Machine learning driven prediction of mechanical properties of rolled aluminum and development of an in-situ quality control method based on electrical resistivity measurement

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@article{102d082e6acd4787b11bb7da02dbf3e0,
title = "Machine learning driven prediction of mechanical properties of rolled aluminum and development of an in-situ quality control method based on electrical resistivity measurement",
author = "Karin Hartl and Marcel Sorger and Helmut Wei{\ss} and Martin Stockinger",
year = "2023",
month = oct,
day = "5",
doi = "10.1016/j.jmapro.2023.09.058",
language = "English",
volume = "106.2023",
pages = "158--177",
journal = "Journal of manufacturing processes",
issn = "0278-6125",
publisher = "Elsevier",
number = "24 November",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Machine learning driven prediction of mechanical properties of rolled aluminum and development of an in-situ quality control method based on electrical resistivity measurement

AU - Hartl, Karin

AU - Sorger, Marcel

AU - Weiß, Helmut

AU - Stockinger, Martin

PY - 2023/10/5

Y1 - 2023/10/5

U2 - 10.1016/j.jmapro.2023.09.058

DO - 10.1016/j.jmapro.2023.09.058

M3 - Article

VL - 106.2023

SP - 158

EP - 177

JO - Journal of manufacturing processes

JF - Journal of manufacturing processes

SN - 0278-6125

IS - 24 November

ER -