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

Viscosity Testing of Plastic Material PA6 with 15% of Glass Fibers

2018 ◽  
Vol 928 ◽  
pp. 133-138
Author(s):  
Karel Ráž ◽  
Martin Zahalka
Keyword(s):  
Injection Molding ◽  
Polymer Material ◽  
Process Parameters ◽  
Plastic Material ◽  
Glass Fibers ◽  
Injection Pressure ◽  
Injection Molding Process ◽  
Spiral Flow ◽  
Molding Process ◽  
Melt Temperature

The main aim of this paper was to describe the viscosity and injection mold filling behavior of PA6 with 15% of glass fibers. Injection molding is one of the most widely used processes for polymer products. The quality of these products is directly linked to correct choice of process parameters. It is necessary to understand the filling behavior of the polymer material during the injection molding process. The spiral flow test was carried out in this study to explore the effects of several injection process parameters. The resulting lengths of spiral flow were compared. The polymer material under test was Polyamide 6 with 15% of short glass fibers (trade name: Durethan BKV 15). Virtual testing as well as real testing was performed. A predominantly linear relationship between the flow length and the mold temperature, melt temperature and injection pressure is described here. A special mold was designed for this test.

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.

scholarly journals Constant Temperature Approach for the Assessment of Injection Molding Parameter Influence on the Fatigue Behavior of Short Glass Fiber Reinforced Polyamide 6

Polymers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1569
Author(s):  
Selim Mrzljak ◽  
Alexander Delp ◽  
André Schlink ◽  
Jan-Christoph Zarges ◽  
Daniel Hülsbusch ◽  
...  
Keyword(s):  
Injection Molding ◽  
Glass Fiber ◽  
Process Parameters ◽  
Fatigue Properties ◽  
Injection Molding Process ◽  
Molding Process ◽  
Short Glass Fiber ◽  
Glass Fiber Reinforced ◽  
Molding Parameter ◽  
Short Glass

Short glass fiber reinforced plastics (SGFRP) offer superior mechanical properties compared to polymers, while still also enabling almost unlimited geometric variations of components at large-scale production. PA6-GF30 represents one of the most used SGFRP for series components, but the impact of injection molding process parameters on the fatigue properties is still insufficiently investigated. In this study, various injection molding parameter configurations were investigated on PA6-GF30. To take the significant frequency dependency into account, tension–tension fatigue tests were performed using multiple amplitude tests, considering surface temperature-adjusted frequency to limit self-heating. The frequency adjustment leads to shorter testing durations as well as up to 20% higher lifetime under fatigue loading. A higher melt temperature and volume flow rate during injection molding lead to an increase of 16% regarding fatigue life. In situ Xray microtomography analysis revealed that this result was attributed to a stronger fiber alignment with larger fiber lengths in the flow direction. Using digital volume correlation, differences of up to 100% in local strain values at the same stress level for different injection molding process parameters were identified. The results prove that the injection molding parameters have a high influence on the fatigue properties and thus offer a large optimization potential, e.g., with regard to the component design.

Ultrasonic measurement of clamping force for injection molding machine

2019 ◽  
Vol 39 (4) ◽  
pp. 388-396 ◽  
Author(s):  
Peng Zhao ◽  
Yao Zhao ◽  
Jianfeng Zhang ◽  
Junye Huang ◽  
Neng Xia ◽  
...  
Keyword(s):  
Injection Molding ◽  
Measurement Method ◽  
Large Scale ◽  
Force Measurement ◽  
Low Cost ◽  
Ultrasonic Method ◽  
Injection Molding Process ◽  
Propagation Time ◽  
Clamping Force ◽  
Molding Process

AbstractAn online and feasible clamping force measurement method is important in the injection molding process and equipment. Based on the sono-elasticity theory, anin situclamping force measurement method using ultrasonic technology is proposed in this paper. A mathematical model is established to describe the relationship between the ultrasonic propagation time, mold thickness, and clamping force. A series of experiments are performed to verify the proposed method. Experimental findings show that the measurement results of the proposed method agree well with those of the magnetic enclosed-type clamping force tester method, with difference squares less than 2 (MPa)2and errors bars less than 0.7 MPa. The ultrasonic method can be applied in molds of different thickness, injection molding machines of different clamping scales, and large-scale injection cycles. The proposed method offers advantages of being highly accurate, highly stable, simple, feasible, non-destructive, and low-cost, providing significant application prospects in the injection molding industry.

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