Minimum Warpage Research Articles

MOLD DESIGN AND FLOW ANALYSIS FOR PRODUCT PROPELLER APC SF 11X4.7 WITH MINIMUM SHRINKAGE AND WARPAGE

MOLD DESIGN AND FLOW ANALYSIS FOR PRODUCT PROPELLER APC SF 11X4.7 WITH MINIMUM SHRINKAGE AND WARPAGE

FOWLP Thermal Debonding: Easing Manufacturing Constraints

Fan-out wafer level packaging enables System-in-package (SiP) and other forms of heterogenous integration. Multi-die configuration or passives for SiP alters the warpage profile of the wafer significantly. Wafers with square-shaped dies typically have bowl or dome type deformations. Wafers with rectangular-shaped dies tend to be deformed in a parabolic shape. However, as the packaging advances to different and more sophisticated technologies, the different chip sizes in one wafer adds complexity to the base structure, which leads to an unpredictable warpage profile and a higher warpage magnitude. Downstream equipment post wafer reconstruction needs to be able to handle this change in magnitude and profile since this could otherwise cause difficulties in high volume production and result in lower yield. The process step that requires the wafer to be placed onto chucks will result in vacuum failure and, if not modified to handle high warpage, the transport mechanism can result in wafer excursion and machine downtime. However, these modifications would require a costly redesign of the machine transport system, handling system, and end effectors. A less cost-intensive solution would be to ensure that the incoming wafer post reconstruction has minimum warpage. Thermal debonding is the last process of the wafer reconstruction flow and it acts as launchpad for layer build-up. It is in this step that warpage is inevitable as the wafer is released from the carrier. Thus, it is essential to have a warpage level that is manageable by these succeeding processes. This paper addresses the issue of warpage in Fan-out Wafer Level Packaging and presents a solution that lessens the warpage in the thermal debonding stage. The efficient processing of warped wafers is critical for the development of Fan-out and the transition to high volume manufacturing of these chips.

Research on warping optimization of automobile mesh based on artificial intelligence algorithm

Automobile mesh is a typical large plastic part prone to warping deformation in the manufacturing process, which seriously affects the quality of products. The traditional method is to find the optimal combination parameters to reduce warping deformation in the forming process through repeated trial or mold flow analysis with high cost. In this study, An optimization method based on an artificial intelligence algorithm is proposed to optimize the warping deformation of the automobile mesh. In the optimization method, artificial intelligence regression algorithm-support vector machine regression was used to establish the nonlinear function relationship (prediction function) between the molding process parameters and the warpage of automobile mesh, and the performance of the prediction function was optimized by a genetic algorithm. The optimized prediction function was taken as the fitness function, the warping value was regarded as the fitness value, and genetic algorithm is used again to obtain a set of optimal process parameters and minimum warpage values. Finally, the reliability of the optimization method is verified by the modal flow analysis software.

Research on Lightweight Injection Molding (CAE) and Numerical Simulation Calculate of New Energy Vehicle

Abstract In this paper, the new energy automobile front-end frame of a factory is taken as the research object, the polypropylene plastic is used to replace the traditional sheet metal material, and the hyper mesh is used to load the front-end frame’s six loading loads, which are the locking limit tension and the cushion impact force, respectively, pedestrian impact strength, condenser support strength, radiator support strength, oil cooler support strength, ensure to achieve certain mechanical properties before production. The maximum warpage deformation is 1.023 mm by mold flow analysis with Moldflow. It does not meet the needs of manufacturers, so it is necessary to optimize the process parameters. We use the mold temperature, melt temperature, holding pressure, injection time, and holding time as test factors, and take the amount of warpage as the optimization goal. Then this paper uses the orthogonal test method to design the test plan and establishes 25 groups of 5-level 5-factor orthogonal tests. Moreover, the effect of the deformation on the warpage is analyzed. These are all to obtain the minimum warpage deformation and find the optimal combination of process parameters. Verification by numerical simulation shows that the warpage is reduced to 0.632 mm. By analyzing the range and variance, the combination of process parameters is optimized, and the warpage is effectively reduced. In the premise of satisfying mechanical properties, plastic instead of steel, to achieve a lightweight design. It provides correct theoretical guidance for setting the actual injection molding process parameters.

Quality prediction and control of thin-walled shell injection molding based on GWO-PSO, ACO-BP, and NSGA-II

Abstract ECG recorders are precision medical devices, but their thin-walled shells are susceptible to warpage and shrinkage during injection molding production due to the injection molding process, which greatly shortens their service life. To address this problem, a multiobjective optimization method for injection molding process parameters based on a combination of a BP neural network model optimized by an ant colony algorithm (ACO-BP) and an improved non-dominated sorting genetic algorithm (NSGA-II) is proposed. The study takes the warpage deformation amount and volume shrinkage rate of the plastic part as the optimization objectives, and the melt temperature, mold temperature, injection pressure, holding pressure, holding time, and cooling time as the design variables. However, for BP neural networks, it is crucial to choose an appropriate number of hidden layer neurons, so the particle swarm algorithm combined with the grey wolf algorithm (GWO-PSO) is used to solve for the optimal number of hidden layer neurons. Firstly, the number of hidden layer neurons of the BP network model was solved based on the samples obtained from the Box–Behnken experimental design and the GWO-PSO algorithm, and the ACO-BP algorithm was used to build the prediction models for warpage and volume shrinkage, respectively, and then combined with NSGA-II for global optimisation. The pareto optimal solution set was subjected to CRITIC analysis and the optimal process parameters were finally obtained, with a minimum warpage of 0.3293 mm and minimum volume shrinkage of 4.993%, a reduction of 8.93 and 6.95% respectively compared to the pre-optimisation period. At the same time, injection molding tests were carried out on the optimum process parameters, and it was found that the molding quality of the plastic parts was better and met the actual production requirements through measurement. The research in this paper provides a theoretical basis for further improving the quality defects of the thin-walled injection molded parts.

Analysis of the Warpage Phenomenon of Micro-Sized Parts with Precision Injection Molding by Experiment, Numerical Simulation, and Grey Theory.

In this study, we determined the effects of design and processing parameters of precision injection molding (PIM) to minimize warpage phenomena of micro-sized parts using various plastics (polyoxymethylene (POM), acrylonitrile-butadiene-styrene (ABS), polypropylene (PP), polyamide (PA), and ABS+ polycarbonate (PC)). We applied a numerical simulation (Moldflow) to determine the runner’s balance in multi-cavities of the micro-sized part and simulate the warpage phenomenon of micro-parts with PIM. We used simulation data to fabricate a steel mold by computer numerical control (CNC) machining. In this, we study manufactured a micro-sized part and measured its warpage value using various PIM process parameters (melt temperature, mold temperature, injection pressure, and filling time). In order to obtain optimal results (i.e., minimum warpage), we employed the Taguchi method and grey theory to discern the influence of each process parameter on PIM. Finally, we determined that the most significant PIM process parameter influencing the warpage phenomenon of micro-sized parts was the mold temperature, regardless of whether in terms of the experimental results, numerical simulations, or grey theory. The PA material had the most suitable properties for application for micro-sized parts, regardless of whether in terms of experimental results, numerical simulations, or grey theory for PIM. This study also illustrates that micro-sized parts can be fabricated by PIM without the use of micro-injection molding, and we determined that the mold temperature required for molding does not need to be higher than the glass-transition temperature of the material.

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

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

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

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

Study on Automotive Back Door Panel Injection Molding Process Simulation and Process Parameter Optimization

A plastic back door car panel was considered as the research object. In order to obtain optimal injection molding simulation parameters, Moldflow2018 is used to simulate injection molding process of plastic back door car panel. Orthogonal experiment is conducted to analyze the influence of injection process parameters on the evaluation index. Melt temperature, mold temperature, cooling time, packing pressure, and packing time are selected as process parameters. Warpage and volumetric shrinkage are taken as evaluation indicators. Test groups are designed to obtain data, and range analysis method is employed to analyze warpage and volumetric shrinkage. Warpage range analysis shows that optimal warpage is 9.3 mm for volumetric shrinkage rate of 14.3%. Range analysis of volumetric shrinkage rate indicates that the best warpage is 8.3 mm for the corresponding volumetric shrinkage rate of 15.05%. A comprehensive evaluation index is established using the gray relational analysis. This analysis shows that warpage is 8.3 mm and volumetric shrinkage rate is 14.2%. Taguchi method is employed to obtain signal‐to‐noise ratio, while range and variance methods are used for the analysis. Optimal warpage is obtained as 8.2 mm, while the volume shrinkage rate is 10.3%. For a single evaluation index warpage, least squares method and artificial fish swarm algorithm are used to find the optimal parameter combination. Moldflow2018 is employed for simulation verification, and minimum warpage is obtained as 6.405 mm.

Optimization for the prepreg compression molding of notebook computer cover using design of experiment and finite element method

Composite materials can be successfully applied to the manufacturing process in the consumer electronics field and can drastically reduce the weight of the several parts. For this reason, it is important to predict the shape change after carrying out compression molding of composite material and to determine the key parameters of the manufacturing process. In this paper, the compression molding process of a notebook computer cover using composite materials was analyzed by the finite element method. In addition, the signal-to-noise ratio has been calculated for the warpage deformations that occurred on the surface of the notebook computer cover. The design of experiment method was applied to determine the optimal parameters in the process. Levels of the optimal process factors were selected for minimum warpage deformation and to verify the proposed method; a physical notebook computer cover was manufactured using these optimal conditions. Experimental results, including those on warpage deformations, show that the proposed method can be useful in the manufacturing of lightweight notebook computer covers using a compression molding machine.

YAKLAŞIK ÇÖZÜM TEKNİKLERİNİ KULLANARAK PLASTİK ENJEKSİYON PROSESİNİN TASARIM METODOLOJİSİ

Bu çalışmada, istatistiksel deney tasarım yöntemleri kullanılarak hazırlanan deney setine uygun olarak yapılan akış analizleri ile enjeksiyon parametreleri optimize edilmiştir. Elde edilen sonuçları değerlendirmek için, ilk olarak istatistiksel deney yazılımı olan MiniTab vasıtası ile uygun bir regresyon modeli oluşturulmuştur, model katsayılarının doğruluğunu saptamak için F testi, parametrelerin sonuçlar üzerindeki etkisini saptamak için ANOVA testi uygulanmıştır. İkinci olarak süreç, bir hipoteze dayanan değişken yaratılarak, Monte Carlo simülasyonu ile optimize edilmiştir. Monte Carlo simülasyonu, analitik olarak belirsiz durumlarda varsayımlara dayanan hesaplamaları yapmak için kullanılır. Kalıp sıcaklığı, soğutma suyu sıcaklığı, enjeksiyon basıncı gibi işlem parametreleri sabit değildir. Bu belirsizliği gidermek için süreç hipoteze dayalı değişken üretmek suretiyle optimize edilmiştir. Son aşamada farklı optimizasyon yöntemleri kullanılarak elde edilen sonuçlar karşılaştırılarak, hangi yöntemin daha doğru sonuç verdiği değerlendirilmiştir. Sonuç olarak Monte Carlo simülasyonu ile minimum çarpılma değeri elde edilmiştir. Sonuç bölümünde yer alan Tablo 7 ve Grafik 1 üzerinden bu sonuç daha net anlaşılmaktadır. Plastik enjeksiyon işlemi gibi birçok parametrenin sonuç üzerinde etkin olduğu üretim yöntemleri için yapılacak optimizasyon çalışmalarında, belirsizlikleri gidermek için hipoteze dayalı rastsal parametre değerleri ile çalışmak avantaj sağlamaktadır.

Minimum Warpage Prediction in Plastic Injection Process using Taguchi Method and Simulation

Plastic materials are used in automobile, electrical and electronic applications, agricultural utilization, household and furniture products, and medical equipments. Among various plastic manufacturing process, injection molding is one of the most commonly used and common methods applied for forming plastic products in the industry. The process requires a molten polymer being injected into a cavity of a mold, which is cooled and the product ejected from the mold. During the Plastic Injection Molding (PIM) process, various defects, such as volumetric shrinkage, warpage, weld line and sink mark can occur. This paper presents a method to minimizing warpage defect on PolyPropylene AZ564 via PIM simulation using Moldflow software. The approach was based on Taguchi method.Through the effectiveness of this proposed method, it is confirmed using simulation by Moldflow software. The effect of the process parameters on the warpage of a motorcycle number plate bracket, is studied using analysis of variance (ANOVA). From the ANOVA, the significant parameters affecting the process are holding time, holding pressure and injection pressure. The result of the Taguchi prediction shown that minimum warpage is 1.078 mm, which is 7.14% and 9.09% different from the simulation result and experiment, respectively.

Warpage and Shrinkage Analysis and Optimization of Rapid Tooling Molded thin Wall Component Using Modified Particle Swarm Algorithm

This paper presents a systematic methodology to determine optimal injection molding conditions for minimum warpage and shrinkage in a thin wall relay part using modified particle swarm optimization algorithm (MPSO). Polybutylene terephthalate (PBT) and polyethylene terephthalate (PET) were injected in a thin wall relay component for different processing parameters: melt temperature, packing pressure and packing time. Further, Taguchi’s L9 (3[Formula: see text] orthogonal array is used for conducting simulation analysis to consider the interaction effects of the above parameters. A predictive mathematical model for shrinkage and warpage is developed in terms of the above process parameters using regression analysis. ANOVA analysis is performed to establish statistical significance within the injection molding parameters. The analytical model is further optimized using a newly developed MPSO algorithm and the process parameters values are predicted for minimizing shrinkage and warpage. The predicted values of shrinkage and warpage using MPSO algorithm are improved by approximately 30% as compared to the initial simulation values and comparable to previous literature results.

Experimental study of warpage and shrinkage in injection molding of HDPE/rPET/wood composites with multiobjective optimization

ABSTRACTIn this paper, injection molding of squared parts with 1.25 mm in thickness, composed of wood plastic composites (high-density polyethylene, recycled polyethylene terephthalate, and wood flour), was done. The warpage and volumetric shrinkage in the parts was determined experimentally with various process conditions (packing time, melt temperature, wood content, and packing pressure). The experiments were done based on Box–Behnken design of experiments. The significance of each parameter and model was evaluated by analysis of variance (ANOVA). ANOVA showed that packing time and melt temperature are the most significant parameters on warpage and wood content is the most significant on volumetric shrinkage. Packing pressure and wood content had no considerable effect on warpage and packing time on shrinkage too. To obtain optimal process conditions for minimum warpage and shrinkage, a multiobjective optimization based on Pareto front was developed. Response surface method was used to find the relationships between input parameters and objective functions, and genetic algorithm presented the Pareto front solutions to determine the optimum solution. It was observed that there was a good agreement between the predicted optimum values and the experiments.

Numerically design the injection process parameters of parts fabricated with ramie fiber reinforced green composites

The warpage deformation plays an important role on the performance of automobile interior components fabricated with natural fiber reinforced composites. The present work investigated the influence of process parameters on the warpage behavior of A pillar trim made of ramie fiber (RF) reinforced polypropylene (PP) composites (RF/PP) via numerical simulation with orthogonal experiment method and range analysis. The results indicated that fiber addition and packing pressure were the most important factors affecting warpage. The A pillar trim can achieved the minimum warpage value as of 2.124 mm under the optimum parameters. The optimal process parameters are: 70% percent of the default value of injection pressure for the packing pressure, 20 wt% for the fiber addition, 185 °C for the melt °C for the mold temperature, 7 s for the filling time and 17 s for the packing time.

Cycle Time Reduction in Injection Molding Process by Selection of Robust Cooling Channel Design

Cycle time of a part in injection molding process is very important as the rate of production and the quality of the parts produced depend on it, whereas the cycle time of a part can be reduced by reducing the cooling time which can only be achieved by the uniform temperature distribution in the molded part which helps in quick dissipation of heat. Conformal cooling channel design is the solution to the problem which basically “conforms” to the shape of cavity in the molds. This paper describes the analytical study of cooling analysis of different types of cooling channel designs. The best cooling channel design is also selected on the basis of minimum time to reach ejection temperature, uniform temperature distribution, and minimum warpage of part. “Creo Elements/Pro 5.0” is used to model the case study, its molds, and the cooling circuit whereas analytical study is done using “Autodesk Moldflow Advisor 2013 (AMFA).”

Warpage Analysis between Straight and Conformal Cooling Channels on Thin Shallow Shell

Warpage is a type mold defect due to non-uniform temperature variation which causes differential shrinkage rate on the moulded parts. An accurate warpage prediction is so important in helping mould designers to achieve successful mold design with minimum warpage defects. This work is performed with a purpose to determine and compare the best parameters can be selected in manufacturing of thin shallow shell using two different types of cooling channels which are straight cooling channels and conformal cooling channels. The results were obtained using Taguchi Method and Analysis of Variance (ANOVA) and run through simulation software. Both parameters are then compared with each other in recommending to the mold designers which is the best to be applied at mold design stage. It has been found from this work that two factors that significantly cause warpage on both cooling channels are packing pressure and filling time.

Optimization Design of Stack Package Warpage Based on Moldflow Software

Currently, 3D multi-chip stack assembly is becoming the future development direction of advanced manufacturing process for printed circuit board. However, during the process of Components stacked packages, Warpage control of chips in the reflow soldering is particularly important. By Combining Taguchi method and injection molding simulation software – Moldflow, this paper studied FBGA and PBGA warpage deformation in the POP, setted four design factors, such as the mold temperature, melt temperature, injection pressure and injection time , find out the minimum warpage combination of design and critical design factors affecting the warpage. Then the factors are ranked according to the degree of the impact of the above experiments. The best combination of levels for these factors is getted, the stacked package parameter is optimized. Experiments show that the value of warpage decreased significantly.

Warpage Measurement of a Micro Electrical Fan on Micro-Injection Molding

This study characterizes warpage of a micro-injection molded micro electrical fan using the Michelson interference method. This study conducts experiments to analyze different polymers-polypropylene (PP), polyamide (PA), acrylonitrile-butadiene styrene (ABS), ABS+ polycarbonate (PC), and polyoxymethylene (POM)-process parameters, such as mold temperature, injection temperature, injection pressure, injection time, packing time, and packing pressure, for a micro electrical fan. To obtain the optimum result (minimum warpage), this study assesses the effect (warpage) of each material on micro-injection molding. PA plastic is the very suitable material for micro electrical fan with Michelson interference analysis on micro-injection molding.

Research on Process Optimization of Thin-Wall Plastics Injection Based on Moldflow and Orthogonal Experiment

Warpage makes the plastic products deviate from the shape of the mold cavity that is one of the common weaknesses of plastics. In this paper, the melt temperature, mold temperature, packing pressure and holding pressure time are chosen as the study objects, and the use of Moldflow combined with orthogonal experiment method determine the best combination of molding process parameters to obtain the minimum warpage value, which improves the quality of injection products.