Washers to Reduce Vibration and Noise From the Injection Molding Process

2006 ◽  
Author(s):  
Kuang-Yih Tsuei ◽  
Shu-Fen Kuo
Keyword(s):  
Injection Molding ◽  
Energy Absorption ◽  
Injection Molding Process ◽  
Performance Tests ◽  
Molding Process ◽  
Noise And Vibration ◽  
Vibration Problems ◽  
And Performance

The noise and vibration problems created by injection molding machines can be moderated by the installation of absorbers. The pull rods of the machine, which are guided to the molding movements, might be a better location for mounting a spring, rubber or hybrid elastomer for energy absorption and reduction of noise and vibration. In this paper, some special washers are designed to fit the guide rods and performance tests are carried out. The results show that noise and vibration decreased over 10 dB and 2 times, respectively.

scholarly journals A Review on Machine Learning Models in Injection Molding Machines

2022 ◽  
Vol 2022 ◽  
pp. 1-28
Author(s):  
Senthil Kumaran Selvaraj ◽  
Aditya Raj ◽  
R. Rishikesh Mahadevan ◽  
Utkarsh Chadha ◽  
Velmurugan Paramasivam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Injection Molding ◽  
Performance Criteria ◽  
Machine Learning Techniques ◽  
Injection Molding Process ◽  
Molding Process ◽  
Injection Speed ◽  
Detection Techniques ◽  
And Performance

One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator’s expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.

scholarly journals About the numerous cost and processing advantages of the microcellular foam injection molding process for thermoplastics materials in the automobile industry

2015 ◽  
Vol 9 (2) ◽  
pp. 6-14
Author(s):  
Alexandru Oprea-Kiss ◽  
Imre Kiss
Keyword(s):  
Injection Molding ◽  
Automobile Industry ◽  
Dimensional Stability ◽  
Injection Molding Process ◽  
Microcellular Foam ◽  
Molding Process ◽  
Microcellular Injection Molding ◽  
And Performance ◽  
Plastic Parts ◽  
Foam Injection Molding

Today one of the goals of the automobile industry is to reduce weight. And physical foaming has already demonstrated its potential in this sector, improving the value and performance of applications under the bonnet: engine and gearbox cases, inlet air filters, cockpits, radiator baffles and so on. Around the world, the microcellular injection molding (MuCell) is used in thousands of applications in the automotive, packaging, technical molding, office machinery and electric and electronic component industries. The research opportunities purpose is to obtain even lighter pieces, with greater dimensional stability and with an excellent surface finish, in other words, perfect plastic parts. More component functionality with reduced weight, and cost control at the same time: MuCell is a process to physically foam thermoplastics, which combines technical and economic objectives. Besides weight reduction, it also provides improved dimensional stability of the moulded parts.

Injection Molding of Rubber: Structure, Processing and Properties

1978 ◽  
Vol 51 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Boh C. Tsai
Keyword(s):  
Injection Molding ◽  
Recovery Process ◽  
Integral Approach ◽  
Design Parameters ◽  
Injection Molding Process ◽  
Anisotropic Behavior ◽  
Molding Process ◽  
Operating Variables ◽  
Molded Part ◽  
And Performance

Abstract The anisotropic behavior of an injection molded part from a disk mold is reported. It is concluded that orientation rather than state of cure is responsible for the anisotropic behavior. The slow strain recovery process of elongational deformation of the uncured rubber is the major factor in introducing orientation. This example is used to illustrate the viewpoint that the total injection molding process should be investigated from an integral approach which takes into account the interactions of parameters in various areas. Engineering concepts such as the unit operations approach have been gradually gaining acceptance in injection molding of rubber, and compound development continues to progress satisfactorily. However, the success of injection molding will largely depend on the skillful manipulation of material characteristics, design parameters, and operating variables under some unfavorable constraints to meet the cost and performance requirements.

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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