scholarly journals Efficient and Precise Micro-Injection Molding of Micro-Structured Polymer Parts Using Micro-Machined Mold Core by WEDM

Polymers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1591 ◽  
Author(s):  
Qianghua Liao ◽  
Chaolan Zhou ◽  
Yanjun Lu ◽  
Xiaoyu Wu ◽  
Fumin Chen ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Injection Molding ◽  
Electrical Discharge Machining ◽  
Minimum Size ◽  
Injection Velocity ◽  
Injection Molding Process ◽  
Replication Rate ◽  
Molding Process ◽  
Micro Injection Molding ◽  
Melt Temperature

In this paper, micro-structured polymer parts were efficiently and accurately fabricated by micro-injection molding using a micro-structured mold core machined by wire electrical discharge machining (WEDM). The objective was to realize low-cost mass production and manufacturing of micro-structured polymer products. The regular micro-structured mold core was manufactured by precise WEDM. The micro-structured polymer workpieces were rapidly fabricated by micro-injection molding and the effects of the micro-injection molding process parameters on replication rate and surface roughness of micro-structured polymers were systematically investigated and analyzed. It is shown that the micro-structured polymer can be rapidly and precisely fabricated by the proposed method. The experimental results show the minimum size machining error of the micro-structured mold core and the maximum replication rate of micro-formed polymer were 0.394% and 99.12%, respectively. Meanwhile, the optimal micro-injection molding parameters, namely, jet temperature, melt temperature, injection velocity, holding pressure and holding time were 195 °C, 210 °C, 40 mm/min, 7 Mpa and 5 s, respectively. The surface roughness Ra at the groove bottom and top of the micro-structured polymer workpieces achieved minimum values of 0.805 µm and 0.972 µm, respectively.

Design and Fabrication of a Polymeric Microfilter for Medical Applications

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Rossella Surace ◽  
Vincenzo Bellantone ◽  
Gianluca Trotta ◽  
Vito Basile ◽  
Francesco Modica ◽  
...  
Keyword(s):  
Injection Molding ◽  
Electrical Discharge Machining ◽  
Mold Temperature ◽  
Machining Process ◽  
Injection Velocity ◽  
Injection Molding Process ◽  
Molding Process ◽  
Micro Injection Molding ◽  
Melt Temperature ◽  
Steel Mold

This paper reports on design, fabrication, and characterization of a microfilter to be used in biomedical applications. The microfilter, with mesh of 80 μm, is fabricated by micro-injection molding process in polymeric material (polyoxymethylene (POM)) using a steel mold manufactured by micro-electrical discharge machining process. The characteristics of the filter are investigated by numerical simulation in order to define a suitable geometry for micro-injection molding. Then, different process configurations of parameters (melt temperature, injection velocity, mold temperature, holding pressure and time, cooling time, pressure limit) are tested in order to obtain the complete part filling via micro-injection molding process preventing any defects. Finally, the component is dimensionally characterized and the process parameters optimized to obtain the maximum filtration capacity.

Effect of Injection Molding Parameters on Residual Stress of Plastic Drawing Board

2012 ◽  
Vol 629 ◽  
pp. 576-580
Author(s):  
Lan Fang Jiang ◽  
Hong Liu ◽  
Chang Guo Hu ◽  
Xian Li Chen ◽  
Zhi Jiang Lei
Keyword(s):  
Residual Stress ◽  
Injection Molding ◽  
Mold Temperature ◽  
Injection Velocity ◽  
Injection Molding Process ◽  
Range Analysis ◽  
Molding Process ◽  
Melt Temperature ◽  
Drawing Board ◽  
Warpage Deformation

Due to large planar scale and small lateral scale of plastic drawing board, it was easy to cause warpage problem in injection molding. Optimization of injection molding process was taken to reduce residual stress and improve quality. Combining orthogonal experimental method and software Moldflow, analyzed the effect of mold temperature, melt temperature, hold pressure and injection velocity on warpage deformation. It changed multi-objective optimization to single-objective optimization by weighted method. Through range analysis obtained the influence trend between parameters and comprehensive optimal object. Lastly got the optimal combination of injection molding process parameters.

scholarly journals Injection Molding of Rubber Compound Influenced by Injection Mold Surface Roughness

2014 ◽  
Vol 1025-1026 ◽  
pp. 283-287 ◽  
Author(s):  
Michal Stanek ◽  
David Manas ◽  
Miroslav Manas ◽  
Martin Ovsik ◽  
Vojtech Senkerik ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Injection Molding ◽  
Flow Characteristics ◽  
Mold Cavity ◽  
Cavity Surface ◽  
Injection Molding Process ◽  
Mold Surface ◽  
Technological Parameters ◽  
Molding Process ◽  
Melt Temperature

Delivery of polymer melts into the mold cavity is the most important stage of the injection molding process. This paper shows the influence of cavity surface roughness and technological parameters on the flow length of rubber into mold cavity. The fluidity of polymers is affected by many parameters (mold design, melt temperature, injection rate and pressures) and by the flow properties of polymers. Results of the experiments carried out with selected types of rubber compounds proved a minimal influence of surface roughness of the runners on the polymer melt flow. This considers excluding (if the conditions allow it) the very complex and expensive finishing operations from the technological process as the influence of the surface roughness on the flow characteristics does not seem to play as important role as was previously thought. Application of the measurement results may have significant influence on the production of shaping parts of the injection molds especially in changing the so far used processes and substituting them by less costly production processes which might increase the competitiveness of the tool producers and shorten the time between product plan and its implementation.

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 Vacuum Venting Enhances the Replication of Nano/Microfeatures in Micro-Injection Molding Process

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Seong Ying Choi ◽  
Nan Zhang ◽  
J. P. Toner ◽  
G. Dunne ◽  
Michael D. Gilchrist
Keyword(s):  
Injection Molding ◽  
Quantitative Study ◽  
Vacuum Pressure ◽  
Injection Molding Process ◽  
Anodized Aluminum ◽  
Molding Process ◽  
Micro Injection Molding ◽  
Sample Shape ◽  
Final Sample ◽  
Qualitative And Quantitative Study

Vacuum venting is a method proposed to improve feature replication in microparts that are fabricated using micro-injection molding (MIM). A qualitative and quantitative study has been carried out to investigate the effect of vacuum venting on the nano/microfeature replication in MIM. Anodized aluminum oxide (AAO) containing nanofeatures and a bulk metallic glass (BMG) tool mold containing microfeatures were used as mold inserts. The effect of vacuum pressure at constant vacuum time, and of vacuum time at constant vacuum pressure on the replication of these features is investigated. It is found that vacuum venting qualitatively enhances the nanoscale feature definition as well as increases the area of feature replication. In the quantitative study, higher aspect ratio (AR) features can be replicated more effectively using vacuum venting. Increasing both vacuum pressure and vacuum time are found to improve the depth of replication, with the vacuum pressure having more influence. Feature orientation and final sample shape could affect the absolute depth of replication of a particular feature within the sample.

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

scholarly journals Experimental Validation of Injection Molding Simulations of 3D Microparts and Microstructured Components Using Virtual Design of Experiments and Multi-Scale Modeling

Micromachines ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 614 ◽  
Author(s):  
Dario Loaldi ◽  
Francesco Regi ◽  
Federico Baruffi ◽  
Matteo Calaon ◽  
Danilo Quagliotti ◽  
...  
Keyword(s):  
Injection Molding ◽  
Process Simulation ◽  
Current Work ◽  
Injection Molding Process ◽  
Process Conditions ◽  
Virtual Design ◽  
Molding Process ◽  
Micro Injection Molding ◽  
Multi Scale ◽  
Filling Time

The increasing demand for micro-injection molding process technology and the corresponding micro-molded products have materialized in the need for models and simulation capabilities for the establishment of a digital twin of the manufacturing process. The opportunities enabled by the correct process simulation include the possibility of forecasting the part quality and finding optimal process conditions for a given product. The present work displays further use of micro-injection molding process simulation for the prediction of feature dimensions and its optimization and microfeature replication behavior due to geometrical boundary effects. The current work focused on the micro-injection molding of three-dimensional microparts and of single components featuring microstructures. First, two virtual a studies were performed to predict the outer diameter of a micro-ring within an accuracy of 10 µm and the flash formation on a micro-component with mass a 0.1 mg. In the second part of the study, the influence of microstructure orientation on the filling time of a microcavity design section was investigated for a component featuring micro grooves with a 15 µm nominal height. Multiscale meshing was employed to model the replication of microfeatures in a range of 17–346 µm in a Fresnel lens product, allowing the prediction of the replication behavior of a microfeature at 91% accuracy. The simulations were performed using 3D modeling and generalized Navier–Stokes equations using a single multi-scale simulation approach. The current work shows the current potential and limitations in the use of micro-injection molding process simulations for the optimization of micro 3D-part and microstructured components.

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.

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.

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