Transcript PowerPoint-esitys - University of Oulu
USE OF BARKHAUSEN NOISE IN INSPECTION OF THE SURFACE CONDITION OF STEEL COMPONENTS Aki Sorsa
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• • • • Background Barkhausen noise ◦ Origin ◦ Literature ◦ Applications BN Studies ◦ Research problem ◦ Approach ◦ Results ◦ Conclusions Summary
CONTENTS
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BACKGROUND
• • • • Material properties can be measured destructively or non-destructively Destructive methods are not applicable to quality control ◦ non-destructive methods are preferred Non-destructive testing methods: visual inspection, ultrasonics, acoustic emission, magnetic methods etc.
Barkhausen noise (BN) is an intriguing technique for ferromagnetic materials ◦ Fast, low costs, simple equipment 3 28.4.2020
BARKHAUSEN NOISE
ORIGIN
• • • The specimen is placed in an external, varying magnetic field ◦ Magnetic domain wall movements ◦ The walls get trapped behind pinning sites ◦ Rapid and stochastic movements caused by walls breaking out of the pinning sites Rapid movements of the domain walls cause a noise-like signal Wall movements are influenced by material properties 4 28.4.2020
BARKHAUSEN NOISE SIGNAL
1500 1000 500 0 -500 -1000 -1500 0 Barkhausen noise 0.2
0.4
Applied magnetic field 300 200 100 0 -100 0.6
Time (relative)
0.8
-200 1 -300 28.4.2020
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BARKHAUSEN NOISE
LITERATURE
• • • BN has been shown to be very sensitive to microstructure and material properties: residual stress, hardness, etc.
Some feature is calculated from the BN signal and compared to the material property studied ◦ RMS, peak height, width and position Results are usually only qualitative Figure from Sorsa (2013) 6 28.4.2020
BARKHAUSEN NOISE
APPLICATIONS
• • • Material characterisation ◦ Quality control Case depth evaluation ◦ Remaining layer thickess Grinding burn detection ◦ Soft spot detection 7 28.4.2020
BN STUDIES
RESEARCH PROBLEM
• • • • Changes in material properties cumulate to the BN signal.
◦ How to distinguish the influence of different factors?
Interactions between material properties and BN are complex.
Stochastic phenomenon ◦ Only averaged properties are reproducible.
Indirect measurement ◦ Models are needed ◦ Significance of calculated features?
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BN STUDIES
APPROACH
• • Mathematical models used for describing the interactions between material properties and BN ◦ Residual stress, hardness Identification of the model divided into 4 steps ◦ Feature generation ◦ Feature selection ◦ Model identification ◦ Model validation 9 28.4.2020
BN STUDIES
APPROACH: STEP 1
• Feature generation ◦ BN signal is useless by itself ◦ Information needs to be converted into useful form ◦ Calculation of features with different mathamatical operations - Statistical - RMS, BN energy and entropy - Factors - Features from BN profile ◦ Produces a big set of features (about 150) 10 28.4.2020
BN STUDIES
APPROACH: STEP 2
• Feature selection ◦ The most significant features are case-dependent ◦ Automatic procedures are needed ◦ Deterministic methods - Stepwise selection (forward-selection, backward elimination and their combinations) - May not lead to optimal solution ◦ Stochastic methods - Simulated annealing, genetic algorithms - Reported to give better results 11 28.4.2020
BN STUDIES
APPROACH: STEP 3
• • • • Model identification ◦ Usually carried out simultaneously with feature selection Different model structures can be used ◦ MLR, PLSR, PCR, ANN … Model is identified with the training data set In BN studies, the number of data points is limited ◦ Cross-validation methods are used (efficient data usage) 12 28.4.2020
BN STUDIES
APPROACH: STEP 4
• Model validation ◦ Prediction models are useless if they are valid only for the data they were trained with.
◦ Models must be validated with an independent testing data set ◦ Validation should also include validation of the selected features with expert knowledge 13 28.4.2020
BN STUDIES
RESULTS
• • Material characterisation: prediction of residual stress All studies carried out in cooperation with the Department of Materials Science, Tampere University of technology 3 4 5
Study
1 2
Feature selection
Manual Forward-selection GA Preselection + GA Preselection + exhaustive
Modelling technique
MLR MLR MLR Nonlinear regression ANN
Reference
Sorsa et al. (2012a) Sorsa et al. (2012b) Sorsa et al. (2013a) Sorsa et al. (2014) Sorsa et al. (2013b) 14 28.4.2020
BN STUDIES
RESULTS: STUDY 1
Data set 1 Data set 2 Perfect fit 800 600 400 200 0 -200 -400 -600 -600 R RMSEP -400 -200 0 200
Measured residual stress Training
0.85
57.68 MPa 400
Validation
0.91
139.37 MPa 600 800 28.4.2020
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BN STUDIES
RESULTS: STUDY 2
Data set 1 Data set 2 Perfect fit 800 600 400 200 0 -200 -400 -600 -600 -400 -200 0 200 400
Measured residual stress [MPa]
600 800 R RMSEP
Training
0.87
53.11 MPa
Validation
0.94
111.82 MPa 28.4.2020
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BN STUDIES
RESULTS: STUDY 3
b)
Training 200 0 -200 -400 -600 -800 -1000 -1000 -800 External validation -600 -400 -200 Perfect fit
Measured residual stress [MPa]
0 200 R RMSEP
Training
0.95
75.62 MPa
Validation
0.96
93.80 MPa 28.4.2020
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BN STUDIES
RESULTS: STUDY 4
18 R RMSEP
Training
0.96
51.95 MPa
Validation
0.92
91.16 MPa 28.4.2020
BN STUDIES
RESULTS: STUDY 5
b) RBF 0 -200 -400 -600 -800 -1000 -1000 training testing R RMSEP -800 -600 -400
Measured residual stress (MPa)
-200
Training
0.88
51.1 MPa
Validation
0.93
45.8 MPa 28.4.2020
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BN STUDIES
RESULTS: CONCLUSIONS 1/2
• Feature selection ◦ Manual selection - Reasonable results but not as good as with other methods - Features based on expert knowledge ◦ Deterministic methods - Efficient - Good results but not as good as with stochastic methods ◦ Stochastic methods - Best results - Computationally expensive 20 28.4.2020
BN STUDIES
RESULTS: CONCLUSIONS 2/2
• Model structures ◦ Linear / MLR - Not as good results as with nonlinear - Robust - Main interactions - Computationally efficient ◦ ◦ Nonlinear regression - Not as good results as with ANN - Computationally expensive - More robust than ANN ANN - Best results - Computationally expensive - Risk of overfitting 21 28.4.2020
SUMMARY
• • • BN is a non-destructive testing method suitable for ferromagnetic materials ◦ Sensitive to many material properties ◦ Indirect measurement models are needed Evaluation of material properties with mathematical models ◦ 4 steps: feature generation, feature selection, model identification, model validation Results illustrated with residual stress predictions ◦ Reasonable results 22 28.4.2020
REFERENCES
• • • • • • Sorsa A, Ruusunen M, Leiviskä K, Santa-aho S, Vippola M and Lepistö T (2014) An attempt to find an empirical model between Barkhausen noise and stress. Materials Science Forum 768-769: 209-216.
Sorsa A (2013) Prediction of material properties based on non-destructive Barkhausen noise measurement. Doctoral thesis, University of Oulu Graduate School, Acta Universitatis Ouluensis, 122p.
Sorsa A, Leiviskä K, Santa-aho S, Vippola M and Lepistö T (2013a) An efficient procedure for identifying the prediction model between residual stress and Barkhausen noise. Journal of Nondestructive Evaluation 32(4): 341-349. Sorsa A, Santa-aho S, Vippola M, Lepistö T and Leiviskä K (2013b) A case study of using radial basis function neural networks for predicting material properties from Barkhausen noise signal. Proceedings of 18th Nordic Process Control Workshop, 6p. Sorsa A, Leiviskä K, Santa-aho S and Lepistö T (2012a) A data-based modelling scheme for estimating residual stress from Barkhausen noise measurements. Insight - Non Destructive Testing and Condition Monitoring 54(5): 278-283.
Sorsa A, Leiviskä K, Santa-aho S and Lepistö T (2012b) Quantitative prediction of residual stress and hardness in case-hardened steel based on the Barkhausen noise measurement. NDT&E International 46: 100-106.
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