METHOD AND DEVICE FOR ASCERTAINING THE ENERGY INPUT OF LASER WELDING USING ARTIFICIAL INTELLIGENCE

Abstract

A method for training a data-based model to ascertain an energy input of a laser welding machine into a workpiece as a function of operating parameters of the laser welding machine. The training is carried out as a function of an ascertained number of spatters.

Claims

1-17. (canceled)

18. A method for training a data-based model to ascertain a variable which characterizes an energy input of a laser welding machine into a workpiece, as a function of operating parameters of the laser welding machine, the method comprising: training the data-based model as a function of an ascertained number of spatters.

19. The method as recited in claim 18, wherein the data-based model is trained to output as a function of the operating parameters the ascertained variable characterizing the energy input as a model output variable, the training of the data-based model being carried out as a function of the number of spatters as an experimentally ascertained measured variable, and the training also being carried out as a function of a simulatively ascertained variable characterizing the energy input as a simulatively ascertained simulation variable.

20. The method as recited in claim 19, wherein during the training, the measured variable and/or the simulation variable are transformed using an affine transformation.

21. The method as recited in claim 20, wherein in the affine transformation, the measured variable and/or the simulation variable is multiplied by a factor, and the factor is selected as a function of a simulative model uncertainty and as a function of an experimental model uncertainty.

22. The method as recited in claim 21, wherein the factor is selected as a function of a quotient of the simulative model uncertainty and the experimental model uncertainty.

23. The method as recited in claim 21, wherein the data-based model includes a simulatively trained first partial model which is a Gaussian process model, and an experimentally trained second partial model which is a Gaussian process model, the simulative model uncertainty being ascertained using the first partial model, and the experimental model uncertainty being ascertained using the second partial model.

24. The method as recited in claim 23, wherein the data-based model includes an experimentally trained third partial model which is a Gaussian process model, and which is trained to output a difference between the experimentally ascertained measured variable and an output variable of the first partial model.

25. The method as recited in claim 24, wherein the second partial model is not trained with the transformed measured variable, but is trained using the measured variable.

26. The method as recited in claim 25, wherein the third partial model is trained using the transformed measured variable.

27. The method as recited in claim 24, wherein when ascertaining the transformed measured variable, the measured variable is transformed using the affine transformation, and the difference is multiplied by the factor.

28. The method as recited in claim 24, wherein to ascertain the model output variable of the data-based model, an output variable of the first partial model and an output variable of the third partial model are added and transformed using an inverse of the affine transformation.

29. The method as recited in claim 24, wherein to ascertain an uncertainty of the model output variable of the data-based model, the uncertainty is ascertained using the second partial model.

30. A method for setting operating parameters of a laser welding machine using Bayesian optimization of a data-based model, the method comprising the following steps: training the data-based model as a function of an ascertained number of spatters; and setting the operating parameters of the laser welding machine using the trained data-based model.

31. The method as recited in claim 30, wherein following the setting of the operating parameters, the laser welding machine is operated using the operating parameters thus set.

32. A test stand for a laser welding machine, the test stand configured to set operating parameters of the laser welding machine using Bayesian optimization of a data-based model, the test stand configured to: train the data-based model as a function of an ascertained number of spatters; and set the operating parameters of the laser welding machine using the trained data-based model.

33. A non-transitory machine-readable memory medium on which is stored a computer program for training a data-based model to ascertain a variable which characterizes an energy input of a laser welding machine into a workpiece, as a function of operating parameters of the laser welding machine, the computer program, when executed by a computer, causing the computer to perform the following: training the data-based model as a function of an ascertained number of spatters.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0044] FIG. 1 schematically shows a structure of a laser welding machine, in accordance with an example embodiment of the present invention.

[0045] FIG. 2 schematically shows a structure of a test stand, in accordance with an example embodiment of the present invention.

[0046] FIG. 3 shows a specific embodiment for operating the test stand in a flowchart, in accordance with the present invention.

[0047] FIG. 4 shows an example of a profile of simulated and measured and trained output variables over an operating variable.

[0048] FIG. 5 shows an example of a profile of further simulated and measured and trained output variables over an operating variable.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0049] FIG. 1 schematically shows a structure of a laser welding machine 2. An activation signal A is provided by an activation logic 40 to activate a laser 10b. The laser beam strikes two material pieces 13, 14 where it generates a weld seam 15.

[0050] FIG. 2 schematically shows a structure of a test stand 3 for ascertaining optimal process parameters x. Present process parameters x are provided by a parameter memory P via an output interface 4 of laser welding machine 2. This machine carries out laser welding as a function of these provided process parameters x. Sensors 30 ascertain sensor variables S, which characterize the result of the laser welding. These sensor variables S are provided as quality properties y.sub.exp to a machine learning block 60 via an input interface 50.

[0051] In the exemplary embodiment, machine learning block 60 includes a data-based model, which is trained as a function of provided quality properties y.sub.exp, as illustrated in FIG. 4 and FIG. 5.

[0052] Varied process parameters x′ may be provided as a function of the data-based model, which are stored in parameter memory P.

[0053] Process parameters x may also, alternatively or additionally to the provision via output interface 4, be provided to an estimation model 5, which provides estimated quality properties y.sub.sim to machine learning block 60 instead of actual quality properties y.sub.exp.

[0054] In the exemplary embodiment of the present invention, the test stand includes a processor 45 which is configured to execute a computer program stored on a computer-readable memory medium 46.

[0055] This computer program includes instructions which prompt processor 45 to carry out the method illustrated in FIGS. 4 and 5 when the computer program is executed. This computer program may be implemented in software or in hardware or in a mixed form of hardware and software.

[0056] FIG. 3 shows a flowchart of a method for setting process parameters x of test stand 3. The method begins 200 in that a particular initialized first Gaussian process model GP.sub.0, second Gaussian process model GP.sub.V, and third Gaussian process model GP.sub.1 are provided. The quantities of the previously recorded experimental data associated with the particular Gaussian process models are each initialized as an empty quantity.

[0057] Then 210, first Gaussian process model GP.sub.0 is simulatively trained. For this purpose, initial process parameters x.sub.init are provided as process parameters x and optionally process parameters x are predefined using a design-of-experiment method and, as described in greater detail hereinafter, simulation data y.sub.sim associated with these process parameters x are ascertained and first Gaussian process model GP.sub.0 is trained using the experimental data thus ascertained.

[0058] Using present process parameters x, a simulation model of laser welding machine 2 is executed and simulative variables y.sub.sim are ascertained 120, which characterize the result of the laser welding.

[0059] For this purpose, the ascertainment of estimated variables y.sub.sim may take place as follows, for example:

[00001]$\begin{array}{cc}T\left(x,y,z\right)-T_0=\frac{1}{2\pi \lambda h}\exp \left(-\frac{v\left(x-x_0\right)}{2a}\right)\left(\begin{array}{c}{q}_{net}{K}_0\left(\frac{\mathrm{vr}}{2a}\right)+2{.Math.}_{m=1}\cos \left(\frac{m\pi z}{h}\right)K_0\\ \left(\frac{\mathrm{vr}}{2a}\sqrt{1+{\left(\frac{2m\pi a}{\mathrm{vh}}\right)}^2}\right)l_m\end{array}\right)\mathrm{where}& \left(13\right)\\ r=\sqrt{{\left(x-x_0\right)}^2+y^2}& \left(14\right)\\ I_m={\int }_0^h{q}_{1net}\left(z\right)\cos \left(\frac{m\pi z}{h}\right)\mathrm{dz}& \left(15\right)\end{array}$

and the parameters
T.sub.0—a predefinable ambient temperature;
x.sub.0—a predefinable offset of the beam of laser 10b to the origin of a coordinate system movable with laser 10b;
λ—a predefinable heat conductivity of material pieces 13, 14;
a—a predefinable temperature conductivity of material pieces 13, 14;
q.sub.net—a predefinable power of laser 10b;
q.sub.1net a predefinable power distribution of laser 10b along a depth coordinate of material pieces 13, 14;
v—a predefinable velocity of laser 10b;
h—a predefinable thickness of material pieces 13, 14;
and Bessel function

[00002]$K_0\left(\omega \right)=\frac{1}{2}{\int }_{-\infty }^{\infty }\frac{e^{i\omega t}}{\sqrt{t^2+1}}dt$

and an ascertained temperature distribution T(x,y,z). A width and a depth of the weld seam may be ascertained from the temperature distribution (for example via the ascertainment of isotherms at a melting temperature of a material of material pieces 13, 14). From the temperature distribution, an entire energy input may directly be ascertained, for example.

[0060] As a function of these variables, a cost function K is evaluated, as may be given, for example, by equation 1, variables y.sub.sim being provided as features q.sub.i and corresponding target values of these variables q.sub.i,target.

[0061] A cost function K is also possible which punishes deviations of the features from the target values, in particular if they exceed a predefinable tolerance distance, and rewards a high productivity. The “punishment” may be implemented, for example, by a high value of cost function K, the “reward” correspondingly by a low value.

[0062] It is then ascertained whether cost function K indicates that present process parameters x are sufficiently good; in the case in which a punishment means a high value and a reward means a low value in that it is checked whether cost function K falls below a predefinable highest cost value. If this is the case, the simulative training ends with present process parameters x.

[0063] If this is not the case, data point x,y.sub.sim thus ascertained made up of process parameters x and associated variables y.sub.sim characterizing the result is added to ascertained experimental data and first Gaussian process model GP.sub.0 is retrained, thus hyperparameters Θ.sub.0,Θ.sub.1 of first Gaussian process model GP.sub.0 are adapted in such a way that a probability that the experimental data arising from first Gaussian process model GP.sub.0 is maximized.

[0064] An acquisition function is then evaluated, as illustrated by way of example in formula 7, and new process parameters x′ are hereby ascertained. The sequence then branches back to the step of evaluating the simulation model, new process parameters x′ being used as present process parameters x, and the method passes through a further iteration.

[0065] After completed simulative training of first Gaussian process model GP.sub.0, subsequently evaluation is carried out using an acquisition function (220), as illustrated as an example in formula 7, and new process parameters x′, which are denoted hereinafter as x.sub.exp, are ascertained 230 to experimentally train second Gaussian process model GP.sub.V and third Gaussian process model GP.sub.2. Laser welding machine 2 is activated using these process parameters x.sub.exp, and measured variables y.sub.exp are ascertained which characterize the actual result of the laser welding and the data-based model is trained using the experimental data thus ascertained as described hereinafter.

[0066] In this case, process parameters x include, for example, laser power resolved in a time-dependent and/or location-dependent manner via characteristic diagrams and/or a focus diameter and/or a focus position and/or a welding speed and/or a laser beam inclination and/or a circular path frequency of a laser wobble and/or parameters which characterize a process inert gas. Measured variables y.sub.exp include, for example, variables which characterize, along weld seam 15, a minimal weld seam depth and/or a minimal weld seam width and/or the productivity and/or a number of weld spatters and/or a number of pores and/or a welding distortion and/or welding residual stress and/or welding cracks.

[0067] To train the data-based model using the ascertained pair made up of process parameters x.sub.exp and measured variables y.sub.exp initially the following variables are ascertained 230: [0068] a simulative model uncertainty σ.sub.P as the square root of variance σ.sup.2 of first Gaussian process model GP.sub.0 at point x.sub.exp, [0069] a simulative model prediction μ.sub.P as the most probable value of first Gaussian process model GP.sub.0 at point x.sub.exp, [0070] an experimental model uncertainty a σ.sub.exp as the square root of variance σ.sup.2 of second Gaussian process model GP.sub.V at point x.sub.exp, [0071] an experimental model prediction y.sub.exp as the most probable value y.sub.exp of third Gaussian process model GP.sub.1 at point x.sub.exp.

[0072] Measured variables y.sub.exp are now each affine transformed 240 according to the following formula:

[00003]$\begin{array}{cc}{y}_{exp}.fwdarw.y_{exp}^{aff}=\frac{{\sigma }_P}{{\sigma }_{exp}}.Math.\left(y_{exp}-{\mu }_{exp}\right)+{\mu }_P& \left(16\right)\end{array}$

[0073] Subsequently, second Gaussian process model GP.sub.V and third Gaussian process model GP.sub.1 are trained 250.

[0074] Second Gaussian process model GP.sub.V is trained for this purpose with the aid of non-transformed measured variables y.sub.exp, in that data point x,y.sub.exp made up of process parameters x and associated measured variables y.sub.exp is added to ascertained experimental data for second Gaussian process model GP.sub.V and second Gaussian process model GP.sub.V is retrained, thus associated hyperparameters Θ.sub.0,Θ.sub.1 of second Gaussian process model GP.sub.V are adapted in such a way that a probability, that the experimental data result from second Gaussian process model GP.sub.V, is maximized.

[0075] Third Gaussian process model GP.sub.1 is trained for this purpose with the aid of affine transformed measured variables V.sub.exp.sup.aff, in that data point x,y.sub.exp.sup.aff made up of process parameters x and associated affine transformed measured variables y.sub.exp.sup.aff is added to the ascertained experimental data for third Gaussian process model GP.sub.1 and third Gaussian process model GP.sub.1 is retrained, thus associated hyper parameters Θ.sub.0,Θ.sub.1 of third Gaussian process model GP.sub.1 are adapted in such a way that a probability, that the experimental data result from third Gaussian process model GP.sub.1, is maximized.

[0076] Similarly to the evaluation of cost function K in step 210, a further cost function K′ is then evaluated 160, as may result, for example, from equation 1, measured variables y.sub.exp being provided as features q.sub.i and corresponding target values of these variables q.sub.i,target.

[0077] It is then ascertained (260) whether cost function K indicates that present process parameters x are sufficiently good. If this is the case (“yes”), the method ends 270 with present process parameters x.

[0078] If this is not the case (“no”), the sequence branches back to step 220.

[0079] FIGS. 4 and 5 show, by way of example for laser welding machine 2, a successfully trained data-based model including the first, second, and third Gaussian process model. FIG. 4 shows a depth ST of a weld seam as a function of velocity v of laser 10b, FIG. 5 shows a number N of spatters which occur during the welding process as a function of velocity v.

[0080] In each case, the output of the simulation model (dashed lines) used for the simulative training of first Gaussian process model GP.sub.0, experimentally ascertained measuring points x,y.sub.exp (black circles), model prediction μ as the most probable value of the data-based model (middle black line), and a prediction inaccuracy (95% confidence interval) of the data-based model (gray shaded area) are shown. FIG. 5 shows the successful training of the data-based model, although it was not possible to simulatively ascertain the experimentally ascertained measured variable of the number of spatters N. However, it has been found that the number of the spatters highly correlates with the simulatively ascertainable energy input, so that this simulatively ascertainable variable is used as simulation data.

[0081] To ascertain model prediction μ as the most probable value of the data-based model with predefined process parameters x, the sum of the model prediction of first Gaussian process model GP.sub.0 and third Gaussian process model GP.sub.1 is used and subsequently transformed using the inverse of formula 16, the parameters being ascertained similarly to step 230.