Comparing Attribute- and Form-Based Machine Learning Techniques for Component Prediction
G. Williams, L. Puentes, J. Nelson, J. Menold, C. Tucker, and C. McComb
2020, ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Online data repositories provide designers and engineers with a convenient source of data. Over time, the wealth and type of readily-available data within online repositories has greatly expanded. This data increase permits new uses for machine learning approaches which rely on large, high-dimensional datasets. However, a comparison of the efficacies of attribute-based data, which lends itself well to traditional machine learning algorithms, and form-based data, which lends itself to powerful deep machine learning algorithms, has not fully been established. This paper presents one such comparison for an exemplar dataset. As the efficacy of different machine learning approaches may be dependent on the specific application, this method is intended to lay the groundwork for future studies that produce more extensive comparisons. Specifically, this work makes use of a manufactured gear dataset for sale price prediction. Two traditional machine learning algorithms, M5Rules and SMOreg, are selected due to their applicability to the gear attribute-based data. These algorithms are compared to a neural network model that is trained on a voxelized version of the gears’ 3D models, the form-based data. Results show that both data types provide comparable predictive accuracy.