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Research Papers

Process Mapping and In-Process Monitoring of Porosity in Laser Powder Bed Fusion Using Layerwise Optical Imaging

[+] Author and Article Information
Farhad Imani, Hui Yang

Industrial and Manufacturing Engineering,
Pennsylvania State University,
State College, PA 16802

Aniruddha Gaikwad, Mohammad Montazeri

Mechanical and Materials Engineering,
University of Nebraska-Lincoln,
Lincoln, NE 68588-0642

Prahalada Rao

Mechanical and Materials Engineering,
University of Nebraska-Lincoln,
Lincoln, NE 68588-0642
e-mail: rao@unl.edu

Edward Reutzel

Applied Research Laboratory,
Pennsylvania State University,
State College, PA 16802

1Corresponding author.

Manuscript received September 14, 2017; final manuscript received June 19, 2018; published online July 27, 2018. Assoc. Editor: Sam Anand.

J. Manuf. Sci. Eng 140(10), 101009 (Jul 27, 2018) (14 pages) Paper No: MANU-17-1575; doi: 10.1115/1.4040615 History: Received September 14, 2017; Revised June 19, 2018

The goal of this work is to understand the effect of process conditions on lack of fusion porosity in parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process, and subsequently, to detect the onset of process conditions that lead to lack of fusion-related porosity from in-process sensor data. In pursuit of this goal, the objectives of this work are twofold: (1) quantify the count (number), size and location of pores as a function of three LPBF process parameters, namely, the hatch spacing (H), laser velocity (V), and laser power (P); and (2) monitor and identify process conditions that are liable to cause porosity through analysis of in-process layer-by-layer optical images of the build invoking multifractal and spectral graph theoretic features. These objectives are important because porosity has a significant impact on the functional integrity of LPBF parts, such as fatigue life. Furthermore, linking process conditions to defects via sensor signatures is the first step toward in-process quality assurance in LPBF. To achieve the first objective, titanium alloy (Ti–6Al–4V) test cylinders of 10 mm diameter × 25 mm height were built under differing H, V, and P settings on a commercial LPBF machine (EOS M280). The effect of these process parameters on count, size, and location of pores was quantified based on X-ray computed tomography (XCT) images. To achieve the second objective, layerwise optical images of the powder bed were acquired as the parts were being built. Spectral graph theoretic and multifractal features were extracted from the layer-by-layer images for each test part. Subsequently, these features were linked to the process parameters using machine learning approaches. Through these image-based features, process conditions under which the parts were built were identified with the statistical fidelity over 80% (F-score).

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Copyright © 2018 by ASME
Topics: Lasers , Porosity , Imaging
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Figures

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Fig. 1

The schematic diagram of the LPBF process

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Fig. 2

Schematic diagram of the location of flash-lamps and camera used to capture in situ powder bed images [44]

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Fig. 3

Cropped image of the powder bed in different light schemes

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Fig. 4

An overview of the methodology for analysis of offline computed tomography data, and in situ images of powder bed fusion process

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Fig. 5

Effect of process conditions on the parts as seen in XCT scan images. Pore count increases as process conditions drift from nominal conditions. Highest number of pores is seen in the part printed at P -50% (c3).

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Fig. 6

An overview of the image processing methodology used to analyze the XCT scan images: (a) XCT scan image of part printed with P -50%, (b) binarization of the XCT scan image of the part, (c) complemented binary image of the XCT scan image, and (d) noise reduced XCT scan image which is used for the spatial distribution analysis

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Fig. 7

An example of the procedure followed to divide XCT scan image of a part into concentric segments. (a) First segment 0–1 mm of the XCT scan image (L1), i.e., the segment that encompasses the center of the XCT scan image, (b) second segment 1–2 mm of the XCT scan image (L2), (c) third segment 2–3 mm of the XCT scan image (L3), (d) fourth segment 3–4 mm of the XCT scan image (L4), and (e) last segment 4–5 mm of the XCT scan image (L5), i.e., the segment which is farthest from the center of the XCT scan image.

Grahic Jump Location
Fig. 8

Count of pores versus Pore size in varying process conditions: (a) In P -50% printing condition, highest number of pores is seen of size R1 (16 μm), and in P0 and P -25% printing condition, very few pores of size R1 (16 μm) are seen. (b) In parts printed with varying hatch spacing only pores of size R1 (16 μm) and R2 (32 μm) are seen, and the highest number of pores is seen in H + 50% printing condition. (c) In comparison with other printing conditions, the lowest number of pores is seen in parts printed with varying velocity. Pores of size R1 (16 μm) are highest in number in V0, V + 25%, and V + 50% printing conditions.

Grahic Jump Location
Fig. 9

Mean pore count versus radius from center of image at varying process conditions. (a) Parts printed with laser power of P -50% have highest number of pores in the third segment (L3 = 2–3mm) of the XCT scan image. Parts printed with P 0 (nominal condition), and P -25% have pores located in second segment (L2 = 1–2 mm) of the XCT scan image. (b) In parts printed with varying hatch spacing highest number of pores are seen in the third segment (L3 = 2–3 mm) of the XCT scan image in all conditions. (c) In parts printed with varying velocity highest number of pores are seen in V + 50% in the third segment (L3 = 2–3 mm), and in V0 and V + 25% conditions, highest number of pores are seen in the second segment (L2 = 1–2 mm) of the XCT scan images.

Grahic Jump Location
Fig. 10

An in situ image of part depicting the row vectors which are used for pairwise comparison

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Fig. 11

Simulated trees by the multifractal iterated function system, (a) IFS tree T1, (b) IFS tree T2, (c), IFS tree T3. All three IFS trees have the same box-counting fractal dimension of 2.0449, but different multifractal spectra as shown in Figure 12.

Grahic Jump Location
Fig. 12

Multifractal spectra of IFS trees show the self-similarity, irregularity, and non-homogeneity of fractal objects that cannot be adequately characterized using a single fractal dimension

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Fig. 13

Lacunarity analysis of IFS trees describes how fractal objects fill the space that cannot be adequately captured using traditional fractal analysis

Grahic Jump Location
Fig. 14

The variations of multifractal spectra with respect to the Andrew's Number for 3132 layerwise images in the LPBF process

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