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

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Khan ◽  
S. Kamran Afaq ◽  
Nizar Ullah Khan ◽  
Saboor Ahmad
Keyword(s):  
Temperature Distribution ◽  
Injection Molding ◽  
Cycle Time ◽  
Analytical Study ◽  
Injection Molding Process ◽  
Cooling Channel ◽  
Uniform Temperature ◽  
Channel Design ◽  
Molding Process ◽  
Uniform Temperature Distribution

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).”

Optimizing the Performance of Plastic Injection Molding Using Weighted Additive Model in Goal Programming

2013 ◽  
pp. 218-229
Author(s):  
Abbas Al-Refaie ◽  
Ming-Hsien Li
Keyword(s):  
Injection Molding ◽  
Goal Programming ◽  
Cycle Time ◽  
Programming Model ◽  
Additive Model ◽  
Injection Molding Process ◽  
Molding Process ◽  
Goal Programming Model ◽  
Process Factors ◽  
Short Product

Injection molding process is increasingly more significant in today’s plastic production industries because it provides high-quality product, short product cycles, and light weight. This research optimizes the performance of this process with three main quality responses: defect count, cycle time, and spoon weight, using the weighted additive goal programming model. The three quality responses and process factors are described by appropriate membership functions. The Taguchi’s orthogonal array is then utilized to provide experimental layout. A linear optimization based on the weighted additive model in goal programming model is built to minimize the deviations of the product/process targets from their corresponding imprecise fuzzy values specified by the process engineer’s preferences. The results show that the average defect count is reduced from an average of 0.75 to 0.16. Moreover, the average cycle time becomes 13.06 seconds, which is significantly smaller than that obtained at initial factor settings (= 15.10 seconds). Finally, the average spoon weight is exactly on its target value of 2.0 gm.

Temperature Distribution in the Molding Process of the Stator of PCP

2013 ◽  
Vol 470 ◽  
pp. 340-343
Author(s):  
Qing Tao Li
Keyword(s):  
Temperature Distribution ◽  
Injection Molding ◽  
Production Process ◽  
Production Efficiency ◽  
Injection Molding Process ◽  
Internal Temperature ◽  
Molding Process ◽  
Influence Of Temperature ◽  
Finite Element Software ◽  
Influence Of Temperature On

Because of the important influence of temperature on the process of manufacturing rubber products and in the actual production process ,the internal temperature of the product is very difficult to measure, so this paper simulated the PCP`s rubber stator injection molding process using finite element software, and detailed analysis of the temperature distribution in the case of injection molding process and injection molding after the completion of the molding, provides the theory support for the design and production process of stator improvement, to further improve the product quality and production efficiency to provide guarantee.

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

Polymers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2515
Author(s):  
Wei-Tai Huang ◽  
Chia-Lun Tsai ◽  
Wen-Hsien Ho ◽  
Jyh-Horng Chou
Keyword(s):  
Temperature Distribution ◽  
Injection Molding ◽  
Process Parameters ◽  
Cooling System ◽  
Injection Molding Process ◽  
Molding Process ◽  
Conformal Cooling ◽  
Characteristic Index ◽  
Average Temperature ◽  
Gray Correlation

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%.

Optimization Design of Hot Runner Injection Molding for Thin Shell Parts Based on Moldflow

2014 ◽  
Vol 599-601 ◽  
pp. 564-567
Author(s):  
Xu Xia Zhu ◽  
Hai Xiao Zheng
Keyword(s):  
Injection Molding ◽  
Cycle Time ◽  
Thin Shell ◽  
Optimization Design ◽  
Design Parameters ◽  
Injection Molding Process ◽  
Injection Mold ◽  
Molding Process ◽  
Shell Part

In this paper, based on the characteristics of shell part, the application of hot runner system for the shell parts injection molding process was analyzed by Moldflow. According to this research, design parameters obtained were optimized. The result shows that hot runner system enables injection mold cooling faster , shortens the molding cycle time , and also improves the quality of the shell part.

Optimizing the Performance of Plastic Injection Molding Using Weighted Additive Model in Goal Programming

2011 ◽  
Vol 1 (2) ◽  
pp. 43-54 ◽  
Author(s):  
Abbas Al-Refaie ◽  
Ming-Hsien Li
Keyword(s):  
Injection Molding ◽  
Goal Programming ◽  
Cycle Time ◽  
Programming Model ◽  
Additive Model ◽  
Injection Molding Process ◽  
Molding Process ◽  
Product Cycles ◽  
Goal Programming Model ◽  
Process Factors

Injection molding process is increasingly more significant in today’s plastic production industries because it provides high-quality product, short product cycles, and light weight. This research optimizes the performance of this process with three main quality responses: defect count, cycle time, and spoon weight, using the weighted additive goal programming model. The three quality responses and process factors are described by appropriate membership functions. The Taguchi’s orthogonal array is then utilized to provide experimental layout. A linear optimization based on the weighted additive model in goal programming model is built to minimize the deviations of the product/process targets from their corresponding imprecise fuzzy values specified by the process engineer’s preferences. The results show that the average defect count is reduced from an average of 0.75 to 0.16. Moreover, the average cycle time becomes 13.06 seconds, which is significantly smaller than that obtained at initial factor settings (= 15.10 seconds). Finally, the average spoon weight is exactly on its target value of 2.0 gm.

scholarly journals Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Saad M. S. Mukras
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Cycle Time ◽  
Optimization Problem ◽  
Extreme Values ◽  
Volumetric Shrinkage ◽  
Injection Molding Process ◽  
Product Cycle ◽  
Molding Process ◽  
Melt Temperature

This paper presents a framework for optimizing injection molding process parameters for minimum product cycle time subjected to constraints on the product defects. Two product defects, namely, volumetric shrinkage and warpage, as well as seven process parameters including injection speed, injection pressure, cooling time, packing pressure, mold temperature, packing time, and melt temperature, were considered. Injection molding experiments were conducted on specifically chosen test points and results were used to compute the volumetric shrinkage and warpage (at each test point). Thereafter, three relationships between the product cycle time (one relationship), the two product defects (two relationships), and the injection molding parameters were constructed using the kriging technique. An optimization problem to minimize the product cycle time (described by the first relationship) subject to constraints on the product defects (described by the latter two relationships) was then formulated. A combination set of points between the lower and upper extreme values of acceptable product defect was generated to serve as constraints for the two product defects. The optimization problem was then solved using the Fmincon function, available in the Matlab optimization toolbox. A plot of the optimization results revealed an appreciable tradeoff between the cycle time and the two product defects. To validate the optimization, an additional injection molding experiment was conducted for one of the optimization results. Results from the additional experiment showed reasonably close agreement with simulation optimization results differing in the cycle time, the warpage and volumetric shrinkage by 6.7%, 3.2%, and 8%, respectively.

Estimate the Melt Flow Length with Internal Induction Heating for the Injection Molding Process

2020 ◽  
Vol 863 ◽  
pp. 97-102
Author(s):  
Huynh Duc Thuan ◽  
Tran Anh Son ◽  
Pham Son Minh
Keyword(s):  
Temperature Distribution ◽  
Injection Molding ◽  
Heating Rate ◽  
Induction Heating ◽  
Heating System ◽  
Mold Temperature ◽  
Heating Process ◽  
Injection Molding Process ◽  
Molding Process ◽  
Internal Induction

In this paper, an induction heating system was applied to the heating stage in the injection molding process. Through simulation and experiment, the heating process was estimated by the temperature distribution and the heating rate. In the simulation, the mold temperature was increased from 30°C to 180°C in 9 s. Therefore, the heating rate was higher than 16°C/s, which represents a positive result in the field of mold heating. Additionally, the temperature distribution revealed that the higher temperature is concentrated on the gate area, while the outside of the mold cavity is at a lower temperature. The same parameters were applied to both the experiment and the simulation, and the results were in good agreement.

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