Research Papers

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

[+] Author and Article Information
Mojtaba Khanzadeh, Ruholla Jafari-Marandi, Brian K. Smith

Industrial and Systems Engineering Department,
Mississippi State University,
Starkville, MS 39759

Prahalada Rao

Department of Mechanical and
Materials Engineering,
University of Nebraska-Lincoln,
Lincoln, NE 68588

Mark A. Tschopp

U.S. Army Research Laboratory,
Aberdeen Proving Ground,
Hillandale, MD 21005

Linkan Bian

Industrial and Systems Engineering Department,
Mississippi State University,
Starkville, MS 39759
e-mail: bian@ise.msstate.edu

1Corresponding author.

Manuscript received June 11, 2017; final manuscript received November 19, 2017; published online December 21, 2017. Assoc. Editor: Zhijian J. Pei. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Manuf. Sci. Eng 140(3), 031011 (Dec 21, 2017) (12 pages) Paper No: MANU-17-1366; doi: 10.1115/1.4038598 History: Received June 11, 2017; Revised November 19, 2017

Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML 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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Fig. 1

The design part used to measure GD&T. Subfigure (a) shows the 3D view of the fabricated test part using FFF process. Subfigure (b) shows the faces used to measure flatness (), circularity (), and cylindricity (); and (c) shows the planes used to measure the thickness—three thickness measurements are taken on each plane [13]. Different colors represent various planes and the point cloud data.

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Fig. 2

The eight points used for alignment of the scan points with the CAD model [3639]

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Fig. 3

Fracture strength of ASTM 638D-Type V samples under different infill percentages. The error bars are ±1 standard deviation (eight samples at each infill percentage level).

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Fig. 4

Flooded contour plots of the benchmark part used in FFF experiments detailed in Sec. 3.1. The material is ABS polymer. The first row (1) shows the top views and the second row (2) contains the bottom views of the parts. (a)–(d) represent different parts, printed under 70%, 80%, 90%, and 100% infill percentages at 230 °C, respectively. The scale bar is in mm (the darker the worse) [14].

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Fig. 5

Micrograph showing the effect of infill percentage on the internal morphology of the circle square diamond part at 230 °C extruder temperature. Shown is the quarter cross section of the part, the circular section is at the top end. At 100% infill, the thermal residual stresses cannot be accommodated without deleteriously affecting the geometric integrity, since there is no vacant space for stress relief.

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Fig. 6

Comparing the flatness and thickness of GD&T characteristics using various set of process conditions (infill (If), extruder temperature (te)). The circle size represents the ratio between thickness and flatness.

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Fig. 7

Clustering point cloud data according to the direction and magnitude of geometric deviations using SOMs to a 4 × 4 quadrilateral space membership map

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Fig. 8

SOM based deviation clustering method applied to different FFF process conditions: (left) te = 225 °C, If = 100%; (right) te = 230 °C, If = 80%

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Fig. 9

(a) Comparing magnitudes of various types of deviations for two distinct parts in the direction in their corresponding x-direction and y-direction, where circles and squares illustrate the magnitude of deviation in the z-direction (te= 225 °C, If = 100%) and (te=230 °C,If= 100%), respectively. (b) Comparing magnitude of various types of deviations for two distinct parts in the z-direction in their corresponding x-direction and y-direction, where triangles and diamonds illustrate the magnitude of deviation in the z-direction for (te= 225 °C, If= 80%) and (te= 230 °C, If = 80%), respectively.

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Fig. 10

Comparing the geometric quality of printed parts using FFF where two levels of (te = 225 °C and 230 °C) and three levels of (If = 80, 90, and 100%) are selected

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Fig. 11

Selecting the top k clusters based on estimated minimum magnitude of deviation

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Fig. 12

Identifying the new scanning areas based on location of point cloud data in top four clusters where (a) illustrates thin-exponentially shaped scanning pattern for designed part using point cloud data based on deviations from cluster 22, (b) demonstrates thin-circular shaped scanning pattern for designed part using point cloud data formed due to deviations of clusters 16 and 21, and (c) shows thin-cubic shaped scanning pattern for designed part using point cloud data depending on deviations of cluster 11 (the scanning pattern demonstrated from top and right view for (a) and (b) and bottom and right view for (c), respectively)

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Fig. 13

Comparing the geometric quality of printed parts using FFF where two levels of (te=225 °C and230 °C) and three levels of (If= 80, 90, and 100%) are selected using K-means clustering method



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