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Research Papers

Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing

[+] Author and Article Information
Bing Yao, Farhad Imani, Aniket S. Sakpal

Department of Industrial and
Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802

E. W. Reutzel

Applied Research Laboratory
The Pennsylvania State University,
P.O. Box 30,
State College, PA 16804-0030

Hui Yang

Department of Industrial and
Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: huy25@psu.edu

1Corresponding author.

Manuscript received July 18, 2017; final manuscript received August 30, 2017; published online January 3, 2018. Assoc. Editor: Zhijian J. Pei.

J. Manuf. Sci. Eng 140(3), 031014 (Jan 03, 2018) (13 pages) Paper No: MANU-17-1444; doi: 10.1115/1.4037891 History: Received July 18, 2017; Revised August 30, 2017

Metal-based powder-bed-fusion additive manufacturing (PBF-AM) is gaining increasing attention in modern industries, and is a promising direct manufacturing technology. Additive manufacturing (AM) does not require the tooling cost of conventional subtractive manufacturing processes, and is flexible to produce parts with complex geometries. Quality and repeatability of AM parts remain a challenging issue that persistently hampers wide applications of AM technology. Rapid advancements in sensing technology, especially imaging sensing systems, provide an opportunity to overcome such challenges. However, little has been done to fully utilize the image profiles acquired in the AM process and study the fractal patterns for the purpose of process monitoring, quality assessment, and control. This paper presents a new multifractal methodology for the characterization and detection of defects in PBF-AM parts. Both simulation and real-world case studies show that the proposed approach effectively detects and characterizes various defect patterns in AM images and has strong potential for quality control of AM processes.

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Figures

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Fig. 1

Three different types of surfaces—(a) rough, (b) sinusoidal, (c) random, and their 2D images (d)–(f)

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Fig. 2

Flowchart of research methodology

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Fig. 3

(a) Illustration of f(q) and α(q) estimated in the range of q values from –3 to 3, (b) Illustration of f(α) spectrum, and (c) Multifractal spectra for the images of rough surface, sinusoidal surface, and random surface in Fig. 1

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Fig. 4

Examples of simulated patterns with varying degrees of dispersion

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Fig. 5

(a) Actual build components from the AM process, (b) 3D visualization of CT scan, and (c) sliced CT-scan image

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Fig. 6

(a) Four different types of simulated images: 1-random pattern, 2-single cluster, 3-matern cluster, and 4-line pattern (Note: the graphs of 2, 3, and 4 are with dispersion level of L2) and (b) multifractal spectra of the four different graphs

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Fig. 7

The variation of single fractal dimension D0 with respect to the dispersion level

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Fig. 8

(a) AM image profiles. (Note: 1 — with no defect; 2 — with balling defect; 3 — with crack defect; 4 — with pore defect.) and (b) multifractal spectrum of each image in (a).

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Fig. 9

Spectra of images with different sizes of pore defects

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Fig. 10

Spectra of images with different number of pore defects

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Fig. 11

Layout of the build setup. Note: The top left corner is the design of experiments to print seven groups of parts under different process conditions, i.e., (H0, V0, P0), H25+=((1+25%)H0, V0, P0), H50+=((1+50%)H0, V0, P0), V25+=(H0(1+25%), V0, P0), V50+=(H0(1+50%), V0, P0), P25−=(H0, V0(1−25%), P0), and P50−=(H0, V0(1−50%), P0).

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Fig. 12

Multifractal spectra of the 120 CT-scan images

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Fig. 13

Residual diagnosis of the regression model

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