Injection Molding Process Parameters Research Articles

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.

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.

OPTIMIZATION OF INJECTION MOLDING PROCESS PARAMETER SETTINGS USING 3k GENERAL FACTORIAL DESIGN AND DATA VISUALIZATION

This paper discusses an experimental design approach to optimize injection molding process parameter settings according to quality targets. The method used is general factorial design, so that it can investigate the effect of each predictor variable (factor) and its interaction effect. In this study, front barrel temperature, injection pressure, holding pressure, and holding time were selected as control factors. Before testing the hypothesis, the results of the experiment are also illustrated to summarize what main characteristics phenomena the data visualization can convey us. Hypothesis testing used linear regression analysis and analysis of variance (ANOVA) with a significance level (a) of 0.05. The results demonstrated that front barrel temperature (A), injection pressure (B), holding pressure (C), and holding time (D) had a significant effect on tensile strength, but only the front barrel temperature factor had a significant effect on net weight. Front barrel temperature is the most influencing factor on the response variables. There are a significant effect of the interaction between factors, namely AB, AC, BC, ABC, ABD, BCD, on tensile strength, whereas only AB interaction has a significant effect on net weight. The optimal settings could be adjusted according to the required quality target.

Predicting and Controlling the Quality of Injection Molding Properties for Fiber-Reinforced Composites

<div>Fiber-reinforced composites are widely used in injection molding processes because of their high strength and high elastic modulus. However, the addition of reinforcing agents such as glass fibers has a significant impact on their injection molding quality. The difference in shrinkage and hardness between the plastic and the reinforcement will bring about warpage and deformation in the injection molding of the product. At the same time, the glass fibers will be oriented in the flow direction during the injection molding process. This will enhance the mechanical properties in the flow direction and increase the shrinkage in the vertical direction, reducing the molding quality of the product. In this study, a test program was developed based on the Box-Behnken test design in the Design-Expert software, using a plastic part as an example. Moldflow software was used for simulation, and data analysis of the experimental data was carried out to investigate the significance of the influence of each injection molding process parameter on the molding quality. In addition to this, a mathematical model between the injection molding process parameters and the quality objectives was established by optimizing the model parameters of the back-propagation (BP) neural network through the ant colony optimization (ACO) algorithm. The established mathematical model is then globally optimized using a multi-objective function optimization based on the non-dominated rank-based sorting genetic algorithm (NSGA-II) to obtain the optimal combination of process parameters. The research in this article provides a theoretical basis for further combining intelligent algorithms to improve injection molding quality.</div>

Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA

Plastic injection molding is one of the most used methods for producing plastic products because it can be produced at a high production rate, low cost, and ease in manufacturing. However, one defect that affects product quality is namely warpage. To reduce plastic product warpage, the injection molding process is required optimal process control to increase plastic product quality. The objective of this paper is to optimize injection molding process parameters for minimizing the warpage of plastic glass. The optimization process is divided into two phases. The Finite Element Method (FEM) was employed in the first phase to simulate 32 experiments under various parameters. The parameters of this process consist of melt temperature ranging from 180 to 230 °C, mold temperature in the range of 20 – 45 °C, filling time from 0.82 to 0.92 s, packing time ranging from 5.88 to 7 s and cooling time of 14 to 18 s. In the second phase, Artificial Neural Network (ANN) combined Genetic Algorithm (GA) was developed to predict the warpage and solve the optimization process to find optimal parameters. Combining the intelligent method shows that ANN and GA effectively find the optimal process parameters that can reduce the warpage of the product by 35.73% from the maximum value.

Characterisation of Polypropylene Composite Reinforced with Chemi-Thermomechanical Pulp from Oil Palm Trunk via Injection Moulding Process.

As the products made from wood-plastic composites (WPCs) become more sophisticated and required more detail profiles, the injection moulding processing method with wood pulp as the reinforcing material is the answer to cater to the rapid change and demands of composite products. The general objective of this study was to study the effects of the material formulation, as well as the injection moulding process parameters, on the properties of a polypropylene composite reinforced with chemi-thermomechanical pulp from oil palm trunks (PP/OPTP composite) via the injection moulding process. The PP/OPTP composite with a material formulation of 70% pulp/26% PP/4% Exxelor PO produced using injection moulding at 80 °C as the mould temperature and with 50 tonnes of injection pressure exhibited the highest physical and mechanical properties. The increment loading of pulp increased the water absorption capacity of the composite. Higher loading of the coupling agent effectively reduced the water absorption capacity and increased the flexural strength of the composite. The increase in mould temperature from unheated to 80 °C prevented excessive heat loss of the flowing material, which enabled the molten material to flow better and filled up all cavities in the mould. The increased injection pressure slightly improved the physical properties of the composite, but the effect on the mechanical properties was insignificant. For the future development of WPCs, further studies should be focused on the viscosity behaviour, as a greater understanding of the processing parameters' effects on the PP/OPTP's viscosity behaviour will lead to improved product design and enable great potential usage of WPCs.

Multi-Objective Optimization of Liquid Silica Array Lenses Based on Latin Hypercube Sampling and Constrained Generative Inverse Design Networks.

Injection molding process parameters have a great impact on plastic production quality, manufacturing cost, and molding efficiency. This study proposes to apply the method of Latin hypercube sampling, and to combine the response surface model and "Constraint Generation Inverse Design Network (CGIDN)" to achieve multi-objective optimization of the injection process, shorten the time to find the optimal process parameters, and improve the production efficiency of plastic parts. Taking the LSR lens array of automotive LED lights as the research object, the residual stress and volume shrinkage were taken as the optimization objectives, and the filling time, melt temperature, maturation time, and maturation pressure were taken as the influencing factors to obtain the optimization target values, and the response surface models between the volume shrinkage rate and the influencing factors were established. Based on the "Constraint-Generated Inverse Design Network", the optimization was independently sought within the set parameters to obtain the optimal combination of process parameters to meet the injection molding quality of plastic parts. The results showed that the optimal residual stress value and volume shrinkage rate were 11.96 MPa and 4.88%, respectively, in the data set of 20 Latin test samples obtained based on Latin hypercube sampling, and the optimal residual stress value and volume shrinkage rate were 8.47 MPa and 2.83%, respectively, after optimization by the CGIDN method. The optimal process parameters obtained by CGIDN optimization were a melt temperature of 30 °C, filling time of 2.5 s, maturation pressure of 40 MPa, and maturation time of 15 s. The optimization results were obvious and showed the feasibility of the data-driven injection molding process optimization method based on the combination of Latin hypercube sampling and CGIDN.

Optimization of injection molding process parameters for the lining of IV hydrogen storage cylinder

The hydrogen storage cylinder lining was taken as the research object. The injection model of the cylinder liner was developed employing 3D software, a two-cavity injection molding system was built, and Moldflow was utilized for analysis to determine the best combination of injection molding process parameters. The effects of injection process parameters (melt temperature, mold temperature, holding pressure, holding time and cooling time) on the evaluation index were analyzed by orthogonal experiment L16(45). The prediction data of IV hydrogen storage cylinder lining under different parameters were obtained by the range analysis method. The multi-objective optimization problem of injection molding process was transformed into a single-objective optimization problem by using the grey correlation analysis method. The optimal parameters such as melt temperature 270 °C, mold temperature 80 °C, packing pressure 55 MPa, packing time 20 s and cooling time 13 s were obtained. Taguchi method was adopted to obtain SNR (signal-to-noise ratio), while range and variance methods were used for analysis. The results showed that warpage was 0.4892 mm, the volume shrinkage was 12.31%, the residual stress in the first direction was 98.13 MPa, and the residual stress in the second direction was 108.1 MPa. The comprehensive index was simultaneously most impacted by the melt temperature.

Analysis of variance of injection moulding process parameters and clay particles size effects on impact strength, shrinkage and warpage of polyethylene/kaolin clay composites

A correlation effect between particle size of kaolin clay and injection moulding process parameters on impact strength, shrinkage and warpage of high-density polyethylene/kaolin clay (HDPE/KC) composites was carried out using analysis of variance (ANOVA). Kaolin clay with particle sizes of < 75, 75–106 and 106–150 μm was used. The process parameters that were taken into consideration were injection temperature, packing pressure and packing time. In general, experimental results showed that the impact strength and shrinkage of the HDPE/KC composites clearly depended on the injection temperature. However, no clear dependency of the warpage on the injection temperature was observed. Furthermore, clay particle size showed to have an influence only on the shrinkage of the composites, where smaller clay particle size led to injected composite parts with relatively less shrinkage. ANOVA showed that the effect of injection temperature on shrinkage of composites containing clay particle sizes of < 75 and 106–150 μm was statistically significant (p = 0.01 and 0.04, respectively). However, the effect of injection temperature on shrinkage of the composite made with clay having particle sizes of 75–106 μm was not significant (p = 0.07). ANOVA also indicated that the injection temperature effect on the impact strength of composites that contain clays with particle sizes of < 75 μm and 75–106 μm was significant (p = 0.03 and p = 0.01, respectively), whereas the injection temperature effect on the impact strength of the composite containing clay with a particle size of 106–150 μm was not significant (p = 0.17). Contrary to shrinkage and impact strength, the effect of the studied parameters on the warpage was not statistically significant, which was in good agreement with the experimental observations.