TY  - JOUR
A1  - Lizarazu, Jorge
A1  - Harirchian, Ehsan
A1  - Shaik, Umar Arif
A1  - Shareef, Mohammed
A1  - Antoni-Zdziobek, Annie
A1  - Lahmer, Tom
T1  - Application of machine learning-based algorithms to predict the stress-strain curves of additively manufactured mild steel out of its microstructural characteristics
JF  - Results in Engineering
N2  - 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, 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.
KW  - Maschinelles Lernen
KW  - Baustahl
KW  - Spannungs-Dehnungs-Beziehung
KW  - Arc-direct energy deposition
KW  - Mild steel
KW  - Dual phase steel
KW  - Stress-strain curve
KW  - OA-Publikationsfonds2023
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20231207-65028
UR  - https://www.sciencedirect.com/science/article/pii/S2590123023007144
VL  - 2023
IS  - Volume 20 (2023)
SP  - 1
EP  - 12
PB  - Elsevier
CY  - Amsterdam
ER  -