Design of Optimization Parameters with Hybrid Genetic Algorithm Method in Multi-Cavity Injection Molding Process

2012 ◽  
Vol 463-464 ◽  
pp. 587-591 ◽  
Author(s):  
Wen Jong Chen ◽  
Jia Ru Lin
Keyword(s):  
Genetic Algorithm ◽  
Injection Molding ◽  
Hybrid Genetic Algorithm ◽  
Injection Molding Process ◽  
Process Conditions ◽  
Molding Process ◽  
Optimization Parameters ◽  
Genetic Algorithm Method ◽  
Artificial Neural Network Ann ◽  
Cae Simulation

This paper combines an artificial neural network (ANN) with a traditional genetic algorithm (GA) method, called hybrid genetic algorithm (HGA), to analyze the warpage of multi-cavity plastic injection molding parts. Simulation results indicate that the minimum and the maximum warpage of the hybrid genetic algorithm (HGA) method were lower than that of the traditional GA method and CAE simulation. These results reveal that, when HGA is applied to multi-cavity plastic warpage analysis, the optimal process conditions are significantly better than those using the traditional GA method or CAE simulation.

Multi-Objective Optimization by Gaussian Genetic Algorithm and its Application in Injection Modeling

2011 ◽  
Vol 399-401 ◽  
pp. 1672-1676
Author(s):  
Xiao Ping Liao ◽  
Ting Ruan ◽  
Wei Xia ◽  
Jun Yan Ma ◽  
Liu Lin Li
Keyword(s):  
Genetic Algorithm ◽  
Injection Molding ◽  
Surrogate Model ◽  
Hybrid Genetic Algorithm ◽  
Injection Molding Process ◽  
Process Conditions ◽  
Multi Objective Optimization ◽  
Molding Process ◽  
Global Optimal ◽  
Quality Indexes

A method of combining Gaussian Process (GP) Surrogate model and Gaussian genetic algorithm is discussed to optimize the injection molding process. GP surrogate model is constructed to map the complex non-linear relationship between process conditions and quality indexes of the injection molding parts. While the surrogate model is established, a Gaussian genetic algorithm (GGA) combined with Gaussian mutation and hybrid genetic algorithm is employed to evaluate the model to search the global optimal solutions. The example presented shows that the GGA is more effective for the process optimization of injection molding.

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.

Experimental determination of friction coefficients between thermoplastics and rapid tooled injection mold materials

2005 ◽  
Vol 11 (3) ◽  
pp. 167-173 ◽  
Author(s):  
Mary E. Kinsella ◽  
Blaine Lilly ◽  
Benjamin E. Gardner ◽  
Nick J. Jacobs
Keyword(s):  
Injection Molding ◽  
Accurate Determination ◽  
Rapid Tooling ◽  
Injection Molding Process ◽  
Process Conditions ◽  
Molding Process ◽  
Friction Coefficients ◽  
Content Type ◽  
Temperature Conditions

PurposeTo determine static friction coefficients between rapid tooled materials and thermoplastic materials to better understand ejection force requirements for the injection molding process using rapid‐tooled mold inserts.Design/methodology/approachStatic coefficients of friction were determined for semi‐crystalline high‐density polyethylene (HDPE) and amorphous high‐impact polystyrene (HIPS) against two rapid tooling materials, sintered steel with bronze (LaserForm ST‐100) and stereolithography resin (SL5170), and against P‐20 mold steel. Friction tests, using the ASTM D 1894 standard, were run for all material pairs at room temperature, at typical part ejection temperatures, and at ejection temperatures preceded by processing temperatures. The tests at high temperature were designed to simulate injection molding process conditions.FindingsThe friction coefficients for HDPE were similar on P‐20 Steel, LaserForm ST‐100, and SL5170 Resin at all temperature conditions. The HIPS coefficients, however, varied significantly among tooling materials in heated tests. Both polymers showed highest coefficients on SL5170 Resin at all temperature conditions. Friction coefficients were especially high for HIPS on the SL5170 Resin tooling material.Research limitations/implicationsApplications of these findings must consider that elevated temperature tests more closely simulated the injection‐molding environment, but did not exactly duplicate it.Practical implicationsThe data obtained from these tests allow for more accurate determination of friction conditions and ejection forces, which can improve future design of injection molds using rapid tooling technologies.Originality/valueThis work provides previously unavailable friction data for two common thermoplastics against two rapid tooling materials and one steel tooling material, and under conditions that more closely simulate the injection‐molding environment.

Sign in / Sign up

Export Citation Format

Share Document