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Research Papers: Design for Manufacture and the Life Cycle

A Knowledge Management System to Support Design for Additive Manufacturing Using Bayesian Networks

[+] Author and Article Information
Yuanbin Wang

Department of Mechanical Engineering,
University of Auckland,
20 Symonds Street,
Auckland 1142, New Zealand
e-mail: ywan821@aucklanduni.ac.nz

Robert Blache

Callaghan Innovation,
69 Gracefield Road,
Lower Hutt 5010, New Zealand
e-mail: robert.blache@callaghaninnovation.govt.nz

Pai Zheng

Department of Mechanical Engineering,
University of Auckland,
20 Symonds Street,
Auckland 1142, New Zealand
e-mail: pzhe539@aucklanduni.ac.nz

Xun Xu

Fellow ASME
Department of Mechanical Engineering,
University of Auckland,
20 Symonds Street,
Auckland 1142, New Zealand
e-mail: xun.xu@auckland.ac.nz

1Corresponding author.

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 11, 2017; final manuscript received January 13, 2018; published online March 14, 2018. Assoc. Editor: Carolyn Seepersad.

J. Mech. Des 140(5), 051701 (Mar 14, 2018) (13 pages) Paper No: MD-17-1625; doi: 10.1115/1.4039201 History: Received September 11, 2017; Revised January 13, 2018

Design for additive manufacturing (DfAM) is gaining increasing attention because of the unique capabilities that additive manufacturing (AM) technologies provide. While they have the ability to produce more complex shapes at no additional cost, AM technologies introduce new constraints. A detailed knowledge of the AM process plays an important role in the design of parts in order to achieve the desired print result. However, research on knowledge management in this area is still limited. The large number of different AM processes, their individual sets of critical parameters and the variation in printing all contribute to a high level of uncertainty in this knowledge domain. Applying AM at the early stages of design projects introduces another source of uncertainty, as requirements are often not well defined at that point. In this paper, a knowledge management system using Bayesian networks (BNs) is proposed to model AM knowledge in cases where there is some uncertainty and fill the knowledge gap between designers and AM technologies. The structure of the proposed model is defined here by introducing the overview layer and detailed information layer. In each layer, different types of nodes and their causal relationships are defined. The system can learn conditional probabilities in the model from different sources of information and inferences can be conducted in both forward and backward directions. To verify the accuracy of the BNs, a sample model for dimensional accuracy in the fused deposition modeling (FDM) process is presented and the results are compared with other methods. A case study is provided to illustrate how the proposed system can help designers with different design questions understand the capabilities of AM processes and find appropriate design and printing solutions.

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Figures

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Fig. 1

Structure of knowledge on additive manufacturing

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Fig. 2

A BN model for surface finish of a printed part using PLA material

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Fig. 3

Updated probability distribution with the evidence of F = f4 and T = t3

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Fig. 4

Workflow of the proposed system

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Fig. 5

Structure of the BN model (“overview layer,” “detailed information layer”)

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Fig. 6

Bayesian network model for the dimensional accuracy of FDM printed part

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Fig. 7

Structure of the developed BN model: (a) overall structure, (b) submodel for material extrusion, and (c) submodel for powder bed fusion (“detailed information layer”)

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Fig. 8

Concept of the mask design

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Fig. 9

Overview of the general properties for (a) material extrusion and (b) powder bed fusion

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Fig. 10

Results from checking dimensional accuracy using (a) backward inference and (b) forward inference

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Fig. 11

Print direction of the design model

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Fig. 12

Features of the concept design model

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Fig. 13

(a) Final design model and (b) printed part

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