Injection Molding Process Parameters Research Articles

Study of injection molding process simulation and mold design of automotive back door panel

Numerical simulation of the injection molding process of the outer panel of the automotive plastic rear door and mold design is presented here. Computer aided three-dimensional interactive application (CATIA) is employed to design the original automotive steel structure, and the modal and thermodynamic properties of the plastic back door outer panel are changed by changing the different injection materials of the back door outer panel. In order to efficiently design the panels, finite element analysis is used to verify whether the designed parts meet the mechanical properties requirements such as light weight, low fuel consumption, short production cycle, strong modeling design, high corrosion resistance and good recovery, the above main parameters have been evaluated, and the above main parameters are carried out evaluate. To simulate the injection molding process, computer aided engineering (CAE) software such as ANSYS and HyperWorks are used to analyze the back door of the selected material. After the numerical analysis, suitable material is selected, so that the modal and thermodynamic properties of the product could be satisfied as well as improved. Unigraphics NX (UG) is employed to design the convex and concave mold for the injection molding of the automobile’s plastic back door panel. Combined with the characteristics of the parts and the design requirements of the injection mold, the multi-scheme design of the pouring and cooling system is carried out. By comparing the effects of different gating and cooling systems on injection molding, the best gating and cooling system is selected. The artificial fish swarm algorithm is used to optimize the process parameters of the injection molding process, and the best combination of the injection molding process parameters of the outer panel of the rear door of the automobile is obtained.

Computer Simulations of Injection Process of Elements Used in Electromechanical Devices.

This paper presents the computer simulations of the injection process of elements used in electromechanical devices and an analysis of the impact of the injection molding process parameters on the quality of moldings. The study of the process was performed in Autodesk Simulation Moldflow Insight 2021. The setting of the injection process of the detail must be based on the material and process technological card data and knowledge of the injection molding machine work. The supervision of production quality in the case of injection moldings is based on the information and requirements received from the customer. The main goal of the analysis is to answer the question: how to properly set up the process of filling the mold cavities in order to meet the quality requirements of the presented molding. In this paper, the simulation was compared with the real process. It is extremely important to optimize the injection, including synchronizing all process parameters. Incorrectly selected values of the parameters may lead to product defects, leading to losses and destruction of raw materials, and unnecessary energy consumption connected with the process.

Comprehensive Areal Geometric Quality Characterisation of Injection Moulded Thermoplastic Gears.

Injection moulding is currently the most widely employed production method for polymer gears. Current standardised gear metrology methods, which are based on metal gear inspection procedures, do not provide the key information regarding the geometric stability of injection moulded gears and are insufficient for a thorough gear inspection. The study developed novel areal quality parameters, along with a so-called moulding runout quality parameter, with a focus on the injection moulding method. The developed parameters were validated on twenty-nine gear samples, produced in the same moulding tool using various processing parameters. The gears were measured using a high-precision structured-light 3D scanner. The influence of injection moulding process parameters on the introduced novel quality parameters was investigated. The developed moulding runout quality parameter proved to be effective in evaluating the shrinkage that can occur in the injection moulding process. The novel moulding runout parameter returned an average value of −21.8 μm in comparison to 29.4 μm exhibited by the standard parameter on all the gears, where the negative value points directly to mould shrinkages. The rate of cooling was determined to be the most influential factor for the shrinkage of the gear. The developed areal parameters demonstrated to be advantageous in characterising the deviations on the teeth more comprehensively.

Integrating Taguchi Method and Gray Relational Analysis for Auto Locks by Using Multiobjective Design in Computer-Aided Engineering

In automobiles, lock parts are matched with inserts, and this is a crucial quality standard for the dimensional accuracy of the molding. This study employed moldflow analysis to explore the influence of various injection molding process parameters on the warpage deformation. Deformation of the plastic part is caused by the nonuniform product temperature distribution in the manufacturing process. Furthermore, improper parameter design leads to substantial warpage and deformation. The Taguchi robust design method and gray correlation analysis were used to optimize the process parameters. Multiobjective quality analysis was performed for achieving a uniform temperature distribution and reducing the warpage deformation to obtain the optimal injection molding process parameters. Subsequently, three water cooling system designs—original cooling, U-shaped cooling, and conformal cooling—were tested to modify the temperature distribution and reduce the warpage. Taguchi gray correlation analysis revealed that the main influencing parameter was the mold temperature followed by the holding pressure. Moreover, the results indicated that the conformal cooling system improved the average temperature distribution.

Effect of morphology development on the low‐temperature tensile properties ofPP/POEblends

AbstractThe application of polypropylene (PP) is greatly restricted by its poor low‐temperature tensile properties. In this work, a polypropylene random copolymer (rPP) and polypropylene/ethylene‐propylene‐diene monomer graft copolymer (gPP) were used to improve the toughening effect of polyolefin elastomer (POE) on PP. The influences of this addition on the phase morphology, crystallization, and rheological and mechanical properties were investigated. The results revealed thatrPP both reduced the size of PP spherulites and improved the interfacial cohesion of the PP/POE blend, which endowed the material with greater tensile properties and melt flow rate. The elongation at break of PP/POE/rPP at 23, 0, −20, and −40°C was 400%, 221%, 205%, and 106%, respectively. These represented increases of more than 200% compared with those of PP/POE. The tensile strengths of PP/POE/rPP were 15.0, 19.1, 21.5, and 27.4 MPa at 23, 0, −20, and −40°C, respectively, which were comparable to those of PP/POE. Finally, the effects of the injection molding process parameters on the mechanical properties and phase morphology ofrPP‐modified PP/POE blends were studied.

Injection molding and shear‐induced stress simulation of poly (styrene‐ethylene‐butylene‐styrene) and polypropylene blends

AbstractIn this article, the structure and rheological properties of poly (styrene‐ethylene‐butylene‐styrene) (SEBS) triblock copolymer blends are analyzed. The influence of polypropylene content on the three SEBS blends was discussed, the viscoelastic response and phase structure evolution during injection molding were studied. According to the capillary flow test data, the least square method is used to fit the viscosity parameters of the cross‐WLF model. The filling length of the spline was tested by adjusting the injection molding process parameters, and its accuracy was verified by comparing the filling simulation with the experimental results. Numerical simulation is used to predict the shear rate distribution of injection‐molded samples, and the shear sensitivity and residual stress of SEBS blends with different phase structures are analyzed. Based on the phase structure analysis and rheological properties, this article predicts the filling properties of triblock copolymer composites, which can be used to construct the flow‐induced structure–property relationship of complex polymer blends.

Optimization of injection molding process parameters based on GA-ELM-GA

The most common optimization method for the optimization of injection mold process parameters is range analysis, but there is often a nonlinear coupling relationship between injection molding process parameters and quality indicators. Therefore, it is difficult to find the optimal process combination in range analysis. In this article, a genetic algorithm optimized extreme learning machine network model (GA-ELM) combined with genetic algorithm (GA) was proposed to optimize the process parameters of the injection mold. Take the injection molding process parameter optimization of an electrical appliance buckle cover shell as an example. In order to find the process parameters corresponding to the minimum warpage deformation, an orthogonal experiment was designed and the results of the orthogonal experiment were analyzed. Then, the corresponding optimal process combination and the degree of influence of process parameters on the warpage deformation were obtained. At the same time, the extreme learning machine network model (GA-ELM) optimized by the genetic algorithm was used to predict the warpage deformation of the plastic part. The trained GA-ELM model can map non-linear coupling relationship between the five process parameters and the warpage deformation well. And the optimal process parameters in the trained GA-ELM network model was searched by the powerful optimization ability of genetic algorithm. Generally speaking, the warpage deformation after optimization by range analysis is reduced by 6.7% compared with the minimum warpage after optimization by orthogonal experiment. But compared to the minimum warpage deformation after orthogonal experiment optimization, the warpage deformation after GAELM-GA optimization is reduced by 22%, which is better than that of the range analysis, thus verifying the feasibility and the optimization of the optimization method. This optimization method provides a certain theoretical reference and technical support for the field involving the optimization of process parameters.

Effect of injection molding cycle time on shrinkage and weight of manufactured polymer gear

In the present study, effect of cycle time on diametric shrinkage and weight of polymer gear was analyzed experimentally and numerically. The experimental investigation was carried out using two plate single cavity gear specimen mold and numerical validation of same was done using the “Fill-Pack-Cool-Warp” analysis sequence. Cycle time mainly consists of injection time, packing time, and cooling time. The injection molding process parameters namely melt temperature of 175 °C, mold temperature of 40 °C, injection pressure of 100 MPa, packing pressure of 100 MPa were fixed and polypropylene homopolymer of grade PP M108 was used in the study. The result shows that with increase in the injection time and packing time, gear shrinkage decreased but it increased with increase in the cooling time. The gear weight increased with increase in the injection time and no effect of packing and cooling time was observed. The minimum shrinkage of 1.657% and 1.972% was obtained in the experimental and numerical study respectively at injection time of 1.5 s, packing time of 15 s and cooling time of 20 s. Similarly, the maximum gear weight of 7.369 g and 7.350 g was also found at the same set of injection molding parameters in experimental and numerical study respectively.

To Explore the Effect of Injection Molding Processing Parameters on Crazing in Acrylic Polymers

Plastic manufacturing industry is the fastest growing industry as the demand for plastic products is exponentially growing worldwide. Poly (methyl methacrylate) (PMMA), also known as acrylic glass, is a transparent and rigid thermoplastic. PMMA is highly resistant to UV light. Weathering has an excellent light transmission and unlimited coloring options compared to other transparent plastic and has been used in wide applications such as architecture, automotive and transportation, lighting (LED lights), medical and healthcare, and furniture. Injection molding is the widely used plastic manufacturing process to produce plastic products for various applications. The quality of the plastic products depends on the injection molding parameters, viz. melting temperature, injection speed, and pressure, holding and cooling time, and holding pressure. Therefore, it is important to control injection molding parameters to reduce injection molding defects or parts. Hence, the main objective of this research is to optimize the injection molding parameters, including the amount of mold releasing agent, to avoid the crazing marks in PMMA which is a long-standing problem in the production of PMMA products or parts such as motorbike headlight lenses. Three different holding pressures (65, 75, and 85 kg/cm2) were varied against three different injection pressures (70, 80, and 90 kg/cm2). The injection speed (60 %), cooling time (4 s), and barrel zones temperature (185-205: 185-205: 190-210: 195-215) were kept constant not to disturb the production cycle, a constraint from the production industry. The minimum criteria required for the motorbike headlight lens selection was based on the LUX intensity test, density, and crazing demanded by the Japanese standard throughout this research. The optimized injection molding parameters and amount of mold releasing agent (Nabakem mold release R2) were 70 kg/cm2 (injection pressure), 65 kg/cm2 (holding pressure), and 1.18 g, respectively. The motorbike headlight lens produced with optimized injection molding parameters showed no crazing. In addition to the desired LUX intensity, density, and no crazing criteria, the motorbike headlight lens also showed improved impact properties and no substantial changes in mechanical properties when compared to virgin PMMA or literature. Hence, it is concluded that the optimized injection molding parameters (thermo-mechanical history) did not much affect the molecular weight and morphology of the PMMA.

Overmolding of Hybrid Long and Short Carbon Fiber Polypropylene Composite: Optimizing Processing Parameters

Injection overmolding was used to produce hybrid unidirectional continuous-short carbon fiber reinforced polypropylene. Polypropylene pellets containing short carbon fibers were melted and overmolded on unidirectional carbon fibers, which act as the core of the composite structure. Four factors were varied in this study: fiber pretension applied to unidirectional fibers, injection pressure, melting temperature, and backpressure used for melting and injecting the composite pellet. This study aimed to evaluate the effect of these factors on fiber volume fraction, flexural strength, and impact strength of the hybrid composite. The relationship between factors and responses was analyzed using Box–Behnken Response Surface Methodology (RSM) and analysis of variance (ANOVA). Each aspect was divided into three levels. There were 27 experimental runs carried out, with three replicated center points. The results showed that the injection molding process parameters had no significant effect on the fiber’s volume fraction. On the other hand, melting temperature and fiber pretension significantly affected impact strength and flexural strength.

Assessment of the Impact of Injection Moulding Process Parameters on the Properties of Mouldings Made of Low-Density Poly(ethylene) Recyclate LDPE

Assessment of the Impact of Injection Moulding Process Parameters on the Properties of Mouldings Made of Low-Density Poly(ethylene) Recyclate LDPE

Minimizing Warpage and Shrinkage of Plastic Car Rear Bumper Fabrication via Simulation Based Optimisation

Car bumpers are designed to absorb small impacts, such as accidentally bumping into another car while backing off a parking spot, not major road accidents. As to make it lighter, plastic is the best available material. To fabricate it by injection molding, thin-wall and complex parts such as car bumper might create a lot of defects such as shrinkage and warpage. So, the stronger plastic material and correct optimized parameters should be applied. Butadiene Styrene (ABS) is selected in this study. This project aims to optimize the injection moulding process parameters of plastic car bumper by using Central Composite Design (CCD) in Response Surface Methodology. Mould temperature, melt temperature, injection time are selected as factors to be studied. As the results, the optimum values suggested by the software were mould temperature of 25°C, 226.36°C melt temperature and 1.88s injection time. The best material suggested is ABS. With small differences in error value between solution and simulation, 0.895% of shrinkage and 10.3% for warpage, the results were acceptable.

Optimization for Injection Moulding process parameters towards Warpage and Shrinkage of HDPE-PBI composites

Injection moulding is a high-quality process that is preferred in modern manufacturing. Due to the defects in HDPE polymer composites the parameter’s had to be adjusted based on the necessity. Therefore, this study of parameters is to improve the quality characteristics of the injection moulding of HDPE-PBI composite samples. As the composition of HDPE composites varies, the quality aspects that must be controlled are shrinkage and warpage. The parameters that are considered as factors are Melting Temperature (°C), Injection pressure (MPa) and cooling time (sec). Using the Taguchi Optimization method of L9 Orthogonal array, the injection moulding process was performed and found that the injection pressure of 65 MPa and temperature of 180-185 °C showed better results. The influence of parameters was studied through this research using Analysis of Variance (ANNOVA).

Calibration of cavity pressure simulation using autoencoder and multilayer perceptron neural networks

AbstractNumerical simulations of polymer melt flow behavior in cavities help predict and optimize injection molding process parameters. However, simulation and actual results may differ because of simplified mathematical models, inaccurate processing conditions, material property settings, and machine aging, among other factors. Therefore, simulated optimal process parameters cannot be directly applied in practice. This study applied machine learning to generate a virtual–actual correction model to improve the accuracy of simulation results, especially the cavity pressure profile, a key indicator of injection‐molding quality for identifying ideal process parameter settings such as filling‐to‐packing switchover time and holding pressure. This method does not require big data for model training to enhance its practicality. Therefore, the correction model is only suitable for specific settings. A set of injection molding machines, molds, and processed materials were used for experimental verification. An autoencoder model was used to extract the features of simulation and actual cavity pressure curves. Then, a multilayer perceptron model was used to determine a relationship between simulation and actual features. The autoencoder was used to decode simulated features into cavity pressure curves. The proposed method was verified with dumbbell‐shaped specimens; the correlation between simulated and actual cavity pressures was greatly improved from 81% to 98%.

Application of Intelligent Modeling Method to Optimize the Multiple Quality Characteristics of the Injection Molding Process of Automobile Lock Parts.

This study focuses on applying intelligent modeling methods to different injection molding process parameters, to analyze the influence of temperature distribution and warpage on the actual development of auto locks. It explores the auto locks using computer-aided engineering (CAE) simulation performance analysis and the optimization of process parameters by combining multiple quality characteristics (warpage and average temperature). In this experimental design, combinations were explored for each single objective optimization process parameter, using the Taguchi robust design process, with the L18 (21 × 37) orthogonal table. The control factors were injection time, material temperature, mold temperature, injection pressure, packing pressure, packing time, cooling liquid, and cooling temperature. The warpage and temperature distribution were analysed as performance indices. Then, signal-to-noise ratios (S/N ratios) were calculated. Gray correlation analysis, with normalization of the S/N ratio, was used to obtain the gray correlation coefficient, which was substituted into the fuzzy theory to obtain the multiple performance characteristic index. The maximum multiple performance characteristic index was used to find multiple quality characteristic-optimized process parameters. The optimal injection molding process parameters with single objective are a warpage of 0.783 mm and an average temperature of 235.23 °C. The optimal parameters with multi-objective are a warpage of 0.753 mm and an average temperature of 238.71 °C. The optimal parameters were then used to explore the different cooling designs (original cooling, square cooling, and conformal cooling), considering the effect of the plastics temperature distribution and warpage. The results showed that, based on the design of the different cooling systems, conformal cooling obtained an optimal warpage of 0.661 mm and a temperature of 237.62 °C. Furthermore, the conformal cooling system is smaller than the original cooling system; it reduces the warpage by 12.2%, and the average temperature by 0.46%.

Effects of Injection Molding Process Parameters on the Chemical Foaming Behavior of Polypropylene and Polystyrene.

In the present study, semi-crystalline polypropylene (PP) and amorphous polystyrene (PS) were adopted as matrix materials. After the exothermic foaming agent azodicarbonamide was added, injection molding was implemented to create samples. The mold flow analysis program Moldex3D was then applied to verify the short-shot results. Three process parameters were adopted, namely injection speed, melt temperature, and mold temperature; three levels were set for each factor in the one-factor-at-a-time experimental design. The macroscopic effects of the factors on the weight, specific weight, and expansion ratios of the samples were investigated to determine foaming efficiency, and their microscopic effects on cell density and diameter were examined using a scanning electron microscope. The process parameters for the exothermic foaming agent were optimized accordingly. Finally, the expansion ratios of the two matrix materials in the optimal process parameter settings were compared. After the experimental database was created, the foaming module of the chemical blowing agents was established by Moldex3D Company. The results indicated that semi-crystalline materials foamed less due to their crystallinity. PP exhibits the highest expansion ratio at low injection speed, a high melt temperature, and a low mold temperature, whereas PS exhibits the highest expansion ratio at high injection speed, a moderate melt temperature, and a low mold temperature.

Optimization of Injection Molding Process of Transparent Complex Multi-Cavity Parts Based on Kriging Model and Various Optimization Techniques

The injection molding process of a mold with multiple cavities can reduce the mold space, but the process is complicated and the product is prone to warpage. In order to reduce the transparent parts warpage of such a mold, by optimizing injection molding process parameters, an optimization technique combining orthogonal experiment, Kriging model and an optimization algorithm is proposed. First, the orthogonal experiment is designed, based on Computer Aided Engineering (CAE) simulation results, while the significant factors affecting the warpage are selected. On this basis, the Kriging model is established to map the nonlinear function relationship between the significant factors and the warpage. Specifically in this paper, four types of correlation functions, affecting the model precision accuracy, are used to establish the Kriging model and the high-precision accuracy model is selected. A numerical optimization algorithm, direct search approach and global exploration method are used to optimize the model, so as to obtain the optimal injection molding parameters producing the minimum warpage. Finally, CAE simulation and experimental validation prove the effectiveness and reliability of the proposed optimization method.

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

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 X-ray 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.

Overview of injection molding process optimization technology

Injection molding process parameters have a very important influence on the molding quality of plastic parts. In this paper, the application of injection molding parameter optimization technology in the study of injection molding process is reviewed. The basic principles and characteristics of various optimization techniques are described. Optimization techniques mainly include: Optimization based on Taguchi method, Optimization of numerical simulation based on CAE technology, Optimization based on genetic algorithm, Optimization based on artificial neural network. The application types of optimization problems applicable to various optimization techniques are discussed.

Study on Injection Molding Process Simulation and Process Parameter Optimization of Automobile Instrument Light Guiding Support

In the present study, Moldflow® software is applied to simulate the injection molding of automobile instrument light guide bracket and optimize the injection gate position. In this regard, Taguchi orthogonal experimental design is adopted, and five processing parameters, the mold temperature, melt temperature, cooling time, packing pressure, and packing time, are considered as the test factors. Moreover, volume shrinkage and warping amount are considered as quality evaluation indices. Then range analysis and variance analysis are carried out to obtain the optimal combination of molding parameters with the volume shrinkage rate and the warpage amount. The grey correlation analysis was used to analyze the test results and obtain the optimal combination of volume shrinkage rate and amount of warping. Based on the performed simulations, it is found that the maximum volume shrinkage rate and the maximum amount of warping in the optimal design are 6.753% and 1.999 mm, respectively. According to the optimal process parameters, the injection molding of the automobile instrument light guide bracket meets the quality requirements.