Multi‐response optimization of shrinkage, clamp force, and part weight in simulated injection molding process of a dialysis micro‐filter

2021 ◽  
pp. 51732
Author(s):  
Behzad Shiroud Heidari ◽  
Nikhilesh Bappoo ◽  
Lachlan J. Kelsey ◽  
Seyed Mohammad Davachi ◽  
Barry Doyle
Keyword(s):  
Injection Molding ◽  
Injection Molding Process ◽  
Molding Process ◽  
Clamp Force ◽  
Response Optimization

Dynamic Response Optimization for Cemented Carbide Injection Molding

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
Sri Yulis M. Amin ◽  
Norhamidi Muhamad ◽  
Khairur Rijal Jamaludin
Keyword(s):  
Injection Molding ◽  
Palm Stearin ◽  
Processing Parameters ◽  
Cemented Carbide ◽  
Metal Injection Molding ◽  
Injection Molding Process ◽  
Molding Process ◽  
Response Optimization ◽  
Injection Molded ◽  
Injection Temperature

The need to optimize the injection molding parameters for producing cemented carbide parts via Metal Injection Molding process is crucial to ensure the system’s robustness towards manufacturer and customer’s satisfactions. Defect free product with best density can be produced while reducing time and cost in manufacturing. In this work, the feedstock consisting of WC-Co powders, mixed with palm stearin and polyethylene binder system was injection molded to produce green parts. Several processing variables, namely powder loading, injection temperature, holding pressure and flowrate, were optimized towards the density of the green body, as the response factor. By considering humidity level at morning and evening conditions as the noise factor, the results show the optimum combination of injection molding parameters that produces best green density. The green part exhibited best density by following this optimum processing parameters, A2B3C1D1, that are flowrate at 20 ccm/s, powder loading at 63% vol., injection temperature at 140°C, and holding pressure at 1700 bar.

Effect of Size of the Common Cup on the Injection Molding Process

2011 ◽  
Vol 308-310 ◽  
pp. 450-454
Author(s):  
Qing Qing Liu ◽  
Wei Guo
Keyword(s):  
Injection Molding ◽  
Time Pressure ◽  
Injection Molding Process ◽  
Fe Model ◽  
Maximum Deflection ◽  
Plastic Part ◽  
Molding Process ◽  
Clamp Force ◽  
Force Increase ◽  
The Common

Plastic injection molding process is an advanced but much complex plastic part molding with multi-factors coupling interactive effects. The size of the part has a significant effect on the injection molding process. In the current work, a kind of common plastic part, common cup, is taken into study. Based on the 3D FE model, the effect laws of size of the common cup on the fill time, pressure at V/P switchover, clamp force needed and maximum deflection considering all effects in the injection molding process have been investigated. The research results show the following: (1) With increasing the initial diameter of the cup, fill time, pressure at V/P switchover and clamp force increase obviously, while overpacking happens for the maximum deflection considering all effects. (2) With increasing the initial height of the common cup, fill time, pressure at V/P switchover and clamp force increase less obvious. Meanwhile, overpacking doesn’t happen. (3) The effect of height to diameter ratio H0/D0 by changing D0 on injection molding process is different from that by changing H0.

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.

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.

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