arXiv subjects

Nitol Saha

Publications and source records attributed to Nitol Saha.

5 recordsLinked to original sources

arXiv2024

VR-based Blockchain-enabled Data Visualization Framework For Manufacturing Industry

This research proposes a blockchain-based data visualization framework integrated with VR to get manufacturing insights. This framework is implemented at the testbed of the Future Factories Lab at the University of South Carolina. The proposed system aims to enhance understanding, analysis, and decision-making processes by immersing users in a VR environment where complex manufacturing data stored using blockchain is translated into intuitive and interactive representations. The project focuses on two main components: blockchain and VR. Hyperledger Fabric is employed to establish a blockchain network, recording data from the Future Factories testbed. This system captures information from various sources, such as potentiometers on robot grippers to measure grip positioning, load cells to gauge pressure, emergency stop buttons, temperature, speed, and vibration sensors on the conveyors. Whenever predefined conditions are met, pertinent data, including sensor ID, timestamp, value, cause, and importance, is securely recorded in the blockchain, signaling the occurrence of a defect within the cell. Data retrieved from the blockchain system is accessed through 'GET' API requests. A VR application is developed using a cross-platform Unity game engine to visualize the data retrieved from the blockchain database. Meta Quest 3 is used as the targeted Head Mounted VR device. The VR application has two C# scripts: one script to query blockchain data using 'GET' API calls and another script converts the JSON object to text data to visualize in the VR system. The proposed system leverages blockchain technology and VR visualization to deliver immersive, actionable insights using secure data transmission. By embracing the proposed framework, manufacturers can unlock new potential for efficiency, sustainability, and resilience in today's increasingly complex and interconnected manufacturing workplace.

cs.CR↗

arXiv2024

IoT-enabled Stability Chamber for the Pharmaceutical Industry

A stability chamber is essential for pharmaceutical facilities to test the stability and quality of products over time by exposing them to different environmental conditions. This paper introduces an IoT-enabled stability chamber designed for the pharmaceutical industry. We constructed four stability chambers by leveraging the existing infrastructure within a manufacturing facility. Each chamber is controlled using a state-of-the-art Proportional Integral Derivative (PID) system based on the Siemens S7-1200 PLC. The Siemens WinCC Runtime Advanced platform, compliant with FDA 21 CFR Part 11, was used for visualizing chamber data. Additionally, an Internet of Things (IoT) application was developed to remotely monitor sensor data through any client application. This research aims to enhance the performance of traditional stability chambers by integrating IoT functionalities, making them more cost-effective and user-friendly.

eess.SY↗

arXiv2024

IoT-Driven Cloud-based Energy and Environment Monitoring System for Manufacturing Industry

This research focused on the development of a cost-effective IoT solution for energy and environment monitoring geared towards manufacturing industries. The proposed system is developed using open-source software that can be easily deployed in any manufacturing environment. The system collects real-time temperature, humidity, and energy data from different devices running on different communication such as TCP/IP, Modbus, etc., and the data is transferred wirelessly using an MQTT client to a database working as a cloud storage solution. The collected data is then visualized and analyzed using a website running on a host machine working as a web client.

eess.SY↗

arXiv2024

Protected QR Code-based Anti-counterfeit System for Pharmaceutical Manufacturing

The pharmaceutical manufacturing faces critical challenges due to the global threat of counterfeit drugs. This paper proposes a new approach of protected QR codes to secure unique product information for safeguarding the pharmaceutical supply chain. The proposed solution integrates secure QR code generation and encrypted data transmission to establish a comprehensive anti-counterfeit ecosystem. The protected QR codes encapsulate product information that cannot be identified using traditional QR code scanners which protect the information against replication and tampering. The system is developed with scalability in mind, which can be easily implemented without introducing any additional modification in the traditional supply chain.

cs.CR↗

arXiv2024

Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform

Two industry-grade datasets are presented in this paper that were collected at the Future Factories Lab at the University of South Carolina on December 11th and 12th of 2023. These datasets are generated by a manufacturing assembly line that utilizes industrial standards with respect to actuators, control mechanisms, and transducers. The two datasets were both generated simultaneously by operating the assembly line for 30 consecutive hours (with minor filtering) and collecting data from sensors equipped throughout the system. During operation, defects were also introduced into the assembly operation by manually removing parts needed for the final assembly. The datasets generated include a time series analog dataset and the other is a time series multi-modal dataset which includes images of the system alongside the analog data. These datasets were generated with the objective of providing tools to further the research towards enhancing intelligence in manufacturing. Real manufacturing datasets can be scarce let alone datasets with anomalies or defects. As such these datasets hope to address this gap and provide researchers with a foundation to build and train Artificial Intelligence models applicable for the manufacturing industry. Finally, these datasets are the first iteration of published data from the future Factories lab and can be further adjusted to fit more researchers needs moving forward.

cs.LG↗