Performance Predictor for Thin-Wall Plastic Parts Produced by Injection Molding

2000 ◽  
Author(s):  
H. P. Wang ◽  
Sreeganesh Ramaswamy ◽  
Irene Dris ◽  
Erin M. Perry ◽  
Dominic Gao
Keyword(s):  
Injection Molding ◽  
Process Development ◽  
Injection Molding Process ◽  
Thin Wall ◽  
Molding Process ◽  
Melt Temperature ◽  
Shear Degradation ◽  
Development Cycle ◽  
Filling Time ◽  
Plastic Parts

Abstract The objective of this work was to develop a numerical simulation tool that is able to predict the processing window for thin-wall plastic parts made by the injection molding process. This performance predictor links the processing conditions (filling time, resin inlet melt temperature, and so on) to the mechanical properties and failure mechanisms of the part, using empirical data developed for the thermal and shear degradation behavior of the resin. Usage of such a performance predictor will help to expedite the long process development cycle time and to reduce the potentially expensive tooling costs associated with the thin-wall segment of the plastics business.

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.

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.

Numerical and Experimental Study on the Injection Moulding of a Thin-Wall Complex Part

2008 ◽  
Author(s):  
Catalin Fetecau ◽  
Ion Postolache ◽  
Felicia Stan
Keyword(s):  
Injection Molding ◽  
Injection Pressure ◽  
Mold Temperature ◽  
Processing Parameters ◽  
Injection Molding Process ◽  
Thin Wall ◽  
Molding Process ◽  
Complex Part ◽  
Filling Time ◽  
Wall Injection

The research presented in this paper involves numerical and experimental efforts to investigate the relative thin-wall injection molding process in order to obtain high dimensional quality complex parts. To better understand the effects of various processing parameters (the filling time, injection pressure, the melting temperature, the mold temperature) on the injection molding of a thin-wall complex part, the molding experiments are regenerated into the computer model using the Moldflow Plastics Insight (MPI) 6.1 software. The computer visualization of the filling phase allows accurate prediction of the location of the flow front, welding lines and air traps. Furthermore, in order to optimize the injection molding process, the effects of the geometry of the runner system on the filling and packing phases are also investigated. It is shown that computational modeling could be used to help the process and mold designer to produce accurate parts.

Analysis of Warpage and Optimization of Parameter for Thin-Wall Plastic Part Moldflow-Based Software

2013 ◽  
Vol 377 ◽  
pp. 133-137 ◽  
Author(s):  
Qiu Hua Sun ◽  
Ming Wei Wang ◽  
Ren Pan ◽  
Wen Xin ◽  
Hong Yan Lv
Keyword(s):  
Three Dimensional ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Thin Wall ◽  
Three Dimensional Model ◽  
Molding Process ◽  
Melt Temperature ◽  
Bottom Cover ◽  
Plastic Parts ◽  
Press Time

The injection molding process of plastic parts is simulated and analyzed through a three-dimensional model of bottom cover of USB (Universal Serial Bus) extender obtained by Pro/E software and MPI module of Moldflow software. Orthogonal test is applied to analyze the influence of press time, press pressure, mold temperature and melt temperature on the warpage of plastic parts. Those being influenced by factors above are listed in order as press pressure, melt tempreture, mold tempreture and press time. The optimum parameters are obtained by analyzing effect trend that the four factors can influence on warpage.

scholarly journals Optimization for Reducing Warpage in Injection Moulding

2018 ◽  
Vol 2 (5) ◽  
pp. 25-31
Author(s):  
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Large Scale ◽  
Simulation Software ◽  
Optimum Process ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Product Cycles ◽  
Filling Time

Injection molding is a standout technique utilized for the fabrication of thermoplastic parts in industry due to short product cycles, high part quality, good mechanical properties and low cost for large scale manufacturing. In molded case circuit breaker (MCCB), Trip-bar is one of the most critical components as safety is concerned which is manufactured by injection molding process. To get it manufactured within the specified warpage and deformities free, number of mold flow simulations is carried out using Creo-MoldFlow. The outcomes of the simulation are used to design the mold tool and the process parameters for injection molding are optimized. For process parameter optimization Taguchi based experimental design and ANOVA analysis is done. The objective of this work is to optimize the injection molding process parameters such as filling time, melt temperature and mold temperature to minimize the warpage. CAE flow simulation software is used to simulate the process and Grey Relational Analysis (GRA) is used to find out optimum process parameters.

Numerical Optimization of Shrinkage and Warpage on the Injection Molding Process Parameters of Electrical Connector

2017 ◽  
Vol 868 ◽  
pp. 183-191 ◽  
Author(s):  
Yun Wang ◽  
Li Yu Chen ◽  
Xia Ming Yang ◽  
Yan Zhao ◽  
Zhen Ying Xu ◽  
...  
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Mold Temperature ◽  
Influential Factors ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Influence Of Process Parameters ◽  
Filling Time ◽  
The Impact

Integrated with orthogonal design method and numerical simulation, injection molding process of the Y-type electrical connectors was conducted to study the influence of process parameters on volume shrinkage rate and maximum warpage, which are regarded as product quality indices. The multi-indices valuation model for the main influencing factors of the process is developed. The influencing sensitivity to the multi-objective of the processing parameters, such as melt temperature, mold temperature, injection time and holding pressure, is determined by range analysis. Through analyzing the diagrams of influential factors, the optimized process parameter diagram is obtained and verified by simulation. The optimum parameters minimizing the warpage defect and shrinkage are: melt temperature (528K), mold temperature (338K), filling time (0.6s), holding pressure (100%) and holding time (10s). The results show that it is effective to balance the impact of process parameters on the shrinkage and warpage. The work can provide optimal design and process reference for the quality control and assembly precision.

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

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.

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