scholarly journals Robust PID Control Parameter Design by Taguchi Method

2006 ◽  
Vol 2006 (0) ◽  
pp. 30-35
Author(s):  
Arata Suzuki ◽  
Kenji Sugimoto
Keyword(s):  
Taguchi Method ◽  
Pid Control ◽  
Control Parameter ◽  
Parameter Design

scholarly journals A Control Parameter Design Method for Hybrid Multi-Terminal HVDC System

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 18669-18680 ◽  
Author(s):  
Bingkun Li ◽  
Yuansheng Liang ◽  
Gang Wang ◽  
Haifeng Li ◽  
Xinquan Chen
Keyword(s):  
Control Parameter ◽  
Design Method ◽  
Parameter Design ◽  
Hvdc System

scholarly journals Optimal Parameter Design for IC Wire Bonding Process by Using Fuzzy Logic and Taguchi Method

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 3034-3045 ◽  
Author(s):  
Jinn-Tsong Tsai ◽  
Cheng-Chung Chang ◽  
Wen-Ping Chen ◽  
Jyh-Horng Chou
Keyword(s):  
Fuzzy Logic ◽  
Taguchi Method ◽  
Optimal Parameter ◽  
Wire Bonding ◽  
Bonding Process ◽  
Parameter Design

Application of the Grey-Taguchi Method to the Optimization of Metal Injection Molding (MIM) Process

2010 ◽  
Vol 443 ◽  
pp. 63-68 ◽  
Author(s):  
Khairur Rijal Jamaludin ◽  
Norhamidi Muhamad ◽  
Mohd Nizam Ab. Rahman ◽  
Sufizar Ahmad ◽  
Mohd Halim Irwan Ibrahim ◽  
...  
Keyword(s):  
Injection Molding ◽  
Taguchi Method ◽  
Mold Temperature ◽  
Parameter Design ◽  
Metal Injection Molding ◽  
Quality Performance ◽  
Surface Appearance ◽  
Grey Relational Grade ◽  
Grey Relational ◽  
Grey Taguchi

The Grey-Taguchi method was adopted in this study to optimize the injection molding parameters for the MIM green compacts with multiple quality performance. A Grey relational grade obtained from the Grey relational analysis is used as the quality performance in the Taguchi method. Then, the optimum injection molding parameters are determined using the parameter design proposed by the Taguchi method. The result concluded that the mold temperature (D) is very significant, by the fact that the ANOVA shows its contribution to excellent surface appearance as well as strong and dense green compacts is 38.82%.

EFFECTS OF SVM PARAMETER OPTIMIZATION BASED ON THE PARAMETER DESIGN OF TAGUCHI METHOD

2011 ◽  
Vol 20 (03) ◽  
pp. 563-575 ◽  
Author(s):  
MEI LING HUANG ◽  
YUNG HSIANG HUNG ◽  
EN JU LIN
Keyword(s):  
Taguchi Method ◽  
Parameter Design ◽  
Support Vector ◽  
Svm Classifier ◽  
Liver Disorders ◽  
Second Stage ◽  
Vector Machines ◽  
Class Labels ◽  
Two Stages ◽  
Decision Boundaries

Support Vector Machines (SVMs) are based on the concept of decision planes that define decision boundaries, and Least Squares Support Vector (LS-SVM) Machine is the reformulation of the principles of SVM. In this study a diagnosis on a BUPA liver disorders dataset, is conducted LS-SVM with the Taguchi method. The BUPA Liver Disorders dataset includes 345 samples with 6 features and 2 class labels. The system approach has two stages. In the first stage, in order to effectively determine the parameters of the kernel function, the Taguchi method is used to obtain better parameter settings. In the second stage, diagnosis of the BUPA liver disorders dataset is conducted using the LS-SVM classifier; the classification accuracy is 95.07%; the AROC is 99.12%. Compared with the results of related research, our proposed system is both effective and reliable.

Optimization of Manufacturing Parameters for Melt Spun Yarns Using Taguchi Method

2014 ◽  
Vol 556-562 ◽  
pp. 4264-4267
Author(s):  
Shu Wen Wang ◽  
Te Li Su
Keyword(s):  
Taguchi Method ◽  
Melt Spinning ◽  
Parameter Design ◽  
Controllable Factors ◽  
Melt Spun ◽  
Spun Yarns ◽  
Spinning Process ◽  
Polypropylene Melt ◽  
Manufacturing Parameters

In melt spinning process, evenness of polypropylene melt spun yarns affects the appearance, hairiness, strength and productivity of yarns, as well as product production and profits, causing rejection due to nonconformity. The research is to find optimal manufacturing parameters of melt spun yarns. Firstly, to proceed the parameter design by Taguchi method, then to select a manufacturing parameter which will affect the quality of melt spun yarns as controllable factors. Also to choose a suitable orthogonal arrays. Meanwhile, according to variation of analysis, to decide optimal manufacturing parameters of melt spun yarns and its remarkable factor. Finally, using 95% confidence interval to proof the experiment’s reliability and repeatability.

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