Purdue University Graduate School
Jack_Girard_Purdue_University_Thesis.pdf (31.33 MB)

Fast error detection method for additive manufacturing process monitoring using structured light three dimensional imaging technique

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posted on 2024-01-19, 18:41 authored by Jack Matthew GirardJack Matthew Girard

Monitoring of additive manufacturing (AM) processes allows for saving time and materials by detecting and addressing errors as they occur. When fast and efficient, the monitored AM of each unit can be completed in less time, thus improving overall economics and allowing the user to accept a higher capacity of AM requests with the same number of machines. Based on existing AM process monitoring solutions, it is very challenging for any approach to analyze full-resolution sensor data that yields three-dimensional (3D) topological information for closed-loop real-time applications. It is also challenging for any approach to be simultaneously capable of plug-and-play operation once AM hardware and sensor subsystems are configured. This thesis presents a novel method to speed up error detection in an additive manufacturing (AM) process by minimizing the necessary three-dimensional (3D) reconstruction and comparison. A structured light 3D imaging technique is developed that has native pixel-by-pixel mapping between the captured two-dimensional (2D) absolute phase image and the reconstructed 3D point cloud. This 3D imaging technique allows error detection to be performed in the 2D absolute phase image domain prior to 3D point cloud generation, which drastically reduces complexity and computational time. For each layer of an AM process, an artificial threshold phase image is generated and compared to the measured absolute phase image to identify error regions. Compared to an existing AM error detection method based on 3D reconstruction and point cloud processing, experimental results from a material extrusion (MEX) AM process demonstrate that the proposed method has comparable error detection capabilities. The proposed method also significantly increases the error detection speed, where the relationship between the speed improvement factor and the percentage of erroneous pixels in the captured 2D image follows a power-law relationship. The proposed method was also successfully used to implement closed-loop error correction to demonstrate a potential process monitoring application.




Degree Type

  • Master of Science in Mechanical Engineering


  • Mechanical Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Song Zhang

Additional Committee Member 2

Monique McClain

Additional Committee Member 3

George Chiu

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