Alikasim Budhwani
Papers
1
Total Citations
49
H-Index
1
About
Alikasim Budhwani is a leading researcher in advanced manufacturing and artificial intelligence, whose work bridges the gap between additive manufacturing and real-time quality control. His primary research areas include laser-directed energy deposition (DED), in-situ monitoring, and deep learning for defect detection. Budhwani’s most significant contribution is the development of a deep-learning-based methodology for detecting surface anomalies during the DED process—a critical advancement for ensuring part integrity in industries like aerospace and biomedical engineering. His landmark 2022 paper, which has garnered 49 citations, demonstrates how convolutional neural networks can analyze melt pool imagery to identify defects such as porosity or cracking as they occur, enabling immediate process adjustments. This work not only reduces material waste but also enhances the reliability of additively manufactured components. By integrating machine learning with manufacturing, Budhwani has pioneered a path toward fully autonomous, self-correcting production systems. His research is widely cited by engineers and data scientists seeking to combine Industry 4.0 principles with practical manufacturing challenges. For students and researchers, Budhwani’s work exemplifies how cross-disciplinary approaches can solve long-standing industrial problems, making him a key figure in the evolution of smart manufacturing.
Research Focus
Key Achievements
Top Papers
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