arrow
Return

Compatibility in microstructural optimization for additive manufacturing

delete2019-03-01
delete126
PRE
AI
G
Garner, Eric
H
H.M.A. Kolken
W
Wang, Charlie C. L.
Z
Zadpoor, Amir A.
J
Jun Wu *
DOI:10.1016/j.addma.2018.12.007delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Microstructures with spatially-varying properties such as trabecular bone are widely seen in nature. These functionally graded materials possess smoothly changing microstructural topologies that enable excellent micro and macroscale performance. The fabrication of such microstructural materials is now enabled by additive manufacturing (AM). A challenging aspect in the computational design of such materials is ensuring compatibility between adjacent microstructures. Existing works address this problem by ensuring geometric connectivity between adjacent microstructural unit cells. In this paper, we aim to find the optimal connectivity between topology optimized microstructures. Recognizing the fact that the optimality of connectivity can be evaluated by the resulting physical properties of the assemblies, we propose to consider the assembly of adjacent cells together with the optimization of individual cells. In particular, our method simultaneously optimizes the physical properties of the individual cells as well as those of neighbouring pairs, to ensure material connectivity and smoothly varying physical properties. We demonstrate the application of our method in the design of functionally graded materials for implant design (including an implant prototype made by AM), and in the multiscale optimization of structures.
Keywords:
Topology optimization
Inverse homogenization
Functionally graded materials
Multiscale optimization
Compatible microstructures
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Additive Manufacturing cover
Additive Manufacturing
IF:
11.1
Papers:
4.6K
Citations:
4.9W

Organization

D
Delft University of Technology
Scholars:
2.6W
Papers: 2.5W
Citations: 3.8W
Cited Papers

Cited Papers

Math Education with Large Language Models: Peril or Promise?
err2023-01-01
err0
PREAI
errHarsh Kumar; David M. Rothschild; Daniel G. Goldstein; Jake Hofman
errShare
errSave
errShare
errSave
Microstructure interpolation for macroscopic design
err2015-10-24
err69
PREAI
errCramer, Andrew D.; Challis, Vivien J.; Roberts, Anthony P.
errShare
errSave
researcher View more