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Research Papers: Design for Manufacture and the Life Cycle

A Study of Design Fixation Related to Additive Manufacturing

[+] Author and Article Information
Esraa S. Abdelall

Department of Industrial and Manufacturing
System Engineering,
Iowa State University,
3004 Black Engineering 2529 Union Drive,
Ames, IA 50011
e-mail: abdelallesra@gmail.com

Matthew C. Frank

Department of Industrial and Manufacturing
System Engineering,
Iowa State University,
3004 Black Engineering 2529 Union Drive,
Ames, IA 50011
e-mail: mfrank@iastate.edu

Richard T. Stone

Department of Industrial and Manufacturing
System Engineering,
Iowa State University,
3004 Black Engineering 2529 Union Drive,
Ames, IA 50011
e-mail: rstone@iastate.edu

1Corresponding author.

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received July 11, 2017; final manuscript received January 5, 2018; published online February 27, 2018. Assoc. Editor: Carolyn Seepersad.

J. Mech. Des 140(4), 041702 (Feb 27, 2018) (10 pages) Paper No: MD-17-1472; doi: 10.1115/1.4039007 History: Received July 11, 2017; Revised January 05, 2018

This study aims to understand the effect of additive manufacturing (AM) on design fixation. Whereas previous research illustrates the positive aspects of AM, the overarching hypothesis of this work is that it might also have negative effects with respect to conventional manufacturability. In this work, participants from two groups, a design for conventional manufacturing (DfCM) group, and a design for additive manufacturing (DfAM) group, were asked to design a basic product. Then, a second iteration of the design asked both groups to design for conventional processes, and to include subtractive and formative methods like machining and casting, respectively. Findings showed that the DfAM fixated on nonproducible manufacturing features and produced harder to conventionally manufacture designs, even when told specifically to DfCM. There was also evidence that the complex designs of the DfAM group limited their modeling success and seemed to encourage them to violate more design constraints. This study draws attention to the negative effect of AM knowledge on designers and provides motivation for treatment methods. This is important if AM is used in prototyping or short run production of parts that are slated for conventional manufacturing later. The issue of design fixation is not a problem if AM is the final manufacturing method—a more common practice nowadays. This work suggests that one should consider the possibility of fixation in design environments where AM precedes larger volume conventional manufacturing.

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Figures

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Fig. 1

Effect of manufacturing process selection on design complexity, jet engine bracket designed for conventional processes (left), and same jet engine bracket designed for additive manufacturing technologies (right). Photo courtesy of General Electric, used with permission.

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Fig. 3

Examples of ideas created by two participants hand sketches (left), their 3D CAD models (right)

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Fig. 4

Part of the Genealogy tree for the DFCM group indicating opening without pain function

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Fig. 5

Average novelty score for designs created by conventional versus additive group. Error bar represents ±1 standard error.

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Fig. 6

Average functionality score of designs created by conventional versus additive group. Error bar represents ±1 standard error.

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Fig. 7

Average manufacturability score of designs created by conventional and additive groups (both original and modified). Error bar represents ± 1 standard error.

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Fig. 8

Examples of designs created by the DfAM group (a, b) and the DfCM group (c, d)

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Fig. 9

Average number of nonproducible features in designs. Error bar represents ±1 standard error.

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Fig. 10

Average frustration score for DFAM group versus DFM group. Error bar represents ±1 standard error.

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Fig. 11

Average redesign difficulty score for DFAM group versus DFM group (U = 159.5, p = 0.2133, one tailed). Error bar represents ±1 standard error.

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Fig. 12

Average success percentage in modeling designs for the DfAM group versus DfCM group (U = 118.5, p = 0.0367, one tailed test). Error bar represents ±1 standard error.

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Fig. 13

Percentage of constraints violated DFAM versus DFM Error bar represents ±1 standard error

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