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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

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

Revista

Título: Fatigue and Fracture of Engineering Materials and Structures

Vol. 47

Páginas: 3116-3132

ISSN: 8756-758X

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 17