Numerical Simulation of Fiber Orientation of Short Fiber Reinforced Polypropylene in Injection Molding

2011 ◽  
Vol 418-420 ◽  
pp. 1194-1201
Author(s):  
He Sheng Liu ◽  
Ai Hua Xiong ◽  
Xing Yuan Huang ◽  
Jia Mei Lai
Keyword(s):  
Numerical Simulation ◽  
Injection Molding ◽  
Fiber Orientation ◽  
Subsurface Layer ◽  
Core Layer ◽  
Injection Molding Process ◽  
Main Process ◽  
Fiber Reinforced ◽  
Molding Process ◽  
Melt Temperature

Based on generalized non-Newtonian fluid with seven parameters Cross-WLF viscosity model and modified 2-double Tait model, the numerical simulation was carried out for the short glass fiber reinforced PP injection molding process of rectangular part. The influence of main process parameters on fiber orientation is investigated. The results show that fiber orientation can be generally divided into three-regional layers in injection molding, that is outer-surface, subsurface and core layer. The degree of fiber orientation in subsurface layer is the highest and that in core layer is the lowest. The influence of fibers interaction coefficient (Ci) and fibers aspect ratio (re) on fiber orientation is significant. There is obvious difference between simulation results and practical results without consideration of Ci. The effect of melt temperature, mold temperature and cooling tubes number on fiber orientation isn’t obvious.

Process comparison on the microstructure and mechanical properties of fiber-reinforced polyphenylene sulfide using MuCell technology

2018 ◽  
Vol 37 (15) ◽  
pp. 1020-1034 ◽  
Author(s):  
Christoph Lohr ◽  
Björn Beck ◽  
Frank Henning ◽  
Kay André Weidenmann ◽  
Peter Elsner
Keyword(s):  
Mechanical Properties ◽  
High Pressure ◽  
Injection Molding ◽  
Fiber Orientation ◽  
Polyphenylene Sulfide ◽  
Injection Molding Process ◽  
Fiber Reinforced ◽  
Molding Process ◽  
Glass Fiber Reinforced ◽  
Foam Injection Molding

The MuCell process is a special injection molding process which utilizes supercritical gas (nitrogen) to create integral foam sandwiches. The advantages are lower weight, higher specific properties and shorter cycle times. In this study, a series of glass fiber-reinforced polyphenylene sulfide foam blanks are manufactured using the MuCell injection molding process. The different variations of the process (low-pressure also known as structural foam injection molding) and high-pressure foam injection molding (also known as “core back expansion,” “breathing mold,” “precision opening,” decompression molding) are used. The sandwich structure and mechanical properties (tensile strength, bending strength, and impact behavior) of the microcellular and glass fiber-reinforced polyphenylene sulfide foams are systematically investigated and compared to compact material. The results showed that the injection parameters (injection speed, foaming mechanism) played an important role in the relative density of microcellular polyphenylene sulfide foams and the mechanical properties. It could be shown that the specific tensile strength decreased while increasing the degree of foaming which can be explained by the increased number of cells and the resulting cell size. This leads to stress peaks which lower the mechanical properties. The Charpy impact strength shows a significant dependence on the fiber orientation. The specific bending modulus of the high-pressure foaming process, however, surpasses the values of the other two processes showing the potential of this manufacturing variation especially with regard to bending loads. Furthermore, a high dependence of the mechanical properties on the fiber orientation of the tested specimens can be found.

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

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