This paper demonstrates how Automated Machine Learning (AutoML) methods can be used as effective surrogate models in engineering design problems. To do so, we consider the challenging problem of structurally-performant bicycle frame design and demonstrate across-the-board dominance by AutoML in regression and classification surrogate modeling tasks. We also introduce FRAMED -- a parametric dataset of 4500 bicycle frames based on bicycles designed by practitioners and enthusiasts worldwide. Accompanying these frame designs, we provide ten structural performance values such as weight, displacements under load, and safety factors computed using finite element simulations for all the bicycle frame designs. We formulate two challenging test problems: a performance-prediction regression problem and a feasibility-prediction classification problem. We then systematically search for optimal surrogate models using Bayesian hyperparameter tuning and neural architecture search. Finally, we show how a state-of-the-art AutoML method can be effective for both regression and classification problems. We demonstrate that the proposed AutoML models outperform the strongest gradient boosting and neural network surrogates identified through Bayesian optimization by an improved F1 score of 24\% for classification and reduced mean absolute error by 12.5\% for regression. Our work introduces a dataset for bicycle design practitioners, provides two benchmark problems for surrogate modeling researchers, and demonstrates the advantages of AutoML in machine learning tasks. The dataset and code are provided at \url{http://decode.mit.edu/projects/framed/}.

After a career that included helping Alphabet Inc's Google build out data centers and speeding packages for Amazon.com Inc to customers, Jim Miller is doing what many Silicon Valley executives do after stints at big companies: finding more time to ride his bike. But this bike is a little different. Arevo Inc, a startup with backing from the venture capital arm of the Central Intelligence Agency and where Miller recently took the helm, has produced what it says is the world's first carbon fiber bicycle with 3D-printed frame. Arevo is using the bike to demonstrate its design software and printing technology, which it hopes to use to produce parts for bicycles, aircraft, space vehicles and other applications where designers prize the strength and lightness of so-called'composite' carbon fiber parts but are put off by the high-cost and labor-intensive process of making them.