Correlating Variations in the Dynamic Resistance Signature to Weld Strength in Resistance Spot Welding Using Principal Component Analysis

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
David W. Adams ◽  
Cameron D. E. Summerville ◽  
Brendan M. Voss ◽  
Jack Jeswiet ◽  
Matthew C. Doolan
Keyword(s):  
Principal Component Analysis ◽  
Quality Control ◽  
Spot Welding ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Weld Strength ◽  
Dynamic Resistance ◽  
Resistance Spot ◽  
Resistance Spot Welds ◽  
Key Features

Traditional quality control of resistance spot welds by analysis of the dynamic resistance signature (DRS) relies on manual feature selection to reduce the dimensionality prior to analysis. Manually selected features of the DRS may contain information that is not directly correlated to strength, reducing the accuracy of any classification performed. In this paper, correlations between the DRS and weld strength are automatically detected by calculating correlation coefficients between weld strength and principal components of the DRS. The key features of the DRS that correlate to weld strength are identified in a systematic manner. Systematically identifying relevant features of the DRS is useful as the correlations between weld strength and DRS may vary with process parameters.

scholarly journals Development of an HPLC-DAD Method for the Quantification of Ten Compounds from Moringa oleifera Lam. and Its Application in Quality Control of Commercial Products

Molecules ◽  
2020 ◽  
Vol 25 (19) ◽  
pp. 4451
Author(s):  
Ramakwala Christinah Chokwe ◽  
Simiso Dube ◽  
Mathew Muzi Nindi
Keyword(s):  
Principal Component Analysis ◽  
Quality Control ◽  
Moringa Oleifera ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Limit Of Quantification ◽  
Simultaneous Quantification ◽  
Pure Solvent ◽  
Relative Standard ◽  
Moringa Oleifera Lam

An HPLC-DAD separation method for the simultaneous quantification of ten compounds from Moringa oleifera plant was developed. The method was validated with pure solvent and different matrices of M. oleifera products. This method was found to be linear in the concentration range of 1 to 10 mg L−1 for all the compounds in the solvent and from 3 to 10 mg kg−1 in the different matrices. The correlation coefficients ranged between 0.9900 and 0.9999. Intra-day and inter-day variability showed that the developed method is both repeatable and precise with percent relative standard deviation values less than 10% and 20%, respectively. Limits of detection ranged between 0.06 and 0.8 mg L−1 for the solvent and 0.1–1.5 mg kg−1 for the matrices, while the limit of quantification ranged between 0.2 and 2.8 mg L−1 and 0.4–4.8 mg kg−1, respectively. The validated method was applied successfully to thirty-two different M. oleifera products, whereby all ten compounds were detected in one of the samples. Principal component analysis was used to assess the correlation and variance between the products. Variations were observed in products from different regions and from different manufacturers.

scholarly journals Principal component analysis for evaluating a ranking method used in the performance testing in sheep of Morada Nova breed

2015 ◽  
Vol 36 (6) ◽  
pp. 3909
Author(s):  
Michelle Santos da Silva ◽  
Luciana Shiotsuki ◽  
Raimundo Nonato Braga Lôbo ◽  
Olivardo Facó
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Correlation Coefficients ◽  
Performance Testing ◽  
Principal Component ◽  
Component Analysis ◽  
Ranking Method ◽  
Daily Weight Gain ◽  
Meat Production ◽  
Body Depth

A multivariate approach was adopted to evaluate the relationship among traits measured in the performance testing of Morada Nova sheep, verify the efficiency of a ranking method used in these tests and identify the most significant traits for use in future analyses. Data from 150 young rams participating in five versions of the performance tests for the Morada Nova breed were used. Twenty traits were measured in each animal: initial weight (IW), final weight (FW), average daily weight gain (ADG), loin eye area (LEA), scrotal circumference (SC), fat thickness (FT), conformation (C), precocity (Pc), muscularity (M), breed features (BF), legs (L), withers height (WH), chest width (CW), rump height (RH), rump width (RW), rump length (RL), body length (BL), body depth (BD), heart girth (HG) and body condition scoring (BCS). The Pearson’s correlation coefficients ranged from –0.10 to 0.93, with the highest correlations were between body weight variables and morphometric measurements. The three first principal components explained 72.28% of the total variability among all traits. The variables related to animal size defined the first principal component, whereas those related to visual appraisal and suitability for meat production defined the second and third principal components, respectively. The combination of traits from the principal component analysis showed that the ranking method currently used in the performance testing of Morada Nova sheep is efficient for selecting larger rams with better breed features and higher degrees of specialization for meat production.

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