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Quantifying Geometric Accuracy with Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts

[+] Author and Article Information
Mojtaba Khanzadeh

Industrial and Systems Engineering Department, Mississippi State University, MS, USA
mk1349@msstate.edu

Prahalad Rao

Department of Mechanical and Materials Engineering, University of Nebraska-Lincoln , NE, USA
rao@unl.edu

Ruholla Jafari-Marandi

Industrial and Systems Engineering Department, Mississippi State University, MS, USA
rj746@msstate.edu

Brian K. Smith

Industrial and Systems Engineering Department, Mississippi State University, MS, USA
smith@ise.msstate.edu

Mark A Tschopp

U.S. Army Research Laboratory, MD, USA
mark.a.tschopp.civ@mail.mil

Linkan Bian

Industrial and Systems Engineering Department, Mississippi State University, MS, USA
bian@ise.msstate.edu

1Corresponding author.

ASME doi:10.1115/1.4038598 History: Received June 11, 2017; Revised November 19, 2017

Abstract

geometric accuracy in FFF; and (2) significant reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised machine learning approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.

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