Date of Award

2025-08-01

Degree Name

Master of Science

Department

Electrical and Computer Engineering

Advisor(s)

Md Fashiar Rahman

Second Advisor

Shian Wang

Abstract

Modern manufacturing is making significant advancements by innovating and automating most processes. However, a major challenge remains: systems are constantly evolving and becoming more complex to analyze. Fortunately, a powerful tool can help, Digital Twin (DT) technology. This technology enables the analysis and optimization of processes like never before. A Digital Twin is a real-time virtual model of a physical system that continuously up dates with live data. One of its greatest features is the ability to create infinite scenarios, allowing hundreds of configurations to be tested virtually, risk-free, and without making any real-world changes that could disrupt ongoing operations. One of the best software solutions for implementing Digital Twins in manufacturing is FlexSim, which specializes in modeling manufacturing and healthcare systems. FlexSim enables users to recreate, analyze, and predict large-scale models. With its real-time analysis tools, users can track performance, utilization, and overall system efficiency with precision. This project developed a Digital Twin (DT) of a conveyor system using FlexSim simulation software, the FlexSim Emulation module, a vision system, and a programmable logic controller (PLC). The system integrates multiple sensors, including a vision sensor, to capture real-time data. The vision system utilizes artificial intelligence (AI) to train a model for object classification. By leveraging a convolutional neural network (CNN) called EfficientNetB0, we created an object classifier capable of identifying object colors and shapes, as well as detecting defective items that the system is not designed to process. A mini-scale prototype was built as a testbed, demonstrating real-time monitoring, re mote oversight, and improved system control. The DT framework is highly scalable, offering significant potential for expanding automation and intelligence in manufacturing. The scholarly contribution of this work lies in the development of a low-cost, scalable Digital Twin framework that integrates AI-driven visual inspection, PLC control, and real-time emulation using FlexSim. Unlike traditional inspection systems, this solution enables near-instant object classification, autonomous decision-making, and full synchronization with physical systems, demonstrating measurable improvements in throughput, inspection speed, and defect handling efficiency.

Language

en

Provenance

Received from ProQuest

File Size

73 p.

File Format

application/pdf

Rights Holder

Jose Francisco Arvizu Astorga

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