Optimizing Injection Parameters of Kenaf Filler Polypropylene Composite by Taguchi Method

2017 ◽  
Vol 894 ◽  
pp. 81-84 ◽  
Author(s):  
Mohd Khairul Fadzly Md Radzi ◽  
Norhamidi Muhamad ◽  
Abu Bakar Sulong ◽  
Zakaria Razak
Keyword(s):  
Injection Molding ◽  
Taguchi Method ◽  
Process Condition ◽  
Injection Pressure ◽  
Poor Quality ◽  
Injection Molding Process ◽  
Molding Process ◽  
Injection Parameters ◽  
Pp Composites ◽  
Injection Temperature

Optimization of injection molding parameters provided a solution to achieve strength improvement of kenaf filler polypropylene composites. Since, molded polymers composites possibility being effected by machine parameters and other process condition that may cause poor quality of composites product. Thus in this study, composite of kenal filler reinforced with thermoplastic polypropylene (PP) were prepared using a sigma blade mixer, followed by an injection molding process. To determine the optimal processing of injection parameters, Taguchi method with L27 orthogonal array was used on statistical analysis of tensile properties of kenaf/PP composites. The results obtained the optimum parameters which were injection temperature 190°C, injection pressure 1300 bar, holding pressure 1900 bar and injection rate 20cm3/s. From the analysis of variance (ANOVA), both flow rate and injection temperature give highest contribution factor to the mechanical properties of the kenaf/PP composites.

scholarly journals Optimization of injection parameters from the aspect of dimension accuracy

2021 ◽  
Vol 1208 (1) ◽  
pp. 012016
Author(s):  
Amir Ćoralić
Keyword(s):  
Injection Molding ◽  
Injection Pressure ◽  
Injection Molding Process ◽  
Molding Process ◽  
Injection Parameters ◽  
Dimension Control

Abstract During the injection molding process, the polymers are subject to shrinkage and bending, which causes an unwanted change in geometry. These deficiencies can be caused by many factors: injection pressure, overpressure, overpressure time, cooling, etc. Within this work, the influence of overpressure and overpressure time on the accuracy of product dimensions is presented. Dimension control was performed by measuring on a 3D coordinate machine. The products are injected in 2 different combinations of parameters. For each combination of parameters, measurements were performed in five injection cycles.

scholarly journals Green Strength Optimization in Metal Injection Molding Applicable with a Taguchi Method L9 (3)4

2015 ◽  
Vol 773-774 ◽  
pp. 115-117 ◽  
Author(s):  
N. Mustafa ◽  
Mohd Halim Irwan Ibrahim ◽  
Rosli Asmawi ◽  
Azriszul Mohd Amin ◽  
S.R. Masrol
Keyword(s):  
Injection Molding ◽  
Taguchi Method ◽  
Complex Geometry ◽  
Signal To Noise Ratio ◽  
Injection Pressure ◽  
Cost Effective ◽  
Metal Injection Molding ◽  
Injection Molding Process ◽  
Molding Process ◽  
Green Strength

Recently Metal injection molding is selected as a vital process in producing large amount of small part with complex geometry and intricate shape. This process is lead to solve cost effective issue in manufacturing fields. Feedstock composition behavior categorized as one of impact factor in determines the victories in metal injection molding process. Thus this paper is focused on optimizing the strength of green part by applied Taguchi Method L9 (34) as optimization tools during injection process. The composition of feedstock is 55% powder loading (PL) were injected by injection molding machine .Several injection parameter were optimized such as injection temperature (A), barrel temperature (B), injection pressure (C) and Speed (D) The results analyzed by using Signal to Noise Ratio (S/N ratio) terms. The highest green strength is A2, B2, C2, and D2

scholarly journals Study on Stability of a Novel Binder System Based on Palm Stearin in Metal Injection Moulding Application

2007 ◽  
Vol 4 (2) ◽  
pp. 1
Author(s):  
Muhammad Hussain Ismail ◽  
Norhamidi Muhamad ◽  
Aidah Jumahat ◽  
Istikamah Subuki ◽  
Mohd Afian Omar
Keyword(s):  
Injection Molding ◽  
Flow Characteristics ◽  
Injection Pressure ◽  
Palm Stearin ◽  
Steel Powder ◽  
Metal Injection Molding ◽  
Injection Molding Process ◽  
Binder System ◽  
Molding Process ◽  
Flow Activation Energy

Metal Injection Molding (MIM) is a wellestablished technology for manufacturing a variety of complex and small precision parts. In this paper, fundamental rheological characteristics of MIM feedstock using palm stearin are theoretically analyzed and presented. The feedstock consisted of gas atomized 316L stainless steel powder at three different particle size distributions and the binder system of palm stearin (PS) and polyethylene (PE). The powder loading used was 60vol % for all samples (monosize 16 µm, monosize 45 µm, and bimodal 16 µm + 45 µm) and the binder system of 40vol %(PS/PE = 40/60). The viscosity of MIM feedstock at different temperatures and shear rates was measured and evaluated. Results showed that, the feedstock containing palm stearin exhibited suitable rheological properties by increasing the fluidity of feedstock in MIM process. The rheological results also showed a pseudoplastic flow characteristics, which poses higher value of shear sensitivity (n) and lower value of flow activation energy (E), that are both favourable for injection molding process. The green parts were successfully injected and exhibited adequate strength for handling by optimizing the injection pressure and temperature.

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

An Intelligent Optimization System for PIM Process

2019 ◽  
Vol 814 ◽  
pp. 203-210
Author(s):  
Wen Chin Chen ◽  
Tai Hao Chen ◽  
Ding Tsair Chang ◽  
Manh Hung Nguyen
Keyword(s):  
Experimental Data ◽  
Injection Molding ◽  
Taguchi Method ◽  
Injection Molding Process ◽  
Optimal Process ◽  
Molding Process ◽  
Training Time ◽  
Intelligent Optimization ◽  
Optimization System ◽  
Modified Pso

This study proposes an intelligent optimization system based on the Taguchi method, back-propagation neural network (BPNN), multilayer perceptron (MLP) and modified PSO-GA to find optimal process parameters in plastic injection molding (PIM). Firstly, the Taguchi method is used to determine the initial combination of parameter settings by calculating the signal-to-noise (S/N) ratios from the experimental data. Significant factors are determined using analysis of variance (ANOVA). The S/N ratio predictors (BPNNS/N) and quality predictors (BPNNQ) are constructed using BPNN with the experimental data. In addition, a modified PSO-GA algorithm in conjunction with MLP is used to find initial weights of BPNN and to reduce the training time of BPNN. In the first stage optimization, the S/N ratio predictors are coupled with GA to reduce the variations of the manufacturing process. In the second stage optimization, The combination of S/N ratio predictors and quality predictors with modified PSO-GA is empoyed to search for the optimal parameters. Finally, three confirmation experiments are performed to assess the effectiveness of these approaches. The experimental results show that the proposed system can create the best performance, and optimal process parameter settings which not only enhance the stability in the whole injection molding process but also effectively improve the PIM product quality. Furthermore, experiences of the novel hybrid optimization system can be transferred into the intelligent PIM machines for the coming up internet of things (IoT) and big data environment.

Comprehensive Online Control Strategies for Plastic Injection Molding Process

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Jaho Seo ◽  
Amir Khajepour ◽  
Jan P. Huissoon
Keyword(s):  
Injection Molding ◽  
Injection Pressure ◽  
Thermal Control ◽  
Plastic Injection Molding ◽  
Injection Molding Process ◽  
Molding Process ◽  
Element Analysis ◽  
Thermal Dynamics ◽  
Uncertain Dynamics ◽  
Plastic Injection

This study proposes an effective thermal control for plastic injection molding (polymer: Santoprene 8211-45 with density of 790 kg/m3, injection pressure: 1400 psi (9,652,660 Pa)) in a laminated die. For this purpose, a comprehensive control strategy is provided to cover various themes. First, a new method for determining the optimal sensor locations as a prerequisite step for modeling and controller design is introduced. Second, system identification through offline and online training with finite element analysis and neural network techniques are used to develop an accurate model by incorporating uncertain dynamics of the laminated die. Third, an additive feedforward control by adding direct adaptive inverse control to self-adaptive PID is developed for temperature control of cavity wall (cavity size: 52.9 × 32.07 × 16.03 mm). A verification of designed controller's performance demonstrates that the proposed strategy provides accurate online temperature tracking and faster response under thermal dynamics with various cycle-times in the injection mold process.

Influence of Technological Parameters on Material Flow

2015 ◽  
Vol 1120-1121 ◽  
pp. 1194-1197 ◽  
Author(s):  
Michal Stanek ◽  
David Manas ◽  
Miroslav Manas ◽  
Vojtech Senkerik ◽  
Adam Skrobak ◽  
...  
Keyword(s):  
Injection Molding ◽  
Injection Pressure ◽  
Polymer Processing ◽  
Injection Rate ◽  
Cavity Surface ◽  
Injection Molding Process ◽  
Technological Parameters ◽  
Molding Process ◽  
Processing Technologies

Injection molding is one of the most extended polymer processing technologies. It enables the manufacture of final products, which do not require any further operations. The tools used for their production – the injection molds – are very complicated assemblies that are made using several technologies and materials. Delivery of polymer melts into the mold cavity is the most important stage of the injection molding process. The fluidity of polymers is affected by many parameters Inc. mold design. Evaluation of set of data obtained by experiments in which the testing conditions were widely changed shows that the quality of cavity surface and technological parameters (injection rate, injection pressure and gate size) has substantial influence on the length of flow.

Numerical and Experimental Study on the Injection Moulding of a Thin-Wall Complex Part

2008 ◽  
Author(s):  
Catalin Fetecau ◽  
Ion Postolache ◽  
Felicia Stan
Keyword(s):  
Injection Molding ◽  
Injection Pressure ◽  
Mold Temperature ◽  
Processing Parameters ◽  
Injection Molding Process ◽  
Thin Wall ◽  
Molding Process ◽  
Complex Part ◽  
Filling Time ◽  
Wall Injection

The research presented in this paper involves numerical and experimental efforts to investigate the relative thin-wall injection molding process in order to obtain high dimensional quality complex parts. To better understand the effects of various processing parameters (the filling time, injection pressure, the melting temperature, the mold temperature) on the injection molding of a thin-wall complex part, the molding experiments are regenerated into the computer model using the Moldflow Plastics Insight (MPI) 6.1 software. The computer visualization of the filling phase allows accurate prediction of the location of the flow front, welding lines and air traps. Furthermore, in order to optimize the injection molding process, the effects of the geometry of the runner system on the filling and packing phases are also investigated. It is shown that computational modeling could be used to help the process and mold designer to produce accurate parts.

Dynamic Response Optimization for Cemented Carbide Injection Molding

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
Sri Yulis M. Amin ◽  
Norhamidi Muhamad ◽  
Khairur Rijal Jamaludin
Keyword(s):  
Injection Molding ◽  
Palm Stearin ◽  
Processing Parameters ◽  
Cemented Carbide ◽  
Metal Injection Molding ◽  
Injection Molding Process ◽  
Molding Process ◽  
Response Optimization ◽  
Injection Molded ◽  
Injection Temperature

The need to optimize the injection molding parameters for producing cemented carbide parts via Metal Injection Molding process is crucial to ensure the system’s robustness towards manufacturer and customer’s satisfactions. Defect free product with best density can be produced while reducing time and cost in manufacturing. In this work, the feedstock consisting of WC-Co powders, mixed with palm stearin and polyethylene binder system was injection molded to produce green parts. Several processing variables, namely powder loading, injection temperature, holding pressure and flowrate, were optimized towards the density of the green body, as the response factor. By considering humidity level at morning and evening conditions as the noise factor, the results show the optimum combination of injection molding parameters that produces best green density. The green part exhibited best density by following this optimum processing parameters, A2B3C1D1, that are flowrate at 20 ccm/s, powder loading at 63% vol., injection temperature at 140°C, and holding pressure at 1700 bar.

Sign in / Sign up

Export Citation Format

Share Document