# US 12,759,092 B2 · Predictive optimization and control for fusion welding of metals

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- Number: US 12,759,092 B2
- Document ID: US12759092B2
- Status: Granted
- Kind: B2
- Application number: 18/126,596
- Filed: 2023-03-27
- Published: 2023-10-12
- Granted: 2026-10-13
- Earliest claimed: 2022-03-28
- Assignees: Rtx Corporation
- Applicants: RTX Corporation
- Inventors: David U. Furrer; Sergei F. Burlatsky
- Web page: https://patentgrep.com/patent/US12759092B2

## CPC

- B23K 31/125: {Weld quality monitoring}
- G01N 33/207: Welded or soldered joints; Solderability
- G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30)
- B23K: SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

A method includes receiving weld data about a weld, and analyzing, using a physics-based model, the weld data to predict a formation of a defect in the weld. The method further includes providing feedback to enable process optimization during a design stage or active control during welding to control a welding machine to correct for and eliminate the formation of the defect in the weld.

## Claims

### Claim 1

A method comprising:
receiving weld data about a weld;
analyzing, using a physics-based model, the weld data to predict a formation of different types of defects in the weld and identify a formation of the different types of defects in the weld; and
providing feedback to enable process optimization during a design stage or active control during welding to control a welding machine to correct for and eliminate the formation of an identified defect in the weld among the different types of defects.

### Claim 2 (depends on 1)

The method of claim 1, wherein receiving the weld data about the weld comprises capturing the weld data about the weld during component and process design.

### Claim 3 (depends on 2)

The method of claim 2, wherein the weld data is computationally processed through a series of algorithms to predict steps in a welding process.

### Claim 4 (depends on 2)

The method of claim 2, wherein the analyzing the weld data comprises analyzing the weld data using algorithms to predict a defect formation.

### Claim 5 (depends on 4)

The method of claim 4, wherein the predicted defect formation provides input to steps of a design optimization or active control inputs.

### Claim 6 (depends on 5)

The method of claim 5, wherein the design optimization is conducted by automated routines or by manual assessment of parameters based on a design and process constraint.

### Claim 7 (depends on 6)

The method of claim 6, wherein the design and process constraint comprises one or more of a weld joint geometry, power settings, and a total power input.

### Claim 8 (depends on 2)

The method of claim 2, wherein the active control during welding is conducted by analysis of instantaneous sensor measurements and prediction of weld stability.

### Claim 9 (depends on 1)

The method of claim 1, wherein receiving the weld data about the weld comprises capturing the weld data about the weld using a sensor, wherein the sensor is one or more of a camera, a pyrometer, or a spectrometer.

### Claim 10 (depends on 9)

The method of claim 9, wherein receiving the weld data about the weld comprises capturing the weld data about the weld from sensors and controls within welding machine.

### Claim 11 (depends on 10)

The method of claim 10, wherein the sensors and controls within the welding machine comprises a power input, a time of power input, a gas flow rate, or a traverse speed.

### Claim 12 (depends on 1)

The method of claim 1, wherein welding machine is a robotic welding machine.

### Claim 13 (depends on 12)

The method of claim 12, wherein robotic welding machine includes control system algorithms to perform defect prediction based on sensor data.

### Claim 14 (depends on 12)

The method of claim 12, wherein robotic welding machine actively calculates stability of instantaneous weld conditions and autonomously adjusts parameters to maintain stability and defect-free welds.

### Claim 15 (depends on 1)

The method of claim 1, wherein a laser power at a weld start is gradually increased.

### Claim 16 (depends on 1)

The method of claim 1, wherein a laser power at a weld stop is gradually decreased.