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Introduction

In recent years, there has been a significant increase in the adoption of advanced technologies such as Artificial Intelligence (AI) in various industries, including manufacturing. One of the emerging trends in this domain is Edge AI, which involves deploying AI algorithms and models on edge devices within the manufacturing environment. This allows for real-time data processing and analysis, enabling faster and more efficient decision-making.

This thesis aims to explore the potential benefits of Edge AI for smart manufacturing and to investigate the challenges and limitations associated with its implementation. By leveraging the capabilities of edge devices, manufacturers can optimize production processes, improve quality control, and enhance overall operational efficiency.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
– Overview of AI in Manufacturing
– Edge Computing in Manufacturing
– Applications of Edge AI in Smart Manufacturing
– Edge AI-enabled Quality Control Systems
– Edge AI for Predictive Maintenance
– Challenges of Implementing Edge AI in Manufacturing
– Case Studies on Edge AI Adoption in Manufacturing
– Future Trends in Edge AI for Smart Manufacturing

Chapter 3: Research Methodology
– Research Design
– Data Collection Methods
– Data Analysis Techniques
– Sampling Procedures
– Ethical Considerations
– Research Limitations
– Research Validity and Reliability
– Pilot Study

Chapter 4: Discussion of Findings
– Analysis of Data Collected
– Comparison of Findings with Existing Literature
– Implications for Smart Manufacturing
– Recommendations for Industry Implementation
– Future Research Directions

Chapter 5: Conclusion and Summary
– Recap of Key Findings
– Contributions to the Field
– Limitations and Future Research Opportunities
– Conclusion and Final Thoughts

Thesis Overview on Edge AI for Smart Manufacturing

Edge AI, also known as Edge Computing, is a cutting-edge technology that brings artificial intelligence algorithms and models closer to the source of data generation in industrial settings such as smart manufacturing. By deploying AI on edge devices within the manufacturing environment, companies can process and analyze data in real-time, enabling faster and more efficient decision-making processes.

This thesis aims to explore the potential benefits of Edge AI for smart manufacturing and to investigate the challenges and limitations associated with its implementation. With the increasing demand for intelligent manufacturing systems, edge AI offers an opportunity to optimize production processes, improve quality control, and enhance overall operational efficiency.

In Chapter 1, the introduction sets the stage by providing an overview of the research topic, background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 delves into a comprehensive literature review covering AI in manufacturing, edge computing, applications of Edge AI in smart manufacturing, challenges, case studies, and future trends.

Chapter 3 focuses on the research methodology, detailing the research design, data collection methods, analysis techniques, sampling procedures, ethical considerations, limitations, validity, reliability, and pilot study. Chapter 4 presents a detailed discussion of findings, including data analysis, comparisons with existing literature, implications for smart manufacturing, recommendations for industry implementation, and future research directions.

In Chapter 5, the conclusion and summary provide a recap of key findings, contributions to the field, limitations, future research opportunities, and final thoughts on the project thesis Edge AI for smart manufacturing. This comprehensive overview aims to contribute to the existing body of knowledge on Edge AI in smart manufacturing and provide valuable insights for practitioners, researchers, and policymakers in the field.

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