scholarly journals Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Peng Zhao ◽  
Zhengyang Dong ◽  
Jianfeng Zhang ◽  
Yi Zhang ◽  
Mingyi Cao ◽  
...  
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Optimization Problem ◽  
Classification Problem ◽  
Support Vector ◽  
Injection Molding Process ◽  
Average Weight ◽  
Optimization Strategy ◽  
Molding Process ◽  
Injection Molded

Product weight is one of the most important properties for an injection-molded part. The determination of process parameters for obtaining an accurate weight is therefore essential. This study proposed a new optimization strategy for the injection-molding process in which the parameter optimization problem is converted to a weight classification problem. Injection-molded parts are produced under varying parameters and labeled as positive or negative compared with the standard weight, and the weight error of each sample is calculated. A support vector classifier (SVC) method is applied to construct a classification hyperplane in which the weight error is supposed to be zero. A particle swarm optimization (PSO) algorithm contributes to the tuning of the hyperparameters of the SVC model in order to minimize the error between the SVC prediction results and the experimental results. The proposed method is verified to be highly accurate, and its average weight error is 0.0212%. This method only requires a small amount of experiment samples and thus can reduce cost and time. This method has the potential to be widely promoted in the optimization of injection-molding process parameters.

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.

Optimization of Plastic Injection Molding Process Parameters for Thin-Wall Plastics Injection Molding

2009 ◽  
Vol 69-70 ◽  
pp. 525-529 ◽  
Author(s):  
Jie Jin ◽  
H.Y. Yu ◽  
S. Lv
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Optimization Method ◽  
Injection Molding Process ◽  
Thin Wall ◽  
Molding Process ◽  
Taguchi Optimization ◽  
Taguchi Optimization Method ◽  
Plastics Injection Molding ◽  
Injection Molded

The effects of the process parameters on the warpge and shrinkage of parts in different thickness are analyzed by Taguchi optimization method. Taguchi optimization method was used for exploiting mold analysis based on three level factorial designs. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) are utilized to find the optimal levels and the effect of process parameters on warpage. It can be concluded that Taguchi method is suitable to solve the quality problem of the injection-molded thermoplastic parts.

Optimization of Injection Molding Process Parameters to Increase the Tensile Strength in Polyamide-Specimen Using the Taguchi Method

2011 ◽  
Vol 341-342 ◽  
pp. 395-399 ◽  
Author(s):  
S. Esmail Mirvar ◽  
Ramin Mohamadi Kaleybar ◽  
Ahmad Afsari
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Injection Molding ◽  
Process Parameters ◽  
Processing Parameters ◽  
Design Parameters ◽  
Injection Molding Process ◽  
Molding Process ◽  
Pressure Time ◽  
Injection Molded

Mechanical properties of plastic play an important role in defining the quality of injection molded products. Many studies have shown that mechanical properties of a product are influenced by the process parameters governing the injection-molding processes .This study employs Taguchi design parameters, to systematically investigate the influence of injection molding processing parameters on the tensile strength of Commercial grade Polyamide (PA-6). The result shows that the holding pressure time has main effect on the tensile strength, followed by cooling time, and holding pressure. Finally, a specimen produced according to the general form of the predictive equation of process parameters and verification test is performed on that. Test results show that the experimental value of tensile strength is very close to the estimated value.

scholarly journals Constant Temperature Approach for the Assessment of Injection Molding Parameter Influence on the Fatigue Behavior of Short Glass Fiber Reinforced Polyamide 6

Polymers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1569
Author(s):  
Selim Mrzljak ◽  
Alexander Delp ◽  
André Schlink ◽  
Jan-Christoph Zarges ◽  
Daniel Hülsbusch ◽  
...  
Keyword(s):  
Injection Molding ◽  
Glass Fiber ◽  
Process Parameters ◽  
Fatigue Properties ◽  
Injection Molding Process ◽  
Molding Process ◽  
Short Glass Fiber ◽  
Glass Fiber Reinforced ◽  
Molding Parameter ◽  
Short Glass

Short glass fiber reinforced plastics (SGFRP) offer superior mechanical properties compared to polymers, while still also enabling almost unlimited geometric variations of components at large-scale production. PA6-GF30 represents one of the most used SGFRP for series components, but the impact of injection molding process parameters on the fatigue properties is still insufficiently investigated. In this study, various injection molding parameter configurations were investigated on PA6-GF30. To take the significant frequency dependency into account, tension–tension fatigue tests were performed using multiple amplitude tests, considering surface temperature-adjusted frequency to limit self-heating. The frequency adjustment leads to shorter testing durations as well as up to 20% higher lifetime under fatigue loading. A higher melt temperature and volume flow rate during injection molding lead to an increase of 16% regarding fatigue life. In situ Xray microtomography analysis revealed that this result was attributed to a stronger fiber alignment with larger fiber lengths in the flow direction. Using digital volume correlation, differences of up to 100% in local strain values at the same stress level for different injection molding process parameters were identified. The results prove that the injection molding parameters have a high influence on the fatigue properties and thus offer a large optimization potential, e.g., with regard to the component design.

scholarly journals Optimization of injection molding process for car fender in consideration of energy efficiency and product quality

2014 ◽  
Vol 1 (4) ◽  
pp. 256-265 ◽  
Author(s):  
Hong Seok Park ◽  
Trung Thanh Nguyen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Optimization Problems ◽  
Computational Cost ◽  
Injection Molding Process ◽  
Molding Process ◽  
Nsga Ii

Abstract Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

scholarly journals Optimization of Process Parameters for Vertical-Faced Polypropylene Bottle Injection Molding

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Youmin Wang ◽  
Zhichao Yan ◽  
Xuejun Shan
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Dwell Time ◽  
Optimization Problem ◽  
Injection Pressure ◽  
Mold Temperature ◽  
Orthogonal Test ◽  
Injection Molding Process ◽  
Injection Time ◽  
Melt Temperature

In order to obtain the optimal combination of process parameters for vertical-faced polypropylene bottle injection molding, with UG, the model of the bottle was drawn, and then, one module and sixteen-cavity injection molding system was established and analyzed using Moldflow. For filling and maintaining pressure during the process of infusion bottle injection molding, the orthogonal test table L25 (56) using CAE was designed for injection molding of the bottle, with six parameters such as melt temperature, mold temperature, injection pressure, injection time, dwell pressure, and dwell time as orthogonal test factors. By finding the best combination of process parameters, the orthogonal experiment was completed, the results were analyzed by range analysis, and the order of influence of each process parameter on each direction of optimization was obtained. The prediction dates of the infusion bottle were gained under various parameters, a comprehensive quality evaluation index of the bottle was formulated, and the multiobjective optimization problem of injection molding process was transformed into a single-objective optimization problem by the integrated weighted score method. The bottle parameters were optimized by analyzing the range date of the weighted scoring method, and the best parameter combination such as melt temperature 200°C, mold temperature 80°C, injection pressure 40 MPa, injection time 2.1 S, dwell pressure 40 MPa, and dwell time 40 S was gained.

Optimization of Injection Molding Process Parameters with Material Properties Based on GA and BP

2013 ◽  
Vol 345 ◽  
pp. 586-590 ◽  
Author(s):  
Xiao Hong Tan ◽  
Lei Gang Wang ◽  
Wen Shen Wang
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Axial Deformation ◽  
Molding Process ◽  
Melt Temperature ◽  
Bp Network ◽  
Injection Parameters ◽  
Moldflow Simulation

To obtain optimal injection process parameters, GA was used to optimize BP network structure based on Moldflow simulation results. The BP network was set up which considering the relationship between volume shrinkage of plastic parts and injection parameters, such as mold temperature, melt temperature, holding pressure and holding time etc. And the optimal process parameters are obtained, which is agreed with actual results. Using BP network to predict injection parameters impact on parts quality can effectively reduce the difficulty and workload of other modeling methods. This method can be extended to other quality prediction in the process of plastic parts.Keyword: Genetic algorithm (GA);Neural network algorithm (BP);Injection molding process optimization;The axial deformation

Sign in / Sign up

Export Citation Format

Share Document