Injection molding simulation with variothermal mold temperature control of highly filled polyphenylene sulfide

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.

Micropowder injection molding (μPIM) is a potential low-cost process for the mass production of metal or ceramic microstructures. In order to obtain good molded microstructures and to avoid molding defects, it is important to select suitable injection molding parameters. In this paper, the selection of injection molding conditions for the production of 316L stainless steel microstructures by μPIM is presented. Silicon mold inserts with 24 × 24 microcavities were injection molded on a conventional injection molding machine. The dimensions of each microcavity were Φ 100 μ m × depth 200 μm, giving an aspect ratio of 2. The distance between each microcavity was 200 μm. Five sets of experiments were conducted by varying one injection molding parameter at a time. The parameters included injection pressure, holding pressure, holding time, mold temperature, and melt temperature. Higher injection pressure and holding pressure were required during the injection molding process due to the small dimensions of the microcavities and the large number of microcavities (576 microcavities). High mold temperature was required for complete filling of the microcavities. Molded microstructures without visual defects were obtained using appropriate injection molding parameters. Catalytic debinding and sintering of the 316L stainless steel microstructures were successfully conducted.

Accurate identification of process parameters from in-mold sensing signals is essential for advancing intelligent and adaptive injection molding systems. This study proposes a data-driven inverse modeling framework that infers key process parameters directly from cavity pressure curves. A stage-wise Autoencoder (AE) is employed to perform nonlinear feature extraction by segmenting the pressure curve into filling, packing, and cooling stages, thereby capturing process-dominant dynamics. The encoded latent features are subsequently mapped to injection speed, packing pressure, nozzle temperature, and mold temperature using a multilayer perceptron (MLP). Eighty-one full-factorial parameter combinations were experimentally conducted, generating 810 molding cycles on an industrial injection molding machine. An independent validation dataset was collected from separate production runs to evaluate generalization capability. The proposed AE–MLP framework achieved high predictive accuracy on unseen data, with R² values of 0.9979 for injection speed, 0.9926 for packing pressure, 0.9571 for mold temperature, and 0.9296 for nozzle temperature. The corresponding RMSE values remained below 1.6 across all parameters. Comparative analysis against PCA–MLP and direct MLP models demonstrates that nonlinear manifold representation significantly improves inverse regression robustness, particularly for thermally coupled parameters. Furthermore, the stability of inverse modeling is analyzed from the perspective of parameter identifiability and many-to-one mapping characteristics inherent in injection molding processes. Results indicate that stage-wise nonlinear feature encoding enhances separability in the latent space and mitigates ambiguity in parameter inference. The proposed framework provides a robust and physically interpretable approach for real-time process parameter estimation, contributing to the development of adaptive and self-optimizing manufacturing systems. Infers molding parameters directly from multi-sensor pressure features. AE–MLP framework learns nonlinear relations between pressure and parameters. Parameter recommendation enables adaptive process condition adjustment. 810-cycle full-factorial experiments confirm accuracy and generalization. Supports smart manufacturing through real-time parameter tuning.

With the warpage of a mobile phone case as the index, a numerical model for injection molding was established by CAE software. The process parameters of 5 factors including injection time, mold temperature, melt temperature, packing time and packing pressure were analyzed by orthogonal experiment, the effect of injection process parameters on warpage was studied and the minimum combination of process parameters was obtained. The simulation results show that the warpage was reduced by 6.98% after optimization, and higher quality plastic parts were obtained, which has important guiding significance for the production practice of thin-wall parts.

This paper presents an application of the Continuous Sensitivity Equation Method (CSEM) for the optimization of the injection molding process and its three-dimensional (3D) simulation by the finite element method. Finding the proper combination of process parameters such as injection speed, and melt and mold temperatures is critical to achieving a part that minimizes warpage and has the desired mechanical properties. Very often a successful design in injection molding comes at the end of a long trial and error process. Design Sensitivity Analysis (DSA) can help manufacturers improve their designs and can produce substantial savings in terms of both time and money. This work explores the ability of sensitivity analysis to predict the effects of design parameters on the performance of an injection molding process. The paper presents results of a 3D finite element solution of the filling stage of the injection molding process. Sensitivities of the solution with respect to different process parameters are computed using the continuous sensitivity equation method. Solutions are shown for the non-isothermal filling of a rectangular plate with a polymer melt behaving as a non-Newtonian fluid. The paper presents the equations for the sensitivity of the velocity, pressure and temperature and their solution by a finite element method. Sensitivities of the solution with respect to the injection speed, the melt and mold temperatures and to the heat transfer coefficient at the cavity/mold interface are shown.

The application of Taguchi method to reduce warpage in an injection moulding process is studied. The objective of this paper is to analyze the effect of injection moulding parameters, i.e., injection time, packing time, melt temperature and mould temperature, on the warpage defect in dumbbell plastics part. Optical comparator horizontal type was used to measure the difference of warpage value on each part. L9 orthogonal array with 3 replications was done with 27 totals of specimens. The result collected was optimized using Taguchi method and percentage of contribution was calculated using analysis of variance (ANOVA). According to the analysis, it is found that the significant factors affected warpage are injection time (32.01%), packing time (29.73%), mould temperature (24.39%) and melt temperature (13.87%). The optimum parameters for minimizing the warpage were injection time (1s), packing time (5s), melt temperature (270 °C) and the mould temperature (21 °C). By using Taguchi method and ANOVA analysis, optimum parameters and the percentage of contribution of parameters can be obtained. Thus, it shows that design of experiment method is the good quality tools to get the best quality for production.

This research studies the effect of injection moulding parameters on the density of green body of Cobalt-30Chromium-6Molybdenum (Co-30Cr-6Mo) for powder injection moulding (PIM) feedstock. In this paper 20 micron Co-Cr-Mo powder was mixed with a palm stearin and polyethylene binder system. L18 orthogonal array by Taguchi Method was used to optimize and predict the future performance. Several injection parameters were optimized such as injection temperature, holding pressure, injection temperature, and mould temperature and injection time. The result shows that the optimum combination of these parameters will produce higher density micro parts. The optimum parameters for 67% powder loading of 20µm Co-30Cr-6Mo powder is 180oC injection temperature, while injection pressure, mold temperature, packing time and injection time are 10 bar, 100oC, 5 s and 7 s respectively.

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.

One of the main challenges in the automotive industry is the development of lightweight components reducing energy consumption and emissions during production and use. For this purpose, material selection and the production process must be optimized. Lightweight, high‐performance materials such as long fiber thermoplastics (LFT) and lightweight technologies such as foam injection molding (FIM) are therefore ideal for achieving high weight reductions through adapted component designs. With the foam injection molding technology ProFoam, it is possible to achieve longer fibers in the component with long‐fiber thermoplastics and thus the weight‐specific properties can be improved. To analyze the potential of the foam injection molding process for the design freedom of lightweight components, plates with ribs are manufactured using injection molding and foam injection molding with a 10 % weight reduction. The process parameters injection speed, melt and mold temperature and the rib geometries are analyzed in this contribution. During foam injection molding process, it is found that cell formation and fiber length are significantly affected by melt temperature and injection speed, which influence further the mechanical properties

Nowadays, micro metal injection molding has become among the promising method in powder metallurgy research to produce small-scale intricate part at an effective process and competitive cost for mass production. This paper investigated the optimization of highest green strength which plays an important characteristic in determining the successful of micro MIM. In this paper, stainless steel SS 316L with D50 = 5.96µm was used with composite binder, which consists of PEG (Polyethelena Glycol), PMMA (Polymethyl Methacrilate) and SA (Stearic Acid). Feedstock with 61.5% with several injection parameters were optimized which highly significant through screening experiment such as injection pressure(A), injection temperature(B), mold temperature(C), injection time(D) and holding time(E). Besides that, interaction effects between injection pressure, injection temperature and mold temperature were also considered to optimize in the Taguchi’s orthogonal array. Analysis of variance (ANOVA) in terms of signal-to-noise ratio (S/N-larger is better) for green density was also presented in this paper. Result shows that interaction between injection temperature and mold temperature(BxC) give highest significant factor followed by interaction between injection pressure and mold temperature(AxC). Single factor that also contributes to significant optimization are mold temperature(C) and injection time(D). This study shows that Taguchi method would be among the best method to solve the problem with minimum number of trials.

This paper is to investigate the optimization of mechanical properties for the maximum tensile strength, elongation, and impact strength of Polycarbonate and Polymethyl methacrylate (PC/PMMA) blends by injection molding process. The PC/PMMA plastics composites with different blending percentage are first blended have been injected as the tensile and impact specimens designed according to ASTM, type V by injection molding machine. Taguchi’s method is then used to find the optimal parameters for the maximum tensile strength, elongation and impact strength. The control factors selected in this study are melt temperature, packing pressure, mold temperature and cooling time. An ANOVA table has been used for determining the significance of injection molding parameters. Results of experiments show that the melt temperature is the most significant parameter for improvement of mechanical properties of PC-PMMA plastics composites. Blends with high PC concentrations result in low tensile strength and high impact strength. Illumination testing of the tatol internal reflection (TIR) of PC/PMMA blends has been proceeded and the TIR lens illumination intensity is compared with three compositions of the PC/PMMA blends. Illumination results show that the PC/PMMA 80/20 blend has the highest intensity of illumination. Results of this study can be applied on the optimization of injection molding parameters for polymer blends of LED lens.

The detailed analyses were conducted on three-dimensional fiber orientation of thin FRTP plate with a pinpoint-gate at the center. To visualize the 3 D fiber orientation, X-ray transmission CT was used and the following conclusions were obtained.1) Owing to the change in flow direction from expanding flow in the filling stage to shear flow in the packing stage, the fiber orientation at the exit of the gate changes from concentric alignment to radial one.2) Molding conditions such as injection speed, mold temperature and resin temperature have slight influence on the fiber orientation.3) The development of shear flow is easily affected by plate thickness and gate dimension. Thinner plate thickness and smaller gate size bring about lower fiber orientation because of less shear flow development.

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.

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

In this study, we investigated the feasibility of injection molding (IM) and injection compression molding (ICM) for fabricating 3.5‐in. light‐guided plates (LGPs). The LGP was 0.4 mm thick with v‐grooved microfeatures (10 μm wide and 5 μm deep). A mold was designed to fabricate LGPs by IM and ICM. Micromachining was used to make the mold insert. The Taguchi method and parametric analysis were applied to examine the effects of the process parameters on the molding quality. The following parameters were considered: barrel temperature, mold temperature, packing pressure, and packing time. Mold temperature in this investigation was in the conventional range. Increasing the barrel temperature and mold temperature generally improved the polymer melt fill in the cavities with microdimensions. The experimental results for the replication of microfeatures by IM and ICM are presented and compared. The height of the v‐grooved microfeatures replicated by ICM was more accurate than those replicated by IM. Additionally, the flatness of the fabricated LGPs showed that ICM was better than IM for thin‐walled molding. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011