概率逻辑
融合
正多边形
不确定度量化
材料科学
传感器融合
人工智能
计算机科学
数学
机器学习
几何学
哲学
语言学
作者
Gang Zhao,Ruikun Wang,Fangyi Li,Jie Liu
标识
DOI:10.1080/17452759.2024.2324429
摘要
ABSTRACTLaser powder bed fusion (LPBF) is increasingly prominent in essential fields such as aerospace. However, due to the characteristics of the manufacturing process and the high test cost, the performance of fabricated structures is inherently uncertain, leading to a challenging characterisation of their performance. This paper proposes a non-probabilistic convex modelling framework involving confirming uncertainties, processing data, and establishing a non-probabilistic convex model, to quantitatively describe the uncertainties concerning the physical properties of structures fabricated by LPBF. Firstly, the non-probabilistic convex modelling framework is proposed and an efficient uncertainty quantification method is developed utilising the non-probabilistic convex model. Then, criteria are set up for evaluating the performance of the developed method, and computational efficiency and accuracy are illustrated via benchmark numerical examples. Last but not least, two types of representative structures, solid and lattice structures, are fabricated via the LPBF method. The physical properties are tested through tensile and compression experiments. By confirming the necessity of accounting for uncertainties with limited data, we employing the proposed framework to the real representative specimens fabricated via the LPBF method, demonstrating that the structures fabricated by LPBF have substantial uncertainty and the proposed framework is practical.
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