ORCID Identifier(s)


Graduation Semester and Year

Spring 2024



Document Type


Degree Name

Doctor of Philosophy in Industrial Engineering


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


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.


Point cloud; Machine learning; Quality assurance; Additive manufacturing; Process mining


Industrial Engineering


Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Available for download on Saturday, May 16, 2026