Computer modeling for detection of discontinuities and remedial actions in fastening systems

Patent metadata

Field	Value
Publication number	US-2022261663-A1
Application number	US-202217734891-A
Country	US
Kind code	A1
Filing date	May 2, 2022
Priority date	Apr 12, 2021
Publication date	Aug 18, 2022
Grant date	—

Abstract

Disclosed herein are systems and methods for identifying welding anomalies and discontinuities in stud welding using AI models. Instead of conventional welding accuracy methods (e.g. destructive and/or image generation methods) a processor may communicate with one or more sensors associated with a joining machine to retrieve joining data and attributes. The processor may then execute an AI model that is trained based on previously performed stud welding, their corresponding welding attributes, and their corresponding discontinuities and/or anomalies. The processor may execute the AI model using data retrieved from the sensors and may calculate a likelihood of a discontinuity and discontinuity attributes, such as, location, depth, and the like. The processor may also execute a second AI model to identify an appropriate course of action to remedy the identified/predicted discontinuity.

First claim

What we claim is:

1 . A method comprising:
receiving, by a processor, a plurality of attributes corresponding to a joining; executing, by the processor, an artificial intelligence model to identify one or more attributes associated with at least one discontinuity or anomaly of the joining,
wherein the artificial intelligence model was trained based on a plurality of attributes corresponding to previous joinings having at least one discontinuity or anomaly; and
wherein the plurality of attributes corresponding to previous joinings comprises at least a voltage value, a current value, or a lift value for each previous joining; and
presenting, by the processor, one or more attributes associated with at least one discontinuity or anomaly of the joining.

2 . The method of claim 1 , wherein the plurality of attributes corresponding to previous joinings further comprise at least one of metal sheet compositional attributes, metal sheet geometric attributes, stud compositional attributes, or stud geometric attributes.

3 . The method of claim 1 , wherein artificial intelligence model calculates a likelihood of a discontinuity or an anomaly being present within the joining.

4 . The method of claim 1 , wherein the artificial intelligence model calculates a depth and a type associated with each discontinuity or anomaly.

5 . The method of claim 1 , further comprising:
transmitting, by the processor, an instruction to a joining machine to modify its welding.

6 . The method of claim 1 , wherein the joining is a stud welding.

7 . The method of claim 1 , wherein the joining is performed via a rivet welding method.

8 . The method of claim 1 , further comprising:
executing, by the processor, a second artificial intelligence model that receives an input of the one or more attributes associated with at least one discontinuity or anomaly generated by the artificial intelligence model and generates an action associated with the at least one discontinuity or anomaly,
wherein the second artificial intelligence model was trained based on one or more attributes associated with at least one discontinuity or anomaly corresponding to previous joinings and one or more corresponding actions; and
presenting, by the processor, for display at least one of:
the one or more attributes associated with at least one discontinuity or anomaly generated by the artificial intelligence model; or
the action generated by the second artificial intelligence model.

9 . The method of claim 9 , wherein the action generated by the second artificial intelligence model is a corrective or preventative action.

10 . A system comprising:
a server comprising a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising:
receiving a plurality of attributes corresponding to a joining;
executing an artificial intelligence model to identify one or more attributes associated with at least one discontinuity or anomaly of the joining,
wherein the artificial intelligence model was trained based on a plurality of attributes corresponding to previous joinings having at least one discontinuity or anomaly; and
wherein the plurality of attributes corresponding to previous joinings comprises at least a voltage value, a current value, or a lift value for each previous joining;

presenting one or more attributes associated with at least one discontinuity or anomaly of the joining.

11 . The system of claim 10 , wherein the plurality of attributes corresponding to previous joinings further comprise at least one of metal sheet compositional attributes, metal sheet geometric attributes, stud compositional attributes, or stud geometric attributes.

12 . The system of claim 10 , wherein artificial intelligence model calculates a likelihood of a discontinuity or an anomaly being present within the joining.

13 . The system of claim 10 , wherein the artificial intelligence model calculates a depth and a type associated with each discontinuity or anomaly.

14 . The system of claim 10 , wherein the instructions further cause the processor to transmit an instruction to a joining machine to modify its welding.

15 . The system of claim 10 , wherein the joining is a stud welding.

16 . The system of claim 10 , wherein the joining is performed via a rivet welding method.

17 . The system of claim 10 , wherein the instructions further cause the processor to execute a second artificial intelligence model that receives an input of the one or more attributes associated with at least one discontinuity or anomaly generated by the artificial intelligence model and generates an action associated with the at least one discontinuity or anomaly,
wherein the second artificial intelligence model was trained based on one or more attributes associated with at least one discontinuity or anomaly corresponding to previous joinings and one or more corresponding actions; and presenting, by the processor, for display at least one of:
the one or more attributes associated with at least one discontinuity or anomaly generated by the artificial intelligence model; or
the action generated by the second artificial intelligence model.

18 . The system of claim 17 , wherein the action generated by the second artificial intelligence model is a corrective or preventative action.

19 . A system comprising:
a joining machine; and a processor in communication with the joining machine, the processor configured to:
receive a plurality of attributes corresponding to a joining;
execute an artificial intelligence model to identify one or more attributes associated with at least one discontinuity or anomaly of the joining, wherein the artificial intelligence model was trained based on a plurality of attributes corresponding to previous joinings having at least one discontinuity or anomaly; and wherein the plurality of attributes corresponding to previous joinings comprises at least a voltage value, a current value, or a lift value for each previous joining; and
present one or more attributes associated with at least one discontinuity or anomaly of the joining.

20 . The system of claim 19 , wherein the processor is further configured to execute a second artificial intelligence model that receives an input of the one or more attributes associated with at least one discontinuity or anomaly generated by the artificial intelligence model and generates an action associated with the at least one discontinuity or anomaly, wherein the second artificial intelligence model was trained based on one or more attributes associated with at least one discontinuity or anomaly corresponding to previous joinings and one or more corresponding actions; and presenting, by the processor, for display at least one of: the one or more attributes associated with at least one discontinuity or anomaly generated by the artificial intelligence model; or the action generated by the second artificial intelligence model.

Assignees

• Newfrey Llc

Inventors

• Kashani Pour Amir Reza
• Dalton Maxim

Classifications

• B23K31/125

  Weld quality monitoring · CPC title

• G06N5/022 Primary

  Knowledge engineering; Knowledge acquisition · CPC title

• B23K31/006

  relating to using of neural networks · CPC title

• G06F7/544 Primary

  for evaluating functions by calculation {(G06F7/4824 takes precedence)} · CPC title

• B23K9/201

  of the extremity of a small piece on a large basis · CPC title