Real-time molten droplet analyzer with spatial modulation in additive manufacturing

Patent metadata

Field	Value
Publication number	US-12454004-B2
Application number	US-202217984113-A
Country	US
Kind code	B2
Filing date	Nov 9, 2022
Priority date	Nov 9, 2022
Publication date	Oct 28, 2025
Grant date	Oct 28, 2025

Abstract

Techniques for determining characteristics of a stream of jetted material in a three-dimensional (3D) printer are disclosed. An example system includes an ejector configured to release molten droplets along a jetting path from the ejector to a build platform. The system also includes a sensor positioned adjacent to the jetting path and configured to generate an electrical signal in response to light emanating from the molten droplets. The system also includes an optical mask positioned adjacent to the jetting path, the optical mask comprising a plurality of regions configured to modulate the electrical signal generated by the sensor as the molten droplets travel along the jetting path. The system also includes one or more processing devices to receive the electrical signal, analyze the electrical signal to identify one or more characteristics of the molten droplets, and control the 3D printer based on the one or more characteristics.

First claim

What is claimed is:

1. A three-dimensional (3D) printer, comprising:
an ejector configured to release molten droplets along a jetting path from the ejector to a build platform;
a sensor positioned adjacent to the jetting path and configured to generate an electrical signal in response to light emanating from the molten droplets;
an optical mask positioned adjacent to the jetting path, the optical mask comprising a plurality of regions configured to modulate the electrical signal generated by the sensor as the molten droplets travel along the jetting path;
one or more processing devices to:
receive the electrical signal;
analyze the electrical signal to identify one or more characteristics of the molten droplets; and
control the 3D printer based on the one or more characteristics.

2. The 3D printer of claim 1 , wherein to analyze the electrical signal comprises to:
identify a frequency component of the electrical signal; and
identify the one or more characteristics of the molten droplets based on the frequency component, wherein the one or more characteristics comprises a travel speed of at least one of the one or more molten droplets.

3. The 3D printer of claim 1 , wherein to analyze the electrical signal comprises to:
identify amplitude variations in the electrical signal; and
identify the one or more characteristics of the molten droplets based on the amplitude variations, wherein the one or more characteristics comprises a size of at least one of the one or more molten droplets.

4. The 3D printer of claim 1 , wherein to analyze the electrical signal comprises to generate a regularity metric describing a degree of regularity of the molten droplets.

5. The 3D printer of claim 1 , wherein to analyze the electrical signal comprises to identify a presence of satellites among the molten droplets.

6. The 3D printer of claim 1 , wherein to analyze the electrical signal comprises to:
identify a duty cycle of the electrical signal; and
identify the one or more characteristics of the molten droplets based on the duty cycle, wherein the one or more characteristics comprises a trajectory of at least one of the one or more molten droplets.

7. The 3D printer of claim 1 , wherein to analyze the electrical signal comprises to convert the electrical signal to a frequency domain representation.

8. The 3D printer of claim 1 , wherein to analyze the electrical signal comprises to:
input the electrical signal or a feature of the electrical signal into a trained neural network; and
receive an output of the trained neural network, wherein the output comprises the one or more characteristics of the molten droplets.

9. The 3D printer of claim 8 , wherein the trained neural network is trained using a training signal labeled to indicate a corresponding droplet characteristic indicated by the training signal and obtained via high-speed imaging.

10. The 3D printer of claim 1 , wherein the sensor is a first sensor, the electrical signal is a first electrical signal, and the optical mask is a first optical mask, and wherein the 3D printer further comprises:
a second sensor positioned adjacent to the jetting path; and
a second optical mask configured to modulate a second electrical signal generated by the second sensor as the molten droplets travel along the jetting path, wherein the one or more processing devices are to analyze the second electrical signal to identify one or more additional characteristics of the molten droplets.

11. A method of sensing characteristics of a stream of jetted material in a 3D printer, the method comprising:
ejecting molten droplets along a jetting path from an ejector to a build platform;
sensing light emanating from the molten droplets to generate an electrical signal corresponding to the light emitted by the molten droplets;
encoding information in the light emanating from the molten droplets using an optical mask positioned adjacent to the jetting path, wherein the optical mask comprises a plurality of regions comprising light-blocking regions and light-passing regions configured to modulate the signal as the molten droplets travel along the jetting path;
analyzing, by a processing device, the electrical signal to identify one or more characteristics of the molten droplets; and
controlling the 3D printer based on the one or more characteristics.

12. The method of claim 11 , wherein analyzing the electrical signal comprises:
identifying a frequency component of the electrical signal; and
identifying the one or more characteristics of the molten droplets based on the frequency component, wherein the one or more characteristics comprises a travel speed of at least one of the one or more molten droplets.

13. The method of claim 11 , wherein analyzing the electrical signal comprises:
identifying amplitude variations in the electrical signal; and
identifying the one or more characteristics of the molten droplets based on the amplitude variations, wherein the one or more characteristics comprises a size of at least one of the one or more molten droplets.

14. The method of claim 11 , wherein analyzing the electrical signal comprises generating a regularity metric describing a degree of regularity of the molten droplets.

15. The method of claim 11 , wherein analyzing the electrical signal comprises identifying a presence of satellites among the molten droplets.

16. The method of claim 11 , wherein analyzing the electrical signal comprises:
identifying a duty cycle of the electrical signal; and
identifying the one or more characteristics of the molten droplets based on the duty cycle, wherein the one or more characteristics comprises a trajectory of at least one of the one or more molten droplets.

17. The method of claim 11 , wherein analyzing the electrical signal comprises:
inputting the electrical signal or a feature of the electrical signal into a trained neural network; and
receiving an output of the trained neural network, wherein the output comprises the one or more characteristics of the molten droplets, wherein the trained neural network is trained using a training signal labeled to indicate a corresponding droplet characteristic indicated by the training signal.

18. A non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a processing device, cause the processing device to:
receive an electrical signal generated by a sensor positioned adjacent to a jetting path of a 3D printer, wherein the electrical signal is generated in response to light emanating from molten droplets as they pass adjacent to an optical mask positioned adjacent to the jetting path;
analyze the electrical signal to identify one or more characteristics of the molten droplets encoded into the electrical signal by the optical mask; and
control the 3D printer based on the one or more characteristics.

19. The non-transitory computer-readable storage medium of claim 18 , wherein to analyze the electrical signal comprises to:
identify a frequency component of the electrical signal and amplitude variations in the electrical signal;
identify a travel speed of at least one of the one or more molten droplets based on the frequency component; and
identify a size of at least one of the one or more molten droplets based on the amplitude variations.

20. The non-transitory computer-readable storage medium of claim 18 , wherein to analyze the electrical signal comprises to:
identify a duty cycle of the electrical signal; and
identify a trajectory of

Assignees

• Xerox Corp

Inventors

• Chen Qiushu
• Kiesel Peter
• Timucin Dogan

Classifications

• B22F12/90

  Means for process control, e.g. cameras or sensors · CPC title

• B33Y30/00

  Apparatus for additive manufacturing; Details thereof or accessories therefor · CPC title

• B33Y10/00 Primary

  Processes of additive manufacturing · CPC title

• B22F10/85

  for controlling or regulating additive manufacturing processes · CPC title

• B33Y50/02

  for controlling or regulating additive manufacturing processes · CPC title