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research-article

A Convolutional Neural Network Model for Predicting a Product's Function, Given Its Form

[+] Author and Article Information
Matthew L. Dering

Computer Science And Engineering, Penn State University, University, PA 16802
mld284@psu.edu

Conrad S. Tucker

Engineering Design and Industrial Engineering, Penn State University, University, PA 16802
ctucker4@psu.edu

1Corresponding author.

ASME doi:10.1115/1.4037309 History: Received February 24, 2017; Revised June 28, 2017

Abstract

Quantifying the ability of a digital design concept to perform a function currently requires the use of costly and intensive solutions such as Computational Fluid Dynamics. To mitigate these challenges, the authors of this work propose a deep learning approach based on 3-Dimensional Convolutions that predicts Functional Quantities of digital design concepts. This work defines the term Functional Quantity to mean a quantitative measure of an artifact's ability to perform a function. Several research questions are derived from this work: i) Are learned 3D Convolutions able to accurately calculate these quantities, as measured by rank, magnitude and accuracy? ii) What do the latent features (that is, internal values in the model) discovered by this network mean? iii) Does this work perform better than other deep learning approaches at calculating Functional Quantities? A case study is presented a proposed network design is tested for its ability to predict several functions (Sitting, Storing Liquid, Emitting Sound, Displaying Images, and Providing Conveyance) based on test form classes distinct from training class. A study is presented which evaluates several approaches to this problem based on a common architecture, with the best approach achieving F Scores of >0.9 in 3 of the 5 functions identified. Testing trained models on novel input also yields accuracy as high as 98% for estimating rank of these functional quantities. This method is also employed to differentiate between decorative and functional head-wear, which yields an 84.4% accuracy and 0.786 precision.

Copyright (c) 2017 by ASME
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