Welding assessment

• US 20030094478A1
• Filed: 09/23/2002
• Published: 05/22/2003
• Est. Priority Date: 12/15/1999
• Status: Active Grant

• Current Assignee: University of Sydney
• Original Assignee: University of Sydney
• Inventors: Simpson, Stephen Walter
• Granted Patent: US 6,660,965 B2
• Time in Patent Office: Days
• US Class Current: 228/9
• CPC Class Codes: B23K 10/02 Plasma welding
• CPC Class Codes: B23K 11/252 using digital means
• CPC Class Codes: B23K 31/125 Weld quality monitoring
• CPC Class Codes: B23K 9/095 Monitoring or automatic control of welding parameters

Abstract

An apparatus and process for determining whether a fault has occurred in a welding process while the process is under way. The invention is applicable to welding and cutting processes where there is an arc plasma, such as gas-metal arc welding, tungsten-inert gas welding, pulsed welding, resistance welding and submerged welding. It involves sampling welding voltage and welding current to provide first and second signals, generating artificial third signals from these dependent on values of the first and second signals through generalised discrete point convolution operations, identifying corresponding values as triplets and collecting triplets of values into groups or regions.

Claims

1. An apparatus for on line welding assessment, comprising:

first sampling means to sample the welding voltage or current to provide a sequence of values for a first signal,

second sampling means to sample the welding current or voltage to provide a sequence of a values of a second signal,

a signal generating means to generate one or more sequences of values for one or more artificial third signals from the first signal and second signals where the artificial signals depend upon values of the first and second signals through generalised discrete point convolution operations,

tripling means to identify corresponding values of the first, second and third signals as triplets,

collection means to collect triplets of values which are useful for quality monitoring into groups or regions.

2. Apparatus according to claim 1, where the first signal is represented as the sequence D₁, D₂, . . . D_η

−

1, D_η

, the second signal is represented as the sequence Γ

₁, Γ

₂, . . . Γ

<sub>η

−

1</sub>, Γ

_η

, the artificial sequence numbered s is the sequence A_1,s, A_2,s, . . . A<sub>η

−

1,s</sub>, A<sub>η

,s</sub>, and the member n of the artificial sequence numbered s. A_n,s, is determined from;

3. Apparatus according to claim 2, where the artificial signal generating means applies equation (1) repeatedly to calculate all values of A_n,s for n varying from 1 to η

, and s varying from 1 to σ

.

4. Apparatus according to claim 3, where ψ

is;

Ψ

(1,κ

,n,s,t)=e<sup>(κ

−

n)[t_n −

st _j]. . . (</sup>κ

−

n)<

0Ψ

(1,κ

,n,s,t)=0. . . (κ

−

n)≧

0Ψ

(2,κ

,n,s,t)=Θ

. . . κ

=nΨ

(2,κ

,n,s,t)= . . . κ

≠

n 

 

(2)

5. Apparatus according to claim 1, where smaller regions have greatest population density, and the size of the regions increasing exponentially in both the first and artificial signal directions, as they progress away from the region of greatest population density.

6. Apparatus according to claim 5, where the region populations are represented by a population density function f_r which is the population of the region numbered r, such that if a given point at {D_n, s, A_n,s} falls within region r, accumulation means increase the population f_r by w_r(D_n, Γ

_n, A_n,s , n, s, t), where t is the time at which D_n and Γ

_n were measured, and w_r(D_n, Γ

_n, A_n,s , n, s, t) is a weight the point is given in region r.

7. Apparatus according to claim 6, where final adjusted region populations p_r function application means apply a single valued monotonic function F to each of the f_r values:

p_r=F(ƒ

_r) 

 

(3)

and the complete set {p₁, p₂ . . . p<sub>ρ

−

1</sub>, p_ρ

} of the p_r collected is a single welding signature.

8. Apparatus according to claim 7, where the weight functions w_r are chosen by defining overlapping regions and decreasing w_r for points closer to the boundary of region r, and the function F is chosen to maximise the sensitivity of the welding signature to faults in the final weld.

9. Apparatus according to claim 7, where several single welding signatures are required, the sampling means collects several sequences of values for first signals and second signals, the apparatus for generating a single welding signature is then used to generate a single signature for each of the sequences of first and second signals, and there are a total of ρ

[m] regions defined for the single signature m, which contains the final adjusted region populations

{p₁[m],p₂[m], p₃[m]. . . p<sub>ρ

[m]</sub>[m]} 

 

(4)

10. Apparatus according to claim 9, where concatenation means then produce a combined welding signature P_T by concatenating all the adjusted region populations in order from each single signature:

P_T={p₁[1], p₂[1] . . . p_p[1][1], p₁[2], p₂[2] . . . p<sub>p[μ

]</sub>[μ

]} 

 

(5)

11. Apparatus according to claim 10, where reference signatures are calculated continuously during welding from previous sampling, the reference is a weighted average of the N signatures H₁, H₂, H₃ . . . H_N where H_N is the most recent signature determined. H_N−

1 is the signature determined before that and so on, and the reference signature X is determined from the weighted average

x_r=W₁h_1r=W₂h_2r+W₃h_3r+ . . . +W_Nh_Nr 

 

(6′

)

for r=1 to ρ

, x_r is the adjusted region population numbered r in the reference signature X;

h_1r to h_Nr are the adjusted region populations numbered r in the signatures H₁ to H_N determined from previous sampling;

ρ

is the total number of regions in each signature; and

W₁ to W_N are the signature weighting factors.

12. Apparatus according to claim 10, where the inner, or dot product, of any two signatures C and G is calculated as

13. Apparatus according to claim 12, where a normalised welding signature C is calculated from a welding signature C as follows:

C=C/({square root}{square root over (C·

C)}) 

 

(8)

14. Apparatus according to claim 12, where a measure of welding consistency is given by the probability:

15. Apparatus according to claim 12, where a measure of welding consistency is given by the probability:

16. Apparatus according to claim 10, where to account for basic anisotropy, first it is necessary to estimate the direction in signature space in which the reference sample shows the greatest deviations from the average, then differences from the average are split into components parallel and perpendicular to that direction and treated independently statistically.

17. Apparatus according to claim 10, where signatures are successively represented by their coordinates in an appropriate subspace with a modified Gram-Schmidt orthogonalization apparatus.

18. Apparatus according to claim 10, where a quantitative measure of “

stability”

is the variability of the welding signature.

19. A method for on line welding assessment, comprising the following steps:

first sampling the welding voltage or current to provide a sequence of values for a first signal,

second sampling the welding current or voltage to provide a sequence of a values of a second signal,

generating one or more sequences of values for one or more artificial third'"'"'signals from the first signal and second signals where the artificial signals depend upon values of the first and second signals through generalised discrete point convolution operations,

identifying corresponding values of the first, second and third signals as triplets,

collecting triplets of values which are useful for quality monitoring into regions.

20. A method according to claim 19, comprising the step of:

determining the member n of the artificial sequence numbered s, A_n,s, from;

$\begin{array}{cc}{A}_{n,s}=\underset{\mathrm{\kappa <br><br>}=1}{\overset{\mathrm{\eta <br><br>}}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\Psi <br><br>}<br><br>\left(1,\mathrm{\kappa <br><br>},n,s,t\right)<br><br>D_{\mathrm{\kappa <br><br>}}+\mathrm{\Psi <br><br>}<br><br>\left(2,\mathrm{\kappa <br><br>},n,s,t\right)<br><br>{\mathrm{\Gamma <br><br>}}_{\mathrm{\kappa <br><br>}}& \left(1\right)\end{array}$

where the first signal is represented as the sequence D₁,D₂, . . . D<sub>η

−

1</sub>,D_η

, the second signal is represented as the sequence Γ

₁,Γ

₂, . . . , Γ

<sub>η

−

1</sub>,Γ

₇₂ , the artificial sequence numbered s is the sequence A_1,s,A_2,s, . . . A<sub>η

−

1,s</sub>,A<sub>η

,s</sub>, and where, the coefficients ψ

may depend on κ

, the location of D_κ

in the first signal data sequence and also the location of Γ

_κ

in the second signal data sequence;

n, the location of A_n,s in the artificial data sequence numbered s;

s, the artificial sequence number; and

t, the time at which D_κ

and Γ

_κ

were measured with respect to some specified time origin.

21. A method according to claim 20, comprising the step of:

the artificial signal generating means applying equation (1) repeatedly to calculate all values of A_n,s for n varying from 1 to η

, and s varying from 1 to σ

.

22. A method according to claim 20, where ψ

is;

Ψ

(1,κ

,n,s,t)=e<sup>[k−

n]t_n−

st₁</sup> . . . (k−

n)<

0Ψ

(1,κ

,n,s,t)=0 . . . (κ

−

n)≧

0Ψ

(2,κ

,n,s,t)=Θ

. . . κ

=nΨ

(2,κ

,n,s,t)=0 . . . κ

≠

n 

 

(2)

23. A method according to claim 19, comprising the step of:

the collection means collecting triplets into regions, with smaller regions where population density is greatest, and the size of the regions increasing exponentially in both the first and artificial signal directions, as they progress away from the region of greatest population density.

24. A method according to claim 23, comprising the step of:

representing the region populations by a population density function f_r which is the population of the region numbered r, such that if a given point at {D_n, s, A_n,s } falls within region r, accumulation means increase the population f_r by w_r(D_n,Γ

_n,A_n,s, n, s, t), where t is the time at which D_n and Γ

_n were measured, and w_r(D_n, Γ

_n, A_n,s, n, s, t) is a weight the point is given in region r.

25. A method according to claim 24, comprising the step of:

final adjusted region populations p_r function application means applying a single valued monotonic function F to each of the f_r values;

p_r=F(ƒ

_r) 

 

(3)

and the complete set {p₁, p₂ . . . p<sub>ρ

−

1</sub>, p<sub>ρ

} of the pr</sub> collected is a single welding signature.

26. A method according to claim 25, comprising the step of:

choosing the weight functions w_r by defining overlapping regions and decreasing w_r for points closer to the boundary of region r, and choosing the function F to maximise the sensitivity of the welding signature to faults in the final weld.

27. A method according to claim 25, where several single welding signatures are required. and the sampling means collects several sequences of values for first signals and second signals, the apparatus for generating a single welding signature is then used to generate a single signature for each of the sequences of first and second signals, and there are a total of ρ

[m] regions defined for the single signature m, which contains the final adjusted region populations

{p₁[m], p₂[m], p₃[m] . . . p<sub>ρ

[m]</sub>[m]}(4) 

 

28. A method according to claim 27, comprising the step of:

concatenation means then producing a combined welding signature P_T by concatenating all the adjusted region populations in order from each single signature;

P_T={p₁[1], p₂[1] . . . p<sub>ρ

[1]</sub>[1], p₁[2], p₂[2] . . . p<sub>ρ

[μ

]</sub>[μ

]} 

 

(5)

29. A method according to claim 28, comprising the step of:

calculating reference signatures continuously during welding from previous sampling, and the reference is a weighted average of the N signatures H₁, H₂, H₃. . . H_N where H_N is the most recent signature determined, H<sub>N−

1</sub> is the signature determined before that and so on, and the reference signature X is determined from the weighted average

x_r=W₁h_1r+W₂h_2r+W₃h_3r+ . . . +W_Nh_Nr 

 

(6′

)

for r=1 to ρ

, x_r is the adjusted region population numbered r in the reference signature X;

h_1r to h_Nr are the adjusted region populations numbered r in the signatures H₁ to H_N determined from previous sampling;

ρ

is the total number of regions in each signature; and

W₁ to W_N are the signature weighting factors.

30. A method according to claim 28, comprising the step of:

calculating the inner, or dot product, of any two signatures C and G as

31. A method according to claim 30, comprising the step of:

calculating a normalised welding signature C′

from a welding signature C as follows;

C′

=C/({square root}{square root over (C·

C)}) 

 

(8)

32. A method according to claim 30, comprising the step of:

giving a measure of welding consistency by the probability;

33. A method according to claim 30, comprising the step of:

giving a measure of welding consistency by the probability;

34. A method according to claim 28, where to account for basic anisotropy, first it is necessary to estimate the direction in signature space in which the reference sample shows the greatest deviations from the average, then differences from the average are split into components parallel and perpendicular to that direction and treated independently statistically.

35. A method according to claim 28, comprising the step of:

successively representing signatures by their coordinates in an appropriate subspace with a modified Gram-Schmidt orthogonalization apparatus.

36. A method according to claim 28, comprising the step of:

giving a quantitative measure of “

stability”

as the variability of the welding signature.