Prediction of Mechanical Properties of Mortise and Tenon Lattice Structures by Fused Deposition Modeling Using Artificial Neural Network
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High strength, lightweight lattice structures are gaining increasing attention in aerospace, automotive, and other fields. Fused deposition modeling (FDM) is a widely used additive manufacturing technique that has significant advantages in the fabrication of lattice structures. However, deposition of inter layers phenomenon affects the mechanical properties of the FDM formed lattice structure, and it is difficult to establish the relationship between the parameters of the lattice structure and the mechanical properties. In this paper, FDM technology was used to prepare 23 groups of mortise and tenon lattice structures (MTLS) with different angles θ , height h and thickness t , and quasi-static compression tests were carried out on them. Artificial neural network (ANN) was used to establish a prediction model of specific energy absorption (SEA) of lattice structures, and the accuracy of the prediction model was verified by experiments. The results show that the SEA of MTLS decreases with increasing θ . With the increase of t and the decrease of h , SEA first increases and then decreases. The SEA values predicted by the ANN with "3-7-1" structure are in good agreement with the experimental values. The ANN tool are validated and can be a favourable tool for lattice energy prediction with available data.
Abstract
High strength, lightweight lattice structures are gaining increasing attention in aerospace, automotive, and other fields. Fused deposition modeling (FDM) is a widely used additive manufacturing technique that has significant advantages in the fabrication of lattice structures. However, deposition of inter layers phenomenon affects the mechanical properties of the FDM formed lattice structure, and it is difficult to establish the relationship between the parameters of the lattice structure and the mechanical properties. In this paper, FDM technology was used to prepare 23 groups of mortise and tenon lattice structures (MTLS) with different angles θ , height h and thickness t , and quasi-static compression tests were carried out on them. Artificial neural network (ANN) was used to establish a prediction model of specific energy absorption (SEA) of lattice structures, and the accuracy of the prediction model was verified by experiments. The results show that the SEA of MTLS decreases with increasing θ . With the increase of t and the decrease of h , SEA first increases and then decreases. The SEA values predicted by the ANN with "3-7-1" structure are in good agreement with the experimental values. The ANN tool are validated and can be a favourable tool for lattice energy prediction with available data.
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