Aug 13, 2018 | By Thomas

3D printing has the ability to create complex geometrical shapes with the help of 3D software. However, quality monitoring is still a big challenge in additive manufacturing, as many 3D printers do not have a designated system to track and monitor the progress of printing process. 3D printers may continue to print the part until all the layers have been completed even if the filament ran out or there are any potential defects in the print.

To help solve the problem, two researchers from the Department of Industrial and Manufacturing Systems Engineering (IMSE) at Kansas State University have developed a new quality monitoring system for the 3D printing process.

Ugandhar Delli and Dr. Shing Chang from IMSE have proposed a method to automatically assess the quality of 3D printed parts in real-time with the integration of a camera, image processing, and supervised machine learning. The researchers used a LulzBot Mini 3D printer for their research, which involved pausing the system at different checkpoints to detect any possible defects and take proper corrective actions such as stopping the print if a potential defect is detected.

Delli and Dr. Chang published a paper on their research, titled “Automated Process Monitoring in 3D Printing Using Supervised Machine Learning,” that demonstrates their new process monitoring method.

The researchers proposed a three-step quality monitoring system to implement the proposed quality monitoring of a 3D printed part during production.

Step 1. Identify proper check points for 3D printing part according to its geometry.
Step 2. Take images of the semi-finished part at each check point with the help of a mounted camera.
Step 3. Perform image processing and analysis. A machine learning method, support vector machine (SVM), is proposed to classify the parts into either ‘good’ or ‘defective’ category.

Image taken at a checkpoint. Credit: Kansas State University/Delli and Dr. Chang.

Parts using ABS and PLA materials were printed to demonstrate the proposed framework. The researchers concluded that the method is capable of detecting both completion failure defects such as filament running out or printing stopped in the mid-progress and structural or geometrical defects. However, this quality monitoring system had its drawbacks.

"The main drawback of the proposed method is that the printing process needs to be paused while the images of a semi-finished part are taken,” Delli and Dr. Chang wrote. “Another drawback is that since only top view images are taken, the proposed method might not be able to detect the defects on the vertical plane which cannot be seen in the top view image. This gives us a direction for future research to incorporate cameras on the sides of the printer as well to detect defects on both the horizontal and vertical planes."

A possible improvement can be made by in situ camera mounting on print head, according to the researchers. Their future work will also focus on the study of impacting factors for choosing proper check points.



Posted in 3D Printing Application



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