Prediction of anisotropic mechanical properties for lattice structures

Munford, Maxwell J.; Hossain, Umar; Ghouse, Shaaz; Jeffers, Jonathan R.T.

doi:10.1016/j.addma.2020.101041

Cited by 23 publications

(25 citation statements)

References 22 publications

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“…[ 24 ] This may be due to our clinically focused workflow which used clinical CT whereas the fabric tensor data is usually created from micro CT methods. [ 37 ] Fifth, specimens had to be frozen once sliced into cubes and thawed before testing, however, the effects of this were mitigated by ensuring that the minimum number of freeze–thaw cycles were used and that all cubes from a given specimen were tested in the same testing session. There may also have been errors introduced by non‐parallel cube geometry.…”

Section: Resultsmentioning

confidence: 99%

Mapping the Multi‐Directional Mechanical Properties of Bone in the Proximal Tibia

Munford

Jeffers

2020

Adv Funct Materials

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The remodeling behavior of bone is influenced by its mechanical environment. By mapping bone's mechanical properties in detail, orthopedic implants with respect to its mechanical properties could stimulate and harness remodeling to improve patient outcomes. In this study, multiaxial apparent modulus and strength of cadaveric proximal tibial bone are mapped and predicted from computed tomography (CT) derived apparent density. Group differences are identified from testing order, subchondral depth, condyle, and sub-meniscal bone with covariates; age and gender. Axial modulus is 50% greater than the transverse modulus. Medial axial modulus is 30% greater than the lateral side. On the lateral side, axial modulus decreases by 50% from proximal to 25 mm distal. On the medial side, axial modulus remains relatively constant. Differences are quantified for density and multiaxial modulus across all subchondral depths, and different power law relationships are provided for each location. Density explains 75% of variation when grouped by subchondral depth and condyle. Yield strength is well-predicted across all test directions, with density predicting 81% of axial strength variation and no differences over subchondral depth. Quantified mapping of bone multiaxial modulus based on condyle and subchondral depth is shown for the first time in a clinically viable protocol using conventional CT.

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Section: Resultsmentioning

confidence: 99%

Mapping the Multi‐Directional Mechanical Properties of Bone in the Proximal Tibia

Munford

Jeffers

2020

Adv Funct Materials

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“…Other researchers have proposed fitting formulae: Maxwell et al built a multiple linear regression model to predict the mechanical properties of stochastic lattice structures in terms of density, fabric, and eigenvalue. For elastic modulus, the off-axis properties ranged from 4.2% to 13%, and the coefficient of determination R 2 ranged from 0.84 to 0.97; for yield strength, the relative error ranged from 5.1% to 10%, and R 2 ranged from 0.84 to 0.94 [16]. Matteo et al used the Gibson-Ashby equation to study the relationship between the mechanical properties of LS and solid materials [17]: the R 2 values were all greater than 0.98.…”

Section: Literature Review 21 Prediction Methods Of Mechanical Properties Of Lsmentioning

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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“…Dies ermöglicht auch die gezielte Nutzung von Anisotropie in der Bauteilgestaltung. [43] Auch die Querkontraktionszahl kann durch die gezielte Wahl der Elementarzelle beeinflusst werden, bis hin zu auxetischen Materialeigenschaften, also einer negativen Querkontraktionszahl [44,45]. Die Querkontraktionszahl muss dabei nicht homogen über das Bauteil verteilt sein, sondern kann einem definierten Verlauf folgen.…”

Section: Programmierbare Eigenschaftenunclassified