Optimizing design with extensive simulation data: A case study of designing a vacuum-assisted biopsy tool

Chi Lun Lin, Dane Coffey, Daniel Keefe, Arthur Erdman

Research output: Contribution to journalArticlepeer-review

Abstract

Design by Dragging (DBD) [1] is a virtual design tool, which displays three-dimensional (3D) visualizations of many simulation results obtained by sampling a large design space and ties this visual display together with a new user interface. The design space is explored through mouse-based interactions performed directly on top of the 3D data visualizations. Our previous study [1] introduced the realization of DBD with a simplistic example of biopsy needle design under a static bending force. This paper considers a realistic problem of designing a vacuum-assisted biopsy (VAB) needle that brings in more technical challenges to include dynamic tissue reaction forces, nonlinear tissue deformation, and progressive tissue damage in an integrated visualization with design suggestions. The emphasis is placed on the inverse design strategy in DBD, which involves clicking directly on a stress (or other output field parameter) contour and dragging it to a new (usually preferable) position on the contour. Subsequently, the software computes the best fit for the design variables for generating a new output stress field based on the user input. Three cases demonstrated how the inverse design can assist users in intuitively and interactively approaching desired design solutions. This paper illustrates how virtual prototyping may be used to replace (or reduce reliance on) purely experimental trial-and-error methods for achieving optimal designs.

Original languageEnglish (US)
Article number021007
JournalJournal of Medical Devices, Transactions of the ASME
Volume12
Issue number2
DOIs
StatePublished - Jun 1 2018

Bibliographical note

Funding Information:
The authors would like to thank Michael Nelson, MD and his fellows who provided demonstrations of the VAB tool and shared their invaluable knowledge. The authors would also like to thank the Minnesota Supercomputing Institute for HPC resources and technical supports. National Science Foundation (NSF) (Grant No. IIS-1251069). National Institutes of Health (NIH) (Grant No. 1R01EB018205-01).

Publisher Copyright:
© 2018 by ASME.

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