Faculty Publications
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Item Intelligent GD&T symbol detection in mechanical drawings: a comparative study of YOLOv11, Faster R-CNN, and RetinaNet for quality assurance(Springer, 2025) Narendra Reddy, T.N.; Kumar, N.; Ponnappa, N.P.; Mohana, N.; Vinod, P.; Herbert, M.A.; Rao, S.S.Geometric dimensioning and tolerancing (GD&T) symbols play a vital role in engineering drawings by specifying allowable variations in part geometry to ensure manufacturing precision and functional performance. Manual identification and extraction of these symbols is labour-intensive, prone to human error, and increasingly unsuitable for fast-paced production environments, as it significantly increases quality inspection time and indirectly delays overall product delivery. This research is specifically conducted to support the development of intelligent quality management systems by integrating machine learning algorithms capable of detecting GD&T symbols directly from CAD-generated mechanical drawings. Such capability is essential for automating inspection processes and enabling reliable data extraction from design files, which are foundational to digital manufacturing workflows. Additionally, with many commercial quality automation tools being prohibitively expensive for small and medium-sized enterprises (SMEs) and micro, small, and medium enterprises (MSMEs), there is a pressing need for cost-effective, indigenous solutions. This study addresses that gap by evaluating three state-of-the-art deep learning-based object detection models—YOLOv11, Faster R-CNN, and RetinaNet—for GD&T symbol recognition. Each model was trained on a custom dataset annotated with diverse GD&T symbols, and performance was assessed using standard evaluation metrics: accuracy, recall, F1 score, and inference speed. The results show that while all three models demonstrate robust performance, YOLOv11 strikes the best balance between detection accuracy and real-time execution. This comparative study not only guides R&D teams in selecting the most suitable model for quality automation tasks but also contributes to the broader goal of enabling affordable, scalable, and intelligent visual inspection systems for SMEs and MSMEs. © The Author(s) 2025.Item NDWC: Global Scaling With Reducing Factor for Influence Ranking in Weighted Complex Networks(Institute of Electrical and Electronics Engineers Inc., 2025) Shetty, R.D.; Manoj, T.; Bhattacharjee, S.; Vasudeva, n.Optimal nodes identification in weighted complex networks is an essential task across diverse areas such as epidemiology, social media, infrastructure, and information diffusion. Traditional centrality measures often fail to capture the nuanced influence of a node when edge weights vary significantly across the network. In the scope of this study, propose a novel centrality measure, Normalized Degree and Weight Centrality (NDWC), that incorporates global scaling and a reducing factor to better assess the importance of nodes in weighted networks. NDWC integrates both structural (degree-based) and strength-based (edge weight) contributions, normalized using global standard deviations to ensure fair comparisons. Furthermore, a reducing factor is introduced to penalize nodes with skewed edge weight distributions, enhancing robustness against local heterogeneity. By combining these elements, NDWC provides a more balanced and representative ranking of nodes. Experimental validation on widely used datasets demonstrates that NDWC outperforms several state-of-the-art methods in identifying influential nodes, particularly in weighted networks. © 2013 IEEE.