Determination of the transition temperature at different cooling rates and its influence on prediction of shrinkage and warpage in injection molding simulation

CNN-Based Defect Inspection for Injection Molding Using Edge Computing and Industrial IoT Systems

Currently, the development of automated quality inspection is drawing attention as a major component of the smart factory. However, injection molding processes have not received much attention in this area of research because of product diversity, difficulty in obtaining uniform quality product images, and short cycle times. In this study, we proposed a defect inspection system for injection molding in edge intelligence. Using data augmentation, we solved the data shortage and imbalance problem of small and medium-sized enterprises (SMEs), introduced the actual smart factory method of the injection process, and measured the performance of the developed artificial intelligence model. The accuracy of the proposed model was more than 90%, proving that the system can be applied in the field.

Experimental Investigation of the Melt Shear Viscosity, Specific Volume and Thermal Conductivity of Low-Density Polyethylene/Multi-Walled Carbon Nanotube Composites Using Capillary Flow

Understanding the flow behavior of polymer/carbon nanotube composites prior to melt processing is important for optimizing the processing conditions and final product properties. In this study, the melt shear viscosity, specific volume and thermal conductivity of low-density polyethylene (LDPE) filled with multi-walled carbon nanotubes (MWCNTs) were investigated for representative processing conditions using capillary rheometry. The experimental results show a significant increase in the melt shear viscosity of the LDPE/MWCNT composite with nanotube loadings higher than 1 wt.%. Upon increasing shear rates, the composites flow like a power-law fluid, with a shear-thinning index less than 0.4. The specific volume decreases with increasing pressure and nanotube loading, while the pVT transition temperature increases linearly with increasing pressure. The thermal conductivity of the LDPE/MWCNT composite is nearly independent of nanotube loading up to the thermal percolation threshold of 1 wt.% and increases linearly with further increases in nanotube loading, reaching 0.35 W/m·K at 5 wt.%. The Carreau-Winter and Cross viscosity models and Tait equation, respectively, are able to predict the shear viscosity and specific volume with a high level of accuracy. These results can be used not only to optimize processing conditions through simulation but also to establish structure-property relationships for the LDPE/MWCNT composites.

Experimental and Numerical Investigation of Shrinkage and Warpage of a U-Shaped Injection Molded Part

Prediction of shrinkage and warpage of injection molded plastic parts is a key issue of injection molding simulation. In the present work a U-shaped part is investigated experimentally and by means of state of the art injection molding simulation software. The part has thick walls in the corner where inner defects such as voids and porous zones may appear. Experiments and simulations were performed with POM and glass fiber reinforced polyamide. Measurements of pressure in the cavities and temperature profiles around the cavities were used to validate the numerical model. In case of POM it was found that warpage predictions are way off when the void volume is high and in good agreement when the void volume is minimized. No such correlation was observed in case of fiber reinforced materials. Here, shrinkage and warpage are dominated by the fiber orientation state with minor influence of the processing conditions. Predicted warpage was found to be extremely dependent on the used closure approximation.

Characterization of a graphene oxide/poly(acrylic acid) nanocomposite by means of molecular dynamics simulations

Graphene oxide/poly(acrylic acid) nanocomposite: static, dynamic, thermal properties and hydrogen bonding, as studied by molecular dynamics simulations.

Measurement and Numerical Simulation of Void and Warpage in Glass Fiber Reinforced Molded Chunky Parts

The aim of this paper is to determine the reason for the large warpage of an injection molded chunky part made of glass fiber reinforced polyamide 6. State of the art commercial 3D injection molding simulation software was used to predict the warpage of the part. While the simulation predicted a product virtually free of warpage the molded parts showed a large deformation. Several molding experiments and X-ray computed tomography (μCT) scans of the moldings have been performed to figure out the reason for this. The μCT scans revealed voids in the thick walls of the part. It was found that these defects were the main cause of the unreliable simulation results. Therefore it is concluded that voids can have a major influence on the reliability of numerical warpage predictions. This paper shows a simplified method to determine the zones were voids will appear and shows the comparison with μCT-data.