Research Papers

Sensor-Based Build Condition Monitoring in Laser Powder Bed Fusion Additive Manufacturing Process Using a Spectral Graph Theoretic Approach

[+] Author and Article Information
Mohammad Montazeri

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

Prahalada Rao

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

1Corresponding author.

Manuscript received January 9, 2018; final manuscript received May 3, 2018; published online June 4, 2018. Assoc. Editor: Zhijian J. Pei.

J. Manuf. Sci. Eng 140(9), 091002 (Jun 04, 2018) (16 pages) Paper No: MANU-18-1023; doi: 10.1115/1.4040264 History: Received January 09, 2018; Revised May 03, 2018

The goal of this work is to monitor the laser powder bed fusion (LPBF) process using an array of sensors so that a record may be made of those temporal and spatial build locations where there is a high probability of defect formation. In pursuit of this goal, a commercial LPBF machine at the National Institute of Standards and Technology (NIST) was integrated with three types of sensors, namely, a photodetector, high-speed visible camera, and short wave infrared (SWIR) thermal camera with the following objectives: (1) to develop and apply a spectral graph theoretic approach to monitor the LPBF build condition from the data acquired by the three sensors; (2) to compare results from the three different sensors in terms of their statistical fidelity in distinguishing between different build conditions. The first objective will lead to early identification of incipient defects from in-process sensor data. The second objective will ascertain the monitoring fidelity tradeoff involved in replacing an expensive sensor, such as a thermal camera, with a relatively inexpensive, low resolution sensor, e.g., a photodetector. As a first-step toward detection of defects and process irregularities that occur in practical LPBF scenarios, this work focuses on capturing and differentiating the distinctive thermal signatures that manifest in parts with overhang features. Overhang features can significantly decrease the ability of laser heat to diffuse from the heat source. This constrained heat flux may lead to issues such as poor surface finish, distortion, and microstructure inhomogeneity. In this work, experimental sensor data are acquired during LPBF of a simple test part having an overhang angle of 40.5 deg. Extracting and detecting the difference in sensor signatures for such a simple case is the first-step toward in situ defect detection in additive manufacturing (AM). The proposed approach uses the Eigen spectrum of the spectral graph Laplacian matrix as a derived signature from the three different sensors to discriminate the thermal history of overhang features from that of the bulk areas of the part. The statistical accuracy for isolating the thermal patterns belonging to bulk and overhang features in terms of the F-score is as follows: (a) F-score of 95% from the SWIR thermal camera signatures; (b) 83% with the high-speed visible camera; (c) 79% with the photodetector. In comparison, conventional signal analysis techniques—e.g., neural networks, support vector machines, linear discriminant analysis were evaluated with F-score in the range of 40–60%.

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Fig. 1

The schematic diagram of the laser powder bed fusion (LPBF) process

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Fig. 2

An LPBF knee implant with an overhang feature shows poor surface finish and coarse microstructure

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Fig. 3

Schematic layout of sensors installed on the LPBF 3D printer

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Fig. 4

Close up and schematic layout of the thermal camera and high-speed video camera

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Fig. 5

(a) The part schematic (all dimensions in millimeter, drawings are not to scale) measuring 16 mm on all sides with 40.5 deg overhang angle, (b) as-built without supports, (c) and (d) side-view and top views of the stripe pattern at the build height of 7.9 mm in the context of the thermal camera position

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Fig. 6

Distinctive meltpool shape for bulk (a) and overhang (b) areas. Note the residual heat for the overhang area resulting from the previously scanned stripe: (a) melting of bulk section and (b) melting of overhang section.

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Fig. 7

Two representative high-speed video images for (a) bulk build conditions and (b) overhang build condition corresponding to the frames in Fig. 6

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Fig. 8

Photodetector signal windows for the overhang and bulk features (a) intensity, (b) Fourier transform, and (c) empirical cumulative distribution function (ECDF) for three consecutive layers

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Fig. 9

The three steps in the proposed spectral graph theoretic approach

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Fig. 10

The four different Rossler systems used for testing the approach

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Fig. 11

Fiedler number pattern for one frame of thermal camera (b) second norm of graph Fourier coefficients of photodetector



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