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Title: High cycle fatigue life prediction of laser additive manufactured stainless steel : a machine learning approach
Authors: Zhang, Meng
Sun, Chen-Nan
Zhang, Xiang
Goh, Phoi Chin
Wei, Jun
Hardacre, David
Li, Hua
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Source: Zhang, M., Sun, C.-N., Zhang, X., Goh, P. C., Wei, J., Hardacre, D., & Li, H. (2019). High cycle fatigue life prediction of laser additive manufactured stainless steel : a machine learning approach. International Journal of Fatigue, 128, 105194-. doi:10.1016/j.ijfatigue.2019.105194
Journal: International Journal of Fatigue
Abstract: Variations in the high cycle fatigue response of laser powder bed fusion materials can be caused by the choice of processing and post-processing strategies. The numerous influencing factors arising from the process demand an effective and unified approach to fatigue property assessment. This work examines the use of a neuro-fuzzy-based machine learning method for predicting the high cycle fatigue life of laser powder bed fusion stainless steel 316L. A dataset, consisting of fatigue life data for samples subjected to varying processing conditions (laser power, scan speed and layer thickness), post-processing treatments (annealing and hot isostatic pressing) and cyclic stresses, was constructed for simulating a complex nonlinear input-output environment. The associated fracture mechanisms, including the modes of crack initiation and deformation, were characterised. Two models, by employing the processing/post-processing parameters and the static tensile properties respectively as the inputs, were developed from the training data. Despite the diverse fatigue and fracture properties, the models demonstrated good prediction accuracy when checked against the test data, and the computationally-derived fuzzy rules agree well with understanding of the fracture mechanisms. Direct application of the model to literature results, however, yielded a range of prediction accuracies because of the variability in the reported data. Retraining the model by incorporating the literature results into the dataset led to improved modelling performance.
ISSN: 0142-1123
DOI: 10.1016/j.ijfatigue.2019.105194
Rights: © 2019 Elsevier. All rights reserved. This paper was published in International Journal of Fatigue and is made available with permission of Elsevier.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SC3DP Journal Articles

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