Mohsin Dalvi
Papers
2
Total Citations
4
H-Index
2
About
Mohsin Dalvi is a researcher focused on the intersection of computer vision, image processing, and intelligent manufacturing. His work primarily addresses automation challenges in industrial settings, with key contributions in weld joint classification and robotic deburring. In his highly cited 2022 paper, Dalvi developed a hybrid deep learning model that integrates a pre-trained CNN with an SVM classifier for automated weld joint type recognition, significantly improving accuracy in industrial inspection tasks. His earlier 2019 work introduced a novel image processing pipeline for burr detection and trajectory generation on 2D workpieces, enabling robots to autonomously identify burr locations and dimensions for precise deburring operations. This approach reduces manual intervention and enhances manufacturing efficiency. With both papers garnering 2 citations each, Dalvi’s research demonstrates practical impact in advancing automated quality control and robotic manipulation. His work is particularly relevant for researchers and engineers seeking to apply computer vision techniques to real-world industrial automation problems, bridging the gap between theoretical image processing algorithms and tangible manufacturing solutions.
Research Focus
Key Achievements
Top Papers
- 1Pre-trained CNN Based SVM Classifier for Weld Joint Type Recognition2 citations · 2022
- 2