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Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

Sanchez, Salomé; Rengasamy, Divish; Hyde, Christopher J.; Figueredo, Grazziela P.; Rothwell, Benjamin

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Authors

Salomé Sanchez

Divish Rengasamy



Abstract

There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters which affect the material microstructure and consequently the mechanical performance as well. Therefore, it is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process–structure–property–performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters. In this work, the predictive capabilities of ML and its ability to develop process–structure–property relationships are applied to the creep properties of laser powder bed fused alloy 718. The input data for the ML model included the Laser Powder Bed Fusion (LPBF) build parameters used—build orientation, scan strategy and number of lasers—and geometrical material descriptors which were extracted from optical microscope porosity images using image analysis techniques. The ML model was used to predict the minimum creep rate of the Laser Powder Bed Fused alloy 718 samples, which had been creep tested at 650∘C and 600MPa. The ML model was also used to identify the most relevant material descriptors affecting the minimum creep rate of the material (determined by using an ensemble feature importance framework). The creep rate was accurately predicted with a percentage error of 1.40 % in the best case. The most important material descriptors were found to be part density, number of pores, build orientation and scan strategy. These findings show the applicability and potential of using ML to determine and predict the mechanical properties of materials fabricated via different manufacturing processes, and to find process–structure–property relationships in AM. This increases the readiness of AM for use in critical applications.

Citation

Sanchez, S., Rengasamy, D., Hyde, C. J., Figueredo, G. P., & Rothwell, B. (2021). Machine learning to determine the main factors affecting creep rates in laser powder bed fusion. Journal of Intelligent Manufacturing, 32(8), 2353–2373. https://doi.org/10.1007/s10845-021-01785-0

Journal Article Type Article
Acceptance Date May 13, 2021
Online Publication Date May 25, 2021
Publication Date 2021-12
Deposit Date May 26, 2021
Publicly Available Date Mar 29, 2024
Journal Journal of Intelligent Manufacturing
Print ISSN 0956-5515
Electronic ISSN 1572-8145
Publisher Springer Science and Business Media LLC
Peer Reviewed Peer Reviewed
Volume 32
Issue 8
Pages 2353–2373
DOI https://doi.org/10.1007/s10845-021-01785-0
Keywords Industrial and Manufacturing Engineering; Software; Artificial Intelligence
Public URL https://nottingham-repository.worktribe.com/output/5572661
Publisher URL https://www.springerprofessional.de/en/machine-learning-to-determine-the-main-factors-affecting-creep-r/19196914

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