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Introduction

Additive manufacturing (AM) technologies have revolutionized the traditional manufacturing processes by enabling the production of complex parts with high precision and efficiency. Among the various types of AM technologies, laser powder bed fusion (LPBF) has gained significant attention due to its ability to produce metal parts with superior mechanical properties. However, the LPBF process is complex and involves a number of parameters that need to be optimized in order to achieve the desired part quality.

Computational modeling plays a crucial role in understanding the underlying physics of the LPBF process and optimizing the process parameters. By simulating the thermal dynamics, powder spreading, and melt pool behavior, researchers can gain valuable insights into the process and make informed decisions to improve part quality. This thesis aims to develop a comprehensive computational model for LPBF and investigate the various factors that influence part quality.

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
2.1 Overview of Additive Manufacturing
2.2 Laser Powder Bed Fusion Process
2.3 Computational Modeling in Additive Manufacturing
2.4 Previous Research on LPBF Modeling
2.5 Material Properties in LPBF
2.6 Process Parameters in LPBF
2.7 Part Quality in LPBF
2.8 Optimization Techniques in LPBF
2.9 Challenges in LPBF Modeling
2.10 Summary of Literature Review

Chapter 3: System Design and Methodology
3.1 Computational Model Development
3.2 Finite Element Analysis
3.3 Modeling Heat Transfer in LPBF
3.4 Powder Bed Dynamics Simulation
3.5 Meltpool Formation Model
3.6 Calibration and Validation
3.7 Sensitivity Analysis
3.8 Optimization Algorithm
3.9 Data Analysis Techniques

Chapter 4: System Implementation
4.1 Software Tools and Programming Languages
4.2 Model Implementation
4.3 Simulation Setup
4.4 Parameter Optimization
4.5 Case Studies
4.6 Results and Discussion
4.7 Comparison with Experimental Data
4.8 Validation of the Model

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Research Directions
5.4 Conclusion
5.5 Recommendations
5.6 Implications for Industry

Thesis Overview

The use of additive manufacturing technologies has seen a significant rise in recent years, with laser powder bed fusion (LPBF) being at the forefront of metal additive manufacturing processes. LPBF offers the ability to produce complex geometries with high precision and mechanical properties, making it an attractive option for various industries such as aerospace, automotive, and medical.

However, the LPBF process is complex and influenced by a multitude of parameters that need to be optimized to ensure part quality and production efficiency. Computational modeling has emerged as a powerful tool to gain insights into the underlying physics of the process, optimize process parameters, and improve part quality.

In this thesis, we seek to develop a comprehensive computational model for LPBF that simulates the thermal dynamics, powder spreading, and melt pool behavior during the process. By understanding these fundamental aspects of LPBF, we aim to optimize process parameters and improve part quality through informed decision-making.

Through a systematic literature review, we will explore the current state-of-the-art in LPBF modeling, material properties, process parameters, and optimization techniques. In the system design and methodology chapter, we will outline the development of the computational model, including finite element analysis, heat transfer modeling, powder bed dynamics simulation, and meltpool formation model. The system implementation chapter will detail the software tools and programming languages used, model implementation, simulation setup, parameter optimization, and case studies.

By the end of this thesis, we hope to provide valuable insights into the LPBF process, contribute to the field of additive manufacturing, and offer recommendations for future research directions. The findings of this research will have significant implications for industry, as they can inform the optimization of LPBF processes and improve part quality in various applications.

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