Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction
Overview & Synopsis
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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www.oapen.org)
Document Information
| Document Title | Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction |
|---|---|
| Author / Organization | Lingelbach, Yannick |
| Subject & Discipline | Data Mining; Case Hardening; Bainitizing; Industrial Heattreatment; Machine Learning; Datenanalyse; Einsatzhärten; Bainitisieren; Industrielle Wärmebehandlung; Maschinelles Lernen |
| Categories | Humanities & Social Sciences |
| Publication Year | 2024 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
OAPEN Library
(www.oapen.org)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-46746 |
| Original Source Link | Visit Source Page on www.oapen.org » |