System and method for quantifying nozzle occlusion in 3D printing
One embodiment can provide a system for detecting occlusion at an orifice of a three-dimensional (3D) printer nozzle while the printer nozzle is jetting liquid droplets. During operation, the system uses one or more cameras to capture an image of the orifice of the printer nozzle while the 3D printer nozzle is jetting liquid droplets. The system performs an image-analysis operation on the captured image to identify occluded regions within the orifice of the 3D printer nozzle, compute an occlusion fraction based on the determined occluded regions, and generate an output based on the computed occlusion fraction, thereby facilitating effective maintenance of the 3D printer.
1. A computer-executed method for determining a maintenance schedule for a three-dimensional (3D) printer nozzle, the method comprising:
obtaining an image of an orifice of the 3D printer nozzle captured by a camera while the 3D printer nozzle is jetting liquid droplets;
performing, by a computer, an image-analysis operation on the captured image to determine an occlusion fraction of the orifice of the 3D printer nozzle; and
determining the maintenance schedule for the 3D printer nozzle based on the occlusion fraction and a predetermined occlusion threshold.
2. The computer-executed method of claim 1 , further comprising adjusting the predetermined occlusion threshold based a quality requirement associated with printed products of the 3D printer.
3. The computer-executed method of claim 1 , further comprising:
obtaining a sequence of images of the orifice captured over a time period; and
performing, by the computer, the image-analysis operation on the sequence of images to determine time-varying changes of one or more occluded regions within the orifice.
4. The computer-executed method of claim 1 , wherein performing the image-analysis operation on the captured image comprises computing a gray-level co-occurrence matrix (GLCM) for the captured image to obtain a texture-analysis outcome associated with the captured image.
5. The computer-executed method of claim 4 , wherein performing the image-analysis operation further comprises:
applying an adaptive thresholding technique on the texture-analysis outcome to obtain a binary pixel map of the captured image.
6. The computer-executed method of claim 1 , wherein performing the image-analysis operation on the captured image comprises applying a machine-learning technique to identify one or more occluded regions within the orifice.
7. The computer-executed method of claim 1 , further comprising performing maintenance on the 3D printer nozzle in response to the occlusion fraction exceeding the predetermined occlusion threshold.
8. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for determining a maintenance schedule for a three-dimensional (3D) printer nozzle, the method comprising:
obtaining an image of an orifice of the 3D printer nozzle captured by a camera while the 3D printer nozzle is jetting liquid droplets;
performing an image-analysis operation on the captured image to determine an occlusion fraction of the orifice of the 3D printer nozzle; and
determining the maintenance schedule for the 3D printer nozzle based on the occlusion fraction and a predetermined occlusion threshold.
9. The non-transitory computer-readable storage medium of claim 8 , wherein the method further comprises adjusting the predetermined occlusion threshold based a quality requirement associated with printed products of the 3D printer.
10. The non-transitory computer-readable storage medium of claim 8 , wherein the method further comprises:
obtaining a sequence of images of the orifice over a time period;
performing the image-analysis operation on the sequence of images to determine time-varying changes of one or more occluded regions within the orifice.
11. The non-transitory computer-readable storage medium of claim 8 , wherein performing the image-analysis operation on the captured image comprises computing a gray-level co-occurrence matrix (GLCM) for the captured image to obtain a texture-analysis outcome associated with the captured image.
12. The non-transitory computer-readable storage medium of claim 11 , wherein performing the image-analysis operation further comprises applying an adaptive thresholding technique on the texture-analysis outcome to obtain a binary pixel map of the captured image.
13. The non-transitory computer-readable storage medium of claim 8 , wherein performing the image-analysis operation on the captured image comprises applying a machine-learning technique to identify one or more occluded regions within the orifice.
14. The non-transitory computer-readable storage medium of claim 8 , wherein the method further comprises performing maintenance on the 3D printer nozzle in response to the occlusion fraction exceeding the predetermined occlusion threshold.
15. A three-dimensional (3D) printer system, comprising:
a printer nozzle;
a processor; and
a storage device coupled to the processor and storing instructions which when executed by the processor cause the processor to perform a method, the method comprising:
obtaining an image of an orifice of the printer nozzle captured by a camera while the printer nozzle is jetting liquid droplets;
performing an image-analysis operation on the captured image to determine an occlusion fraction of the orifice of the printer nozzle; and
determining a maintenance schedule for the printer nozzle based on the occlusion fraction and a predetermined occlusion threshold.
16. The 3D printer system of claim 15 , wherein the method further comprises adjusting the predetermined occlusion threshold based a quality requirement associated with printed products of the 3D printer.
17. The 3D printer system of claim 15 , wherein the method further comprises:
obtaining a sequence of images of the orifice over a time period;
performing the image-analysis operation on the sequence of images to determine time-varying changes of one or more occluded regions within the orifice.
18. The 3D printer system of claim 15 , wherein performing the image-analysis operation on the captured image comprises computing a gray-level co-occurrence matrix (GLCM) for the captured image to obtain a texture-analysis outcome associated with the captured image.
19. The 3D printer system of claim 18 , wherein performing the image-analysis operation further comprises applying an adaptive thresholding technique on the texture-analysis outcome to obtain a binary pixel map of the captured image.
20. The 3D printer system of claim 15 , wherein performing the image-analysis operation on the captured image comprises applying a machine-learning technique to identify one or more occluded regions within the orifice.