Injection Molding Process Parameters Research Articles

Research on residual stress control in polycarbonate molding based on orthogonal analysis

Abstract Residual internal stress is a primary factor affecting the performance and quality of transparent food containers formed by polycarbonate injection molding. Adjusting the injection molding process parameters stands out as the most effective means to control residual internal stress. This study employs numerical simulation to analyze the influence of injection molding process parameters on material internal stress during the polymer injection molding process, providing a reference for optimizing molding process parameters. Initially, through an analysis of the mechanism of internal stress formation, it is determined that the internal stress of beverage containers is mainly composed of cooling internal stress. Consequently, a mathematical model based on thermo-viscoelastic constitutive relations is selected to numerically simulate and calculate the formation of cooling internal stress during the injection molding process of PC materials. Numerical simulation results are obtained accordingly. Building upon this, orthogonal experiments are conducted based on the analysis data from numerical simulation, and through experimental comparison, the consistency between numerical simulation results and experimental results is validated. Finally, combining the results of experiments with numerical simulation, it is concluded that melt temperature is the primary factor influencing molding internal stress, followed by mold temperature and holding pressure, with injection speed being the least influential factor on residual stress.

Multi-objective optimization of microcellular injection molding process parameters to reduce energy consumption and improve product quality

Multi-objective optimization of microcellular injection molding process parameters to reduce energy consumption and improve product quality

Fuzzy assessment of process parameter interactions on warpage defect modeling in plastic injection molding

AbstractStudying the interactions among major plastic injection molding process parameters is necessary to understand how they collectively influence major defects such as warpage hence enabling optimization of the process for improved product quality. Existing process parameter interaction studies have used statistical approximations, which have limitations such as reduced predictive power and limited accuracy. To overcome these limitations, this study presents an alternative method of analysis of the interactions among process parameters based on fuzzy logic intelligent algorithm. Through computer aided engineering, factorial design of experiment and fuzzy logic modelling, the study evaluated the effects of major injection molding process parameter interactions on warpage. The results obtained indicated a general increase in warpage with increase in parameters such as melt temperature, mold temperature, injection pressure and cool time whereas an increase in parameters such as ambient temperature and packing pressure decreased warpage. Parameter interactions were obtained both statistically and based on fuzzy logic model and their significance tested through ANOVA. Ambient temperature (30.6%) and melt temperatures (18.7%) had the greatest effect on warpage all with P-values of 0.000 whereas cooling time (0.1%) had the least effect with P-value of 0.250. The largest two way interaction affecting warpage involved melt temperature and cooling time with a contribution of 12.2% whereas the largest three way interaction involves ambient temperature, packing pressure and injection pressure with a contribution of 2.7%. Also, despite cooling time having the least mains effect, most interaction terms with greater effect on warpage involved cooling time and melt temperature. The results from this study provides an insight on targeted injection molding process parameter control for defect minimization.

Multi-Objectives Optimization of Plastic Injection Molding Process Parameters Based on Numerical DNN-GA-MCS Strategy.

An intelligent optimization technique has been presented to enhance the multiple structural performance of PA6-20CF carbon fiber-reinforced polymer (CFRP) plastic injection molding (PIM) products. This approach integrates a deep neural network (DNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Monte Carlo simulation (MCS), collectively referred to as the DNN-GA-MCS strategy. The main objective is to ascertain complex process parameters while elucidating the intrinsic relationships between processing methods and material properties. To realize this, a numerical study on the PIM structural performance of an automotive front engine hood panel was conducted, considering fiber orientation tensor (FOT), warpage, and equivalent plastic strain (PEEQ). The mold temperature, melt temperature, packing pressure, packing time, injection time, cooling temperature, and cooling time were employed as design variables. Subsequently, multiple objective optimizations of the molding process parameters were employed by GA. The utilization of Z-score normalization metrics provided a robust framework for evaluating the comprehensive objective function. The numerical target response in PIM is extremely intricate, but the stability offered by the DNN-GA-MCS strategy ensures precision for accurate results. The enhancement effect of global and local multi-objectives on the molded polymer-metal hybrid (PMH) front hood panel was verified, and the numerical results showed that this strategy can quickly and accurately select the optimal process parameter settings. Compared with the training set mean value, the objectives were increased by 8.63%, 6.61%, and 9.75%, respectively. Compared to the full AA 5083 hood panel scenario, our design reduces weight by 16.67%, and achievements of 92.54%, 93.75%, and 106.85% were obtained in lateral, longitudinal, and torsional strain energy, respectively. In summary, our proposed methodology demonstrates considerable potential in improving the, highlighting its significant impact on the optimization of structural performance.

Experimental Investigation of Processing Temperature Effect on Adhesive Bond Strength Between Engineering Thermoplastics in the Plastic Injection Molding Process

Abstract Multicomponent injection molding industry is experiencing a growth due to its ability to reduce production costs and streamline processes. However, compared to single injection, multicomponent injection molding introduces interface regions where multiple engineering polymers meet. Consequently, it is essential to comprehend and enhance the adhesive bonding strength properties of these polymers. This study investigates the adhesive bond strength of polymer–polymer multimaterial molding using two-shot bi-injection and overmolding techniques. The research also emphasizes the influence of injection molding process parameters of mold temperature and melt temperature on the adhesive bond strength of polycarbonate (PC), polycarbonate–acrylonitrile butadiene styrene (PC–ABS), acrylonitrile butadiene styrene (ABS), and styrene ethylene butadiene styrene (SEBS). Tensile strength results revealed that the bi-injection method yields the highest interface strength, approximately 10 MPa lower than the reference value for single-material hard–hard plastics. Results from overmolded samples for both injection sequences are presented, indicating that material with low melting temperature was found to be the first injected part for better adhesion strength. Empirical equations for estimating adhesion strength were derived as a function of interface temperature obtained from CAE numerical simulations and polymer glass transition temperatures. The proposed equation achieved R2 values greater than 0.96. This empirically derived equation will serve as a guide for multi-injection manufacturing processes.

Multi-objective optimization method of injection molding process parameters based on hierarchical sampling and comprehensive entropy weights

Multi-objective optimization method of injection molding process parameters based on hierarchical sampling and comprehensive entropy weights

Evaluation of plastic injection molding for PA6/ABS/CaCO3 nanocomposites using Taguchi method and Moldflow simulation approach

Nowadays, nanocomposites are widely used in the industry. Polymer nanocomposites are widely used in the automotive industry because they have very favorable properties. These properties, including mechanical, electrical, and thermal characteristics, change depending on the combination of materials used in composite synthesis. In this paper, the injection molding of an automobile part called a control arm protector is investigated. Since warpage and shrinkage are general and important challenges in injection molded parts, Taguchi test design and Autodesk Moldflow® simulation approach are used to find the best injection condition for the mentioned part. To perform this, PA6/ABS polymer composite combined with nano CaCO3 is used. Three main injection molding process parameters, including melt temperature, mold temperature, and injection pressure, as well as nano CaCO3 amount, are evaluated. Moreover, analysis of the gate location is investigated. In the following, an analysis of variance is conducted to identify the significant parameters. Regression correlations were also established. Finally, optimization of the process is carried out by the desirability method.

High-Density Polyethylene/Kaolin Clay Composites: Optimization of The In-jection Moulding Process Parameters Towards Minimum Shrinkage and Warpage

High-Density Polyethylene/Kaolin Clay Composites: Optimization of The In-jection Moulding Process Parameters Towards Minimum Shrinkage and Warpage

Integration of the multivariate statistical control chart and machine learning to identify faults in the quality characteristics for polylactic acid with glass fiber composites in injection molding

Complex processing parameters need to be adjusted for expected qualities in injection molding processing. Once the process is abnormal, it is essential to spend time and human work on fault diagnosis. In this study, we focus on fault diagnosis of injection molding processing parameters for polylactic acid/glass fiber composites. The injection molding processing parameters include the melt temperature, injection speed, packing pressure, packing time, and cooling time. The qualities include tensile strength, hardness, impact strength, and flexure strength. When processing parameters deviate from the optimal process condition, the multivariate statistical control chart monitors downgraded qualities. The machine is operated at the optimal process conditions to generate normal samples and the corresponding four qualities of data are chosen as the historical data. Hotelling’s T2 is used to calculate the upper control limit (UCL) from the historical data to detect abnormal samples. If the T2 value exceeds the UCL, the corresponding sample is considered abnormal. Then, the residuals of qualities for abnormal samples are obtained by a residual control chart. They are chosen as the feature values for the backpropagation neural network (BPNN) to identify the abnormal processing parameters. The experimental results proved that the BPNN can achieve a 100% recognition rate for single-factor abnormal samples. For the single-/double-factor mixture, the accuracy rate of double-factor classification can reach 97.44%. This proposed study has the advantage of high stability, being non-destructive, high precision, and low cost, and can be widely promoted in injection molding industries.

Application of machine learning methods in the analysis of interface bonding strength for overmolded hybrid thermoset‐thermoplastic composites

AbstractThe focus of this study is to apply finite element method (FEM) and machine learning methods to investigate the interfacial bonding strength of continuous fiber reinforced thermoset composite (TSC)‐thermoplastic structures manufactured through co‐curing and overmolding processes, with polyamide 6 (PA 6) as the thermoplastic material. A model for interfacial healing degree in TSC‐PA 6 structures was developed. Then, FEM was employed to study the influence of various injection overmolding process parameters on the interfacial bonding strength of TSC‐PA 6 structures. The results show that there is a strong correlation between the degree of interface healing and the bonding strength. Subsequently, six machine learning methods were employed to correlate interfacial healing degree with diverse injection molding process parameters. Simulation data were utilized for training, calibration, and validation of the six machine learning models. Based on the results of simulation and machine learning predictions, a quantitative analysis of the significance of injection molding process parameters on healing degree was conducted. These parameters are ranked in descending order of importance as follows: insert temperature, melt temperature, and injection rate.Highlights The interfacial healing degree model of PA 6 was established and validated through the integration FEM and experiment. Six machine learning models were built to predict the interfacial healing degree. Grad boosting performed best in predicting the interfacial healing degree. Insert temperature had the greatest impact on the interface healing degree.

Multi-objective Optimization of Injection Molding Process Based on One-Dimensional Convolutional Neural Network and the Non-dominated Sorting Genetic Algorithm II

<div>In the process of injection molding, the vacuum pump rear housing is prone to warping deformation and volume shrinkage, which affects its sealing performance. The main reason is the improper control of the injection process and the large flat structure of the vacuum pump rear housing, which does not meet its production and assembly requirements (the warpage deformation should be controlled within 1.1 mm and the volume shrinkage within 10%). To address this issue, this study initially utilized orthogonal experiments to obtain training samples and conducted a preliminary analysis using gray relational analysis. Subsequently, a predictive model was established based on a one-dimensional convolutional neural network (1D CNN). Input parameters from the injection molding process, including melt temperature, mold temperature, packing pressure, packing time, injection pressure, injection time, and cooling time, were used while warping deformation and volume shrinkage were considered as outputs. Global optimization was performed using the non-dominated sorting genetic algorithm II (NSGA-II), and the optimal combination of process parameters was evaluated using the criterion importance through intercriteria correlation—technique for order preference by similarity to ideal solution (CRITIC-TOPSIS). Moldflow analysis demonstrated that the obtained indicators outperformed the optimization results from orthogonal experiments, confirming the effectiveness of the injection molding process parameter optimization method based on 1D CNN-NSGA-II. In comparison to the pre-optimization results, product warping deformation decreased by 40.68%, and volume shrinkage reduced by 18.14%, and all of them meet the production requirements.</div>

Study on the influence of in-mold sequential injection molding process parameters on mechanical properties of self-reinforced single composites

Abstract In this paper, the self-reinforced single polymer composites (SR-SPCs) with different mechanical properties were obtained by compound injection molding technology, and the micro-morphology of these samples was observed. Then, using structured statistical methods, analysis of variance, and response surface methodology, study the effects of various molding variables on material morphology and properties and determine the most important molding variables and their interactions. Finally, the associated experimental data are fitted by the least squares minimization program, and the relevant dimensionless equations are obtained. The purpose is to objectively analyze the influence mechanism of molding parameters on SR-SPCs and establish a mechanism model. It was found that temperature change was the most important factor affecting the morphology and mechanical properties. The degree of molecular orientation is the most important factor to determine the tensile strength and elastic modulus of the sample. The change of crystallinity is the most important factor related to the elongation at break. By establishment relevant dimensionless equations, the influence of molding parameters on the mechanical properties of SR-SPCs, such as tensile strength and elastic modulus, was preliminarily studied.

Comparative Analysis of Different Injection Molding Process Parameters through Injection Molding Flow Simulation for Various Gate Locations

This study deals with part quality concerning gate location on the part for injection molding. Through injection molding filling simulations, basic parameters such as injection pressure, bulk and surface temperature, shear rate, solidification, shrinkage, air entrapment, weld lines and others were analysed. The simulation was conducted on a fan blade model with Polycarbonate material. Polycarbonate is a commonly used polymer in the industry and is famous for its rigidity, strength-to-weight ratio and reliability in critical system. Simulation is done at six possible gate locations labelled A1 through A6. Injection molding filling simulations was performed to evaluate the performance of each gate location. Among the considered gate locations, gate A4 exhibited a considerably high pressure drop, while gate A5 exhibited a comparatively uniform pressure drop across the mould cavity, also gate A5 showed consistent and smooth filling along with uniform cooling implying less chance of defects. Gate A4 and A5 exhibit uniform distribution across the cavity in shear rate analysis. Gate locations A2, A3, A4, and A5 exhibited controlled solidification, indicating consistent part quality. However, Gate A6 exhibited problems with volume shrinkage, which requires process optimization. Gate A6 exhibited a significant reduction in air entrapment as compared to other gate locations.

Effects of Plastic Injection Moulding Process Parameters on Material Elastic Modulus on Complex Geometry and Parameter Optimization for Getting Maximum Stiffness

Plastic injection is widely used in white goods sector in case of highly production rates, low transformation costs and recyclability of products. There are many parameters affecting on the quality, dimensional stability and mechanical strength in plastic injection process. In washing machine tubs which are exposed to dynamic loads during spinning, it is necessary to optimize process parameters. In this research; packing pressure, packing transition point, packing time and injection time is optimized by using Box Behnken methodology via Moldlfow elastic modulus outputs and structural analyses results. Optimum process parameters are obtained to achieve best material orientation.

Multi-Objective Optimization of Injection Molding Process Parameters for Moderately Thick Plane Lens Based on PSO-BPNN, OMOPSO, and TOPSIS

Injection molding (IM) is an ideal technique for the low-cost mass production of moderately thick plane lenses (MTPLs). However, the optical performance of injection molded MTPL is seriously degraded by the warpage and sink marks induced during the molding process with complex historical thermal field changes. Thus, it is essential that the processing parameters utilized in the molding process are properly assigned. And the challenges are further compounded when considering the MTPL molding energy consumption. This paper presents a set of procedures for the optimization of injection molding process parameters, with warpage, sink marks reflecting the optical performance, and clamping force reflecting the molding energy consumption as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the S/N response shows that these three objectives cannot reach the optimal values simultaneously. Second, considering the experimental data scale, the back propagation neural network updated by the particle swarm optimization method (PSO-BPNN) was applied to establish the complex nonlinear mapping relationship between the process parameters and these three trade-off objectives respectively. Then, the Pareto optimal frontier of the multi-objective optimization problem was attained by multi-objective particle swarm optimization using a mutation operator and dominance coefficient algorithm (OMOPSO). And the competitive relationship between these objectives was further confirmed by the corresponding pairwise Pareto frontiers. Additionally, the TOPSIS method with equal weights was employed to achieve the best optimal solution from the Pareto optimal frontier. The simulation results yielded that the maximum values of warpage, sink marks, and clamping force could be reduced by 7.44%, 40.56%, and 5.56%, respectively, after optimization. Finally, MTPL products were successfully fabricated.

Comparative analysis of artificial neural network model and analysis of variance for predicting defect formation in plastic injection moulding processes

This study investigates the impact of plastic injection moulding process parameters on overflow defect formation. Experiments were conducted using a Taguchi L27 orthogonal array design. Multilayer Perceptron (MLP) artificial neural networks is explored and compared with ANOVA predictions. To assess model performance, the Root Mean Squared Error (RMSE) and the coefficient of determination (R2) is applied. The study considered temperature, speed, pressure, and packing force when constructing the MLP model using the back-propagation algorithm in Python. Results show that among the configured MLP neural networks, the 3-layer MLP architecture with sigmoid activation functions in hidden layers and a linear function in the output layer exhibited the lowest prediction error and the highest coefficient of determination. Comparative analysis reveals that the MLP neural network outperforms the ANOVA model, indicating superior prediction accuracy. The predicted outcomes from the ANN align well with experimental values, demonstrating the effectiveness of the ANN model in forecasting defect formation under specific process conditions. This research sheds light on the significance of process parameters and showcases the potential of MLP neural networks as a valuable tool in predicting and mitigating overflow defects in plastic injection moulding.

Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III.

This study aimed to improve the injection molding quality of LSR material lenses by optimizing the process parameters. To achieve this goal, we employed the population-based optimization algorithm NSGA-III, which can simultaneously optimize multiple objective functions and identify an equilibrium point among them, thereby reducing the time required to find the optimal process parameters. We utilized analysis software to simulate the injection molding process of LSR material lenses, with a specific focus on examining the relationship between tie bar elongation and the optimized process parameters. During the study, we intentionally varied key process parameters, including the melt temperature, holding pressure, and holding time, to analyze their impact on the residual stress of the final product. In order to investigate the intricate relationship between the tie bar yield, injection molding process parameters, and lens residual stress, we installed strain sensors on the tie bar to continuously monitor changes in clamping force throughout the injection molding process. The experimental results showed that both the tie bar force and mold cavity pressure exerted significant influence on residual stresses. By applying the NSGA-III algorithm for optimization, we successfully determined the optimal process parameters, which included a melt temperature of 34.92 °C, a holding pressure of 33.97 MPa, and a holding time of 9.96 s. In comparison to the initially recommended process parameters during the design phase, the optimized parameters led to reductions of 12.98% in clamping force and 47.14% in residual stress. Furthermore, the average transmittance of the actual product remained within the range of 95-98%. In summary, this approach not only enables the prediction of the lens's residual stress trends based on the tie bar elongation, but also leads to a substantial enhancement of lens quality, characterized by reduced residual stress and improved transmittance through the optimization of process parameters. This methodology can serve as a valuable guide for optimizing real-world injection molding processes.

Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning

Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning

Optimization of plastic injection molding process parameters for cowl B (L/R) sink mark defects by using Taguchi methods and ANOVA

The plastic injection molding process on Cowl B (L/R) products that have been carried out has sink mark defects. The defects that arise occur because the composition of injection molding parameter values is not optimal in the variables of melt temperature, mold temperature, packing time, packing pressure, and cooling time. The purpose of this study is to find the optimal composition of parameter values for each variable, to minimize sink mark defects in the product. The analysis process begins with the preparation of an orthogonal array matrix to determine the design parameters to be simulated on Autodesk mold flow. These results are evaluated with a signal-to-noise ratio to determine the effect of each parameter value composition on the results of the analysis process. The Analysis of Variance (ANOVA) method is used to estimate the contribution of each independent variable to all response measurements (the dependent variable). The optimization results for sink mark defects in the sink mark index value of 1.4494%, volumetric shrinkage of 0.5053%, and sink mark estimate of 0.0608 mm are found in the composition of the parameter values of melt temperature 200°C, mold temperature 80°C, packing time 30 seconds, packing pressure 80 MPa and a cooling time of 13,365 seconds. This data is used as a reference in determining parameters before production is carried out on plastic injection molding machines so that the time and cost of testing the injection molding process are optimal.

The potential of RHCM technology in injection molding using a simple convention heating and cooling system

In conventional injection molding, low mold temperatures are used; however, the thermal conditions, which vary during the processing, affect the properties of the injection molded parts and the injection molding process parameters. Due to the rapid cooling of the molten polymer against the mold wall, the flow resistance of the molten polymer increases, and the hydraulic injection pressure is directly affected by this. The rapid cooling of the plastic parts affects the quality of the parts, which can result in surface defects.Using the rapid heat cycle molding (RHCM) approach, a simple convection heating and cooling system was added to a conventional injection molding process to study the potential of RHCM technology. We assessed the influence of the mold temperature on the part's morphological features (namely, the frozen layer) and mechanical properties (namely, the Young's modulus), as well as the hydraulic injection pressure.Using RHCM to heat the molding surface up to 100 °C was found to entirely prevent the formation of a frozen layer, with an expected positive effect on the previously mentioned defects. Note that while the decrease of the frozen layer could have been expected to reduce Young's modulus, this did not happen, which is a very positive outcome related to the specific orientation at which the frozen layer is formed and its morphology. The higher the mold surface temperature, the lower the hydraulic injection pressure, which means less strict structural requirements for the mold. These results highlight the potential of RHCM technology in injection molding, namely as a strategy to mitigate or solve molding defects.