Weighted principal component analysis combined with Taguchi’s signal-to-noise ratio to the multiobjective optimization of dry end milling process: a comparative study

2016 ◽  
Vol 39 (5) ◽  
pp. 1663-1681 ◽  
Author(s):  
Danielle M. D. Costa ◽  
Gabriela Belinato ◽  
Tarcísio G. Brito ◽  
Anderson P. Paiva ◽  
João R. Ferreira ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Multiobjective Optimization ◽  
Comparative Study ◽  
End Milling ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Milling Process ◽  
Signal To Noise ◽  
Weighted Principal Component ◽  
End Milling Process

Optimization of Tool Wear, Surface Roughness and Material Removal Rate in the Milling Process of Al 6061 Using Taguchi and Weighted Principal Component Analysis (WPCA)

2014 ◽  
Vol 493 ◽  
pp. 535-540 ◽  
Author(s):  
Laily Ulfiyah ◽  
Bambang Pramujati ◽  
Bobby Oedy Pramoedyo Soepangkat
Keyword(s):  
Principal Component Analysis ◽  
Taguchi Method ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Principal Component ◽  
Milling Process ◽  
Performance Characteristics ◽  
Weighted Principal Component ◽  
End Milling Process

In the metal cutting industry, end milling has an important role in cutting metal to obtain the various required shapes and size. This study takes Al 6061 as working material and investigates three performance characteristics, i.e., tool wear (VB), surface roughness (Ra) and material removal rate (MRR), with Taguchi method and WPCA for determining the optimal parameters in the end milling process. The performance characteristic of MRR is larger-the-better while VB and Ra are having smaller-the-better performance characteristic. Based on Taguchi method, an L18 mixed-orthogonal array was chosen for the experiments. The optimization was conducted by using weighted principal component analysis (WPCA). As a result, the optimization of complicated multiple performance characteristics was transformed into the optimization of single response performance index. The most significant machining parameters which affected the multiple performance characteristics were type of milling operation, spindle speed, feed rate and depth of cut. Experimental result have also shown that machining performance characteristics of end milling process can improved effectively through the combination of Taguchi method and WPCA.

scholarly journals Near-field sound source localization using principal component analysis–multi-output support vector regression

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091640
Author(s):  
Lanmei Wang ◽  
Yao Wang ◽  
Guibao Wang ◽  
Jianke Jia
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Signal To Noise ◽  
Noise Ratio

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.

Enhancing the Signal-to-Noise Ratio of X-ray Diffraction Profiles by Smoothed Principal Component Analysis

2005 ◽  
Vol 77 (20) ◽  
pp. 6563-6570 ◽  
Author(s):  
Zeng Ping Chen ◽  
Julian Morris ◽  
Elaine Martin ◽  
Robert B. Hammond ◽  
Xiaojun Lai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray Diffraction ◽  
Signal To Noise ◽  
X Ray ◽  
Noise Ratio

scholarly journals Pemampatan Citra Pas Foto dengan Menggunakan Algoritma Kompresi Joint-Photograpic Experts Group (JPEG) dan Principal Component Analysis (PCA)

2018 ◽  
Vol 17 (1) ◽  
pp. 102
Author(s):  
M. Azman Maricar ◽  
Oka Widyantara
Keyword(s):  
Principal Component Analysis ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

Penelitian ini bertujuan untuk membandingkan hasil kompresi dari algoritma Joint-Photograpic Experts Group (JPEG) dan Principal Component Analysis (PCA) terhadap citra pas foto, guna menemukan hasil terbaik dari hasil citra kompresi yang kualitas hasilnya tidak berbeda jauh dengan citra aslinya. Alat ukur yang digunakan adalah Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR). Hasil yang diperoleh dalam penelitian ini adalah rata-rata MSE dan PNSR algoritma PCA dapat dikatakan tinggi jika dibandingkan dengan algoritma JPEG. Namun dari segi kualitas citra yang dihasilkan tidak jauh berbeda dengan algoritma JPEG.Dapat dikatakan bahwa algoritma JPEG mampu menghasilkan citra yang lebih baik dibandingkan algoritma PCA. Namun, algoritma PCA tidaklah buruk untuk dijadikan alternatif dalam kompresi citra pas foto.

scholarly journals Comparison of functional and discrete data analysis regimes for Raman spectra

2021 ◽  
Author(s):  
Rola Houhou ◽  
Petra Rösch ◽  
Jürgen Popp ◽  
Thomas Bocklitz
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectra ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Peak Position ◽  
Signal To Noise ◽  
Linear Discriminant ◽  
The Mean ◽  
Raman Spectral Data ◽  
Raman Spectral

AbstractRaman spectral data are best described by mathematical functions; however, due to the spectroscopic measurement setup, only discrete points of these functions are measured. Therefore, we investigated the Raman spectral data for the first time in the functional framework. First, we approximated the Raman spectra by using B-spline basis functions. Afterwards, we applied the functional principal component analysis followed by the linear discriminant analysis (FPCA-LDA) and compared the results with those of the classical principal component analysis followed by the linear discriminant analysis (PCA-LDA). In this context, simulation and experimental Raman spectra were used. In the simulated Raman spectra, normal and abnormal spectra were used for a classification model, where the abnormal spectra were built by shifting one peak position. We showed that the mean sensitivities of the FPCA-LDA method were higher than the mean sensitivities of the PCA-LDA method, especially when the signal-to-noise ratio is low and the shift of the peak position is small. However, for a higher signal-to-noise ratio, both methods performed equally. Additionally, a slight improvement of the mean sensitivity could be shown if the FPCA-LDA method was applied to experimental Raman data.

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