Research Papers

Optimizing Cutting Planes for Advanced Joining and Additive Manufacturing

[+] Author and Article Information
Brandon Massoni

Mechanical, Industrial,
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331

Matthew I. Campbell

Mechanical, Industrial,
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: matt.campbell.oregonstate.edu

1Corresponding author.

Manuscript received December 13, 2016; final manuscript received October 24, 2017; published online December 21, 2017. Assoc. Editor: Sam Anand.

J. Manuf. Sci. Eng 140(3), 031001 (Dec 21, 2017) (9 pages) Paper No: MANU-16-1640; doi: 10.1115/1.4038509 History: Received December 13, 2016; Revised October 24, 2017

While additive manufacturing allows more complex shapes than conventional manufacturing processes, there is a clear benefit in leveraging both new and old processes in the definition of metal parts. For example, one could create complex part shapes where the main “body” is defined by extrusion and machining, while small protruding features are defined by additive manufacturing. This paper looks at how optimization and geometric reasoning can be combined to identify cutting planes within complex three-dimensional (3D) shapes. These cutting planes are used to divide realistic mechanical parts into subparts that can be joined together through additive manufacturing or linear friction welding (LFW). The optimization method presents possible manufacturing alternatives to an engineering designer where optimality is defined as a minimization of cost. The paper presents and compares several cutting planes identification methods and describes how the optimization finds the optimal results for several example parts.

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Fig. 1

Traditional stock material versus wire feed and LFW (all prior to machining)

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Fig. 2

Isometric view of a test part, beam A

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Fig. 3

Graph of area decomposition with largest changes in area locating two possible cutting planes

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Fig. 4

Graph of area decomposition along X-axis for beam A

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Fig. 5

Graph of volume reduction along X-axis for beam A

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Fig. 6

Cutting plane with highest volume reduction

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Fig. 7

(top) Bracket with dependent planes, (bottom) graph of area decomposition with possible volume reduction shown as the center red box

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Fig. 8

Results for bearing bracket (522 vertices, volume = 1500 cm3)

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Fig. 9

Results for angled arm (3734 vertices, volume = 30 cm3)

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Fig. 10

Results for square support (1274 vertices, volume = 8670 cm3)

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Fig. 11

Results for beam A (14,631 vertices, volume = 4700 cm3)



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