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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.…
Document Type: | Article |
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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): | Creative Commons 4.0 - Namensnennung (CC BY 4.0) |