A time-variant reliability analysis framework for selective laser melting fabricated lattice structures with probability and convex hybrid models

ABSTRACT

We propose a time-variant reliability analysis framework to quantitatively predict the lifetime of the lattice structures fabricated by selective laser melting (SLM), including confirming hybrid uncertainties, establishing a hybrid model, and proposing an efficient time-variant reliability method. We first design and manufacture a representative and complex L-shaped body-centred cubic (BCC) lattice structure utilising the SLM method, followed by morphology and microstructure observations to indicate the necessity of accounting for material uncertainty. Further considering loading fluctuation, we develop an effective time-variant reliability analysis method utilising the mixed probability and convex set model. One benchmark numerical example has been employed to shed a light on the high computational efficiency and acceptable computational accuracy of the developed time-variant reliability method. Finally, the proposed framework is performed to a real L-shaped BCC lattice structure to predict its lifetime, finding that the failure probability after ten years can reach more than 40 times the initial design.

KEYWORDS:

• Time-variant reliability analysis framework
• mixed probability and convex set model
• process discretization scheme
• morphology and microstructure observations
• sequential iterative scheme
• SLM

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The raw/processed data required to reproduce these findings cannot be shared at this time due to legal or ethical reasons.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China: (Grant Numbers 12172095, 51905116), the Science and Technology Programme of Guangzhou: (Grant Number 202102010428), and the Natural Science Foundation of Guangdong Province: (Grant Number Nos. 2021A1515010320, 2019A1515011683).

Notes on contributors

Fangyi Li

Fangyi Li received the Ph.D. degree in Mechanical Engineering from Hunan University. He is currently an Associate Professor with the School of Mechanical and Electric Engineering, Guangzhou University. His research interest work focusing on structural uncertainty analysis and optimization.

Ruikun Wang

Ruikun Wang is a lecturer at Guangzhou University. He received his Ph.D. degree from the South China University of Technology in 2017. His current research focuses on corrosion protection of metallic materials and functional nanomaterials. Up to date, he has (co)authored more than 10 SCI papers.

Zhijun Zheng

Zhijun Zheng is a Professor at South China University of Technology. He received his Ph.D. degree from the South China University of Technology in 2012. His current research focuses on Metal Additive Manufacturing Process and Performance Research. Up to date, he has (co)authored more than 50 SCI papers.

Jie Liu

Jie Liu received a Ph.D. degree in mechanical engineering from Hunan University, Changsha, China, in 2017. He was a co-trained Ph.D. student at RMIT University, Melbourne, VIC, Australia, from 2016 to 2017. He is currently an Associate Professor with the School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, China. His research interests include soft robots, structural optimization, and lightweight structure design. He has published more than 30 SCI papers as (co)author.