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Application of machine learning-based algorithms to predict the stress-strain curves of additively manufactured mild steel out of its microstructural characteristics

  • The study presents a Machine Learning (ML)-based framework designed to forecast the stress-strain relationship of arc-direct energy deposited mild steel. Based on microstructural characteristics previously extracted using microscopy and X-ray diffraction, approximately 1000 new parameter sets are generated by applying the Latin Hypercube Sampling Method (LHSM). For each parameter set, aThe study presents a Machine Learning (ML)-based framework designed to forecast the stress-strain relationship of arc-direct energy deposited mild steel. Based on microstructural characteristics previously extracted using microscopy and X-ray diffraction, approximately 1000 new parameter sets are generated by applying the Latin Hypercube Sampling Method (LHSM). For each parameter set, a Representative Volume Element (RVE) is synthetically created via Voronoi Tessellation. Input raw data for ML-based algorithms comprises these parameter sets or RVE-images, while output raw data includes their corresponding stress-strain relationships calculated after a Finite Element (FE) procedure. Input data undergoes preprocessing involving standardization, feature selection, and image resizing. Similarly, the stress-strain curves, initially unsuitable for training traditional ML algorithms, are preprocessed using cubic splines and occasionally Principal Component Analysis (PCA). The later part of the study focuses on employing multiple ML algorithms, utilizing two main models. The first model predicts stress-strain curves based on microstructural parameters, while the second model does so solely from RVE images. The most accurate prediction yields a Root Mean Squared Error of around 5 MPa, approximately 1% of the yield stress. This outcome suggests that ML models offer precise and efficient methods for characterizing dual-phase steels, establishing a framework for accurate results in material analysis.show moreshow less

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  • Gefördert durch das Programm Open-Access-Publikationskosten der DFG und den Publikationsfonds der Bauhaus-Universität Weimar.

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Metadaten
Document Type:Article
Author: Jorge LizarazuORCiD, Dr.-Ing. Ehsan HarirchianORCiDGND, Umar Arif Shaik, Mohammed Shareef, Annie Antoni-Zdziobek, Prof. Dr. Tom LahmerORCiDGND
DOI (Cite-Link):https://doi.org/10.1016/j.rineng.2023.101587Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20231207-65028Cite-Link
URL:https://www.sciencedirect.com/science/article/pii/S2590123023007144
Parent Title (English):Results in Engineering
Publisher:Elsevier
Place of publication:Amsterdam
Language:English
Date of Publication (online):2023/12/06
Date of first Publication:2023/11/27
Release Date:2023/12/07
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Institut für Strukturmechanik (ISM)
Volume:2023
Issue:Volume 20 (2023)
Pagenumber:13
First Page:1
Last Page:12
Tag:OA-Publikationsfonds2023
Arc-direct energy deposition; Dual phase steel; Mild steel; Stress-strain curve
GND Keyword:Maschinelles Lernen; Baustahl; Spannungs-Dehnungs-Beziehung
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften
BKL-Classification:31 Mathematik / 31.80 Angewandte Mathematik
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2023
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)