scholarly journals Mechanistic understanding of 3D printed polycarbonate process yielding comparable dielectric strength with injection molding process

2020 ◽  
Vol 2 (7) ◽  
Author(s):  
Anshita Sudarshan ◽  
S. M. Swamy ◽  
Nitesh Shet ◽  
Hari Prasad ◽  
Juha-Matti Levasalmi ◽  
...  
Keyword(s):  
Injection Molding ◽  
Dielectric Strength ◽  
Injection Molding Process ◽  
Molding Process ◽  
3D Printed ◽  
Mechanistic Understanding

scholarly journals Performance Analysis of Enhanced 3D Printed Polymer Molds for Metal Injection Molding Process

Metals ◽  
2018 ◽  
Vol 8 (6) ◽  
pp. 433 ◽  
Author(s):  
Khurram Altaf ◽  
Junaid Qayyum ◽  
A. Rani ◽  
Faiz Ahmad ◽  
Puteri Megat-Yusoff ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Injection Molding ◽  
Metal Injection Molding ◽  
Injection Molding Process ◽  
Molding Process ◽  
3D Printed

scholarly journals Improving the Performance of 3D Printed Molds for Plastic Injection Molding

10.29007/9dk4 ◽  
2019 ◽  
Author(s):  
Faryar Etesami ◽  
Christopher Mullens ◽  
Ramsey Sahli ◽  
Trey Webb
Keyword(s):  
Mechanical Properties ◽  
High Temperature ◽  
Injection Molding ◽  
Surface Hardness ◽  
Plastic Injection Molding ◽  
Injection Molding Process ◽  
Molding Process ◽  
High Temperature Mechanical Properties ◽  
3D Printed ◽  
Plastic Injection

Additive manufacturing technology has become a viable solution for making molds for plastic injection molding applications. The molds are usually made of high temperature plastic resins suitable for plastic injection molding. Molding resins have superior mechanical properties necessary to withstand the high temperatures and pressures of the injection molding process. It is known that high temperature mechanical properties of resins influence mold performance but it is not established which properties are most important and to what extent they influence the mold performance. Identifying the most important properties influencing mold performance would help resin manufacturers to develop better mold-making materials. In order to study the performance of mold materials we have built a device for measuring the mechanical properties of 3D printed resins including their strength, surface hardness, and wear resistance at molding temperatures of up to 260 oC. We then quantified the mechanical properties of three high-temperature resins along with ABS at the injection molding temperatures. This paper describes the test device and the results of characterizing the mechanical properties of the selected plastics.

scholarly journals Metal Injection Molding Process Parameters as A Function of Filling Performance of 3D Printed Polymer Mold

2018 ◽  
Vol 225 ◽  
pp. 06004
Author(s):  
Junaid A. Qayyum ◽  
Khurram Altaf ◽  
A. Majdi A. Rani ◽  
Faiz Ahmad ◽  
Hafiz A. Qadir ◽  
...  
Keyword(s):  
Injection Molding ◽  
Metal Injection Molding ◽  
Injection Molding Process ◽  
Metal Mold ◽  
Molding Process ◽  
Injection Parameters ◽  
3D Printed ◽  
Low Volume ◽  
Parts Manufacturing ◽  
Polymer Mold

Metal injection molding (MIM) is a swift manufacturing process, which can produce complex and intricate parts with good repeatability and accuracy. However, to quickly address low-volume demands of customized MIM parts, manufacturing of mold could be a potential challenge. Typically, machined metal molds are used for MIM, but they are expensive and need more lead time. The machined metal mold becomes useless once the design is changed or requirement of MIM parts is met. Therefore, for MIM production of a low volume of highly customized parts, machined metal mold could be substituted by 3D printed polymer molds. However, knowledge of filling behavior of MIM feedstock in polymer mold is a grey area, which demands study to investigate the effects of injection parameters on mold filling. The present study investigates the effects of machine injection parameters on feedstock filling behavior in 3D printed polymer molds. An attempt has been made to determine the trend of feedstock filling in the polymer mold as a function of injection parameters. Further, the design of experiment (DOE) has been used to estimate the weight of injection parameters.

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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