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

During the production of thin shell plastic parts by injection molding, warpage depending on the process conditions is often encountered. In this study, efficient minimization of warpage on thin shell plastic parts by integrating finite element (FE) analysis, statistical design of experiment method, response surface methodology (RSM), and genetic algorithm (GA) is investigated. A bus ceiling lamp base is considered as a thin shell plastic part example. To achieve the minimum warpage, optimum process condition parameters are determined. Mold temperature, melt temperature, packing pressure, packing time, and cooling time are considered as process condition parameters. FE analyses are conducted for a combination of process parameters organized using statistical three-level full factorial experimental design. The most important process parameters influencing warpage are determined using FE analysis results based on analysis of variance (ANOVA) method. A predictive response surface model for warpage data is created using RSM. The response surface (RS) model is interfaced with an effective GA to find the optimum process parameter values.

Injection molding technology is known as the most widely used method in mass production of plastic products. To meet the quality requirements, a lot of methods were applied in optimization of injection molding process parameter. In this study the optimization based on Taguchi orthogonal array and Grey relational analysis (GRA) is used to optimize the injection molding process parameters on a LED lens. The four process parameters are: packing pressure, injection speed, melt temperature and mold temperature. The multi-response quality characteristics are total displacement, volumetric shrinkage, and thermal residual stress. The optimal molding parameters are packing pressure (90 MPa), injection speed (300 mm/sec), melt temperature (270 °C) and mold temperature (90 °C). The luminous uniformity of the LED is 92.61 % and the viewing angle of the LED is 124.76°. Among the four factors, packing pressure plays the key role in reducing total displacement, volumetric shrinkage, and thermal residual stress.

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%.

Incorporating a new design combined with a thermoplastic material without having a prior process database may cause numerous defects in the molded part. Along with the initial setup cost, the scrape due to defects can add additional costs to the production. To address such issues, the numerical analysis tool Moldex3D was used to conduct simulations, and Taguchi Design of Experiment (DOE) was performed to optimize the process parameters. For this study, a classic medical device like the safety goggle model has been chosen as a subject. An amorphous material, polycarbonate, was used because of its transparent and significant thermal properties. The most influential defects, like the warpage and short shot, needed to be addressed together. Process parameters like melt temperature, injection speed, coolant temperature, and packing pressure were considered to address those defects. The goal of this work was to improve both warpage and short-shot defects at the same time using the same combination of process parameters. By performing the simulation and statistical analysis method, 25% of warpage defects had been reduced in the molded part. A total of 2.3% of any potential short-shot defect reduction was recorded. Additionally, 300 °C melt temperature, 80 mm/s injection speed, 10 MPa packing pressure, and 100 °C coolant temperature were the best combinations for the part that had uneven thickness and narrow flow channels. The combinations to reduce potential short shots were 280 °C melt temperature, 40 mm/s injection speed, 10 MPa packing pressure, and 90 °C coolant temperature. Moreover, packing pressure for the warpage and melt temperature for the short shots were the most significant factors of all.

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.

The study on the tribological properties of fiber-reinforced PBT composites for various injection molding process parameters

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.

The paper investigated the optimization design of plastic injection molding process parameters for preformed bottle production using the grey-Taguchi method to minimize shrinkage defects. Diametrical (radial) and length (axial) shrinkage were taken as the responses for one-liter PET plastic preform bottle to perform optimization by controlling four injection molding process parameters. The obtained optimum parameter combination was 260°C melting temperature, 70°C molding temperature, 120 MPa holding pressure, and 15 s cooling time. Melting temperature was the most significant factor according to the ANOVA in PET plastic preform of bottle production. The experimental validation test was also performed using the optimal level settings, five samples of preforms were taken, and the result showed that the shrinkage of the preform was reduced by 22.55%. The average grey relational grade, 0.853, of the confirmation results was found between the confidence intervals of 0.581 and 1.125 for the 95% confidence level. Therefore, the confirmation test showed a good agreement between the predicted and experimental values.

Multi Response optimization of injection moulding Process parameters to reduce cycle time and warpage

With the rapid development of IC technologies, topics of 3D stacked IC packages are now being studied vastly around the world. In 3D stacked IC technologies, many electrical characteristics have been proved to have great improvements, but their corresponding thermo-mechanical problems are raising as well. Due to the reason of ICs stacking, thermo-mechanical problems are becoming more serious than those in traditional single IC package. These problems include the heat dissipation, die breaking, interfacial delamination, via cracking and so on. These corresponding problems not only cause failures in 3D stacked IC packages but also have significant influences on reliability. For the sake of realizing critical stress distributions in 3D stacked IC packages, the three dimensional finite element analysis (FEA) modeling has been employed in this paper. The 3D-WLP (wafer-level-package) with ten layer chips stacking is selected as our test vehicle. By using three dimensional FEA modeling, thermal induced stress distributions in 3D-WLP when subjected to a uniform temperature change can be illustrated. Further, the design of experiments (DOE) analysis for design optimization has also been used to obtain the sensitivities of geometry and material properties of 3D-WLP. Several geometric dimensions and material properties are selected as main design factors and the average value of maximum von Mises stress in copper vias and chips is chosen as the response to be optimized. Through the DOE analysis, the optimum geometry and material properties of 3D-WLP and this result in smaller von Mises stresses in copper via and chip configurations can be obtained. These results can be most effectively used as design guidelines to engineers when optimum stress solutions in 3D-WLP are required.

<div class="htmlview paragraph">A DoE analysis was carried out to investigate the effects of the compression ratio, injection timing, injector nozzle hole size and number on performance and emissions in a diesel marine engine, aiming to find out the optimal combination between all the abovementioned parameters.</div> <div class="htmlview paragraph">The study was performed on a six cylinder in line, 100 liter total displacement, diesel marine engine, by means of a 1-D engine simulation fluid-dynamic code, coupled with a multi-zone combustion model for oxide of nitrogen (NOx) and particulate (PM) prediction.</div> <div class="htmlview paragraph">A preliminary detailed validation process, based on an extensive experimental data set, was carried out on the engine model concerning, in particular, the predicted heat release rate, the in-cylinder pressure trace and NOx emissions for several operating points of a propeller load curve.</div> <div class="htmlview paragraph">Afterward a full factorial Design of Experiments (DoE) analysis was carried out in order to find out the optimal combination between compression ratio, injection timing, injector nozzle hole size and number aiming to reach the lowest brake specific fuel consumption value while complying with NOx emissions and peak firing pressures constraints.</div>

Todays, there are various of optimisation methods that have been studied by many researchers in order to find the appropriate combination of processing parameters setting in the injection moulding process. From the previous literatures, the optimisation works have been proven will improve the moulded part quality. In this study, the application of optimisation work to improve warpage of front panel housing has been explored. By selecting cooling time, coolant temperature, packing pressure and melt temperature as the variable parameters, design of experiment (DOE) have been constructed by using the rotatable central composite design (CCD) approach. Response Surface Methodology (RSM) was performed in order to define the optimal processing parameters setting which will optimise the warpage condition. Based on the results, melt temperature is the most significant factor contribute to the warpage condition and warpage have optimised by 47.1% after optimisation. The findings show that the application of optimisation work offers the best quality of moulded part produced.

In this study, a mold is designed in single and dual type of gate in order to investigate the deflection of warpage for thick component in injection molding process. Autodesk Moldflow Insight software was used as a medium for experimental tested. Nessei NEX 1000 injection molding machine and P20 mold material details were entered in this study to get more accurate data on top of Acrylonitrile Butadiene Styrene (ABS) as a molded thermoplastic material. Taguchi orthogonal array, analysis of Signal to Noise (S/N) ratio and Analysis of Variance (ANOVA) were implemented to get the best combination of parameter and significant factor that affect the warpage problem for both types of gates. Coolant inlet temperature, melt temperature, packing pressure and packing time are the selected parameter that used in this study. A conformation test is conducted to verify the combination parameters optimized. From the result, multi gates used was founded that can decrease the deflection of warpage for thick product. From ANOVA, the most significant factor is melt temperature for single gate, and coolant inlet temperature for multi gate. Packing pressure and packing time were slightly influence on warpage problem for both studies.

This study is focused on the effects of process parameters in plastic injection molding towards shrinkage and warpage problem of High Density Polyethylene (HDPE) specimen. The aim was to identify the main effects of different process parameters on warpage and shrinkage defects via numerical simulation software and experimental validation. Series of simulations were carried out using Cadmould 3D-F in order to utilize the combination of process parameters based on three level of L9 Taguchi orthogonal design. The Signal to Noise (S/N) ratio and the Analysis of Variance (ANOVA) were used to optimize the levels and to point out the impact of the process parameters on warpage and shrinkage. From the results, it showed that the most affected parameter on the warpage and shrinkage is packing time, with the P value of 48.93%, followed by melting temperature with 40.58%. The filling time and packing pressure were not a significant factor because they only contribute 7.30% and 3.19% of P value, respectively.

Computer models of complex mechanical systems are being used more frequently and effectively due to ever increasing computing power. These models have improved the design process by allowing designers to create virtual prototypes of systems. Computer models permit iterative designs, thereby allowing the designer to run hundreds of simulations in the time it would take to make one physical model and test it. This ease of iteration has made design of experiments (DOE) analyses easy to carry out on the computer models. A DOE analysis provides excellent information regarding the specific output function being examined, but may overlook some of the global effects that these changes may have on the system. A new design tool is developed in this work that addresses this deficiency. The new method greatly improves upon existing design tools such as DOE. Specifically, the new method augments a traditional DOE study with an Activity study, which focuses on the energy flow in the system and provides a larger picture of the system dynamics. Instead of using only a DOE analysis to determine which elements’ properties should be changed, the Activity of the elements is also considered. This helps prevent altering a parameter or set of parameters to achieve a desired response at the expense of other unforeseen results. This idea is demonstrated by investigating the issue of designing a vehicle for improved ride quality. A simple quarter car model is used so that the results can be clearly seen. The example shows that design changes based on a DOE analysis alone can improve the ride quality, but also can alter other system responses significantly. When an Activity analysis is combined with the DOE study, the ride quality is improved, but the effects on other aspects of the system are greatly reduced. Augmentation of DOE with Activity allows a specific design goal to be achieved and also provides global information about the system. This allows the engineer to make informed decisions about the effects of changes on the system.