System and method to evaluate the integrity of spot welds

Patent metadata

Field	Value
Publication number	US-11301980-B2
Application number	US-202016854235-A
Country	US
Kind code	B2
Filing date	Apr 21, 2020
Priority date	Apr 21, 2020
Publication date	Apr 12, 2022
Grant date	Apr 12, 2022

Abstract

A method to evaluate the integrity of spot welds includes one or more of the following: projecting light from a light source at a spot weld to illuminate the spot weld; capturing an image of the illuminated spot weld with a camera; transmitting information about the image of the illuminated spot weld to a central processing unit (CPU); and evaluating with the CPU the information about the image of the illuminated spot weld coupled with an artificial intelligence neural networked-based algorithm to determine the integrity of the spot weld in real time.

First claim

What is claimed is:

1. A method to evaluate the integrity of spot welds, the method comprising:
projecting light from a light source at a spot weld to illuminate the spot weld;
capturing an image of the illuminated spot weld with a camera;
transmitting information about the image of the illuminated spot weld to a central processing unit (CPU);
evaluating with the CPU the information about the image of the illuminated spot weld coupled with an artificial intelligence neural network-based algorithm to determine the integrity of the spot weld in real time, the neural network-based algorithm including a training data base that is continuously updated, the training data base that is continuously updated being a first input data and the information about the image of the illuminated spot weld being a second input data, and
wherein the first input data includes process and material data, lab test data, sensitivity analysis data and correlation data, and wherein the sensitivity analysis includes changing one welding parameter while other welding parameters are kept constant and analysis of variations in mechanical and electrical machine setup of the process to produce spot welds.

2. The method of claim 1 , wherein the spot weld is illuminated with different patterns, the second input data being a picture image or a video image of the spot weld that is colored or black and white, the picture image or the video image being converted to pixels.

3. The method of claim 1 , wherein the camera and the light source are housed in an assembly, each of the camera and the light source being independently movable.

4. The method of claim 3 , wherein the assembly is static.

5. The method of claim 3 , wherein the assembly is movable by a robot.

6. The method of claim 1 , wherein the artificial intelligence neural networked-based algorithm is stored as software in a non-transitory memory system that communicates with the CPU.

7. The method of claim 1 further comprising performing a corrective action in response to results of evaluating with the CPU.

8. The method of claim 7 , wherein the corrective action is performed so that even with a maintenance issue a suitable weld is produced.

9. A method to evaluate the integrity of spot welds, the method comprising:
projecting light with different patterns from at least one light source at a spot weld to illuminate the spot weld;
capturing an image of the illuminated spot weld with at least one camera;
transmitting information about the image of the illuminated spot weld to a central processing unit (CPU);
evaluating with the CPU the information about the image of the illuminated spot weld coupled with an artificial intelligence neural network-based algorithm to determine the integrity of the spot weld in real time, the neural network-based algorithm including a training data base that is continuously updated, the training data base that is continuously updated being a first input data and the information about the image of the illuminated spot weld being a second input data; and
performing a corrective action in response to results of evaluating with the CPU,
wherein the first input data includes process and material data, lab test data, sensitivity analysis data and correlation data.

10. The method of claim 9 , wherein the sensitivity analysis includes changing one welding parameter while other welding parameters are kept constant and analysis of variations in mechanical and electrical machine setup of the process to produce spot welds.

11. The method of claim 9 , wherein the second input data is a picture image or a video image of the spot weld that is colored or black and white, the picture image or the video image being converted to pixels.

12. The method of claim 9 , wherein the at least one camera and the at least one light source are housed in an assembly, each of the at least one camera and the at least one light source being independently movable.

13. The method of claim 9 , wherein the artificial intelligence neural network-based algorithm is stored as software in a non-transitory memory system that communicates with the CPU.

14. The method of claim 9 , wherein the corrective action is performed so that even with a maintenance issue a suitable weld is produced.

15. A system to evaluate the integrity of spot welds, the system comprising:
at least one light source that projects light with different patterns at a spot weld to illuminate the spot weld;
a camera that captures an image of the illuminated spot weld; and
a central processing unit (CPU) that receives information about the image of the illuminated spot weld,
wherein the CPU evaluates the information about the image of the illuminated spot weld coupled with an artificial intelligence neural network-based algorithm to determine the integrity of the spot weld in real time, the artificial intelligence neural network-based algorithm being stored as software in a non-transitory memory system that communicates with the CPU, and
wherein the neural network-based algorithm includes a training data base that is continuously updated, the training data base that is continuously updated being a first input data and the information about the image of the illuminated spot weld being a second input data, and
wherein the first input data includes process and material data, lab test data, sensitivity analysis data and correlation data, the sensitivity analysis including changing one welding parameter while other welding parameters are kept constant and analysis of variations in the mechanical and electrical machine setup of the process to produce spot welds.

16. The system of claim 15 , wherein the second input data is a picture image or a video image of the spot weld that is colored or black and white, the picture image or the video image being converted to pixels.

17. The system of claim 15 , wherein the at least one camera and the at least one light source are housed in an assembly, each of the at least one camera and the at least one light source being independently movable.

18. The system of claim 15 further comprising performing a corrective action in response to results of evaluating with the CPU.

Assignees

• Gm Global Tech Operations Llc

Inventors

• Sakuramoto Carlos Y
• Huang Ningjian
• Monte Sena Fabio Do
• Nunes Adriana

Classifications

• H04N23/51

  Housings · CPC title

• H04N23/56

  provided with illuminating means · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/08

  Learning methods · CPC title