Discovery of New Materials and Heat Treatments: Accelerated Metallurgy and the Case of Ferrous and Magnesium Alloys

2014 ◽  
Vol 783-786 ◽  
pp. 2188-2193 ◽  
Author(s):  
I. Toda-Caraballo ◽  
Enrique I. Galindo-Nava ◽  
Pedro E.J. Rivera-Díaz-del-Castillo
Keyword(s):  
Mechanical Properties ◽  
Heat Treatment ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Process Models ◽  
Statistical Techniques ◽  
Trial And Error ◽  
New Materials ◽  
Ferrous Alloys ◽  
Expensive Process

Traditionally, the discovery of new materials has been the result of a trial and error process. This has resulted in an extremely time-consuming and expensive process. Models for guiding the discovery of new materials have been developed within the European Accelerated Metallurgy project. The application of statistical techniques to large materials datasets has lead to the discovery of unexpected regularities among their properties. This work focuses on mechanical properties. In particular, the interplay between yield strength, ultimate tensile strength and elongation. A methodology based on principal component analysis, and Kocks-Mecking modelling has led to a tool for finding optimal compositional and heat treatment scenarios. The model is first presented for wide ranges of alloys, and the application to the discovery of new magnesium and ferrous alloys is outlined.

Tactile evaluation of down jacket fabric by the comprehensive handle evaluation system for fabrics and yarns

2020 ◽  
pp. 004051752097720
Author(s):  
Yuan Tian ◽  
Yi Sun ◽  
Zhaoqun Du ◽  
Dongming Zheng ◽  
Haochen Zou ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Principal Component Analysis ◽  
Evaluation System ◽  
Principal Component ◽  
Objective Evaluation ◽  
Component Analysis ◽  
Quality Level ◽  
Fabric Handle ◽  
Good Agreement

Down jacket fabric is greatly important in determining the quality of a down jacket. In order to enrich the research on fabric handle, subjective and objective evaluations were made for down jacket fabrics that were less studied. The comprehensive handle evaluation system for fabrics and yarns (CHES-FY) can be used to evaluate the tactile handle of the fabric by accurately and efficiently measuring the basic mechanical properties of the fabric. Therefore, the CHES-FY was used to link the objective evaluation with the subjective handle, so as to effectively estimate the total handle value of the down jacket fabric. Fifty-two kinds of down jacket fabrics were objectively tested through measuring 17 extracted parameters, and principal component analysis was adopted to establish the five main handle characteristics of fullness, softness, stiffness, smoothness, looseness and tightness to characterize basic style of the down jacket fabrics. The results showed that the subjective and objective results were in good agreement. These characteristics can be used as indicators to characterize fabric performance, and the principal component expression to characterize fabric handle can better predict the handle characteristics of down jacket fabrics. This also proves that the CHES-FY can quickly and accurately obtain the fabric handle value, and can also evaluate the fabric quality level.

Relationships between sites used in the interstate wheat variety trials

1981 ◽  
Vol 32 (5) ◽  
pp. 691 ◽  
Author(s):  
PN Fox ◽  
AJ Rathjen
Keyword(s):  
Principal Component Analysis ◽  
Wheat Variety ◽  
Principal Component ◽  
Component Analysis ◽  
Climatic Variables ◽  
Statistical Techniques ◽  
Strong Similarity ◽  
Variety Trials ◽  
Variety Trial ◽  
The Mean

A combination of statistical techniques was used to present useful information for breeders concerning the 197.5 Interstate Wheat Variety Trial. Grouping of sites was similar for all techniques, but was shown most clearly by the principal component analysis. Within three of the four groups of sites there was strong similarity between members. Some groups included widely geographically separated sites, which suggests that in the final stages of varietal testing, it might be possible to use widely separated sites as an alternative to testing over several years within a region. One group dominated the overall mean yields of the trial because it included more sites and because these sites were more uniform than sites within other groups. This domination, illustrated by regression and ranking techniques, may reduce the value to industry of the Interstate Wheat Variety Trials if these sites are not representative of extensive areas of wheat production. The differences in relative performances of varieties between sites could not be related either to differences in the mean yields at these sites or to edaphic or climatic variables. The need for such analysis of each year's data from the Interstate Wheat Variety Trials is stressed.

Characterization of Balsamic Vinegar by Multivariate Statistical Analysis of Trace Element Content

1998 ◽  
Vol 81 (5) ◽  
pp. 1087-1095 ◽  
Author(s):  
Antonella Del Signore ◽  
Barbara Campisi ◽  
Franco Di Giacomo
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Trace Element Content ◽  
The Other ◽  
Statistical Techniques ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Balsamic Vinegar ◽  
And Cluster Analysis

Abstract To characterize vinegars according to the types prescribed by Italian regulations, 8 trace elements (Cr, Mn, Co, Ni, Cu, Zn, Cd, and Pb) were determined. The data collected were successively elaborated by 3 statistical techniques: linear principal component analysis (LPCA), linear discriminant analysis (LDA), and cluster analysis (CA). LDA and LPCA best classified and discriminated the 3 types of vinegar under study, separating traditional balsamic vinegars from the other 2 types, nontraditionally aged balsamic vinegars and common vinegars. The latter 2 types were appreciably distinguished only by LDA through bidimensional analysis of discriminant scores

Principal Component Analysis Using the Factor Procedure

2010 ◽  
pp. 171-193
Author(s):  
Sean Eom
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Techniques ◽  
Data Input ◽  
Number Of Factors ◽  
Rotation Method ◽  
Definition Of

This chapter describes the factor procedure. The first section of the chapter begins with the definition of factor analysis. This is the statistical techniques whose common objective is to represent a set of variables in terms of a smaller number of hypothetical variables (factor). ACA uses principal component analysis to group authors into several catagories with similar lines of research. We also present many different approaches of preparing datasets including manual data inputs, in-file statement, and permanent datasets. We discuss each of the key SAS statements including DATA, INPUT, CARDS, PROC, and RUN. In addition, we examine several options statements to specify the followings: method for extracting factors; number of factors, rotation method, and displaying output options.

Thermomechanical Behavior of Nonwovens

1976 ◽  
Vol 46 (7) ◽  
pp. 513-518 ◽  
Author(s):  
Charles J. Shimalla ◽  
John C. Whitwell
Keyword(s):  
Mechanical Properties ◽  
Principal Component Analysis ◽  
Bonding Temperature ◽  
Principal Component ◽  
Thermomechanical Behavior ◽  
Thermal Bonding ◽  
Fiber Concentration ◽  
Nonwoven Fabrics ◽  
Quench Temperature ◽  
Chemical Constitution

A systematic study was undertaken to correlate qualitatively a multiplicity of responses of nonwoven fabrics to a selected set of controlled variables. The nonwovens were bonded under heat and pressure using a bilateral bonding fiber mixed with a base fiber. The variables were the chemical constitution of the base fiber, concentration of binder fibers, and three thermal bonding variables: bonding temperature, extent of annealing, and quench temperature. Of 40 responses originally recorded, 23 mechanical properties are analyzed by regression on the first 5 new responses generated by a principal component analysis. The results of this method, which eliminates redundancies in the responses, are compared to the results which would have been obtained using the 23 separate responses. Improvement in ability to quantify effects of the variables was achieved.

Principal Component Analysis and Independent Component Analysis-Based Prognostic Health Monitoring of Electronic Assemblies Subjected to Simultaneous Temperature-Vibration Loads

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas
Keyword(s):  
Principal Component Analysis ◽  
Printed Circuit Board ◽  
Failure Strain ◽  
Principal Component ◽  
Component Analysis ◽  
Circuit Board ◽  
Statistical Techniques ◽  
Time Frequency ◽  
Printed Circuit ◽  
Strain Signal

Abstract This paper discusses methods for estimating different feature vectors from strain signals of an electronic assembly under combined temperature and vibration load. A vibrational load of 14 G acceleration-level with an ambient temperature of 55 °C is selected as the operating conditions for this experiment. Strain signals were measured at different time intervals during the vibration of the printed circuit board, and resistance values of the packages on the printed circuit board are monitored to identify the failure. The frequency response was measured by taking the fast Fourier transform of the signal and quantized by frequency quantization techniques. These techniques were able to identify the increase in the number of higher frequency components in the strain signal before failure with increase vibration time. The time-frequency response was also compared by employing different time–frequency analysis, joint time–frequency analysis, and statistical techniques such as principal component analysis (PCA), and independent component analysis (ICA). Statistical techniques like PCA and ICA were used to identify the different patterns of the original strain and filtered signals. These techniques discretely separated the before and after failure strain signals but were unable to predict the progression of failure in the packages. The instantaneous frequency of the strain signal displayed an interesting behavior, in which the variance of the PCA components of the instantaneous frequency had an increasing trend and reached a maximum value before continuously decreasing and reaching a lower value just before failure, indicating a progression of the before failure strain components.

scholarly journals Water Quality Assessment of Porsuk River, Turkey

2010 ◽  
Vol 7 (2) ◽  
pp. 593-599 ◽  
Author(s):  
Suheyla Yerel
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Water ◽  
Surface Water Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Porsuk River

The surface water quality of Porsuk River in Turkey was evaluated by using the multivariate statistical techniques including principal component analysis, factor analysis and cluster analysis. When principal component analysis and factor analysis as applied to the surface water quality data obtain from the eleven different observation stations, three factors were determined, which were responsible from the 66.88% of total variance of the surface water quality in Porsuk River. Cluster analysis grouped eleven observation stations into two clusters under the similarity of surface water quality parameters. Based on the locations of the observation stations and variable concentrations at these stations, it was concluded that urban, industrial and agricultural discharge strongly affected east part of the region. Finally, this study shows that the usefulness of multivariate statistical techniques for analysis and interpretation of datasets and determination pollution factors for river water quality management.

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