2026-09-22T21:18:22Zhttps://ubir.buffalo.edu/oai/request

oai:ubir.buffalo.edu:10477/864812025-07-15T18:32:14Zcom_10477_77914col_10477_86252

Predicting Build Orientation of Additive Manufactured Parts with Mechanical Machining Features Using Deep Learning Eranpurwala, Aliakbar; 0000-0003-1970-5556 Lewis, Kemper Mechanical and Aerospace Engineering mechanical engineering M.S. 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. **To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.** 2025-02-21T17:22:51Z 2025-02-21T17:22:51Z 2020 Text Thesis http://hdl.handle.net/10477/86481 eng Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions. Copyright retained by author. application/pdf State University of New York at Buffalo