Fracture and failure behavior of additive manufactured Ti6Al4V lattice structures under compressive load

Li, PeiYao; E., Yu; Sun, Wenbo; Qian, Xudong; Zhang, Weihong; Wang, Zhenhai

doi:10.1016/j.engfracmech.2021.107537

Cited by 40 publications

(9 citation statements)

References 43 publications

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“…FEA is commonly used for simulating the experimental process and validating testing results. Researchers usually conducted FEA for performance evaluation [50][51][52], structure design [53], investigating configurational effects [54], and studying the failure mechanism [55,56]. Digital image correlation (DIC) is a 3D, full-field, non-contact optical technique to measure contour, deformation, vibration, and strain on almost any material.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Compressive and Tensile Behavior of Stainless Steel/Dissolvable Aluminum Bimetallic Composites by Finite Element Modeling and Digital Image Correlation

Ghasri-Khouzani

Bogno

et al. 2021

Materials

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This study reports fabrication, mechanical characterization, and finite element modeling of a novel lattice structure based bimetallic composite comprising 316L stainless steel and a functional dissolvable aluminum alloy. A net-shaped 316L stainless steel lattice structure composed of diamond unit cells was fabricated by selective laser melting (SLM). The cavities in the lattice structure were then filled through vacuum-assisted melt infiltration to form the bimetallic composite. The bulk aluminum sample was also cast using the same casting parameters for comparison. The compressive and tensile behavior of 316L stainless steel lattice, bulk dissolvable aluminum, and 316L stainless steel/dissolvable aluminum bimetallic composite is studied. Comparison between experimental, finite element analysis (FEA), and digital image correlation (DIC) results are also investigated in this study. There is no notable difference in the tensile behavior of the lattice and bimetallic composite because of the weak bonding in the interface between the two constituents of the bimetallic composite, limiting load transfer from the 316L stainless steel lattice to the dissolvable aluminum matrix. However, the aluminum matrix is vital in the compressive behavior of the bimetallic composite. The dissolvable aluminum showed higher Young’s modulus, yield stress, and ultimate stress than the lattice and composite in both tension and compression tests, but much less elongation. Moreover, FEA and DIC have been demonstrated to be effective and efficient methods to simulate, analyze, and verify the experimental results through juxtaposing curves on the plots and comparing strains of critical points by checking contour plots.

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Section: Introductionmentioning

confidence: 99%

Investigation of Compressive and Tensile Behavior of Stainless Steel/Dissolvable Aluminum Bimetallic Composites by Finite Element Modeling and Digital Image Correlation

Ghasri-Khouzani

Bogno

et al. 2021

Materials

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Section: Methodology 31 Entropy Of 3d Modelsmentioning

confidence: 99%

“…Lattice structures (LS), whether inspired by nature or created by mathematicians, are considered promising candidates for lightweight energy absorption and heat dissipation because their unique geometric shape can realise different functions. As FIGURE 1 shows, the most applied LS are body-centred cubic (BCC) [1], face-centred cubic (FCC) [2], BBC with vertical struts (BCCZ) [3], and triply periodic minimal surface (TPMS) structures [4]. Parts composed of LS are designed by arraying the LS unit; however, they are hard to fabricate via traditional manufacturing methods because of their complex interior shapes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties of Three-Dimensional Printed Lattice Structures Through Machine Learning

Tang

Liu

et al. 2021

Journal of Computing and Information Science in Engineering

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Lattice structures (LS) manufactured by 3D printing are widely applied in many areas, such as aerospace and tissue engineering, due to their lightweight and adjustable mechanical properties. It is necessary to reduce costs by predicting the mechanical properties of LS at the design stage since 3D printing is exorbitant at present. However, predicting mechanical properties quickly and accurately poses a challenge. To address this problem, this study proposes a novel method that is applied to different LS and materials to predict their mechanical properties through machine learning. First, this study voxelized 3D models of the LS units and then calculated the entropy vector of each model as the geometric feature of the LS units. Next, the porosity, material density, elastic modulus, and unit length of the lattice unit are combined with entropy as the inputs of the machine learning model. The sample set includes 57 samples collected from previous studies. Support vector regression (SVR) was used in this study to predict the mechanical properties. The results indicate that the proposed method can predict the mechanical properties of LS effectively and is suitable for different LS and materials. The significance of this work is that it provides a method with great potential to promote the design process of lattice structures by predicting their mechanical properties quickly and effectively.

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“…To calculate the entropy of a voxelised part, first, the 3D models of LS units were voxelised into 3D voxels. To avoid too much data and ensure sufficient precision, 20 3 , 50 3 , 100 3 , 150 3 , 200 3 , and 300 3 resolution were tested. The porosities of re-built voxelised models were calculated and compared with the 3d models, thus, 100 × 100 × 100 resolution was adopted in this study.…”

Section: Methodology 31 Entropy Of 3d Modelsmentioning

confidence: 99%

Predicting Mechanical Properties of 3D Printed Lattice Structures

Tang

et al. 2021

Volume 2: 41st Computers and Information in Engineering Conference (CIE)

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Lattice structures (LS) manufactured by 3D printing are widely applied in many areas, such as aerospace and tissue engineering, due to their lightweight and adjustable mechanical properties. It is necessary to reduce costs by predicting the mechanical properties of LS at the design stage since 3D printing is exorbitant at present. However, predicting mechanical properties quickly and accurately poses a challenge. To address this problem, this study proposes a novel method that is applied to different LS and materials to predict their mechanical properties through machine learning. First, this study voxelised 3D models of the LS units and then calculated the entropy vector of each model as the geometric feature of the LS units. Next, the porosity, material density, elastic modulus, and unit length of the lattice unit are combined with entropy as the inputs of the machine learning model. The sample set includes 57 samples collected from previous studies. Support vector regression was used in this study to predict the mechanical properties. The results indicate that the proposed method can predict the mechanical properties of LS effectively and is suitable for different LS and materials. The significance of this work is that it provides a method with great potential to promote the design process of lattice structures by predicting their mechanical properties quickly and effectively.

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Fracture and failure behavior of additive manufactured Ti6Al4V lattice structures under compressive load

Cited by 40 publications

References 43 publications

Investigation of Compressive and Tensile Behavior of Stainless Steel/Dissolvable Aluminum Bimetallic Composites by Finite Element Modeling and Digital Image Correlation

Investigation of Compressive and Tensile Behavior of Stainless Steel/Dissolvable Aluminum Bimetallic Composites by Finite Element Modeling and Digital Image Correlation

Prediction of Mechanical Properties of Three-Dimensional Printed Lattice Structures Through Machine Learning

Predicting Mechanical Properties of 3D Printed Lattice Structures

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