Cyclic Plasticity and Low Cycle Fatigue of an AISI 316L Stainless Steel: Experimental Evaluation of Material Parameters for Durability Design

AISI 316L stainless steels are widely employed in applications where durability is crucial. For this reason, an accurate prediction of its behaviour is of paramount importance. In this work, the spotlight is on the cyclic response and low-cycle fatigue performance of this material, at room temperature. Particularly, the first aim of this work is to experimentally test this material and use the results as input to calibrate the parameters involved in a kinematic and isotropic nonlinear plasticity model (Chaboche and Voce). This procedure is conducted through a newly developed calibration procedure to minimise the parameter estimates errors. Experimental data are eventually used also to estimate the strain–life curve, namely the Manson–Coffin curve representing the 50% failure probability and, afterwards, the design strain–life curves (at 5% failure probability) obtained by four statistical methods (i.e., deterministic, “Equivalent Prediction Interval”, univariate tolerance interval, Owen’s tolerance interval for regression). Besides the characterisation of the AISI 316L stainless steel, the statistical methodology presented in this work appears to be an efficient tool for engineers dealing with durability problems as it allows one to select fatigue strength curves at various failure probabilities depending on the sought safety level

A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturing

Defects in additively manufactured materials are one of the leading sources of uncertainty in mechanical fatigue. Fracture mechanics concepts are useful to evaluate their influence, nevertheless, these approaches cannot account for the real morphology of defects. Preliminary attempts to exploit a more comprehensive description of defects can be found in the literature, by using Machine Learning. These approaches are notoriously data-hungry and neither physics laws nor phenomenological rules are introduced to assess the soundness of the outcome. Hereby, to overcome this limitation, an approach to predicting fatigue finite life of defective materials, based on a Physics-Informed Neural Network framework, is presented for the first time. The training process of a Neural Network is reinforced by introducing novel Fracture Mechanics constraints. Experimental results obtained from the literature, including detailed defect analysis from computer tomography and fractography, were used to check its accuracy. The proposed predictive tool fully exploits the advanced capabilities of machine learning to account for morphological aspects of defects that could not be accounted for otherwise, while at the same time obeying fracture mechanics laws and requiring a smaller experimental dataset. The approach paves the way for new structural design approaches with an unprecedented degree of accuracy.Team Luca Laurent

On the factors influencing the elastoplastic cyclic response and low cycle fatigue failure of AISI 316L steel produced by laser-powder bed fusion

The growing interest in additively manufactured metals for structural purposes renders their mechanical characterisation of foremost importance. In that context, low cycle fatigue (LCF) properties are lacking, especially for the AISI 316L stainless steel which is the material studied in this work alongside its elastoplastic response. Corroborating fractography analysis unveils premature failures ascribed to the presence of lack-of-fusion defects. A comparison with literature data revealed a generally good agreement in terms of fatigue, while contrasting results emerged for the cyclic response. The study comprehensively outlines critical factors influencing the LCF of the material and provides insights for future improvements