Faculty Publications
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Item An Industry 4.0 Approach: Data Acquisition and Machine Monitoring for Welding Machines(Springer Nature, 2024) Narendra Reddy, T.N.; Ponnappa, N.P.; Prasad, P.; Vinod, P.; Herbert, M.A.; Rao, S.S.The research introduces an innovative application of Industry 4.0 principles in welding by employing IIoTbased performance monitoring equipment. One the important aspect of Industry 4.0 adaption is understanding the requirement from the customer and develop/ provide the acceptable solution to them is crucial. An attempt has been made to develop a solution which can be used for any kind of welding machines including legacy welding machines. The developed solution delivers real-time updates on shop floor welding processes with the help of Operational Technology (OT) and Information technology (IT) with the help of hall effect sensors and voltage transducers by connecting them to the Programmable logic controller (PLC). Additionally, it facilitates real-time feedback, alerts, and report generation. The study comprehensively assesses the effectiveness and production capacity of an industrial welding system, presenting a detailed design overview and practical demonstration. Potential enhancements, such as integrating machine learning, emphasizing remote monitoring, evaluating energy efficiency, addressing cybersecurity, and assessing scalability, are explored. The research includes a cost–benefit analysis for the shopfloor and provides insights into the real-world effectiveness of IIoT-based welding performance monitoring in industrial contexts. The developed solution has been tested, validated, and deployed in one of the welding industries, and it has helped in real-time monitoring, scheduling the work and data analytics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Item Development of Low-Cost IIoT Solution for Smart Factories in MSME Industries: Utilizing Current Measurements for Machine and Factory Monitoring(Springer Science and Business Media Deutschland GmbH, 2025) Narendra Reddy, T.N.; Vinod, S.P.; Nachappa, P.P.; Herbert, M.A.; Rao, S.S.In response to an evolving industrial landscape, monitoring industrial assets and systems involves aggregating data from diverse sources within a production system to assess the performance of machinery, and associated processes. This paper is dedicated to the development of an affordable IIoT solution for productivity monitoring in MSME industries and also being implemented in the development cell for manufacturing setup. The devised solution leverages cost-effective hardware for sensing, a controller for data acquisition and analysis, and an edge gateway for cloud storage. The collected data are processed and analyzed through web-based front-end and back-end software technologies designed for machine and factory monitoring. The solution offers insights into factory and machine statuses, facilitates productivity tracking, and enables part count measurements. Furthermore, the software also provides users with automated reporting through Email and SMS, allowing for the creation of customized reports on a daily and monthly basis. Subsequently, the resulting experimental setup was successfully introduced as a low-cost solution to an industry, further validating its practicality and relevance in real-world settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.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 Open-source solutions for real-time data retrieval in industrial automation and IoT environments(Inderscience Publishers, 2025) Hegde, S.B.; Narendra Reddy, T.N.; Prasannan, P.; Manjunath, K.V.; Herbert, M.A.; Rao, S.S.Digitalisation of the manufacturing industries due to the implementation of the ‘industrial internet of things (IIOT)’ is a key enabler for improved productivity and reliability at a reduced labour cost. The industrial IOT connects all the industrial machines such as PLCs, CNCs, and robots through a robust network. The generated data by these end devices plays a vital role in industrial automation, however acquiring the data from machines specifically legacy machines using various communication protocols is the biggest challenge and costly process, especially for MSMEs. Hence this paper discusses the usage of the open-source framework for real-time data acquisition from industrial machines and its implication in Industry 4.0. The paper implements and validates the possibility of the usage of an open-source framework for data acquisition instead of vendor-specific licensed software using several test cases. The paper also validates and proves ‘Wireshark’ can be a universal open-source solution for data acquisition using any standard communication protocols from various vendor-specific machines. Hence this work provides a novel solution for the digitalisation of the MSME manufacturing industries efficiently at the reduced maintenance cost and improve their productivity. © © 2025 Inderscience Enterprises Ltd.