4.5 Article

An Efficient Procedure for Identifying the Prediction Model Between Residual Stress and Barkhausen Noise

Related references

Note: Only part of the references are listed.
Article Engineering, Industrial

Development of Barkhausen noise calibration blocks for reliable grinding burn detection

Suvi Santa-aho et al.

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2012)

Article Materials Science, Multidisciplinary

Barkhausen noise characterisation during elastic bending and tensile-compression loading of case-hardened and tempered samples

Suvi Santa-aho et al.

JOURNAL OF MATERIALS SCIENCE (2012)

Article Engineering, Electrical & Electronic

A New Method for Evaluation of Mechanical Stress Using the Reciprocal Amplitude of Magnetic Barkhausen Noise

L. Mierczak et al.

IEEE TRANSACTIONS ON MAGNETICS (2011)

Article Engineering, Electrical & Electronic

Evaluation of Barkhausen Noise and Magnetoacoustic Emission Signals Properties for Plastically Deformed Armco Iron

Leszek Piotrowski et al.

IEEE TRANSACTIONS ON MAGNETICS (2010)

Article Engineering, Electrical & Electronic

Magneto-Acoustic Emission and Magnetic Barkhausen Emission for Case Depth Measurement in En36 Gear Steel

John W. Wilson et al.

IEEE TRANSACTIONS ON MAGNETICS (2009)

Article Automation & Control Systems

A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm

Roberto Kawakami Harrop Galvao et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2008)

Article Materials Science, Multidisciplinary

Effect of hardness and composition gradients on Barkhausen emission in case hardened steel

M Blaow et al.

JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS (2006)

Article Chemistry, Analytical

Enhancing electronic nose performance by sensor selection using a new integer-based genetic algorithm approach

JW Gardner et al.

SENSORS AND ACTUATORS B-CHEMICAL (2005)

Article Automation & Control Systems

A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models

A Alexandridis et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2005)

Article Automation & Control Systems

Correlation ranking procedure for factor selection in PC-ANN modeling and application to ADMETox evaluation

B Hemmateenejad

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2005)

Article Materials Science, Multidisciplinary

Magnetic Barkhausen noise analysis of stress in steel

DM Stewart et al.

CURRENT APPLIED PHYSICS (2004)

Article Nanoscience & Nanotechnology

Effect of deformation in bending on magnetic Barkhausen noise in low alloy steel

M Blaow et al.

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING (2004)

Article Chemistry, Analytical

Cross-validation as the objective function for variable-selection techniques

K Baumann

TRAC-TRENDS IN ANALYTICAL CHEMISTRY (2003)

Article Automation & Control Systems

Comparison of selection methods of explanatory variables in PLS regression with application to manufacturing process data

JP Gauchi et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2001)

Article Automation & Control Systems

The successive projections algorithm for variable selection in spectroscopic multicomponent analysis

MCU Araújo et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2001)

Article Materials Science, Multidisciplinary

Plastic versus elastic deformation effects on magnetic Barkhausen noise in steel

CG Stefanita et al.

ACTA MATERIALIA (2000)

Article Physics, Multidisciplinary

Dynamics of domain magnetization and the Barkhausen effect

DC Jiles

CZECHOSLOVAK JOURNAL OF PHYSICS (2000)