Browsing by Autor "V. Ganesan"

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AI-Driven Intelligent Control Strategies for Industrial Robotics: A Reinforcement Learning Approach
(2025) Kishore Kunal; M. Kathiravan; Vairavel Madeshwaren; T. Chandrakala; V. Ganesan; Sheifali Gupta
This study proposes an AI-driven adaptive control strategy to enhance the learning, adaptability, and autonomous performance of robotic ‎manipulators in dynamic and unstructured industrial environments. Moving beyond the limitations of conventional model-based controllers, ‎the research introduces a self-learning framework that integrates real-time sensor data from LiDAR and stereo vision cameras. This data ‎continuously informs and optimizes the robot’s motion trajectories in both simulated and real-world tasks. The system’s core innovation lies ‎in combining Reinforcement Learning (RL) with Deep Neural Networks (DNNs) for adaptive trajectory planning and error compensation. ‎Specifically, the Proximal Policy Optimization (PPO) algorithm is employed to fine-tune control strategies based on real-time sensory ‎feedback, allowing the robotic system to autonomously adapt to variations in object positions and unexpected disturbances. An Edge-AI ‎module is embedded into the architecture to enhance decision-making speed and reduce latency during task execution. Experimental ‎validation, including scenarios like arc welding and sealant dispensing, shows the proposed system outperforms traditional PID-based ‎adaptive controllers. The AI-driven solution demonstrated improved precision, faster convergence, and superior adaptability under complex ‎and fluctuating manufacturing conditions. The study also opens pathways for future integration of hybrid AI techniques—such as fuzzy ‎logic and genetic algorithms—for even more intelligent and responsive robotic systems‎.
Federated Learning for Secure AI-Driven Predictive Maintenance in Smart Manufacturing
(2025) Kishore Kunal; Vairavel Madeshwaren; S. Leena Nesamani; A. Banushri; V. Ganesan; Sheifali Gupta
Background: In the Industry 4.0 landscape, integrating artificial intelligence (AI) with smart manufacturing is essential for enhancing automated monitoring, predictive maintenance, and system optimization. However, traditional centralized AI model training poses critical risks to data privacy, security, and scalability, especially when sensitive operational data from factory machines is shared across platforms. Methods: This study proposes a decentralized, intelligent framework designed for real-time machine monitoring that enhances fault detection accuracy while safeguarding data privacy. The approach begins with real-time sensor data acquisition—capturing vibration, temperature, and acoustic signals from distributed factory units via edge devices. These signals undergo preprocessing and advanced feature extraction using Wavelet Transform and Empirical Mode Decomposition (EMD) to reveal critical fault characteristics. Results: A hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks is used for classification. CNNs are responsible for extracting spatial features, whereas LSTMs identify temporal dependencies in time-series. With the federated learning (FL) framework, model training can be done collaboratively across edge devices without the need to transfer sensitive raw data. Conclusion: This ensures security and enhances model generalization. Results from experiments indicate that the suggested FL-based hybrid model exceeds centralized architectures regarding detection accuracy, computational efficiency, and adaptability. This research provides a scalable and secure solution that enhances intelligent monitoring for Industry 4.0 systems.
Intelligent 3D Printing Algorithms: Accelerating Additive Manufacturing with Precision and Defect-Free Fabrication
(2025) M. Kathiravan; Kishore Kunal; Vairavel Madeshwaren; P. Lavanya; V. Ganesan; Sheifali Gupta
Additive Manufacturing (AM) has transformed modern production by enabling the fabrication of complex geometries with enhanced material ‎efficiency. However, traditional 3D printing techniques often face challenges such as incomplete fusion, material inconsistencies, and thermal ‎warping, which affect overall quality and productivity. This study introduces an intelligent 3D printing framework that integrates Artificial ‎Intelligence (AI) to enable real-time monitoring, defect detection, and adaptive process control, thereby addressing these limitations. The ‎proposed system utilizes Convolutional Neural Networks (CNNs) for computer vision-based quality inspection, enabling the detection of ‎structural anomalies during the printing process. Reinforcement Learning (RL) is employed for dynamic adjustment of parameters like nozzle ‎temperature, deposition speed, and material feed rate in response to real-time feedback, significantly reducing defect occurrence. Adaptive ‎machine learning algorithms like Random Forests and Gradient Boosting also facilitate process optimization and predictive maintenance. ‎Stereolithography (SLA), Selective Laser Sintering (SLS) and Fused Deposition Modeling (FDM) are among the AM platforms that use this AI-‎AI-enhanced closed-loop control approach. Material use, energy efficiency, production time, print quality and defect mitigation have all significantly ‎improved, as confirmed by experimental validation. With its ability to guarantee accuracy and dependability in contemporary 3D printing ‎processes, the framework shows great promise for developing industrial and biomedical applications‎.