Kashif Hasan Kazmi
Papers
10
Total Citations
106
H-Index
6
About
Kashif Hasan Kazmi is an emerging researcher specializing in wire arc additive manufacturing (WAAM) and the integration of artificial intelligence into advanced metal fabrication processes. His work sits at a compelling intersection of materials science, robotics, and machine learning, making significant contributions to one of the most promising frontiers in modern manufacturing engineering. Kazmi's research has systematically advanced understanding of WAAM across multiple dimensions. His investigations into aluminum alloys — particularly ER-4043 and aluminum-5356 — have yielded critical insights into bead geometry, microstructure, mechanical properties, and tribological behavior, establishing foundational knowledge for industrial application. Notably, his exploration of machine learning for surface roughness minimization (28 citations) and deep learning for anomaly detection represent pioneering efforts to bring intelligent automation to WAAM quality control. His work on path planning strategies, deposition angles, and multi-objective genetic algorithm optimization further demonstrates a holistic approach to process refinement. With over 100 cumulative citations across publications spanning just two years, Kazmi has established rapid and meaningful traction within the additive manufacturing community. His research addresses real industrial challenges — reducing defects, improving surface quality, and optimizing deposition parameters — making his contributions both scientifically rigorous and practically valuable for engineers and researchers working in next-generation metal manufacturing.
Research Focus
Key Achievements
Top Papers
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- 7Deep learning for anomaly detection in wire-arc additive manufacturing6 citations · 2023
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