Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm

This study conducts the simulation on optimisation of injection moulding process parameters using Autodesk Moldflow Insight (AMI) software. This study has applied some process parameters which are melt temperature, mould temperature, packing pressure, and cooling time in order to analyse the warpage value of the part. Besides, a part has been selected to be studied which made of Polypropylene (PP). The combination of the process parameters is analysed using Analysis of Variance (ANOVA) and the optimised value is obtained using Response Surface Methodology (RSM). The RSM as well as Genetic Algorithm are applied in Design Expert software in order to minimise the warpage value. The outcome of this study shows that the warpage value improved by using RSM and GA.

Warpage is one of the common defect that occur during the injection molding process and one of the causes was process condition or parameter setting. The minimization of warpage in x and y direction were studied by going through a few stages of simulation using Integrating Finite Element (FE) Analysis, Moldflow analysis and Response Surface Methodology (RSM). The plastic part that examines in this study was front panel housing part with a thickness of 2.5 mm. There were four parameters that were selected by reviewing the previous research study, which were melt temperature, packing time, cooling time and packing pressure. All these parameters were combined and analyzed by conduct a two full factorial experimental design in Design Expert software. The FE analysis result obtains via analysis of variance (ANOVA) method was run as to determine the percentage contribution among these four parameters toward the warpage occur.

Plastic injection moulding is a popular manufacturing method not only it is reliable, but also efficient and cost saving. It able to produce plastic part with detailed features and complex geometry. However, defects in injection moulding process degrades the quality and aesthetic of the injection moulded product. The most common defect occur in the process is warpage. Inappropriate process parameter setting of injection moulding machine is one of the reason that leads to the occurrence of warpage. The aims of this study were to improve the quality of injection moulded part by investigating the optimal parameters in minimizing warpage using Response Surface Methodology (RSM). Subsequent to this, the most significant parameter was identified and recommended parameters setting was compared with the optimized parameter setting using RSM. In this research, the mobile phone case was selected as case study. The mould temperature, melt temperature, packing pressure, packing time and cooling time were selected as variables whereas warpage in y-direction was selected as responses in this research. The simulation was carried out by using Autodesk Moldflow Insight 2012. In addition, the RSM was performed by using Design Expert 7.0. The warpage in y direction recommended by RSM were reduced by 70 %. RSM performed well in solving warpage issue.

Injection molding is the advance technologies in manufacturing plastic parts in modern industries nowadays. The quality of the moulded parts are highly depends on parameter settings of injection moulding process. In this research, there are 4 process parameter used which are melt temperature, mould temperature, cooling time and packing time. By using Autodesk Moldflow Insight (AMI) 2012, the recommended process parameter settings are melt temperature of 230 °C, mould temperature of 60°C, cooling time of 15.60s and packing time of 31.34s. These settings yield warpage value of 0.4295mm. From Response Surface Methodology (RSM), the warpage value optimised to 0.412728mm with melt temperature increases to 230.32 °C, mould temperature increases to 63.04 °C, cooling time increases to 15.77s and packing time decreases to 28.25s. From the validation test, the percentage error obtained is 0.0075%, which is very little different with RSM value. So, the results of process parameter settings in RSM is valid.

Warpage reduction, dimensional tolerance accomplishment and time and cost saving are some of the most important problems in the injection molding production process. In this study, a real case of a thermoplastic injected part is analyzed. The mold for a pipeline connecting element has been designed, according to known technical and economical criterion to fulfill customer requirements. Particularly for this element, warpage of a specified surface and two diameter sizes are fundamental for correct part functionality and assembling. The analysis is centered on the effects of four process parameters, i. e. packing pressure, packing time, melt temperature and cooling time that heavily influence final results. A finite element model has been used to evaluate their effects on four important final objectives that are the geometrical entities and production cycle time. Each variable has been varied into its proper range, in accordance with a central composite design DoE plan; 25 simulations have been executed and results have been represented using response surfaces. Pareto Front for above listed objectives has been extracted using a genetic algorithm and the best set of parameters has been determined after application of specific selection criteria and a weighted objective function. After numerical evaluation, the CCD DoE plan has been experimentally repeated. Results have been measured on real components, and then represented with response surfaces as well. The same algorithm and objective function have been used for optimization, to determine the experimental optimum parameter set. Finally, the two parameter sets have been compared.

This paper discusses on an analysis run via injection moulding process in determination of the optimum processing parameters used for manufacturing side arms of catheters in minimizing the warpage issues. The optimization method used was RSM. Moreover, in this research tries to find the most significant factor affecting the warpage. From the previous literature review,4 most significant parameters on warpage defect was selected. Those parameters were melt temperature, packing time, packing pressure, mould temperature and cooling time. At the beginning, side arm was drawn using software of CATIA V5. Then, software Mouldflow and Design Expert were employed to analyses on the popular warpage issues. After that, GSO artificial intelligence was apply using the mathematical model from Design Expert for more optimization on RSM result. Recommended parameter settings from the simulation work were then compared with the optimization work of RSM and GSO. The result show that the warpage on the side arm was improved by 3.27 %

For many years, injection molding is adopted as the most suitable manufacturing process in the production of complex plastic components. It is very difficult to boost the product's quality stability to attain a higher forming speed and higher automation in the injection moulding process because the process often disrupted by numerous inevitable deviations. The settings for different process parameters and machine parameters have quite a major influence on the overall product quality and its performance of the injection moulding process, which shows the final product quality as output responses. Identifying the perfect process parameter settings so that numerous responses may be optimized at almost the same time is becoming a significant research challenge these days. Smart manufacturing is at the heart of the new industrial revolution, and it will continue to be the primary means of transforming and upgrading the manufacturing industry in the upcoming decades. Melt temperature, the temperature of mould, injection pressure, and velocity of molten polymer while injection, injection time, pressure of molten polymer while holding into the mold cavity, holding time of molten polymer, and cooling time are the variety of input process parameters.  While mechanical characteristics, flaws such as warpage, volumetric or dimensional shrinkage, sink mark on outer surface, residual stress, and surface finish/roughness are examples of responses. To study and optimize the molding process, researchers have used a wide range of methods. Mathematical modeling, simulation, design of experiments, Taguchi methodology, analysis of variance - ANOVA, response surface methodology - RSM, evolutionary algorithm (EA), artificial neural networks - ANN, genetic algorithms - GA, fuzzy logic, particle swarm optimization - PSO, are examples of these techniques. This paper presents a review of the numerous strategies that used till date for optimizing the key injection moulding parameters, as well as their benefits and challenges. In the review, it is discovered that a perfect injection molding process, which takes care of overall quality control is still being researched upon. The process parameters involved in injection molding, required response, and the methodologies used for optimization of overall injection molding process are discussed throughout this paper.

Weld lines are a serious defect observed in plastic injection molded parts, impacting both their cosmetic appearance and mechanical properties. Controlling the conditions of plastic injection is crucial to mitigate these weld lines. This study introduces a novel approach to identify polypropylene injection molding (PIM) conditions aimed at reducing weld lines in polypropylene parts. The PIM conditions considered in this study include melt temperature, injection pressure, packing pressure, packing time, and cooling time. An orthogonal array Taguchi L 27 design was employed for the experimental setup, producing 27 polypropylene parts with varying combinations of process conditions. The width of weld lines generated on the parts’ surfaces was measured using an optimum microscope for all trials. Parametric analysis was conducted using response surface plots and contour plots to estimate the process conditions yielding minimum weld lines. Analysis of variance and regression analysis were employed to interpret the experimental data, with the resulting regression equation used to predict weld lines for a set of PIM process conditions. Finally, two efficient optimization algorithms, genetic algorithm (GA), and particle swarm optimization (PSO), were implemented using MATLAB programming to estimate the optimum process conditions for minimizing weld lines. The GA and PSO predicted weld line widths of 6.12302 μm and 6.123 μm, respectively, representing an 18.51% improvement in results. These findings demonstrate that the novel approach presented in this study can be effectively and reliably applied to address plastic product defects in the industry.

The injection molded housing part with thin shell feature could be produced to increase the internal space for packing more components. In this study, injection velocity, packing pressure, mold temperature, and melt temperature were selected as effective parameters for injection molding process. For the purpose of reducing dimension shrinkage variation of thin shell molded part, the response surface methodology was utilized to determine the relationship between input parameters and responses. Then the optimization condition was obtained according to the desirability function. Results show that melt temperature is the most significant factor on dimension shrinkage variation in transverse direction, followed by packing pressure, mold temperature, and injection velocity. However, in the longitudinal direction, packing pressure has the greatest influence on the dimension shrinkage variation, followed by injection velocity, melt temperature, and mold temperature. In accordance with verification experiments, the difference between the experimental data and predicted values ranges from −9.8% to 1.8%. To obtain the optimal condition, the overall desirability must be larger than 0.9. Based on analysis of variance, the proposed models look reasonably accurate.

This study focuses on the analysis of plastic injection moulding process simulation using Autodesk Moldflow Insight (AMI) software in order to correlate between process parameters as an input and warpage as an output for single and dual gates mould design. Nessei NEX 1000 injection moulding machine and P20 mould material details are incorporated in this study on top of Acrylonitrile Butadiene Styrene (ABS) as a moulded thermoplastic material. Coolant inlet temperature, material melt temperature, packing pressure and packing time are selected as a variable parameter. Design Expert software is obtained as a medium for analysis and optimization of input variables in order to minimize the warpage. RSM method as well as Analysis of Variance (ANOVA) has been applied in this study. The results of ANOVA show that some interactions between factors are significant towards warpage existence, which is coolant inlet temperature, material melt temperature and packing pressure. Furthermore, the model created using RSM can be used for warpage prediction and improvement due to a minimum value of error. From this study, the dual gate is the best solution which able to improve the warpage up to 80% instead of single.

The quality and the aspect of outer lens fabricated by an injection molding process are an essential part for the optical properties of vehicular lighting system. This work is accurately addressed to identify the influential factors on the red color of the outer lens made of blends of polycarbonate and acrylonitrile butadiene styrene, which are mixed with a red masterbatch. Seven factors were investigated in this work: masterbatch concentration, mold and nozzle temperatures, holding and packing pressures, and holding and packing times. The main influential factors were found through a design of experiments in a linear approximation. The outer lens's red color is mainly influenced by the masterbatch concentration, the nozzle temperature, holding time, and the holding pressure, in a decreasing order. In contrast, the mold temperature, packing pressure, and packing time are not statically significant in the color appearance.

Background: The method was proposed to optimize the warpage and volume shrinkage of the blade of a plant protection unmanned aerial vehicle (UAV) during injection molding. The influence of molding defects on the geometric parameters of airfoil was analyzed from two aspects of blade geometry structure and molding process based on blade element theory. This paper focuses on reducing the warpage and volume shrinkage of the blade in the production process and improving molding quality through moldflow analysis and calculation. Objective: The purpose of this patent study was to combine injection molding CAE with orthogonal test and response surface method to reduce product warpage and volume shrinkage, improve dimensional accuracy and production efficiency, and combine fluid dynamics simulation experiments to prove the experiment's reliability. Methodology: The optimization method of injection molding process parameters was proposed based on the orthogonal test and response surface method, combining the principles of aerodynamics mathematics and polymer injection molding. The Taguchi experiment was designed with melt temperature injection time and mold temperature pressure holding time as the optimization variable. The Box-Behnken Design (BBD) experiment was designed, and the response surface model was established with Design-Expert software to analyze the mapping relationship between process parameters and warpage and volume shrinkage. Moldflow software was used for flow analysis on the basis of analysis by response surface method, and the deformed parts were analyzed by computational fluid dynamics (CFD) with ANSYS software to prove the reliability of the calculation process. Results: The warpage and volume shrinkage of the product can be significantly reduced based on the orthogonal test and response surface model. The aerodynamic performance of the blade can be improved by optimization injection molding, making the actual production of the blade close to the original design blade model to the greatest extent and reducing the actual production trial and error cost. Conclusion: An optimization method of the injection molding process of the UAV rotor was proposed based on the response surface method and blade element theory. Through the systematic design and optimization of injection molding parameters, the molding quality and performance of the rotor can be improved, which provides a new way to optimize the dynamic components of massproduced UAVs.

Simulation and experimental study in determining injection molding process parameters for thin-shell plastic parts via design of experiments analysis

1. In this study, neural network (NN) and response surface (RS) models were developed to investigate the response [average daily gain (ADG) and feed efficiency (FE)] of young broiler chickens to dietary protein and lysine. For this purpose, data on their responses to dietary protein and lysine were extracted from the literature and separate NN and RS models were constructed. 2. Comparison between the NN and RS models revealed higher accuracy of prediction with the NN models compared to the RS models. In terms of R 2 values, the NN models developed for both ADG (R 2 = 0.923) and FE (R 2 = 0.904) were far superior to the RS models (R 2 for ADG = 0.511; R 2 for FE = 0.67). This suggests that the NN models can serve as an alternative option to conventional regression approaches including use of RS models. 3. Optimisation of the NN models developed for response to protein and lysine showed that diets containing 220.7 (g/kg of diet) protein and 12.85 (g/kg of diet) lysine maximise ADG, whereas maximum FE is achieved with diets containing 241.3 and 13.12 (g/kg) protein and lysine, respectively. Based on the optimisation results, optimal dietary protein and lysine concentrations for maximum FE in broiler chickens during the starting period are higher than for ADG.

In this study, an adaptive optimization method based on artificial neural network model is proposed to optimize the injection molding process. The optimization process aims at minimizing the warpage of the injection molding parts in which process parameters are design variables. Moldflow Plastic Insight software is used to analyze the warpage of the injection molding parts. The mold temperature, melt temperature, injection time, packing pressure, packing time, and cooling time are regarded as process parameters. A combination of artificial neural network and design of experiment (DOE) method is used to build an approximate function relationship between warpage and the process parameters, replacing the expensive simulation analysis in the optimization iterations. The adaptive process is implemented by expected improvement which is an infilling sampling criterion. Although the DOE size is small, this criterion can balance local and global search and tend to the global optimal solution. As examples, a cellular phone cover and a scanner are investigated. The results show that the proposed adaptive optimization method can effectively reduce the warpage of the injection molding parts.