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.
Skip Nav Destination
Article navigation
March 2019
Research-Article
Layerwise Anomaly Detection in Laser Powder-Bed Fusion Metal Additive Manufacturing
Mohamad Mahmoudi,
Mohamad Mahmoudi
Industrial and Systems Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: mahmoudi@tamu.edu
Texas A&M University,
College Station, TX 77843
e-mail: mahmoudi@tamu.edu
Search for other works by this author on:
Ahmed Aziz Ezzat,
Ahmed Aziz Ezzat
Industrial and Systems Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: aa.ezzat@tamu.edu
Texas A&M University,
College Station, TX 77843
e-mail: aa.ezzat@tamu.edu
Search for other works by this author on:
Alaa Elwany
Alaa Elwany
Industrial and Systems Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: elwany@tamu.edu
Texas A&M University,
College Station, TX 77843
e-mail: elwany@tamu.edu
Search for other works by this author on:
Mohamad Mahmoudi
Industrial and Systems Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: mahmoudi@tamu.edu
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
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
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. Mar 2019, 141(3): 031002 (13 pages)
Published Online: January 17, 2019
Article history
Received:
February 15, 2018
Revised:
November 19, 2018
Citation
Mahmoudi, M., Ezzat, A. A., and Elwany, A. (January 17, 2019). "Layerwise Anomaly Detection in Laser Powder-Bed Fusion Metal Additive Manufacturing." ASME. J. Manuf. Sci. Eng. March 2019; 141(3): 031002. https://doi.org/10.1115/1.4042108
Download citation file:
Get Email Alerts
Special Section: Manufacturing Science Engineering Conference 2024
J. Manuf. Sci. Eng (November 2024)
Anisotropy in Chip Formation in Orthogonal Cutting of Rolled Ti-6Al-4V
J. Manuf. Sci. Eng (January 2025)
Modeling and Experimental Investigation of Surface Generation in Diamond Micro-Chiseling
J. Manuf. Sci. Eng (February 2025)
Estimation of Temperature Rise in Magnetorheological Fluid-Based Finishing of Thin Substrate: A Theoretical and Experimental Study
J. Manuf. Sci. Eng (February 2025)
Related Articles
A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing
J. Manuf. Sci. Eng (December,2014)
Engineering-Guided Deep Learning of Melt-Pool Dynamics for Additive Manufacturing Quality Monitoring
J. Comput. Inf. Sci. Eng (October,2024)
Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control
J. Manuf. Sci. Eng (April,2019)
High-fidelity Sensing Modality for Anomaly Detection in Inkjet Printing
J. Manuf. Sci. Eng (January,0001)
Related Proceedings Papers
Related Chapters
Process Monitoring Using Online Sensor Signals
Ultrasonic Welding of Lithium-Ion Batteries
Fatigue Performance of Bearing Rollers Manufactured by Laser Powder Bed Fusion
Bearing and Transmission Steels Technology
Research on the Application of Artificial Intelligence in Industrial Process Monitoring Integrated System
International Conference on Mechanical and Electrical Technology, 3rd, (ICMET-China 2011), Volumes 1–3