Sonu Rajak
Papers
6
Total Citations
108
H-Index
6
About
Sonu Rajak is an emerging researcher at the intersection of advanced manufacturing and artificial intelligence, with a specialized focus on wire-arc additive manufacturing (WAAM) and machine learning-driven process optimization. His work addresses some of the most pressing challenges in metal additive manufacturing, particularly improving surface quality, predicting bead geometry, and enabling real-time anomaly detection during deposition processes. Rajak's most cited contribution — a comparative study of machine learning algorithms for bead geometry prediction in WAAM (38 citations) — established a foundational framework for intelligent process control in this rapidly growing field. His subsequent work implementing machine learning to minimize surface roughness in robotic WAAM (28 citations) further demonstrated the practical value of data-driven approaches in industrial manufacturing settings. Notably, his application of deep learning and the YOLO algorithm for real-time melt pool monitoring and anomaly detection (18 citations) showcases his forward-thinking integration of computer vision into digital manufacturing workflows. Beyond computational methods, Rajak has also contributed valuable insights into the mechanical properties, microstructure, and tribological behavior of aluminum alloy components produced via WAAM. With nearly 110 cumulative citations across six publications, Rajak is establishing himself as a significant voice in the smart manufacturing research community.
Research Focus
Key Achievements
Top Papers
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- 6Deep learning for anomaly detection in wire-arc additive manufacturing6 citations · 2023