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Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

Author
Organization
Abstract
The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.
Keywords
Mechanical Engineering, Biophysics

Citation

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MLA
Cheng, Liangliang, et al. “Quality Inspection of Complex-Shaped Metal Parts by Vibrations and an Integrated Mahalanobis Classification System.” STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2021, doi:10.1177/1475921720979707.
APA
Cheng, L., Yaghoubi Nasrabadi, V., Van Paepegem, W., & Kersemans, M. (2021). Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL. https://doi.org/10.1177/1475921720979707
Chicago author-date
Cheng, Liangliang, Vahid Yaghoubi Nasrabadi, Wim Van Paepegem, and Mathias Kersemans. 2021. “Quality Inspection of Complex-Shaped Metal Parts by Vibrations and an Integrated Mahalanobis Classification System.” STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL. https://doi.org/10.1177/1475921720979707.
Chicago author-date (all authors)
Cheng, Liangliang, Vahid Yaghoubi Nasrabadi, Wim Van Paepegem, and Mathias Kersemans. 2021. “Quality Inspection of Complex-Shaped Metal Parts by Vibrations and an Integrated Mahalanobis Classification System.” STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL. doi:10.1177/1475921720979707.
Vancouver
1.
Cheng L, Yaghoubi Nasrabadi V, Van Paepegem W, Kersemans M. Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL. 2021;
IEEE
[1]
L. Cheng, V. Yaghoubi Nasrabadi, W. Van Paepegem, and M. Kersemans, “Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system,” STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2021.
@article{8685574,
  abstract     = {{The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.}},
  articleno    = {{147592172097970}},
  author       = {{Cheng, Liangliang and Yaghoubi Nasrabadi, Vahid and Van Paepegem, Wim and Kersemans, Mathias}},
  issn         = {{1475-9217}},
  journal      = {{STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL}},
  keywords     = {{Mechanical Engineering,Biophysics}},
  language     = {{eng}},
  title        = {{Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system}},
  url          = {{http://dx.doi.org/10.1177/1475921720979707}},
  year         = {{2021}},
}

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