Numerical Simulation and Process Optimization of a 3D Thin-Walled Polymeric Part Using Injection Compression Molding

2021 ◽  
Vol 36 (4) ◽  
pp. 459-467
Author(s):  
D. Sönmez ◽  
A. A. Eker
Keyword(s):  
Numerical Simulation ◽  
Injection Molding ◽  
Process Parameters ◽  
Compression Molding ◽  
Injection Molding Process ◽  
Mold Surface ◽  
Clamping Force ◽  
Molding Process ◽  
Thin Walled ◽  
Injection Compression Molding

Abstract Injection compression molding (ICM) is a hybrid injection molding process for manufacturing polymer products with high precision and surface accuracy. In this study, a 3D flow simulation was employed for ICM and injection molding (IM) processes. Initially, the process parameters of IM and ICM were discussed based on the numerical simulations. The IM and ICM processes were compared via numerical simulation by using CAE tools of Moldflow software. The effect of process parameters of mold surface temperature, melting temperature, compression force and injection time on clamping force and pressure at the injection location of molded 3D BJ998MO Polypropylene (MFI 100) part was investigated by Taguchi analysis. In conclusion, it was found that the ICM has a relatively lower filling pressure than ICM, which results in reduced clamping force for producing a 3D thin-walled polymeric part.

scholarly journals Analysis of Reactive Injection Compression Molding by Numerical Simulations and Experiments

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Giorgio Ramorino ◽  
Silvia Agnelli ◽  
Matteo Guindani
Keyword(s):  
Injection Molding ◽  
Experimental Tests ◽  
Curing Kinetics ◽  
Compression Molding ◽  
Computational Time ◽  
Injection Molding Process ◽  
Molding Process ◽  
Viscosity Models ◽  
Element Simulation ◽  
Injection Compression Molding

Injection compression molding is an injection molding process with the addition of a compression stage after the injection. This process is useful for the injection molding of precision parts. A stable and controlled manufacturing process is needed to guarantee reliability of complex products, and usually process optimization is achieved by experimental and time consuming approaches. However, for being competitive a minimal market time is a very important requirement and computer simulations can help to optimize the process at the only expense of computational time. This paper reports and discusses for the first time the results of a 3D finite element simulation of reactive injection compression molding (RICM) by commercial software for the production of rubber diaphragms. In particular, the stages of mold filling dynamics and material curing are analyzed and the results verified with experimental tests. To get an accurate representation of the process, the rheological behavior, thermal properties, and kinetic behavior during curing of the real rubber compound were described by mathematical models. A differential scanning calorimeter (DSC) and a capillary rheometer are employed to characterize the rubber material in order to achieve an appropriate curing reaction and viscosity models, respectively. The computations are found to be in good agreement with the experimental results, indicating that reliable information on material viscosity and curing kinetics can play a key role in making well-founded predictions and avoiding trial and error methods.

scholarly journals Optimization of Injection-Molding Process for Thin-Walled Polypropylene Part Using Artificial Neural Network and Taguchi Techniques

Polymers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 4158
Author(s):  
Mehdi Moayyedian ◽  
Ali Dinc ◽  
Ali Mamedov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Holding Time ◽  
Injection Molding Process ◽  
Molding Process ◽  
Filling Time ◽  
Thin Walled

Plastics are commonly used engineering materials, and the injection-molding process is well known as an efficient and economic manufacturing technique for producing plastic parts with various shapes and complex geometries. However, there are certain manufacturing defects related to the injection-molding process, such as short shot, shrinkage, and warpage. This research aims to find optimum process parameters for high-quality end products with minimum defect possibility. The Artificial Neural Network and Taguchi Techniques are used to find a set of optimal process parameters. The Analytic Hierarchy Process is used to calculate the weight of each defect in the proposed thin-walled part. The Finite Element Analysis (FEA) using SolidWorks plastics is used to simulate the injection-molding process for polypropylene parts and validate the proposed optimal set of process parameters. Results showed the best end-product quality was achieved at a filling time of 1 s, cooling time of 3 s, pressure-holding time of 3 s, and melt temperature of 230 °C. The end-product quality was mostly influenced by filling time, followed by the pressure-holding time. It was found that the margin of error for the proposed optimization methods was 1.5%, resulting from any uncontrollable parameters affecting the injection-molding process.

scholarly journals Constant Temperature Approach for the Assessment of Injection Molding Parameter Influence on the Fatigue Behavior of Short Glass Fiber Reinforced Polyamide 6

Polymers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1569
Author(s):  
Selim Mrzljak ◽  
Alexander Delp ◽  
André Schlink ◽  
Jan-Christoph Zarges ◽  
Daniel Hülsbusch ◽  
...  
Keyword(s):  
Injection Molding ◽  
Glass Fiber ◽  
Process Parameters ◽  
Fatigue Properties ◽  
Injection Molding Process ◽  
Molding Process ◽  
Short Glass Fiber ◽  
Glass Fiber Reinforced ◽  
Molding Parameter ◽  
Short Glass

Short glass fiber reinforced plastics (SGFRP) offer superior mechanical properties compared to polymers, while still also enabling almost unlimited geometric variations of components at large-scale production. PA6-GF30 represents one of the most used SGFRP for series components, but the impact of injection molding process parameters on the fatigue properties is still insufficiently investigated. In this study, various injection molding parameter configurations were investigated on PA6-GF30. To take the significant frequency dependency into account, tension–tension fatigue tests were performed using multiple amplitude tests, considering surface temperature-adjusted frequency to limit self-heating. The frequency adjustment leads to shorter testing durations as well as up to 20% higher lifetime under fatigue loading. A higher melt temperature and volume flow rate during injection molding lead to an increase of 16% regarding fatigue life. In situ Xray microtomography analysis revealed that this result was attributed to a stronger fiber alignment with larger fiber lengths in the flow direction. Using digital volume correlation, differences of up to 100% in local strain values at the same stress level for different injection molding process parameters were identified. The results prove that the injection molding parameters have a high influence on the fatigue properties and thus offer a large optimization potential, e.g., with regard to the component design.

Ultrasonic measurement of clamping force for injection molding machine

2019 ◽  
Vol 39 (4) ◽  
pp. 388-396 ◽  
Author(s):  
Peng Zhao ◽  
Yao Zhao ◽  
Jianfeng Zhang ◽  
Junye Huang ◽  
Neng Xia ◽  
...  
Keyword(s):  
Injection Molding ◽  
Measurement Method ◽  
Large Scale ◽  
Force Measurement ◽  
Low Cost ◽  
Ultrasonic Method ◽  
Injection Molding Process ◽  
Propagation Time ◽  
Clamping Force ◽  
Molding Process

AbstractAn online and feasible clamping force measurement method is important in the injection molding process and equipment. Based on the sono-elasticity theory, anin situclamping force measurement method using ultrasonic technology is proposed in this paper. A mathematical model is established to describe the relationship between the ultrasonic propagation time, mold thickness, and clamping force. A series of experiments are performed to verify the proposed method. Experimental findings show that the measurement results of the proposed method agree well with those of the magnetic enclosed-type clamping force tester method, with difference squares less than 2 (MPa)2and errors bars less than 0.7 MPa. The ultrasonic method can be applied in molds of different thickness, injection molding machines of different clamping scales, and large-scale injection cycles. The proposed method offers advantages of being highly accurate, highly stable, simple, feasible, non-destructive, and low-cost, providing significant application prospects in the injection molding industry.

scholarly journals Optimization of injection molding process for car fender in consideration of energy efficiency and product quality

2014 ◽  
Vol 1 (4) ◽  
pp. 256-265 ◽  
Author(s):  
Hong Seok Park ◽  
Trung Thanh Nguyen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Optimization Problems ◽  
Computational Cost ◽  
Injection Molding Process ◽  
Molding Process ◽  
Nsga Ii

Abstract Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

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

Sign in / Sign up

Export Citation Format

Share Document