NDLI: Approximate Functionally Graded Materials for Multi-Material Additive Manufacturing

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Leung, Yuen-Shan Mao, Huachao Chen, Yong
Copyright Year	2018
Abstract	Functionally graded materials (FGM) possess superior properties of multiple materials due to the continuous transitions of these materials. Recent progresses in multi-material additive manufacturing (AM) processes enable the creation of arbitrary material composition, which significantly enlarges the manufacturing capability of FGMs. At the same time, the fabrication capability also introduces new challenges for the design of FGMs. A critical issue is to create the continuous material distribution under the fabrication constraints of multi-material AM processes. Using voxels to approximate gradient material distribution could be one plausible way for additive manufacturing. However, current FGM design methods are non-additive-manufacturing-oriented and unpredictable. For instance, some designs require a vast number of materials to achieve continuous transitions; however, the material choices that are available in a multi-material AM machine are rather limited. Other designs control the volume fraction of two materials to achieve gradual transition; however, such transition cannot be functionally guaranteed. To address these issues, we present a design and fabrication framework for FGMs that can efficiently and effectively generate printable and predictable FGM structures. We adopt a data-driven approach to approximate the behavior of FGM using two base materials. A digital material library is constructed with different combinations of the base materials, and their mechanical properties are extracted by Finite Element Analysis (FEA). The mechanical properties are then used for the conversion process between the FGM and the dual material structure such that similar behavior is guaranteed. An error diffusion algorithm is further developed to minimize the approximation error. Simulation results on four test cases show that our approach is robust and accurate, and the framework can successfully design and fabricate such FGM structures.
Sponsorship	Design Engineering Division Computers and Information in Engineering Division
File Format	PDF
ISBN	9780791851722
DOI	10.1115/DETC2018-86391
Volume Number	Volume 1A: 38th Computers and Information in Engineering Conference
Conference Proceedings	ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Language	English
Publisher Date	2018-08-26
Publisher Place	Quebec City, Quebec, Canada
Access Restriction	Subscribed
Subject Keyword	Approximation Design methodology Mechanical properties Additive manufacturing Diffusion (physics) Machinery Design Errors Algorithms Simulation results Functionally graded materials Manufacturing Finite element analysis
Content Type	Text
Resource Type	Article

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