IP Library › Granted Patent US 12,697,781
Granted Patent B1
US 12,697,781 · App. 18/483,958 · Granted Aug 4, 2026

Machine learning for real-time monitoring and control of additive manufacturing

Inventors: Devin John Roach (Albuquerque, NM); Andrew Rohskopf (San Jose, CA); William Derek Reinholtz (Albuquerque, NM); Adam Wade Cook (Albuquerque, NM); Leah N. Appelhans (Tijeras, NM)
Assignee: National Technology & Engineering Solutions of Sandia, LLC
B29C64/393B29C64/118B33Y50/02
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Quick Facts
Patent No.
US 12,697,781
App. No.
18/483,958
Filed
Oct 10, 2023
Granted
Aug 4, 2026
Kind
B1
Art Unit
1745
USPC
264/40.1
Abstract

A machine learning (ML) approach enables real-time direct ink write (DIW) print-parameter optimization through in-situ monitoring of printed line geometry. The method can use an invertible neural network (INN) to solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and INNs, DIW printing parameters can be autonomously optimized to print a target line width in a matter of seconds. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The method eliminates user-intensive, time-consuming, iterative parameter discovery approaches that currently limit accelerated implementation of DIW and other extrusion-based additive manufacturing processes.

Claims (9)

1 . A method for in-situ process monitoring and optimization of direct ink write (DIW) printing, comprising:

providing a DIW printer configured to print an ink subject to one or more printing parameters,

measuring an output of the printed ink in real-time using computer vision,

comparing the measured output to a target output,

when the output differs from the target output, adjusting the one or more printing parameters of the DIW printer in real time using a pre-trained invertible neural network model to produce the target output, wherein the invertible neural network models both a forward and an inverse relationship between the one or more printing parameters and the target output, and

printing the ink on a substrate subject to the optimized one or more printing parameters in a single printing step.

2 . The method of claim 1 , wherein the one or more printing parameters comprises a nozzle diameter, print speed, linear extrusion rate, or height from the substrate.

3 . The method of claim 1 , wherein the output comprises a line width, line type, or line shape.

4 . The method of claim 1 , wherein the invertible neural network model and optimizing step finds the one or more printing parameters associated with the target line width that are closest in value to current printing parameters.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 3, 2023
From: ROACH, DEVIN JOHN; ROHSKOPF, ANDREW; REINHOLTZ, WILLIAM DEREK; COOK, ADAM WADE; APPELHANS, LEAH N.
To: NATIONAL TECHNOLOGY & ENGINEERING SOLUTIONS OF SANDIA, LLC
Reel/Frame 065446/0772 →
CONFIRMATORY LICENSE Recorded Oct 26, 2023
From: NATIONAL TECHNOLOGY & ENGINEERING SOLUTIONS OF SANDIA, LLC
To: U.S. DEPARTMENT OF ENERGY
Reel/Frame 065355/0004 →
Continuity (1)
Provisional Application 63464057 · May 4, 2023
References Cited (10)
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