Characterizing liquid reflective surfaces in 3D liquid metal printing

Patent metadata

Field	Value
Publication number	US-12002265-B2
Application number	US-202117356604-A
Country	US
Kind code	B2
Filing date	Jun 24, 2021
Priority date	Jun 24, 2021
Publication date	Jun 4, 2024
Grant date	Jun 4, 2024

Abstract

A method includes defining a model for a liquid while the liquid is positioned at least partially within a nozzle of a printer. The method also includes synthesizing video frames of the liquid using the model to produce synthetic video frames. The method also includes generating a labeled dataset that includes the synthetic video frames and corresponding model values. The method also includes receiving real video frames of the liquid while the liquid is positioned at least partially within the nozzle of the printer. The method also includes generating an inverse mapping from the real video frames to predicted model values using the labeled dataset. The method also includes reconstructing the liquid in the real video frames based at least partially upon the predicted model values.

First claim

What is claimed is:

1. A method, comprising:
defining a model for a liquid while the liquid is positioned at least partially within a nozzle of a printer;
synthesizing video frames of the liquid using the model to produce synthetic video frames;
generating a labeled dataset that comprises the synthetic video frames and corresponding model values;
receiving real video frames of the liquid while the liquid is positioned at least partially within the nozzle of the printer;
generating an inverse mapping from the real video frames to predicted model values using the labeled dataset;
reconstructing the liquid in the real video frames based at least partially upon the predicted model values; and
extracting one or more metrics from the reconstructed liquid, wherein the metrics comprise a carrier oscillation frequency, a pulse-to-pulse covariance, a waveform decay rate, or a combination thereof.

2. The method of claim 1 , wherein the model comprises a parametric model.

3. The method of claim 1 , wherein the model values comprise:
a relative weight of a function that describes an oscillation of the liquid;
a constant offset;
a constant representing a steady state shape of the liquid at rest;
or a combination thereof.

4. The method of claim 1 , wherein the predicted model values comprise a subset of the model values.

5. The method of claim 1 , wherein generating the inverse mapping comprises training an artificial neural network using the labeled dataset to predict the predicted model values in the real video frames.

6. The method of claim 1 , wherein generating the inverse mapping comprises selecting an entry from the labeled dataset that is most similar, in mean-square error, to the real video frames using direct nearest-neighbor matching.

7. The method of claim 1 , wherein the liquid comprises a meniscus, and wherein reconstructing the liquid comprises reconstructing a shape, a motion, or both of the meniscus.

8. The method of claim 1 , further comprising adjusting one or more parameters of the printer based at least partially upon the one or more metrics to adjust the liquid positioned at least partially within the nozzle.

9. The method of claim 8 , wherein the one or more parameters comprise an amplitude, a frequency, or both of a waveform from a power source that causes the liquid to be jetted through the nozzle.

10. A visual-based method for performing diagnostics on a printer, the comprising:
defining a parametric model for a surface of a liquid while the liquid is positioned at least partially within a nozzle of the printer, wherein the surface of the liquid comprises a meniscus;
synthesizing video frames of the meniscus of the liquid with a graphics simulator using the parametric model to produce synthetic video frames;
generating a labeled dataset that comprises the synthetic video frames and corresponding parametric model values;
receiving real video frames of the liquid while the liquid is positioned at least partially within the nozzle of the printer, wherein the synthetic video frames simulate the real video frames;
generating an inverse mapping from the real video frames to predicted parametric model values using the labeled dataset, wherein the predicted parametric model values are a subset of the parametric model parameter values;
reconstructing a shape and a motion of the meniscus of the liquid in the real video frames based at least partially upon the predicted parametric model values; and
extracting one or more metrics from the reconstructed meniscus, wherein the metrics comprise a carrier oscillation frequency, a pulse-to-pulse covariance, a waveform decay rate, or a combination thereof.

11. The method of claim 10 , wherein the parametric model values comprise:
a first relative weight of a first Bessel function that describes an oscillation of the liquid;
a second relative weight of a second Bessel function that describes the oscillation of the liquid;
a constant offset;
a constant representing a steady state shape of the meniscus at rest; or
a combination thereof.

12. The method of claim 10 , wherein generating the inverse mapping comprises training an artificial neural network using the labeled dataset to predict the predicted parametric model values in the real video frames.

13. The method of claim 10 , wherein generating the inverse mapping comprises selecting an entry from the labeled dataset that is most similar, in mean-square error, to the real video frames using direct nearest-neighbor matching.

14. The method of claim 10 , further comprising adjusting one or more parameters of the printer based at least partially upon the one or more metrics to adjust the meniscus.

15. A method for characterizing a behavior of a liquid while the liquid is positioned at least partially within a nozzle of a printer, the method comprising:
defining a parametric model for a surface of the liquid while the liquid is positioned at least partially within the nozzle of the printer, wherein the surface of the liquid comprises a meniscus;
synthesizing video frames of the meniscus of the liquid with a graphics simulator using the parametric model to produce synthetic video frames;
generating a labeled dataset that comprises the synthetic video frames and corresponding parametric model values, wherein the parametric model values comprise:
a first relative weight of a first Bessel function that describes an oscillation of the liquid;
a second relative weight of a second Bessel function that describes the oscillation of the liquid;
a constant offset; and
a constant representing a steady state shape of the meniscus at rest;

jetting the liquid through the nozzle;
illuminating the liquid with a light source when the liquid is positioned at least partially within the nozzle;
receiving real video frames of the liquid while the liquid is positioned at least partially within the nozzle, wherein the synthetic video frames simulate the real video frames;
generating an inverse mapping from the real video frames to predicted parametric model values using the labeled dataset, wherein the predicted parametric model values are a subset of the parametric model parameter values, and wherein generating the inverse mapping comprises:
training an artificial neural network using the labeled dataset to predict the predicted parametric model values in the real video frames; or
selecting an entry from the labeled dataset that is most similar, in mean-square error, to the real video frames using direct nearest-neighbor matching;

reconstructing a shape and a motion of the meniscus of the liquid in the real video frames based at least partially upon the predicted parametric model values;
extracting one or more metrics from the reconstructed meniscus, wherein the metrics comprise a carrier oscillation frequency, a pulse-to-pulse covariance, a waveform decay rate, or a combination thereof; and
adjusting one or more parameters of the printer based at least partially upon the one or more metrics to adjust the meniscus.

16. The method of claim 15 , wherein illuminating the liquid with the light source comprises illuminating the liquid with a plurality of light sources that are oriented at different angles with respect to the liquid.

17. The method of claim 16 , wherein the light sources each have a different color.

18. The method of claim 15 , wherein:
the first relative weight has a value between −0.5 and 0.5;
the second relative weight has a value between −0.1 and 0.1;
the constant offset has a value between −1 and 1; and
the constant

Assignees

• Xerox Corp

Inventors

• Price Robert R
• Bala Raja
• Korneev Svyatoslav
• Somarakis Christoforos
• Shreve Matthew
• Lew Adrian
• Ramesh Palghat

Classifications

• G06N3/0475

  Generative networks · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0455

  Auto-encoder networks; Encoder-decoder networks · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title