Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction
This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift c...
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| フォーマット: | Online |
| 言語: | 英語 |
| 出版事項: |
KIT Scientific Publishing
2024
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| 主題: | |
| オンライン･アクセス: | https://library.oapen.org/handle/20.500.12657/92444 |
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| _version_ | 1869523832653479936 |
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| author | Lingelbach, Yannick |
| author_browse | Lingelbach, Yannick |
| author_facet | Lingelbach, Yannick |
| author_sort | Lingelbach, Yannick |
| collection | Directory of Open Access Books |
| description | This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework. |
| format | Online |
| id | doab-20.500.12854ir-142637 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1426372025-05-27T06:17:22Z Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction Lingelbach, Yannick Data Mining; Case Hardening; Bainitizing; Industrial Heattreatment; Machine Learning; Datenanalyse; Einsatzhärten; Bainitisieren; Industrielle Wärmebehandlung; Maschinelles Lernen thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework. 2024-07-31T05:49:06Z 2024-07-31T05:49:06Z 2024-07-29T08:30:58Z 2024 book https://library.oapen.org/handle/20.500.12657/92444 9783731513520 https://directory.doabooks.org/handle/20.500.12854/142637 eng Schriftenreihe des Instituts für Angewandte Materialien, Karlsruher Institut für Technologie open access image/jpeg image/jpeg image/jpeg image/jpeg Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International https://library.oapen.org/bitstream/20.500.12657/92444/1/application-of-data-mining-and-machine-learning-methods-to-industrial-heat-treatment-processes-for-hardness-prediction.pdf https://library.oapen.org/bitstream/20.500.12657/92444/1/application-of-data-mining-and-machine-learning-methods-to-industrial-heat-treatment-processes-for-hardness-prediction.pdf https://library.oapen.org/bitstream/20.500.12657/92444/1/application-of-data-mining-and-machine-learning-methods-to-industrial-heat-treatment-processes-for-hardness-prediction.pdf https://library.oapen.org/bitstream/20.500.12657/92444/1/application-of-data-mining-and-machine-learning-methods-to-industrial-heat-treatment-processes-for-hardness-prediction.pdf KIT Scientific Publishing 10.5445/KSP/1000169018 10.5445/KSP/1000169018 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731513520 AG Universitätsverlage 278 open access |
| spellingShingle | Data Mining; Case Hardening; Bainitizing; Industrial Heattreatment; Machine Learning; Datenanalyse; Einsatzhärten; Bainitisieren; Industrielle Wärmebehandlung; Maschinelles Lernen thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials Lingelbach, Yannick Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction |
| title | Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction |
| title_full | Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction |
| title_fullStr | Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction |
| title_full_unstemmed | Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction |
| title_short | Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction |
| title_sort | application of data mining and machine learning methods to industrial heat treatment processes for hardness prediction |
| topic | Data Mining; Case Hardening; Bainitizing; Industrial Heattreatment; Machine Learning; Datenanalyse; Einsatzhärten; Bainitisieren; Industrielle Wärmebehandlung; Maschinelles Lernen thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials |
| topic_facet | Data Mining; Case Hardening; Bainitizing; Industrial Heattreatment; Machine Learning; Datenanalyse; Einsatzhärten; Bainitisieren; Industrielle Wärmebehandlung; Maschinelles Lernen thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials |
| url | https://library.oapen.org/handle/20.500.12657/92444 |
| work_keys_str_mv | AT lingelbachyannick applicationofdataminingandmachinelearningmethodstoindustrialheattreatmentprocessesforhardnessprediction |