Evaluating the Stress-Strain Relationship of the Additively Manufactured Lattice Structures

Zhang, Long, Bibi, Farzana, Hussain, Imtiyaz, Sultan, Muhammad, Arshad, Adeel, Hasnain, Saqib, Alarifi, Ibrahim M., Alamir, Mohammed A. and Sajjad, Uzair (2022) Evaluating the Stress-Strain Relationship of the Additively Manufactured Lattice Structures. Micromachines, 14 (1). p. 75. ISSN 2072-666X

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Official URL: https://doi.org/10.3390/mi14010075


Extensive amount of research on additively manufactured (AM) lattice structures has been made to develop a generalized model that can interpret how strongly operational variables affect mechanical properties. However, the currently used techniques such as physics models and multi-physics simulations provide a specific interpretation of those qualities, and are not general enough to assess the mechanical properties of AM lattice structures of different topologies produced on different materials via several fabrication methods. To tackle this problem, this study develops an optimal deep learning (DL) model based on more than 4000 data points, which has been optimized by analyzing three different hyper-parameters optimization schemes including gradient boost regression trees (GBRT), gaussian process (GP), and random forest (RF) with different data distribution schemes such as normal distribution, nth root transformation, and robust scaler. With the robust scaler and nth root transformation, the accuracy of the model increases from R2 = 0.85 (for simple distribution) to R2 = 0.94 and R2 = 0.88, respectively. After feature engineering and data correlation, the stress, unit cell size, total height, width, and relative density are chosen to be the input parameters to model the strain. The optimal DL model is able to predict the strain of different topologies of lattices (such as circular, octagonal, Gyroid, truncated cube, Truncated cuboctahedron, Rhombic do-decahedron, and many others) with decent accuracy (R2 = 0.936, MAE = 0.05, and MSE = 0.025). The parametric sensitivity analysis and explainable artificial intelligence (by using DeepSHAP library) based insights confirm that stress is the most sensitive input to the strain followed by the relative density from the modeling perspective of the AM lattices. The findings of this study would be helpful for the industry and the researchers to design AM lattice structures of different topologies for various engineering applications.

Item Type: Article
Additional Information: Funding information: L.Z. has received the funds from the Gansu Provincial University Innovation Fund Project(2020A-218). U.S. has received financial support from the National Science and Technology Council Taiwan under the Contract No. NSTC 111-2222-E-027-009.
Uncontrolled Keywords: additive manufacturing; lattice structure; stress-strain relationship; Bayesian optimization; deep learning; explainable artificial intelligence; mechanical properties
Subjects: H200 Civil Engineering
H300 Mechanical Engineering
H700 Production and Manufacturing Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Elena Carlaw
Date Deposited: 10 Feb 2023 15:53
Last Modified: 10 Feb 2023 16:00
URI: https://nrl.northumbria.ac.uk/id/eprint/51380

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