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ASTM Selected Technical Papers
Progress in Additive Manufacturing 2021
Editor
Nima Shamsaei
Nima Shamsaei
Symposium Chair and STP Editor
1
Auburn University
,
Auburn, AL,
US
Search for other works by this author on:
Nik Hrabe
Nik Hrabe
Symposium Chair and STP Editor
2
National Institute of Standards and Technology
,
Boulder, CO,
US
Search for other works by this author on:
Mohsen Seifi
Mohsen Seifi
Symposium Chair and STP Editor
3
ASTM International
,
Washington, DC,
US
Search for other works by this author on:
ISBN:
978-0-8031-7735-2
No. of Pages:
274
Publisher:
ASTM International
Publication date:
2022

Laser metal deposition is an additive manufacturing technology in which the process parameters greatly influence the product quality and geometry. Therefore, it is generally concluded that real-time control and monitoring of the process are required to deliver quality parts. However, during the building process it is only possible to control the liquid state of the melt pool. Monitoring of the melt pool typically involves image processing and comes with an associated computational cost. CPU-based systems are limited in the real-time field due to their slower response than their field-programmable gate array (FPGA) counterparts for parallel computing and related latency. This paper presents an FPGA-based vision system that extracts geometric and intensity statistical features in real time of the melt pool based on a low-cost visible and near-infrared camera. The extraction of features of the melt pool is achieved by a thresholding approach and a 2D blob analysis using image moments. A pyrometer is used synchronously with the camera to simultaneously measure the temperature in the melt pool. The observed melt pool shape and intensity features are compared with the temperature values, and the results are discussed and correlated.

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