Quantifying uncertainty in fatigue crack growth of SLM 316L through advanced predictive modeling

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Haselibozchaloee, D (Author) Other; Correia, JAFO (Author) FEUP; Braga, DFO (Author) Other; Cipriano, G (Author) Other; Reis, L (Author) Other; Manuel, L (Author) Other; Moreira, PMGP (Author) Other

Journal

Title: Fatigue and Fracture of Engineering Materials and Structures

Vol. 47

Pages: 3116-3132

ISSN: 8756-758X

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: P-010-M2A

DOI: 10.1111/ffe.14361

Abstract (EN): Optimizing structural designs is crucial today, with additive manufacturing, particularly selective laser melting, gaining prominence. Thorough mechanical characterization of new materials remains vital. This paper investigates fatigue crack growth behavior in SLM 316L specimens under cyclic loading conditions. The study addresses result uncertainties by using CT specimens aligned along three building directions per ASTM E647 standards and a constant loading ratio (R = 0.1), necessitating mean value and confidence interval predictions. Departing from linear prediction models, innovative Bootstrap Polynomial and Power Regression Models and Bayesian Nonlinear Regression Model updated posterior distribution by Markov Chain Monte Carlo are employed. These approaches leverage bootstrapping to construct confidence intervals, offering robustness and flexibility in handling non-normal data behavior and limited sample sizes. They provide tailored fits to data curvature, revealing limitations of linear prediction models in capturing observed nonlinear behavior, enhancing reliability in additive manufacturing applications, and advancing material science and engineering. Fatigue crack growth in SLM316L is evaluated. Robust nonlinear regression techniques are utilized. Distribution approximation is done using Kernel estimator. Confidence intervals are estimated employing Bootstrap and Bayesian regression models.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17