Predicting build orientation of additively manufactured parts with mechanical machining features using deep learning

Additive Manufacturing (AM) is a revolutionary development that is being viewed as a core technology for fabricating current and future engineered products. While AM has many advantages over subtractive manufacturing processes, one of the primary limitations of AM is to swiftly evaluate precise part build orientations. Current algorithms are either computationally expensive or provide multiple alternative orientations, requiring additional decision tradeoffs. To hasten the process of finding accurate part build orientation, a data-driven predictive model is introduced by mapping standard machining features to build orientation angles. A combinatory learning algorithm of classification and regression is utilized for the prediction of build orientation. The framework uses 54,000 voxelized standard tessellated language (STL) files as input to train the classification algorithm for eighteen standard machining features using a nine-layer 3D Convolutional Neural Network (CNN). Additionally, a multi-machining feature dataset of 1000 voxelized STL files are evaluated in parallel by performing quaternion rotations to obtain build orientation angles based on minimization of support structure volume. A regression model is then developed to establish a relationship between the machining features and orientation angles to predict optimal build orientation for new parts.