-injection shot, gas pressure, delay time and melt temperature were observed by using multi-factor orthogonal experimental method. According to the analysis of the factors' impact on evaluation index, the optimized parameter combination is obtained. Therefore the optimization design of technological parameters is done. The results show that during the gas-assisted injection molding, optimum pre-injection shot is 94%,gas pressure is 15MPa,delay time is 0.5s,melt temperature is 240 oC. This study provided a more practical approach for the gas-assisted injection molding process optimization.

Filling Behavior of Polymer Material into Sub-^|^mu;m Structure in Injection Molding Process

2013 ◽  
Vol 133 (4) ◽  
pp. 105-111
Author(s):  
Chisato Yoshimura ◽  
Hiroyuki Hosokawa ◽  
Koji Shimojima ◽  
Fumihiro Itoigawa
Keyword(s):  
Injection Molding ◽  
Polymer Material ◽  
Injection Molding Process ◽  
Molding Process

scholarly journals Study on External Gas-Assisted Mold Temperature Control with the Assistance of a Flow Focusing Device in the Injection Molding Process

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 965 ◽  
Author(s):  
Nguyen Truong Giang ◽  
Pham Son Minh ◽  
Tran Anh Son ◽  
Tran Minh The Uyen ◽  
Thanh-Hai Nguyen ◽  
...  
Keyword(s):  
Injection Molding ◽  
Temperature Control ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Melt Flow ◽  
Flow Focusing ◽  
Molding Process ◽  
Heating Efficiency ◽  
Flow Length ◽  
Insert Plate

In the injection molding field, the flow of plastic material is one of the most important issues, especially regarding the ability of melted plastic to fill the thin walls of products. To improve the melt flow length, a high mold temperature was applied with pre-heating of the cavity surface. In this paper, we present our research on the injection molding process with pre-heating by external gas-assisted mold temperature control. After this, we observed an improvement in the melt flow length into thin-walled products due to the high mold temperature during the filling step. In addition, to develop the heating efficiency, a flow focusing device (FFD) was applied and verified. The simulations and experiments were carried out within an air temperature of 400 °C and heating time of 20 s to investigate a flow focusing device to assist with external gas-assisted mold temperature control (Ex-GMTC), with the application of various FFD types for the temperature distribution of the insert plate. The heating process was applied for a simple insert model with dimensions of 50 mm × 50 mm × 2 mm, in order to verify the influence of the FFD geometry on the heating result. After that, Ex-GMTC with the assistance of FFD was carried out for a mold-reading process, and the FFD influence was estimated by the mold heating result and the improvement of the melt flow length using acrylonitrile butadiene styrene (ABS). The results show that the air sprue gap (h) significantly affects the temperature of the insert and an air sprue gap of 3 mm gives the best heating rate, with the highest temperature being 321.2 °C. Likewise, the actual results show that the height of the flow focusing device (V) also influences the temperature of the insert plate and that a 5 mm high FFD gives the best results with a maximum temperature of 332.3 °C. Moreover, the heating efficiency when using FFD is always higher than without FFD. After examining the effect of FFD, its application was considered, in order to improve the melt flow length in injection molding, which increased from 38.6 to 170 mm, while the balance of the melt filling was also clearly improved.

scholarly journals Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

2021 ◽  
Vol 112 (11-12) ◽  
pp. 3501-3513
Author(s):  
Yannik Lockner ◽  
Christian Hopmann
Keyword(s):  
Injection Molding ◽  
Transfer Learning ◽  
Injection Molding Process ◽  
Process Data ◽  
Molding Process ◽  
Melt Temperature ◽  
Processing Industry ◽  
Source Models ◽  
Plastics Processing ◽  
Artificial Neural

AbstractThe necessity of an abundance of training data commonly hinders the broad use of machine learning in the plastics processing industry. Induced network-based transfer learning is used to reduce the necessary amount of injection molding process data for the training of an artificial neural network in order to conduct a data-driven machine parameter optimization for injection molding processes. As base learners, source models for the injection molding process of 59 different parts are fitted to process data. A different process for another part is chosen as the target process on which transfer learning is applied. The models learn the relationship between 6 machine setting parameters and the part weight as quality parameter. The considered machine parameters are the injection flow rate, holding pressure time, holding pressure, cooling time, melt temperature, and cavity wall temperature. For the right source domain, only 4 sample points of the new process need to be generated to train a model of the injection molding process with a degree of determination R2 of 0.9 or and higher. Significant differences in the transferability of the source models can be seen between different part geometries: The source models of injection molding processes for similar parts to the part of the target process achieve the best results. The transfer learning technique has the potential to raise the relevance of AI methods for process optimization in the plastics processing industry significantly.

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