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research-article

Design Heuristics for Additive Manufacturing Validated Through a User Study

[+] Author and Article Information
Alexandra Bloesch-Paidosh

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

Kristina Shea

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

1Corresponding author.

ASME doi:10.1115/1.4041051 History: Received December 22, 2017; Revised July 30, 2018

Abstract

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 re-design 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 re-design than a lecture on DfAM alone. Future research is planned to validate the impact with expert designers and in original design scenarios.

Copyright (c) 2018 by ASME
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