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.


Predicting Build Orientation of Additively Manufactured Parts With Mechanical Machining Features Using Deep Learning

Division / Sponsors: 
Design Engineering Division and Computers & Information In Engineering Division


Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2020/volume/83907

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