Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction

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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.

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Yannick Lingelbach

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Maschinelles Lernen Industrielle Wärmebehandlung Bainitisieren Einsatzhärten Datenanalyse Machine Learning Industrial Heattreatment Bainitizing Case Hardening Data Mining

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