Design of a Dual-Resonance Excitation Langevin Piezoelectric Actuator Using Taguchi Method

2016 ◽  
Author(s):  
Yu-Jen Wang ◽  
Shyang-Jye Chang ◽  
Kuo-Chieh Fu ◽  
Chien-Erh Weng
Keyword(s):  
Injection Molding ◽  
Taguchi Method ◽  
Piezoelectric Actuator ◽  
Resonance Excitation ◽  
Injection Molding Process ◽  
Molding Process ◽  
Knock Out ◽  
Dual Resonance ◽  
Ejector System ◽  
Langevin Transducer

For injection molding, knock-out pin will leave ejector marks on the parts of product. While making micro-structured components, the pin for normal ejector system is usually bigger than the micro-structured one. It would leave marks while part ejection process and it would even destroy the exterior of micro-structured components sometimes. In order to avoid this situation, this research is to design a Langevin transducer using the theory of Smooth Impact Drive Mechanism (SIDM) as a linear actuator. The dimensions of the Langevin piezoelectric actuator was determined using the Taguchi method to set the first and third minimum impedance frequency ratio at nearly 1:2. The mold equipped with the double layer Langevin transducer could demold the parts without knock-out pin in the plastic injection molding process.

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.

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 Fill Time Analysis of 8 Cavities Parison Mould using Taguchi Method

2020 ◽  
Vol 9 (3) ◽  
pp. 2757-2762
Keyword(s):  
Injection Molding ◽  
Taguchi Method ◽  
Optimal Parameter ◽  
Mold Temperature ◽  
Design Parameters ◽  
Injection Molding Process ◽  
Molding Process ◽  
Injection Process ◽  
Plastic Injection ◽  
Illustrative Application

In the plastic injection molding process, the optimization of the process parameters is a complex task. This paper presents the optimum conditions of the injection process for 8 cavities mold for 20g parison filled with Polyethylene Terephthalate (PET) by utilizing the Taguchi method. In the Taguchi method, the performance parameter is assumed to be the optimal parameter for injection molding process. An L16 (43) orthogonal array is considered as an experimental plan for the design parameters as suggested in Minitab version. The objective of this study is to propose an approach for efficiently optimizing injection molding parameter, i.e. fill time, with three different outputs, i.e. melting temperature, runner size and mold temperature. The illustrative application and comparison of results show that the proposed methodology outperforms the existing methods and can help injection molding process to efficiently and effectively identify optimal fill time process parameter. The result indicates the best performance for the highest contribution for each respond. This is due to the interaction of factors and it also gives the percentage contribution with 95% confidence level. The analysis using the Taguchi method showed the optimized fill time. The results show that the optimal parameters for the fill time during injection process of 8 cavities mold 20g parison is A1B1C4.

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