COMBINING TAGUCHI METHOD, PRINCIPAL COMPONENT ANALYSIS AND FUZZY LOGIC TO THE TOLERANCE DESIGN OF A DUAL-PURPOSE SIX-BAR MECHANISM

2010 ◽  
Vol 34 (2) ◽  
pp. 277-293 ◽  
Author(s):  
Fu-Chen Chen ◽  
Yih-Fong Tzeng ◽  
Meng-Hui Hsu ◽  
Wei-Ren Chen
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Logic ◽  
Taguchi Method ◽  
Hybrid Approach ◽  
Principal Component ◽  
Component Analysis ◽  
Tolerance Design ◽  
Dual Purpose ◽  
Control Factors ◽  
Key Dimensions

A hybrid approach of combining Taguchi method, principal component analysis and fuzzy logic for the tolerance design of a dual-purpose six-bar mechanism is proposed. The approach is to firstly use the Taguchi orthogonal array to carry out experiments for calculating the S/N ratios of the positional errors to the angular error of the dual-purpose six-bar mechanism. The principal component analysis is then applied to determine the principal components of the S/N ratios, which are transformed via fuzzy logic reasoning into a multiple performance index (MPI) for further analysis of the effect of each control factors on the quality of the mechanism. Through the analysis of response table and diagram, key dimensional tolerances can be classified, which allows the decision of either to tighten the key tolerances to improve mechanism quality or to relax the tolerance of non-key dimensions to reduce manufacturing costs to be made.

The use of the Taguchi method and principal component analysis for the sensitivity analysis of a dual-purpose six-bar mechanism

2008 ◽  
Vol 223 (3) ◽  
pp. 733-741 ◽  
Author(s):  
F C Chen ◽  
Y F Tzeng ◽  
W R Chen ◽  
M H Hsu
Keyword(s):  
Principal Component Analysis ◽  
Taguchi Method ◽  
Principal Component ◽  
Component Analysis ◽  
Dual Purpose ◽  
Control Factors ◽  
Response Table ◽  
Key Dimensions ◽  
The Cost

In this paper, the Taguchi method and the principal component analysis were applied to a dual-purpose six-bar mechanism for investigating the influence of manufacturing tolerance and joint clearance on the quality of the mechanism. Experiments were carried out based on the orthogonal array from the Taguchi method, which calculated the S/ N ratios of the positional and angular errors of a dual-purpose six-bar mechanism. Using the principal component analysis, the S/ N ratios were transformed into a multiple performance index to further understand the effect of the control factors on the quality of the six-bar mechanism. Using the analysis of response table and response diagram, the key dimensions of the mechanism could be identified and their tolerance optimized, i.e. decreasing the tolerance of important dimensions and increasing the rest, with the objective of simultaneously improving the quality of the mechanism and reducing the cost.

Optimization of Process Parameters for Submerged Arc Welding by Weighted Principal Component Analysis Based Taguchi Method

2012 ◽  
Vol 622-623 ◽  
pp. 45-50 ◽  
Author(s):  
Joydeep Roy ◽  
Bishop D. Barma ◽  
J. Deb Barma ◽  
S.C. Saha
Keyword(s):  
Principal Component Analysis ◽  
Taguchi Method ◽  
Arc Welding ◽  
Principal Component ◽  
Traverse Speed ◽  
Component Analysis ◽  
Submerged Arc Welding ◽  
Bead Geometry ◽  
Weighted Principal Component ◽  
Submerged Arc

In submerged arc welding (SAW), weld quality is greatly affected by the weld parameters such as welding current, traverse speed, arc voltage and stickout since they are closely related to weld joint. The joint quality can be defined in terms of properties such as weld bead geometry and mechanical properties. There are several control parameters which directly or indirectly affect the response parameters. In the present study, an attempt has been made to search an optimal parametric combination, capable of producing desired high quality joint in submerged arc weldment by Taguchi method coupled with weighted principal component analysis. In the present investigation three process variables viz. Wire feed rate (Wf), stick out (So) and traverse speed (Tr) have been considered and the response parameters are hardness, tensile strength (Ts), toughness (IS).

scholarly journals Genetic Evaluation of Dual-Purpose Buffaloes (Bubalus bubalis) in Colombia Using Principal Component Analysis

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0132811 ◽  
Author(s):  
Divier Agudelo-Gómez ◽  
Sebastian Pineda-Sierra ◽  
Mario Fernando Cerón-Muñoz
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Bubalus Bubalis ◽  
Genetic Evaluation ◽  
Dual Purpose

scholarly journals Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects

2014 ◽  
Vol 2 (1) ◽  
pp. 291-308 ◽  
Author(s):  
Baris Yuce ◽  
Ernesto Mastrocinque ◽  
Michael Sylvester Packianather ◽  
Duc Pham ◽  
Alfredo Lambiase ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Taguchi Method ◽  
Network Design ◽  
Principal Component ◽  
Component Analysis ◽  
Wood Veneer

scholarly journals A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2229 ◽  
Author(s):  
Mansoor Khan ◽  
Tianqi Liu ◽  
Farhan Ullah
Keyword(s):  
Principal Component Analysis ◽  
Renewable Energy ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Algorithm ◽  
Hybrid Approach ◽  
Principal Component ◽  
Component Analysis ◽  
Wind Data ◽  
Deep Learning Algorithm

Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results.

scholarly journals Principal component analysis of conformation traits in Hungarian Simmental cows

2021 ◽  
Vol 66 (No. 2) ◽  
pp. 39-45
Author(s):  
Evelin Török ◽  
István Komlósi ◽  
Béla Béri ◽  
Imre Füller ◽  
Barnabás Vágó ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Analysis Data ◽  
Component Analysis ◽  
Linear Type ◽  
Dual Purpose ◽  
Type Traits ◽  
And Cluster Analysis ◽  
Trait Groups

The aim of the current research was to analyze the linear type traits of Hungarian Simmental dual-purpose cows scored in the first lactation using principal component analysis and cluster analysis. Data collected by the Association of Hungarian Simmental Breeders were studied during the work. The filtered database contained the results of 8 868 cows, born after 1997. From the evaluation of main conformation traits, the highest correlations (r = 0.35, P < 0.05) were found between mammary system and feet and legs traits. Within linear type traits, the highest correlation was observed between rump length and rump width (r = 0.81, P < 0.05). Using the principal component analysis, main conformation traits were combined into groups. There were three factors having 84.5 as total variance ratio after varimax rotation. Cluster analysis verified the results of the principal component analysis as most of the trait groups were similar. The strongest relationship was observed between feet and legs and mammary system (main conformation traits) and between rump length and rump width (linear type traits).

scholarly journals Bleeding recognition technique in wireless capsule endoscopy images using fuzzy logic and principal component analysis

2021 ◽  
Vol 11 (3) ◽  
pp. 2688
Author(s):  
A. Al Mamun ◽  
P. P. Em ◽  
T. Ghosh ◽  
M. M. Hossain ◽  
M. G. Hasan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Logic ◽  
Capsule Endoscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Wireless Capsule Endoscopy ◽  
Innovative Technology ◽  
Support Vector ◽  
Bleeding Detection ◽  
Wireless Capsule

Wireless capsule endoscopy is the most innovative technology to perceive the entire gastrointestinal (GI) tract in recent times. It can diagnose inner diseases like bleeding, ulcer, tumor, Crohn's disease, and polyps. in a discretion way. It creates immense pressure and onus for clinicians to perceive a huge number of image frames, which is time-consuming and makes human oversight errors. Therefore a computer-automated system has been introduced for bleeding detection. A unique fuzzy logic technique is proposed to extract the specified bleeding and non-bleeding information from the image data. A particular quadratic support vector machine (QSVM) classifier is employed to classify the obtained statistical features from the bleeding and non-bleeding images incorporating principal component analysis (PCA). After extensive experiments on clinical data, 98% sensitivity, 98.4% accuracy, 98% specificity, 93% precision, 95.4% F1-score, and 99% negative predicted value have been achieved, which outperforms some of the states of art methods in this regard. It is optimistic that the proposed methodology would significantly contribute to bleeding detection techniques and diminish the additional onus of the physicians.

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