A HYBRID DEEP LEARNING MODEL OF PROCESS-BUILD INTERACTIONS IN ADDITIVE MANUFACTURING
Open Access
- Author:
- Mojahed Yazdi, Reza
- Graduate Program:
- Industrial Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- July 06, 2020
- Committee Members:
- Hui Yang, Thesis Advisor/Co-Advisor
Steven James Landry, Program Head/Chair
Soundar Rajan Tirupatikumara, Committee Member
Clyde Lee Giles, Committee Member - Keywords:
- Deep Learning
Wavelet Analysis
Process Monitoring
Texture Analysis
Additive Manufacturing
Laser Powder Bed Fusion
Data Analysis
Machine Learning - Abstract:
- Laser powder bed fusion (LPBF) is a technique of additive manufacturing (AM) that is often used to construct a metal object layer-by-layer. The quality of AM builds depends to a great extent on the minimization of different defects such as porosity and cracks that could occur by process deviation during printing operation. Therefore, there is a need to develop new analytical methods and tools to equip the LPBF process with the inspection frameworks that assess the process condition and monitor the porosity defect in real-time. Advanced sensing is recently integrated with the AM machines to cope with process complexity and improve information visibility. This opportunity lays the foundation for online monitoring and assessment of the in-process build layer. This study presents a hybrid deep neural network structure with two types of input data to monitor the process parameters that result in porosity defect in cylinders’ layers. Results demonstrate that statistical features extracted by wavelet transform and texture analysis along with original powder bed images, assist the model to reach a robust performance. In order to illustrate the fidelity of the proposed model, the capability of the main pipeline is examined and compared with different machine learning models. Eventually, the proposed framework identified the process conditions with an F-score of 97.14\%. This salient flaw detection ability is conducive to repair the defect in real-time and assure the quality of the final part before the completion of the process.