A Strategy for Problem Solving of Filling Imbalance in Geometrically Balanced Injection Molds

Wilczyński, Krzysztof; Narowski, Przemysław

doi:10.3390/polym12040805

Cited by 17 publications

(13 citation statements)

References 11 publications

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“…It was concluded in the review that a complete intelligent technique operable without human interference is yet to be developed. Very recently, Wilczyński and Narowski [ 122 ] also discussed the optimization issues of the injection molding process.…”

Section: Optimization For Extrusionmentioning

confidence: 99%

Optimization and Scale-Up for Polymer Extrusion

Nastaj

Wilczyński

2021

Polymers

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A review paper is presented on optimization and scale-up for polymer extrusion, both single screw and twin screw extrusion. Optimization consists in obtaining a multidimensional space of process output variables (response surface) on the basis of an appropriate set of input data and searching for extreme values in this space. Scaling consists in changing the scale of the process according to specific criteria, that is, changing the process while maintaining the scaling parameters at a level that is as close to the reference process parameters as possible. It consists in minimizing the differences between the parameters characterizing the reference process and the resulting process. This may be obtained by using optimization techniques leading to the minimization of discrepancies between the parameters of scaled processes. In the paper, it was stated that optimization and scale-up based on process simulation are more effective than those based on experimentation which is time consuming and expensive. The state-of-the-art on extrusion process modeling which is the basis of optimization and scale-up has been presented. Various optimization techniques have been discussed, and the Genetic Algorithms have been identified as powerful and very efficient. Optimization and scale-up based on the process simulation using Genetic Algorithms have been broadly reviewed and discussed. It was concluded that, up to date, there is a lack of optimization studies on the counter-rotating twin screw extrusion, although the global models of this process are known. There is also a lack of process simulation-based scaling-up studies, both on the counter-rotating twin screw extrusion and on the starve fed single screw extrusion. Finally, development perspectives in this field have been discussed.

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Section: Optimization For Extrusionmentioning

confidence: 99%

Optimization and Scale-Up for Polymer Extrusion

Nastaj

Wilczyński

2021

Polymers

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“…Several experimental design strategies may be considered for improving injection molded product quality based on process parameter evaluation. Among the various experimental design strategies, the Taguchi method and response surface methodology have been widely used to determine the optimal process parameters for injection molding [ 22 , 23 ]. In the small factor change problem [ 24 ], because changing numerous factors is undesirable, some variation on a one-factor-at-a-time (OFAT) strategy would be appropriate for undertaking quality improvement with minimal changes to factor levels.…”

Section: Introductionmentioning

confidence: 99%

Effects of Injection Molding Process Parameters on the Chemical Foaming Behavior of Polypropylene and Polystyrene

et al. 2021

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In the present study, semi-crystalline polypropylene (PP) and amorphous polystyrene (PS) were adopted as matrix materials. After the exothermic foaming agent azodicarbonamide was added, injection molding was implemented to create samples. The mold flow analysis program Moldex3D was then applied to verify the short-shot results. Three process parameters were adopted, namely injection speed, melt temperature, and mold temperature; three levels were set for each factor in the one-factor-at-a-time experimental design. The macroscopic effects of the factors on the weight, specific weight, and expansion ratios of the samples were investigated to determine foaming efficiency, and their microscopic effects on cell density and diameter were examined using a scanning electron microscope. The process parameters for the exothermic foaming agent were optimized accordingly. Finally, the expansion ratios of the two matrix materials in the optimal process parameter settings were compared. After the experimental database was created, the foaming module of the chemical blowing agents was established by Moldex3D Company. The results indicated that semi-crystalline materials foamed less due to their crystallinity. PP exhibits the highest expansion ratio at low injection speed, a high melt temperature, and a low mold temperature, whereas PS exhibits the highest expansion ratio at high injection speed, a moderate melt temperature, and a low mold temperature.

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“…Then, the warpage and average temperature conditions were further observed, in order to obtain the manufacturing process parameters of auto lock parts with the optimal design for each single objective. Taguchi methods are the most widely applied robust design methods in the planning of process parameters, because they reveal the effects of various combinations of parameters on a relevant single quality characteristic [24][25][26][27]. The combinations of parameters for the experimental injection molded sample were assessed with the Taguchi L 18 (2 1 × 3 7 ) OA, using the warpage and average temperature as single quality characteristics.…”

Section: Taguchi Robust Design Processmentioning

confidence: 99%

Application of Intelligent Modeling Method to Optimize the Multiple Quality Characteristics of the Injection Molding Process of Automobile Lock Parts

Huang

Tsai

et al. 2021

Polymers

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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%.

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A Strategy for Problem Solving of Filling Imbalance in Geometrically Balanced Injection Molds

Cited by 17 publications

References 11 publications

Optimization and Scale-Up for Polymer Extrusion

Optimization and Scale-Up for Polymer Extrusion

Effects of Injection Molding Process Parameters on the Chemical Foaming Behavior of Polypropylene and Polystyrene

Application of Intelligent Modeling Method to Optimize the Multiple Quality Characteristics of the Injection Molding Process of Automobile Lock Parts

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