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Research Papers: Design Theory and Methodology

Design Heuristics for Additive Manufacturing Validated Through a User Study1

[+] Author and Article Information
Alexandra Blösch-Paidosh

Engineering Design and Computing Laboratory,
Department for Mechanical and
Process Engineering,
ETH Zürich Tannenstrasse 3,
Zürich 8092, Switzerland
e-mail: apaidosh@ethz.ch

Kristina Shea

Fellow ASME
Engineering Design and Computing Laboratory,
Department for Mechanical and Process Engineering,
ETH Zürich Tannenstrasse 3,
Zürich 8092, Switzerland
e-mail: kshea@ethz.ch

1A shorted version of this paper has been accepted to the International Design Engineering Technical Conferences and the Computers and Information in Engineering Conference (IDECT/CIE) 2018 in Quebec, Canada. It is titled “Preliminary User Study on Design Heuristics for Additive Manufacturing.” Paper Number: DETC2018-85908.

2Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received December 22, 2017; final manuscript received July 30, 2018; published online January 11, 2019. Assoc. Editor: Carolyn Seepersad.

J. Mech. Des 141(4), 041101 (Jan 11, 2019) (8 pages) Paper No: MD-17-1838; doi: 10.1115/1.4041051 History: Received December 22, 2017; Revised July 30, 2018

Additive manufacturing (AM) has unique capabilities when compared to traditional manufacturing, such as shape, hierarchical, functional, and material complexity, a fact that has fascinated those in research, industry, and the media for the last decade. Consequently, designers would like to know how they can incorporate AM's special capabilities into their designs but are often at a loss as how to do so. Design for additive manufacturing (DfAM) methods are currently in development, but the vast majority of existing methods are not tailored to the needs and knowledge of designers in the early stages of the design process. Therefore, we propose a set of process-independent design heuristics for AM aimed at transferring the high-level knowledge necessary for reasoning about functions, configurations, and parts to designers. Twenty-nine design heuristics for AM are derived from 275 AM artifacts. An experiment is designed to test their efficacy in the context of a redesign scenario with novice designers. The heuristics are found to positively influence the designs generated by the novice designers and are found to be more effective at communicating DfAM concepts in the early phases of redesign than a lecture on DfAM alone. Future research is planned to validate the impact with expert designers and in original design scenarios.

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Figures

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Fig. 1

The existing DfAM research in relation to the product development process

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Fig. 6

Mean cumulative number of heuristics utilized (with heuristics) or appearing (all groups, all parts) per participant with the 95% confidence interval indicated by the error bars

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Fig. 7

Normalized cumulative number of interpreted heuristic appearances

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Fig. 2

Design heuristics for AM derivation method

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Fig. 5

Normalized cumulative number of indicated heuristic utilizations

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Fig. 4

Experimental design task

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Fig. 3

Diagram describing experimental procedure

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Fig. 8

Mean number of design modifications per participant with the 95% confidence intervals indicated by the error bars

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Fig. 9

Figure indicating some common (indicated by bold text) and unique design modifications generated by the participants. The relevant design heuristics are also indicated.

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