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Research Papers: Design for Manufacture and the Life Cycle

Towards a Numerical Approach of Finding Candidates for Additive Manufacturing-Enabled Part Consolidation

[+] Author and Article Information
Sheng Yang

Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: sheng.yang@mail.mcgill.ca

Florian Santoro

Department of Ergonomics Design and
Mechanical Engineering,
Université de Technologie of
Belfort-Montbéliard,
Beflort 90010, France
e-mail: florian.santoro@mail.mcgill.ca

Yaoyao Fiona Zhao

Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: yaoyao.zhao@mcgill.ca

1Corresponding author.

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received April 11, 2017; final manuscript received December 7, 2017; published online January 30, 2018. Assoc. Editor: Carolyn Seepersad.

J. Mech. Des 140(4), 041701 (Jan 30, 2018) (13 pages) Paper No: MD-17-1262; doi: 10.1115/1.4038923 History: Received April 11, 2017; Revised December 07, 2017

Part consolidation (PC) is one of the typical design freedoms enabled by additive manufacturing (AM) processes. However, how to select potential candidates for PC is rarely discussed. This deficiency has hindered AM from wider applications in industry. Currently available design guidelines are based on obsolete heuristic rules provided for conventional manufacturing processes. This paper first revises these rules to take account of AM constraints and lifecycle factors so that efforts can be saved and used at the downstream detailed design stage. To automate the implementation of these revised rules, a numerical approach named PC candidate detection (PCCD) framework is proposed. This framework is comprised of two steps: construct functional and physical interaction (FPI) network and PCCD algorithm. FPI network is to abstractly represent the interaction relations between components as a graph whose nodes and edges have defined physical attributes. These attributes are taken as inputs for the PCCD algorithm to verify conformance to the revised rules. In this PCCD algorithm, verification sequence of rules, conflict handling, and the optimum grouping approach with the minimum part count are studied. Compared to manual ad hoc design practices, the proposed PCCD method shows promise in repeatability, retrievability, and efficiency. Two case studies of a throttle pedal and a tripod are presented to show the application and effectiveness of the proposed methods.

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Figures

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Fig. 1

An illustrative example of assigning components into feasible groups

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Fig. 2

PC candidate detection framework

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Fig. 3

Inputs and outputs of PCCD algorithm

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Fig. 4

Pseudo code for steps 1 and 2

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Fig. 5

Kernel function of finding minimum number of node groups

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Fig. 6

A simple example of the break sequence and the restore sequence

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Fig. 7

Pseudo code for steps 6 and 7

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Fig. 8

Exploded view of a throttle pedal

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Fig. 9

FPI matrix G0(V,E,W)

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Fig. 10

Graphic view of PCCD matrix transition: (a) Matrix G1: R4, R6, R5, (b) Matrix G2: R1, R2, R7, R5, (c) Matrix S2: R3, and (d) Matrix S1: R5

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Fig. 11

Final outputs of PCCD algorithm: (a) conflict hub, (b) final node groups, and (c) PCCD result

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Fig. 12

Physical view of a tripod: (a) Bottom section, (b) top section, and (c) tripod

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Fig. 13

Group division result using PCCD kernel function: (a) original network, (b) Renishaw RenAM 500M, (c) SLM500, (d)imaginary size: (a) original FPI network, (b) [350, 250, 250], (c) [500, 365, 280], and (d) [800, 800, 800]

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Fig. 14

Dynamics of searching the minimum grouping solution

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Fig. 15

Final grouping solution with R3 in consideration: (a) Renishaw Ren 500M, (b) SLM500, and (c) imaginary building volume: (a) [350, 250, 250], (b) [500, 365, 280], and (c) [800, 800, 800]

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