Machine learning mechanical properties of steel sheets from an industrial production route

Millner, Gerfried; Mücke, Manfred; Romaner, Lorenz; Scheiber, Daniel

doi:10.1016/j.mtla.2023.101810

Materialia

2023

DOI: 10.1016/j.mtla.2023.101810

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Machine learning mechanical properties of steel sheets from an industrial production route

Gerfried Millner,

Manfred Mücke,

Lorenz Romaner

et al.

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2024

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Cited by 3 publications

References 22 publications

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Vorantreiben der digitalen und umweltfreundlichen Produktion durch MCL-Aktivitäten

Raninger,

Sistaninia,

Bloder

et al. 2024

Berg Huettenmaenn Monatsh

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Vorantreiben der digitalen und umweltfreundlichen Produktion durch MCL-Aktivitäten

Raninger,

Sistaninia,

Bloder

et al. 2024

Berg Huettenmaenn Monatsh

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Influence of typical elements and heat treatment parameters on hardenability in steel: a review

Wang,

Zhu,

Zhang

et al. 2024

J. Iron Steel Res. Int.

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Tensile strength prediction of steel sheets: an insight into data-driven models, dimensionality reduction, and feature importance

Millner,

Mücke,

Romaner

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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In this work we apply data-driven models for predicting tensile strength of steel coils from chemical composition and process parameters. The data originates from steel production and includes a full chemical analysis, as well as many process parameters and the resulting strength properties from tensile tests. We establish a data pre-processing pipeline, where we apply data cleaning and feature engineering to create a machine-readable dataset suitable for various modeling tasks.We compare prediction quality, complexity and interpretability of pure machine learning models, either with the full feature set or a reduced one. Dimensionality reduction methods are used to reduce the number of features and therefore reduce complexity, either with a smart selection method or feature encoding, where features are combined and the included information is preserved. In order to determine key features of our models, we are investigating feature importance ratings, which can be used as a feature selection criteria.Furthermore, we are highlighting methods to explain predictions and determine the impact of every feature in every observation applicable for any machine learning model.

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Machine learning mechanical properties of steel sheets from an industrial production route

Cited by 3 publications

References 22 publications

Vorantreiben der digitalen und umweltfreundlichen Produktion durch MCL-Aktivitäten

Vorantreiben der digitalen und umweltfreundlichen Produktion durch MCL-Aktivitäten

Influence of typical elements and heat treatment parameters on hardenability in steel: a review

Tensile strength prediction of steel sheets: an insight into data-driven models, dimensionality reduction, and feature importance

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