Long-Term Ultrasonic Benchmarking for Microstructure Characterization with Bayesian Updating
Ultrasonic non-destructive characterization is an appealing technique for identifying the microstructures of materials in place of destructive testing. However, the existing ultrasonic characterization techniques do not have sufficient long-term gage repeatability and reproducibility (GR&R), sin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/12/7/1088 |
| _version_ | 1797416991646023680 |
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| author | Feng Zhang Yongfeng Song Xiongbing Li Peijun Ni |
| author_facet | Feng Zhang Yongfeng Song Xiongbing Li Peijun Ni |
| author_sort | Feng Zhang |
| collection | DOAJ |
| description | Ultrasonic non-destructive characterization is an appealing technique for identifying the microstructures of materials in place of destructive testing. However, the existing ultrasonic characterization techniques do not have sufficient long-term gage repeatability and reproducibility (GR&R), since benchmarking data are not updated. In this study, a hierarchical Bayesian regression model was utilized to provide a long-term ultrasonic benchmarking method for microstructure characterization, suitable for analyzing the impacts of experimental setups, human factors, and environmental factors on microstructure characterization. The priori distributions of regression parameters and hyperparameters of the hierarchical model were assumed and the Hamilton Monte Carlo (HMC) algorithm was used to calculate the posterior distributions. Characterizing the nodularity of cast iron was used as an example, and the benchmarking experiments were conducted over a 13-week transition period. The results show that updating a hierarchical model can increase its performance and robustness. The outcome of this study is expected to pave the way for the industrial uptake of ultrasonic microstructure characterization techniques by organizing a gradual transition from destructive sampling inspection to non-destructive one-hundred-percent inspection. |
| first_indexed | 2024-03-09T06:13:27Z |
| format | Article |
| id | doaj.art-2f6b8a02c5aa469c9eff7454680600b4 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T06:13:27Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-2f6b8a02c5aa469c9eff7454680600b42023-12-03T11:56:07ZengMDPI AGMetals2075-47012022-06-01127108810.3390/met12071088Long-Term Ultrasonic Benchmarking for Microstructure Characterization with Bayesian UpdatingFeng Zhang0Yongfeng Song1Xiongbing Li2Peijun Ni3School of Traffic and Transportation Engineering, Central South University, Changsha 410075, ChinaSchool of Traffic and Transportation Engineering, Central South University, Changsha 410075, ChinaSchool of Traffic and Transportation Engineering, Central South University, Changsha 410075, ChinaInner Mongolia Metallic Materials Research Institute, Ningbo 315103, ChinaUltrasonic non-destructive characterization is an appealing technique for identifying the microstructures of materials in place of destructive testing. However, the existing ultrasonic characterization techniques do not have sufficient long-term gage repeatability and reproducibility (GR&R), since benchmarking data are not updated. In this study, a hierarchical Bayesian regression model was utilized to provide a long-term ultrasonic benchmarking method for microstructure characterization, suitable for analyzing the impacts of experimental setups, human factors, and environmental factors on microstructure characterization. The priori distributions of regression parameters and hyperparameters of the hierarchical model were assumed and the Hamilton Monte Carlo (HMC) algorithm was used to calculate the posterior distributions. Characterizing the nodularity of cast iron was used as an example, and the benchmarking experiments were conducted over a 13-week transition period. The results show that updating a hierarchical model can increase its performance and robustness. The outcome of this study is expected to pave the way for the industrial uptake of ultrasonic microstructure characterization techniques by organizing a gradual transition from destructive sampling inspection to non-destructive one-hundred-percent inspection.https://www.mdpi.com/2075-4701/12/7/1088ultrasonic benchmarkingmicrostructure characterizationBayesian updatinghierarchical regression model |
| spellingShingle | Feng Zhang Yongfeng Song Xiongbing Li Peijun Ni Long-Term Ultrasonic Benchmarking for Microstructure Characterization with Bayesian Updating Metals ultrasonic benchmarking microstructure characterization Bayesian updating hierarchical regression model |
| title | Long-Term Ultrasonic Benchmarking for Microstructure Characterization with Bayesian Updating |
| title_full | Long-Term Ultrasonic Benchmarking for Microstructure Characterization with Bayesian Updating |
| title_fullStr | Long-Term Ultrasonic Benchmarking for Microstructure Characterization with Bayesian Updating |
| title_full_unstemmed | Long-Term Ultrasonic Benchmarking for Microstructure Characterization with Bayesian Updating |
| title_short | Long-Term Ultrasonic Benchmarking for Microstructure Characterization with Bayesian Updating |
| title_sort | long term ultrasonic benchmarking for microstructure characterization with bayesian updating |
| topic | ultrasonic benchmarking microstructure characterization Bayesian updating hierarchical regression model |
| url | https://www.mdpi.com/2075-4701/12/7/1088 |
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