Spot welding system and method for sensing welding conditions in real time

• US 20020053555A1
• Filed: 07/06/2001
• Published: 05/09/2002
• Est. Priority Date: 09/21/2000
• Status: Active Grant

• Current Assignee: Massachusetts Institute of Technology
• Original Assignee: Massachusetts Institute of Technology
• Inventors: Matsuyama, Kin-ichi
• Granted Patent: US 6,506,997 B2
• Time in Patent Office: Days
• US Class Current: 219/110
• CPC Class Codes: B23K 11/252 using digital means
• CPC Class Codes: B23K 11/253 the measured parameter being a displacement or a position
• CPC Class Codes: B23K 11/257 the measured parameter being an electrical current
• CPC Class Codes: B23K 11/258 the measured parameter being a voltage
• CPC Class Codes: B23K 11/3063 Electrode maintenance, e.g. cleaning, grinding

Abstract

Methods and apparatus are provided for monitoring resistance spot welding process conditions in real time by using welding data in an energy balance model in integral form to estimate the mean weld temperature and to predict process conditions such as occurrence of splash, and weld diameter. Using predicted welding process conditions, welding parameters are modified in real time to prevent splash and produce sound welds.

Claims

1. A method for monitoring a resistance spot welding process performed on a workpiece having a target volume comprising the steps of:

monitoring at least one welding parameter; and

computing a mean temperature estimate based on an energy balance model which includes said at least one welding parameter and a thickness of said workpiece.

2. The method of claim 1 further comprising the step of predicting a process condition associated with said resistance spot welding process based on said mean temperature estimate.

3. The method of claim 2 wherein the step of predicting a process condition comprises at least one of:

predicting an occurrence of splash;

predicting a weld diameter;

predicting a growth in the weld diameter; and

predicting an electrode contact diameter.

4. The method of claim 3 wherein said splash predicting step comprises:

comparing said mean temperature estimate to a threshold temperature value; and

predicting the occurrence of splash in response to said mean temperature estimate exceeding said threshold temperature value.

5. The method of claim 3 wherein said weld diameter predicting step comprises:

experimentally generating a function relating a weld diameter to a mean temperature in said target volume; and

using said function to determine said weld diameter in response to said mean temperature estimate.

6. The method of claim 2 further comprising the step of controlling at least one welding parameter in real time in response to said predicted process condition.

7. The method of claim 1 wherein said energy balance model further includes a target volume heat loss term.

8. The method of claim 7 wherein an approximation of said heat loss term is determined experimentally.

9. The method of claim 7 wherein said heat loss term is a function of the thickness of said workpiece.

10. The method of claim 1 wherein said energy balance model is a function of:

a welding voltage across the workpiece (v);

a heat conductivity constant in an electrode tip (K_e);

a heat conductivity constant in the workpiece (K);

a contact diameter at a plate-electrode interface at a time t (d);

a current density correction factor (f(d/h));

a temperature (I) in the target volume at a time τ

; and

a total workpiece thickness (h_t).

11. The method of claim 10 wherein said energy balance model corresponds to:

12. The method of claim 10 further comprising the step of computing a real time estimate of said contact diameter at a plate-electrode interface (d) based on a resistance value calculated from the welding voltage v and a welding current i, a resistivity of the workpiece, the total workpiece thickness, and the current density correction factor.

13. The method of claim 1 wherein said mean temperature estimate is a function of heat (Q) in said target volume, a specific heat of a workpiece (C), a density of the workpiece (σ

), and a resistivity of the workpiece (ρ

).

14. The method of claim 13 wherein said mean temperature estimate is given by:

15. The method of claim 1 wherein said monitoring step includes monitoring at least one of a welding voltage and a welding current.

16. The method of claim 1 further comprising the step of defining said target volume as being bounded by a surface of a first electrode in contact with said workpiece and a surface of a second electrode in contact with said workpiece.

17. The method of claim 1 further comprising:

providing an analog-digital converter having an input multiplexer having at least three channels;

sampling a welding current to obtain a first welding current value;

sampling a welding voltage;

sampling the welding current to obtain a second welding current value; and

averaging the first and the second welding current values to approximate a resistive component value between a first electrode tip and a second electrode tip.

19. The system of claim 18 wherein said at least one process condition is one of an occurrence of splash, a weld diameter, a growth in the weld diameter, and a contact diameter.

18. A resistance spot welding system comprising a power control unit and a force control unit, and a spot welding machine providing a welding parameter to a welding data processor during a welding operation in which a weld is formed in a workpiece having a target volume, said system comprising:

a weld temperature prediction processor operable to predict a mean temperature in said target volume using at least one welding parameter and a thickness of said workpiece, wherein at least one condition associated with said welding operation is predicted during the welding operation in response to said mean temperature prediction.

20. A resistance spot welding system comprising a welding controller coupled to a welding machine for controlling a welding process performed on a workpiece comprising:

a welding data processor having an output port and an input port coupled to said welding machine; and

an energy balance processor having an input port coupled to said welding data processor output port to receive welding process data, wherein said energy balance processor is operative to calculate an estimate of the amount of energy in a target volume in response to said welding process data.

21. The controller of claim 20 wherein the integral energy balance processor is responsive to a thickness of said workpiece for calculating said estimate.

22. The controller of claim 20 further comprising a weld temperature prediction processor coupled to said welding data processor for predicting a temperature in a target volume of said workpiece.

23. The controller of claim 20 further comprising a nugget diameter prediction processor coupled to said weld temperature prediction processor for predicting a nugget diameter in said workpiece in response to said mean temperature prediction.

24. The controller of claim 20 further comprising an adaptive control processor coupled to said weld temperature prediction processor for adaptively controlling said welding process in response to said mean temperature prediction.

25. The controller of claim 24 wherein said adaptive control processor modifies at least one of a welding current, a welding time, and an electrode force.

26. The controller of claim 20 further comprising a splash prediction processor coupled to said weld temperature prediction processor for predicting the occurrence of splash in response to said mean temperature prediction.