http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/1921 2026-04-21T14:14:23Z 2026-04-21T14:14:23Z Sangwan, Kuldip Singh http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19955 2025-11-04T09:05:02Z 2025-01-01T00:00:00Z

Vision-driven robotic grasping order generation using segmentation and relative positioning in a cluttered environment 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.

2025-01-01T00:00:00Z Sangwan, Kuldip Singh http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19954 2025-11-04T08:58:55Z 2025-01-01T00:00:00Z

Machine learning-based predictive life cycle assessment approach during product design Sangwan, Kuldip Singh Most of the past efforts for the environmental impact assessment has been carried out based on the manufactured product. Environmental improvement strategies intertwined with the product design will be more effective as 70-80% of the environmental impacts are fixed during the design phase. However, the major challenge to carry out the life cycle assessment (LCA) during design phase – predictive LCA – is the data scarcity during the design phase. Therefore, this paper proposes data augmentation using deep learning techniques to overcome this challenge for predictive LCA. This paper proposes a four-phase predictive LCA methodology consisting of (i) identification of design requirements and environmental aspects, (ii) database building based upon product descriptors & environmental performance data, (iii) deep learning assisted data preprocessing, and (iv) machine learning based predictive LCA models – random forest, support vector machine, and neural network. It was found that random forest gives the better prediction based on model evaluation metrics of mean absolute error (MAE), mean square error (MSE), root mean squared error (RMSE), and R-square (R2). This research equips environmentalists, companies, researchers, and businesses with a predictive environmental conscious approach to their decision-making processes early in the design phase, thereby, fostering a sustainable approach right from the inception of a product’s design.

2025-01-01T00:00:00Z Sangwan, Kuldip Singh http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19953 2025-11-04T07:15:11Z 2024-07-01T00:00:00Z

A cognitive digital twin for process chain anomaly detection and bottleneck analysis Sangwan, Kuldip Singh Bottleneck detection and management plays a significant role in the context of Industry 4.0, wherein process chains have become more intricate. The dynamic nature of process chains shifts the bottleneck location, which requires an integrated methodology capable of identifying current as well as predicting future bottlenecks. The paper proposes a cognitive digital twin (CDT) with a novel explainable artificial intelligence (XAI) model. The proposed CDT is capable of (i) detecting existing bottlenecks, (ii) detecting data anomalies and process chain anomalies (iii) estimating shifting bottlenecks due to anomalies, (iv) predicting near future bottlenecks, and (v) the XAI model supports operational and strategic decision making. The usefulness of proposed CDT is demonstrated and validated experimentally on an industry 4.0 compliant learning factory. The proposed novel CDT effectively addresses the process chain bottlenecks (existing, shifting, and future) while the XAI model enhances transparency and trustworthiness for practical implementation.

2024-07-01T00:00:00Z Sangwan, Kuldip Singh http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19952 2025-11-04T04:54:51Z 2025-06-01T00:00:00Z

Uncertainty analysis of workpiece orientation: a mathematical decision support system for circular geometry measurements Sangwan, Kuldip Singh The article aims at quantifying and controlling the measurement uncertainties contributed by the workpiece orientation during the automated inspection of a circular geometry by using Coordinate Measuring Machines. The paper proposes a mathematical model to compute angular error and circular variance to quantify and minimize the measurement uncertainty associated with the workpiece orientation. The proposed methodology involves part programming, acquisition of the raw coordinate data points through experiments, identification of the potential factors influencing measurement results, development of the mathematical model to estimate and correct measurement uncertainties, and finally supports the user to minimize the variations in the measurement results. It was found that diameter, circularity and centroid measurements are affected by the workpiece orientation and probe start position has a significant effect on the measurement results. The proposed model reduced uncertainties as the data tends to spread uniformly along the geometric feature. The overall measurement results improved by 12 %.

2025-06-01T00:00:00Z