Graduation Semester and Year
Spring 2024
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Industrial Engineering
Department
Industrial and Manufacturing Systems Engineering
First Advisor
Dr. Chen Kan
Second Advisor
Dr. Shouyi Wang
Third Advisor
Dr. Xin Liu
Fourth Advisor
Dr. Amir Ameri
Abstract
The rapid advancement in Additive Manufacturing (AM) technologies has developed significant innovations in various sectors, including medical, aerospace, and automotive industries. Despite these benefits, the adoption of AM is often hindered by quality inconsistencies related to the surface defects and geometrical inaccuracies in the fabricated products. These defects can significantly undermine the mechanical properties of the products, leading to material waste and potential safety issues. This dissertation addresses the critical challenges in quality control of AM processes through the integration of 3D point cloud data and machine learning techniques, aiming to enhance the reliability and efficiency of AM systems. The primary objective of this research is to develop a comprehensive framework that utilizes real-time 3D point cloud data for anomaly detection and evaluation during the AM processes. The methodologies proposed in this dissertation leverage the strengths of 3D point cloud data, including its ability to provide detailed and accurate geometric representations, which are essential for high-quality AM production. The integration of these data with advanced analytical and machine learning techniques presents a significant step forward in the automation and optimization of AM quality control processes. This research contributes to the field by providing robust tools for enhancing the reliability and efficiency of AM systems, potentially leading to broader adoption and industrial applications of these technologies.
Keywords
Point cloud; Machine learning; Quality assurance; Additive manufacturing; Process mining
Disciplines
Industrial Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Ye, Zehao, "3D Point Cloud Sensing and Analytics with Applications in Process Mining and Quality Control of Additive Manufacturing" (2024). Industrial, Manufacturing, and Systems Engineering Dissertations. 1.
https://mavmatrix.uta.edu/industrialmanusys_dissertations/1