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Research Papers

Layerwise Anomaly Detection in Laser Powder-Bed Fusion Metal Additive Manufacturing

[+] Author and Article Information
Mohamad Mahmoudi

Industrial and Systems Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: mahmoudi@tamu.edu

Ahmed Aziz Ezzat

Industrial and Systems Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: aa.ezzat@tamu.edu

Alaa Elwany

Industrial and Systems Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: elwany@tamu.edu

1Corresponding author.

Manuscript received February 15, 2018; final manuscript received November 19, 2018; published online January 17, 2019. Assoc. Editor: Sam Anand.

J. Manuf. Sci. Eng 141(3), 031002 (Jan 17, 2019) (13 pages) Paper No: MANU-18-1094; doi: 10.1115/1.4042108 History: Received February 15, 2018; Revised November 19, 2018

A growing research trend in additive manufacturing (AM) calls for layerwise anomaly detection as a step toward enabling real-time process control, in contrast to ex situ or postprocess testing and characterization. We propose a method for layerwise anomaly detection during laser powder-bed fusion (L-PBF) metal AM. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of the following four-step anomaly detection procedure: (1) using the captured thermal images, a process signature of a just-fabricated layer is generated. Next, a signature difference is obtained by subtracting the process signature of that particular layer from a prespecified reference signature, (2) a screening step selects potential regions of interests (ROIs) within the layer that are likely to contain process anomalies, hence reducing the computational burden associated with analyzing the full layer data, (3) the spatial dependence of these ROIs is modeled using a Gaussian process model, and then pixels with statistically significant deviations are flagged, and (4) using the quantity and the spatial pattern of the flagged pixels as predictors, a classifier is trained and implemented to determine whether the process is in- or out-of-control. We validate the proposed method using a case study on a commercial L-PBF system custom-instrumented with a dual-wavelength imaging pyrometer for capturing the thermal images during fabrication.

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Figures

Grahic Jump Location
Fig. 1

Representative melt pool thermal image captured during laser melting

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Fig. 2

Process of generating a thermal signature for a rectangular layer: (a) the very first melt pool image captured can be seen at the bottom right corner, (b) more melt pool images are combined as the laser progresses and scans the rest of the rectangle; the arrows schematically show the laser scan path, and (c) the complete thermal signature of the layer

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Fig. 3

(a) Grayscale image for a sample rectangular layer, (b) the binary image with the islands detected after thresholding indicating possible anomalies, and (c) ROIs positioned at the centers of the island clusters

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Fig. 7

Regions of interests detected in the screening step corresponding to the thermal signature of the faulty layer 151

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Fig. 6

Thermal signature corresponding to layer 151 with the cavity in the center

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Fig. 5

(a) Schematic of the prism showing the cavity and (b) as-printed prism

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Fig. 8

Top row: SIZER output for a faulty ROI, (a) spatial detection and (b) independent detection. Bottom row: SIZER output for a faultless ROI, (c) spatial detection, and (d) independent detection.

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Fig. 9

ROC for LR classifier with spatial detection (solid red line), with independent detection (solid blue line). Dashed green line corresponds to random guess.

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Fig. 4

The pyrometer used for temperature measurement

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