Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology

German Omar  Barrionuevo; Jose Luis Mullo; Jorge Andres Ramos-Grez

doi:10.1007/S00170-021-07469-6

Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology

German Omar Barrionuevo, Jose Luis Mullo, Jorge Andres Ramos-Grez

Department of Engineering

Research output: Contribution to journal › Article › peer-review

Original language	Undefined/Unknown
Journal	The International Journal of Advanced Manufacturing Technology
DOIs	https://doi.org/10.1007/S00170-021-07469-6
State	Published - 30 Jun 2021

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10.1007/S00170-021-07469-6

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Barrionuevo, G. O., Luis Mullo, J., & Andres Ramos-Grez, J. (2021). Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/S00170-021-07469-6

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title = "Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology",

author = "Barrionuevo, {German Omar} and {Luis Mullo}, Jose and {Andres Ramos-Grez}, Jorge",

year = "2021",

month = jun,

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doi = "10.1007/S00170-021-07469-6",

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journal = "The International Journal of Advanced Manufacturing Technology",

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Barrionuevo, GO, Luis Mullo, J & Andres Ramos-Grez, J 2021, 'Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology', The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/S00170-021-07469-6

Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology. / Barrionuevo, German Omar ; Luis Mullo, Jose; Andres Ramos-Grez, Jorge.
In: The International Journal of Advanced Manufacturing Technology, 30.06.2021.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology

AU - Barrionuevo, German Omar

AU - Luis Mullo, Jose

AU - Andres Ramos-Grez, Jorge

PY - 2021/6/30

Y1 - 2021/6/30

UR - https://publons.com/wos-op/publon/48326150/

U2 - 10.1007/S00170-021-07469-6

DO - 10.1007/S00170-021-07469-6

M3 - Artículo

JO - The International Journal of Advanced Manufacturing Technology

JF - The International Journal of Advanced Manufacturing Technology

ER -

Barrionuevo GO, Luis Mullo J, Andres Ramos-Grez J. Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology. The International Journal of Advanced Manufacturing Technology. 2021 Jun 30. doi: 10.1007/S00170-021-07469-6

Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology

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