Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning Algorithms
X-ray diffractograms of high-speed steels are analyzed using machine learning algorithms to accurately classify various heat treatments. These differently heat-treated steel samples are also investigated by dilatometric analysis and by metallographic analysis in order to label the samples accordingl...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Metals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4701/15/2/194 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850238718146772992 |
|---|---|
| author | Manfred Wiessner Ernst Gamsjäger |
| author_facet | Manfred Wiessner Ernst Gamsjäger |
| author_sort | Manfred Wiessner |
| collection | DOAJ |
| description | X-ray diffractograms of high-speed steels are analyzed using machine learning algorithms to accurately classify various heat treatments. These differently heat-treated steel samples are also investigated by dilatometric analysis and by metallographic analysis in order to label the samples accordingly. Both agglomerative hierarchical clustering and t-distributed stochastic neighbor embedding are employed to automatically classify preprocessed X-ray datasets. The clusters obtained by this procedure agree well with the labeled data. By supervised learning via a support vector machine, hyperplanes are constructed that allow separating the clusters from each other based on the X-ray measurements. The exactness of these hyperplanes is analyzed by cross-validation. The machine learning algorithms used in this work are valuable tools to separate different microstructures based on their diffractograms. It is demonstrated that the separation of martensitic, bainitic, and pearlitic microstructures is possible based on the diffractograms only by means of machine learning algorithms, while the same problem is error-prone when looking at the diffractograms only. |
| format | Article |
| id | doaj-art-e30aae86be354625aacaef919443b042 |
| institution | OA Journals |
| issn | 2075-4701 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj-art-e30aae86be354625aacaef919443b0422025-08-20T02:01:23ZengMDPI AGMetals2075-47012025-02-0115219410.3390/met15020194Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning AlgorithmsManfred Wiessner0Ernst Gamsjäger1DUAL Analytics, Södingberg 282, 8152 Geistthal-Södingberg, AustriaChair of Mechanics, Montanuniversität Leoben, Franz-Josef Str. 18, 8700 Leoben, AustriaX-ray diffractograms of high-speed steels are analyzed using machine learning algorithms to accurately classify various heat treatments. These differently heat-treated steel samples are also investigated by dilatometric analysis and by metallographic analysis in order to label the samples accordingly. Both agglomerative hierarchical clustering and t-distributed stochastic neighbor embedding are employed to automatically classify preprocessed X-ray datasets. The clusters obtained by this procedure agree well with the labeled data. By supervised learning via a support vector machine, hyperplanes are constructed that allow separating the clusters from each other based on the X-ray measurements. The exactness of these hyperplanes is analyzed by cross-validation. The machine learning algorithms used in this work are valuable tools to separate different microstructures based on their diffractograms. It is demonstrated that the separation of martensitic, bainitic, and pearlitic microstructures is possible based on the diffractograms only by means of machine learning algorithms, while the same problem is error-prone when looking at the diffractograms only.https://www.mdpi.com/2075-4701/15/2/194high-speed steelX-ray diffractionagglomerative hierarchical clusteringsupport vector machinet-distributed stochastic neighbor embedding |
| spellingShingle | Manfred Wiessner Ernst Gamsjäger Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning Algorithms Metals high-speed steel X-ray diffraction agglomerative hierarchical clustering support vector machine t-distributed stochastic neighbor embedding |
| title | Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning Algorithms |
| title_full | Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning Algorithms |
| title_fullStr | Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning Algorithms |
| title_full_unstemmed | Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning Algorithms |
| title_short | Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning Algorithms |
| title_sort | characterization of high speed steels experimental data and their evaluation supported by machine learning algorithms |
| topic | high-speed steel X-ray diffraction agglomerative hierarchical clustering support vector machine t-distributed stochastic neighbor embedding |
| url | https://www.mdpi.com/2075-4701/15/2/194 |
| work_keys_str_mv | AT manfredwiessner characterizationofhighspeedsteelsexperimentaldataandtheirevaluationsupportedbymachinelearningalgorithms AT ernstgamsjager characterizationofhighspeedsteelsexperimentaldataandtheirevaluationsupportedbymachinelearningalgorithms |