Simulation of Ceramic Injection Molding for Zirconia Optical Ferrule

2007 ◽  
Vol 336-338 ◽  
pp. 997-1000 ◽  
Author(s):  
Mei Min Zhang ◽  
Bin Lin
Keyword(s):  
Injection Molding ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Ceramic Injection Molding ◽  
Rectangular Distribution ◽  
Simulation Results ◽  
Die Temperature ◽  
Ram Speed ◽  
Short Shot

Zirconia Ferrule is a key part for manufacturing fiber connectors. The ceramic injection molding (CIM) process of the optical ferrule was simulated with the commercial CAE software moldflow. In order to obtain the optimum results, the orthogonal method was introduced to discuss the influence of each parameter such as die temperature, melt temperature, ram speed and gate dimension with the two kinds of distribution layout system respectively. The simulation results show that the curved distribution runner system is more suitable than the rectangular distribution one in the optical ferrule molding. Moreover, the effect of gravity on the ceramic injection molding process was discussed for determining a more reasonable balanced runner system of the special designed two-plate mold with six die cavities. It was found that short shot occurred at the top of the die cavity while other five cavities were filled well in the original designed mold. And when the top die cavity’s round runner with section diameter of 4.0mm was increased to 4.17 mm, each cavity was balanced filled without short shot.

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.

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.

Optimization of Injection Molding Process Parameters with Material Properties Based on GA and BP

2013 ◽  
Vol 345 ◽  
pp. 586-590 ◽  
Author(s):  
Xiao Hong Tan ◽  
Lei Gang Wang ◽  
Wen Shen Wang
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Axial Deformation ◽  
Molding Process ◽  
Melt Temperature ◽  
Bp Network ◽  
Injection Parameters ◽  
Moldflow Simulation

To obtain optimal injection process parameters, GA was used to optimize BP network structure based on Moldflow simulation results. The BP network was set up which considering the relationship between volume shrinkage of plastic parts and injection parameters, such as mold temperature, melt temperature, holding pressure and holding time etc. And the optimal process parameters are obtained, which is agreed with actual results. Using BP network to predict injection parameters impact on parts quality can effectively reduce the difficulty and workload of other modeling methods. This method can be extended to other quality prediction in the process of plastic parts.Keyword: Genetic algorithm (GA);Neural network algorithm (BP);Injection molding process optimization;The axial deformation

Injection Molding Process Windows Considering Two Conflicting Criteria: Simulation Results

2012 ◽  
Author(s):  
Alicia B. Rodríguez ◽  
Esmeralda Niño ◽  
Jose M. Castro ◽  
Marcelo Suarez ◽  
Mauricio Cabrera
Keyword(s):  
Injection Molding ◽  
Multiple Criteria ◽  
Process Window ◽  
Injection Molding Process ◽  
Processing Conditions ◽  
Molding Process ◽  
Multiple Criteria Optimization ◽  
Simulation Results ◽  
Main Ideas

In this work, two criteria in conflict are considered simultaneously to determine a process window for injection molding. The best compromises between the two criteria are identified through the application of multiple criteria optimization concepts. The aim with this work is to provide a formal and realistic strategy to set processing conditions in injection molding operations. In order to keep the main ideas manageable, the development of the strategy is constrained to two controllable variables in computer simulated parts.

The simulation of the warpage rule of the thin-walled part of polypropylene composite based on the coupling effect of mold deformation and injection molding process

2018 ◽  
Vol 25 (3) ◽  
pp. 593-601 ◽  
Author(s):  
Jixiang Zhang ◽  
Xiaoyi Yin ◽  
Fengzhi Liu ◽  
Pan Yang
Keyword(s):  
Injection Molding ◽  
Coupling Effect ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Plastic Part ◽  
Molding Process ◽  
Melt Temperature ◽  
Packing Pressure ◽  
Thin Walled ◽  
Packing Time

Abstract Aiming at the problem that a thin-walled plastic part easily produces warpage, an orthogonal experimental method was used for multiparameter coupling analysis, with mold structure parameters and injection molding process parameters considered synthetically. The plastic part deformation under different experiment schemes was comparatively studied, and the key factors affecting the plastic part warpage were analyzed. Then the injection molding process was optimized. The results showed that the important order of the influence factors for the plastic part warpage was packing pressure, packing time, cooling plan, mold temperature, and melt temperature. Among them, packing pressure was the most significant factor. The optimal injection molding process schemes reducing the plastic part warpage were melt temperature (260°C), mold temperature (60°C), packing pressure (150 MPa), packing time (2 s), and cooling plan 3. In this situation, the forming plate flatness was better.

The Effect of Injection Molding Process Parameters on the Buckling Properties of PBT/Short Glass Fiber Composites

2011 ◽  
Vol 284-286 ◽  
pp. 550-556 ◽  
Author(s):  
Ming Hsiung Ho ◽  
Pin Ning Wang ◽  
Chin Ping Fung
Keyword(s):  
Injection Molding ◽  
Glass Fiber ◽  
Process Parameters ◽  
Injection Molding Process ◽  
Skin Area ◽  
Molding Process ◽  
Melt Temperature ◽  
Short Glass Fiber ◽  
Filling Time ◽  
Short Glass

This study investigates the effect of various injection molding process parameters and fiber amount on buckling properties of Polybutylene Terephthalate (PBT)/short glass fiber composite. The buckling specimens were prepared under injection molding process. These forming parameters about filling time, melt temperature and mold temperature that govern injection molding process are discussed. The buckling properties of neat PBT, 15 wt%, and 30 wt% are obtained using two ends fixed fixture and computerized closed-loop server-hydraulic material testing system. The fracture surfaces are observed by scanning electron microscopy (SEM). The global buckling forces are raised when increased the fiber weight percentage of PBT. Also, the fracture mechanisms in PBT and short glass fiber matrix are fiber pullout in skin area and fiber broken at core area. It is found that the addition of short glass fiber can significantly strengthen neat PBT.

In-Mold Monitoring of Temperature and Cavity Pressure During the Injection Molding Process

2014 ◽  
Author(s):  
Catalin Fetecau ◽  
Felicia Stan ◽  
Laurentiu I. Sandu
Keyword(s):  
Injection Molding ◽  
Pressure Sensors ◽  
Temperature Sensors ◽  
Injection Molding Process ◽  
Cavity Pressure ◽  
Molding Process ◽  
Shear Rates ◽  
Wide Range ◽  
Simulation Results ◽  
Two Temperature

This paper focuses on the in-mold monitoring of temperature and cavity pressure. The melt contact temperature and the cavity pressure along the flow path were directly measured using two pressure sensors and two temperature sensors fitted into the cavity of a spiral mold. Three melt temperatures and dies of different heights (1.0, 1.5 and 2 mm) were used to achieve a wide range of practically relevant shear rates. In order to analyze the extent to which the numerical simulation can predict the behavior of the molten polymer during the injection molding process, molding experiments were simulated using the Moldflow software and the simulation results were compared with the experimental data under the same injection molding conditions.

Optimization of the Injection Molding Process Parameters Based on Moldflow and Orthogonal Experiment

2013 ◽  
Vol 561 ◽  
pp. 239-243 ◽  
Author(s):  
Yong Nie ◽  
Hui Min Zhang ◽  
Jia Teng Niu
Keyword(s):  
Injection Molding ◽  
Orthogonal Experiment ◽  
Mold Temperature ◽  
Processing Parameters ◽  
Influential Factors ◽  
Injection Molding Process ◽  
Range Analysis ◽  
Molding Process ◽  
Melt Temperature ◽  
Plastic Parts

This article is using Moldflow analysis and orthogonal experimental method during the whole experiment. The injection molding process of motor cover is simulated under various technological conditions.After forming the maximum amount of warpage of plastic parts for evaluation.According to the range analysis of the comprehensive goal, the extent of the overall influence to the processing parameters, such as gate location, melt temperature, mold temperature and holding pressure is clarified.Through analyzing the diagrams of influential factors resulted from the simulation result,the optimized process parameter scheme is obtained and further verified by simulation.

scholarly journals ANALYSIS ON VOLUMETRIC SHRINKAGE OF PLASTIC FOOD CONTAINER MADE FROM AN INJECTION MOLDING PROCESS

2020 ◽  
Vol 8 (2) ◽  
pp. 22-31
Author(s):  
Rosidah Jaafar ◽  
◽  
Hambali Arep ◽  
Effendi Mohamad ◽  
Jeefferie Abd Razak ◽  
...  
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Food Packaging ◽  
Plastic Injection Molding ◽  
Volumetric Shrinkage ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Plastic Injection

The plastic injection molding process is one of the widely used of the manufacturing process to manufacture the plastic product with high productivity. Moreover, the food packaging manufacturing industry undergoes the trials and errors to obtain the optimal setting of the process parameters in order to minimize the quality issues and these trials and errors are time consuming and costly. The aim of this study is to improve the quality of the butter tub by minimizing the volumetric shrinkage. This study is to deal with the application of Moldflow integrating with the statistical technique to minimize the volumetric shrinkage the butter tub which depends on the process parameters of the plastic injection molding. For this purpose, the rectangular shape of butter tub is designed by utilizing the SolidWorks. Molflow is used to simulate the plastic filling of the single cavity mold of butter tub based on the Taguchi’s �!" orthogonal array table. In addition, the analysis of variance (ANOVA) is applied to investigate significant impact of the process parameters on the quality of the butter tub. Minitab is used to optimize the response of the volumetric shrinkage by selecting the most appropriate process parameters that maximizing the desirability value. Furthermore, the butter tub has a uniform thickness which was 1.2 mm and its factor of safety was 3.383 and the volumetric shrinkage response have optimized by 0.956 %. The melt temperature and mold temperature are found to be the most significant process parameters for the plastic injection molding process of butter tub and the volumetric shrinkage value obtained from the simulation is verified by the calculated volumetric shrinkage value.

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