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contributor authorRegenwetter, Lyle
contributor authorCurry, Brent
contributor authorAhmed, Faez
date accessioned2022-05-08T08:25:53Z
date available2022-05-08T08:25:53Z
date copyright10/29/2021 12:00:00 AM
date issued2021
identifier issn1050-0472
identifier othermd_144_3_031706.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4283917
description abstractIn this paper, we present “BIKED,” a dataset composed of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and support the development of data-driven design methods. The dataset is comprised of a variety of design information including assembly images, component images, numerical design parameters, and class labels. In this paper, we first discuss the processing of the dataset, and then highlight some prominent research questions that BIKED can help address. Of these questions, we further explore the following in detail: (1) How can we explore, understand, and visualize the current design space of bicycles and utilize this information? We apply unsupervised embedding methods to study the design space and identify key takeaways from this analysis. (2) When designing bikes using algorithms, under what conditions can machines understand the design of a given bike? We train a multitude of classifiers to understand designs, then examine the behavior of these classifiers through confusion matrices and permutation-based interpretability analysis. 3) Can machines learn to synthesize new bicycle designs by studying existing ones? We test Variational Autoencoders on random generation, interpolation, and extrapolation tasks after training on BIKED data. The dataset and code are available online.
publisherThe American Society of Mechanical Engineers (ASME)
titleBIKED: A Dataset for Computational Bicycle Design With Machine Learning Benchmarks
typeJournal Paper
journal volume144
journal issue3
journal titleJournal of Mechanical Design
identifier doi10.1115/1.4052585
journal fristpage31706-1
journal lastpage31706-15
page15
treeJournal of Mechanical Design:;2021:;volume( 144 ):;issue: 003
contenttypeFulltext


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