Term of Award
Spring 2024
Degree Name
Master of Science, Mechanical Engineering
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mechanical Engineering
Committee Chair
Hossain Ahmed
Committee Member 1
Haijun Gong
Committee Member 2
Mujibur Khan
Committee Member 3
Sourav Banerjee
Committee Member 3 Email
banerjes@cec.sc.edu
Abstract
This study presents a novel approach to overcoming process reliability challenges in Material Extrusion (ME), a prominent additive manufacturing (AM) technique. Despite ME's advantages in cost, versatility, and rapid prototyping, it faces significant barriers to commercial-scale production, primarily due to quality issues such as overextrusion and underextrusion, which compromise final part performance. Traditional manual monitoring methods severely lack the capability to efficiently detect these defects and highlight the necessity for an efficient and real-time monitoring solution. Considering these challenges, an innovative and field-deployable infrared thermography-based in-situ real-time defect detection and feedback control system is proposed in this thesis. A novel experimental setup and data acquisition framework are developed and automated to collect consistent thermal data during the extrusion process. The study elaborates on the preprocessing of this data for deep learning model training, including sub-segmentation and temporal trimming. Six deep learning architectures are trained and evaluated for their ability to detect defects in real-time. One classification model incorporating CNN-SelfAttention architecture is selected for online implementation based on its performance. The proposed closed-loop control system demonstrates the dynamic adjustment of the relative flow rate within 3 seconds of process deviation. The developed methodology ensures consistent production of high-quality parts by reducing percentage volumetric change by factors of 10. This approach not only ensures the production of defect-free parts within tolerable ranges but also sets a foundation for future research in automated monitoring systems for additive manufacturing. The findings and methodologies detailed in this thesis contribute to the advancement of AM and offer a path toward overcoming one of its major barriers to commercial and industrial application.
Recommended Citation
Sadaf, Asef Ishraq, "Integration of Infrared Thermography and Deep Learning for Real-Time In-Situ Defect Detection and Rapid Elimination of Defect Propagation in Material Extrusion" (2024). Electronic Theses and Dissertations. 2749.
https://digitalcommons.georgiasouthern.edu/etd/2749
Research Data and Supplementary Material
No
