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Research Papers: D3 Methods

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 Park, PA 16802
e-mail: mld284@psu.edu

Conrad S. Tucker

Engineering Design and Industrial Engineering,
Penn State University,
University Park, PA 16802
e-mail: ctucker4@psu.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 24, 2017; final manuscript received June 28, 2017; published online October 2, 2017. Assoc. Editor: Charlie C. L. Wang.

J. Mech. Des 139(11), 111408 (Oct 02, 2017) (14 pages) Paper No: MD-17-1178; doi: 10.1115/1.4037309 History: Received February 24, 2017; Revised June 28, 2017

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 three-dimensional (3D) convolutions that predict 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? In the case study, 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. This study evaluates several approaches to this problem based on a common architecture, with the best approach achieving F scores of >0.9 in three of the five 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 headwear, which yields an 84.4% accuracy and 0.786 precision.

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References

Figures

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Fig. 1

Cross sections of selected layers of a 3D CNN. These show the activations of certain kernels of the layers in the trained network on the voxelized input shownleft.

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Fig. 2

An artificial neuron. The inputs are represented by xi that are multiplied by the weights wi, summed with a bias term b, and activated by a function f to produce an output y. Each layer type principally defines how the inputs are mapped to theprevious layer, along with which activation function is employed. The rest of the terms are learned.

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Fig. 3

Selected 3 × 3 × 3 learned kernels

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Fig. 4

The convolution and pooling operation

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Fig. 5

The scaling scheme of the functional quantity targets

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Fig. 6

Confusion matrix for the binary (qualitative) variation of this network

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Fig. 7

Confusion matrix for the softmax regression variation of this network

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Fig. 8

Confusion matrix for the absolute regression variation of this network

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Fig. 9

Confusion matrix for the aggregated left out classes

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Fig. 10

Performance on this task by VoxNet Network [34]

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Fig. 11

Activations of the left out networks on novel inputs for well performing inputs

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Fig. 12

Headwear from the dataset [33], the left helmet is functional, while center is purely decorative. Right depicts the confusion matrix of this experiment.

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