How Universities Are Using Xiris Cameras To Advance The Scientific Field

Dmytro Havrylov
Written by Dmytro Havrylov on May 28, 2024

The advancement of scientific research relies upon the precision and reliability of observation tools.

Xiris weld cameras have emerged as such tools, widely recognized and employed as exemplars of quality within the scientific community.

Let's take a deep dive into the numerous scientific articles that showcase the diverse applications and invaluable insights facilitated by Xiris cameras. 


Fig. 1: Metal transfer in Fronius CMT process monitored with a Xiris XIR-1800 thermal camera.


The Reach of Xiris Cameras in Scientific Research 

Xiris cameras have become synonymous with quality and reliability in scientific studies, with their usage documented in numerous articles over the years. 

A staggering number of publications, exceeding 150 since 2019, attest to the widespread adoption of Xiris cameras across various fields of study.

From monitoring melt pools in different welding processes to providing insights into additive manufacturing and beyond, these cameras offer researchers a unique vantage point into the dynamics of their experiments. 

Exploring Advanced Welding Techniques at the University of Kentucky with Xiris Cameras

Among the Xiris camera lineup, the XVC series stands out as a cornerstone in scientific research. Models such as the XVC-1100 color weld cameras, monochrome XVC-1000, and the rugged XVC-1000E cameras have garnered widespread acclaim in numerous studies.

For instance, researchers at the University of Kentucky, including Rui Yu et al. [1, 2], leveraged the XVC-1100 to predict penetration in GTAW (Gas Tungsten Arc Welding) using advanced deep learning techniques.

Similarly, other studies have utilized color weld cameras for tasks such as melt pool segmentation, demonstrating their versatility in academic research. 

RMIT University's Laser Direct Energy Deposition Study with XVC-1000 Cameras

The monochrome visible and near-infrared XVC-1000 cameras have emerged as favorites among researchers due to their ease of customization for scientific applications.

This monochrome nature allows researchers to apply various light filters tailored to their specific needs.

For example, researchers at RMIT University in Melbourne, led by Zefeng Wu et al. [3], utilized specialized 1030 nm filters to study Laser Direct Energy Deposition (Laser DED) processes, highlighting the adaptability of Xiris cameras to different wavelengths and applications. 


Fig. 2: A research setup with an XVC-1000 weld camera used for defect classification in aluminum welding.


Defect Classification and Process Optimization at the University of Birmingham and the Federal University of Rio de Janeiro

Xiris cameras are not just passive observers but active tools for defect classification and process optimization.

Daniel Bacioiu et al. [4] from the University of Birmingham and TWI Ltd. utilized XVC-1000 cameras for defect classification in GTAW of aluminum, training neural networks to identify various welding flaws accurately.

Meanwhile, Marcus O. Couto et al. [5, 6] from the Federal University of Rio de Janeiro monitored weld pool width for Wire Arc Additive Manufacturing (WAAM), showcasing the practical utility of Xiris cameras in process optimization. 


Fig. 3: Weld defects in aluminum GTAW and their appearance on a Xiris XVC-1000 weld camera.


Advanced Welding Research with XIR-1800 Thermal Cameras at the University of Tennessee and Ghent University Studies

In addition to the XVC line, the scientific community has begun exploring the capabilities of the XIR-1800 short-wave infrared (SWIR) weld thermal camera.

Despite being relatively new to the market, this camera has quickly gained traction in weld research, particularly in processes such as Wire Arc Additive Manufacturing (WAAM). 

M. A. Roach et al. [7] from the University of Tennessee utilized the XIR-1800 thermal weld camera to explore IoT and ROS2 sensors for thermal monitoring and path planning for WAAM with the Fronius CMT process.

Similarly, researchers at Ghent University in Belgium, led by Rafael Nunes et al. [8], used the XIR-1800 to observe metal transfer cycles in WAAM

Using the XIR-1800 Thermal Camera to observe WAAM at Tennessee Technological University and Colorado School of Mines

The XIR-1800 has also proven invaluable in material science research. Md Abdul Karim et al. [9] from Tennessee Technological University and Oak Ridge National Laboratory utilized the XIR-1800 to observe temperature distribution profiles in WAAM of Aluminum - Stainless steel intermetallic structures.

Additionally, researchers from the Colorado School of Mines, including Luc Hagen et al. [10], employed the XIR-1800 to observe WAAM of 316L stainless steel, including the detection of lack of fusion. 



Fig. 4: Metal transfer in CMT process with the respective wire feed speed (WFS), current (I) and voltage(U).



In conclusion, Xiris cameras, both within the XVC line and the emerging XIR-1800 thermal cameras, continue to play a pivotal role in advancing scientific research.

By providing researchers with unparalleled insights into their experiments, these cameras drive innovation and push the boundaries of knowledge across various disciplines.

As technology evolves and new challenges emerge, Xiris cameras stand ready to unlock new insights, empowering researchers to make significant contributions to their fields. 






[1] Mucllari, E., Yu, R., Cao, Y., Ye, Q., & Zhang, Y. (2023). Do We Need a New Foundation to Use Deep Learning to Monitor Weld Penetration?. IEEE Robotics and Automation Letters. 


[2] Yu, R., Chen, Y., Zhang, J., Ye, Q., & Zhang, Y. (2023). Monitoring Weld Penetration by Training A Deep Learning Model Using Inaccurate Labels. Automation, Robotics & Communications for Industry 4.0/5.0, 17. 


[3] Wu, Z., O’Toole, P., Hagenlocher, C., Qian, M., Brandt, M., & Watts, J. (2023). Melt pool dynamics on different substrate materials in high-speed laser directed energy deposition process. Journal of Laser Applications, 35(4). 


[4] Bacioiu, D., Melton, G., Papaelias, M., & Shaw, R. (2019). Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks. Journal of manufacturing processes, 45, 603-613. 


[5] de Oliveira Couto, M. V., Rodrigues, A. G., Costa, R. R., Lizarralde, F. C., Leite, A. C., Juliano, D. R., ... & da Cruz Payão Filho, J. (2020). Weld bead width monitoring in a carbon steel wire and arc additive manufacturing system. 


[6] Couto, M. O., Rodrigues, A. G., Coutinho, F., Costa, R. R., Leite, A. C., Lizarralde, F., & Filho, J. C. P. (2022). Mapping of bead geometry in wire arc additive manufacturing systems using passive vision. Journal of Control, Automation and Electrical Systems, 33(4), 1136-1147. 




[8] Nunes, R., Vandermeiren, N., Verlinde, W., Boruah, D., Motte, R., & De Waele, W. (2023). A benchmark of mechanical properties and operational parameters of different steel filler metals for wire arc additive manufacturing. The International Journal of Advanced Manufacturing Technology, 127(1), 599-613. 


[9] Karim, M. A., Jadhav, S., Kannan, R., Pierce, D., Lee, Y., Nandwana, P., & Kim, D. B. (2024). Investigating stainless steel/aluminum bimetallic structures fabricated by cold metal transfer (CMT)-based wire-arc directed energy deposition. Additive Manufacturing, 81, 104015. 


[10] Hagen, L., Yu, Z., Clarke, A., Clarke, K., Tate, S., Petrella, A., & Klemm-Toole, J. (2023). High deposition rate wire-arc directed energy deposition of 316L and 316LSi: Process exploration and modelling. Materials Science and Engineering: A, 880, 145044. 


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