Sagar Nikam
Lecturer (Assistant Professor) in Mechanical Engineering
United KingdomResearch Interests
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Positions (1)
Sagar Nikam
Ulster University
8 months ago
Ulster University is offering a fully funded PhD studentship in the area of Mechanical Engineering, specifically focusing on the development of a Real-time Weld Monitoring System for Robotic Arc Welding Process . This project is conducted in collaboration with the industry partner Nugent Trailers Ltd, providing a unique opportunity for students interested in industrial applications of engineering research. The research will address advanced topics in weld monitoring, automation, and robotics, aiming to enhance the efficiency and quality of robotic arc welding processes. The successful candidate will join the School of Computing, Engineering and Intelligent Systems at Ulster University, working under the supervision of Dr. Sagar Nikam, Lecturer (Assistant Professor) in Mechanical Engineering. The project is open to applicants worldwide, and the university encourages candidates with a strong background in Mechanical Engineering or related fields to apply. Funding: The studentship is fully funded, covering tuition fees and providing a stipend. The funding is supported by Nugent Trailers Ltd, ensuring strong industry engagement and practical relevance. Eligibility: Applicants should have a solid academic record in Mechanical Engineering or a closely related discipline. International and domestic candidates are eligible. Additional requirements may include a minimum degree classification, English language proficiency, and relevant research or industry experience. Please consult the official project page for comprehensive eligibility details. Application Deadline: Friday, 27 February 2026, by 04:00 PM (UK time). How to Apply: Interested candidates should review the full project details and application instructions at the provided Ulster University link. For further inquiries or to discuss the opportunity, contact Dr. Sagar Nikam at [email protected]. Keywords: Weld Monitoring, Robotic Arc Welding, Mechanical Engineering, Real-time Systems, Industrial Collaboration, Engineering, Automation.
8 months ago
Articles (6)
This paper presents a deep learning approach to identify and classify various defects in the laser-directed energy manufactured components. It mainly focuses on the Convolutional Neural Network (CNN) architectures, such as VGG16, AlexNet, GoogLeNet and ResNet to perform the automated classification of defects. The main objectives of this research are to manufacture components using the laser-directed energy deposition process, prepare a dataset of horizontal wall structure, vertical wall structure and cuboid structure with three defective classes such as voids, flash formation, and rough textures, and one non-defective class, use this dataset with a deep learning algorithm to classify the defect and use the efficient algorithm to detect defects. The next objective is to compare the performance parameters of VGG16, AlexNet, GoogLeNet and ResNet used for classifying defects. It has been observed that the best results were obtained when the VGG16 architecture was applied to an augmented dataset. With augmentation, the VGG16 architecture gave a test accuracy of 94.7% and a precision of 80.0%. The recall value is 89.3% and an F1-Score is 89.5%. The VGG16 architecture with augmentation is highly reliable for automating the defect detection process and classifying defects in the laser additive manufactured components.
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2023
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2022
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2021
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2020
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2020
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2020
Collaborators (4)
Deepika Nikam
Lecturer
University of Ulster
Shuo YIN
Assistant Professor
Trinity College Dublin
Shaun McFadden
Senior Lecturer
University of Ulster
Hao Wu
University of Birmingham
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