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Author: search.filters.author.Sangwan, Kuldip SinghSubject: search.filters.subject.Convolutional neural networks (CNNs)

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    Vision-driven robotic grasping order generation using segmentation and relative positioning in a cluttered environment
    (Elsevier, 2025) Sangwan, Kuldip Singh
    In this paper, a multichannel vision-based approach for intelligent robotic grasping in cluttered environments is proposed. The experiments are conducted with an open-source synthetic dataset consisting of color and depth images to address this general problem. The proposed approach involves the use of a modified Cascade Mask R-CNN-based semantic segmentation model to detect and classify objects in the scene. The results show a high mAP@0.5-0.95 score of 93.85% for the customized Meta-Grasp dataset using this model. The captured depth data is processed based on the segmented mask regions to approximate their position in a 3D coordinate system. The affinity between the edge profiles is calculated to estimate the relation between the segmented objects in 3D space. This information is used to generate a priority order for object pickup such that only the objects in the top layer are picked first, followed by the underlying layers. The methodology was evaluated for various placement options for a 6-class subset of the dataset with a varying number of objects. The actual object classes and their mask positions were obtained successfully, and the priority order was calculated so that no lower-layered object was picked before the upper-lying object. Overall, the proposed two-stage decision pipeline has demonstrated its effectiveness in generating the pickup priority and sorting order for a multi-object scene and has potential applications in fully automated factories or smart manufacturing.
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    Detecting additive manufacturing anomalies with shallow convolutional neural networks
    (Springer, 2025-10) Sangwan, Kuldip Singh
    Additive manufacturing often known as 3D printing, has been significant in the manufacturing industry in recent decades. However, the method encounters significant challenges in the form of printing errors, adversely impacting end-user product experience and obstacles to widespread adoption. The current manual and sensor-based continuous monitoring techniques lack a clear distinction between anomalies and healthy data points, making them ineffective for implementation in industrial environments. This research introduces a computer vision-based methodology for detecting anomalies in real-time. Two Convolutional Neural Networks versions are created, Model V1 using residual connection with decreased parameters and computational complexity and Model V2 to facilitate effortless deployment on constraint devices without compromising performance. The proposed CNN networks are evaluated against state-of-the-art classification models, namely ResNet18, ResNet34, and Deep LSTM classifier, to assess their performance. Model V1 and Model V2 achieved comparable performances with 86.7% and 11.86% reduced parameters compared to ResNet18. Afterward, quantization is applied to produce a compact model representation for edge-device deployment. The quantization model proposed has no loss in performance. Lastly, an inference study is conducted on multiple edge devices where the TI AM68A board proved fast, with 0.246 and 0.04 s inference time for models V1 and V2 respectively.