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Plain2Fun: Augmenting Ordinary Objects with Surface Painted Circuits

Published:20 April 2018Publication History

ABSTRACT

The growing makers' community demands better supports for designing and fabricating interactive functional objects. Most of the current approaches focus on embedding desired functions within new objects. Instead, we advocate re-purposing existing objects and authoring interactive functions onto them. We present Plain2Fun, a design and fabrication pipeline enabling users to quickly transform ordinary objects into interactive and functional ones. Plain2Fun allows users to directly design the circuit layouts onto the surfaces of the scanned 3D model of existing objects. Our design tool automatically generates as short as possible circuit paths between any two points while avoiding intersections. Further, we build a digital machine to construct the conductive paths accurately. With a specially designed housing base, users can simply snap the electronic components onto the surfaces and obtain working physical prototypes. Moreover, we evaluate the usability of our system with multiple use cases.

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References

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    • Published in

      cover image ACM Conferences
      CHI EA '18: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems
      April 2018
      3155 pages
      ISBN:9781450356213
      DOI:10.1145/3170427

      Copyright © 2018 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 20 April 2018

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      Acceptance Rates

      CHI EA '18 Paper Acceptance Rate1,208of3,955submissions,31%Overall Acceptance Rate6,164of23,696submissions,26%

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