METHOD FOR ANALYZING A LASER MACHINING PROCESS, SYSTEM FOR ANALYZING A LASER MACHINING PROCESS, AND LASER MACHINING SYSTEM COMPRISING SUCH A SYSTEM

Abstract

A method for analyzing a laser machining process for machining workpieces includes the steps of acquiring at least one sensor data set for the laser machining process and determining a value of at least one physical property of a machining result of the laser machining process based on the at least one sensor data set using a transfer function. The transfer function is formed by a trained neural network. A system for analyzing a laser machining process and a laser machining system including such a system are also disclosed.

Claims

1. A method for analyzing a laser machining process, said method comprising the steps of: acquiring at least one sensor data set for the laser machining process; and determining a value of at least one physical property of a machining result of the laser machining process based on the at least one sensor data set using a transfer function, said transfer function being formed by a trained neural network.

2. The method according to claim 1, wherein acquiring at least one sensor data set is based on a measurement of process radiation of the laser machining process and/or on a measurement of at least one process parameter of the laser machining process.

3. The method according to claim 2, wherein the at least one process parameter comprises a keyhole depth, a focus position, a focus diameter and/or a distance of a laser machining head carrying out the laser machining process from a workpiece.

4. The method according to claim 1, wherein the at least one sensor data set is based on a measurement of a radiation intensity of process radiation of the laser machining process and/or on an image of a machined surface of a workpiece.

5. The method according to claim 4, wherein the radiation intensity is measured for a predetermined period of time and/or in at least one predetermined wavelength range and/or at at least one predetermined wavelength and/or in a spatially resolved manner and/or in a frequency-resolved manner.

6. The method according to claim 2, wherein the process radiation of the laser machining process comprises at least one of temperature radiation, plasma radiation, and laser radiation reflected from a surface of a workpiece.

7. The method according to claim 1, wherein the value of the at least one physical property is determined based on at least two sensor data sets that have been acquired by different sensors for the same period of time.

8. The method according to claim 1, wherein the physical property of the machining result is selected from a group comprising a tensile strength, a compressive strength, an electrical conductivity, a keyhole depth, a welding depth, a gap size of a gap between two workpieces joined by the laser machining process, a roughness of a cut edge of a workpiece cut by the laser machining process, a burr of a cut edge of a workpiece cut by the laser machining process, a burr height of a cut edge of a workpiece cut by the laser machining process, a steepness of the cutting front and a perpendicularity of a cut edge of a workpiece cut by the laser machining process.

9. The method according to claim 1, wherein the at least one sensor data set is acquired during and/or after the execution of the laser machining process, and/or wherein the value of the physical property is determined while the laser machining process is performed and/or after the laser machining process has been completed.

10. The method according to claim 1, wherein the value of the physical property is further determined based on at least one control data set of the laser machining process.

11. The method according to claim 9, wherein the at least one control data set comprises control data for a laser power, a distance between a laser machining head carrying out the laser machining process and the workpiece, a focus position, a focus diameter, a path signal, a workpiece material and/or a workpiece thickness.

12. A system for analyzing a laser machining process, wherein said system is configured to carry out the method according to claim 1, said system comprising: a sensor unit configured to acquire the at least one sensor data set for the laser machining process; and an analysis unit configured to determine the value of the at least one physical property by means of the transfer function formed by the trained neural network.

13. The system according to claim 12, wherein said sensor unit comprises a diode, a photodiode, an image sensor, a line sensor, a camera, a spectral sensor, a multispectral sensor and/or a hyperspectral sensor.

14. The system according to claim 12, wherein said analysis unit is configured to determine the value of the at least one physical property in real time and to output control data to a laser machining system carrying out the laser machining process.

15. A laser machining system for machining a workpiece by means of a laser beam, said laser machining system comprising: a laser machining head for radiating a laser beam onto a workpiece to be machined; and a system according to claim 12.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Embodiments of the present invention are described in detail below with reference to figures.

[0047] FIG. 1 shows a schematic diagram of a laser machining system for machining workpieces using a laser beam and a system for analyzing a machining result of a laser machining process according to embodiments of the present invention;

[0048] FIG. 2 shows a method for analyzing a machining result of a laser machining process according to embodiments of the present invention; and

[0049] FIG. 3 shows a diagram of an objective function when training a neural network according to embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0050] Unless otherwise noted, the same reference symbols are used below for the same elements and those with the same effect.

[0051] FIG. 1 shows a schematic diagram of a laser machining system 100 for machining a workpiece by means of a laser beam 10 according to embodiments of the present disclosure. The laser machining system 100 is configured to carry out a laser machining process, in particular laser welding, laser soldering or laser cutting, and the method for analyzing a machining result of the laser machining process according to embodiments of the present invention.

[0052] The laser machining system 100 includes a laser machining head 101, in particular a laser soldering, laser cutting or laser welding head, and a system 200 for analyzing a machining result of the laser machining process according to embodiments of the present invention. The laser machining system 100 further includes a control unit 120 for controlling the laser machining system 100. The laser machining head 101 is used to provide a laser beam 10 (also referred to as “machining beam” or “machining laser beam”) and may include elements for beam shaping and guidance of the laser beam 10 (not shown). When the laser machining process is carried out, the laser beam 10 is directed or radiated onto the workpiece 1. In the process, material of the workpiece 1 is melted and/or evaporated, as a result of which a vapor capillary and a melt pool surrounding the vapor capillary are formed, for example during welding or soldering. This area of interaction between the laser beam 10 and the workpiece 1 may also be referred to as the “machining area”.

[0053] According to embodiments, the laser machining system 100 or parts thereof, such as the laser machining head 101, may be movable in a machining direction 20 relative to the workpiece 1. Alternatively or additionally, the workpiece 1 may be movable relative to the laser machining system 100 or parts thereof counter to the machining direction 20. The machining direction 20 may be a cutting, welding, soldering direction and/or a direction of movement of the laser machining system 100, for example the laser machining head 101, with respect to the workpiece 1. In particular, the machining direction 20 may be a horizontal direction. The machining direction 20 may also be referred to as “feed direction”.

[0054] The system 200 for analyzing a machining result of the laser machining process includes a sensor unit 210 for acquiring a sensor data set of the laser machining process. The sensor data set includes sensor data based, for example, on a measurement of process radiation or radiation intensity of a process radiation of the laser machining process from the machining area and a radiation emitted or reflected by a surface of the workpiece. The process radiation may include thermal radiation, plasma radiation and reflected or backscattered laser radiation. For this purpose, the sensor unit 210 may comprise a diode, a photodiode, a line sensor, an image sensor, a camera, a spectrometer, a multispectral sensor and/or a hyperspectral sensor. For example, data over a specific wavelength range may be acquired in a spatially resolved manner using image sensors or not in a spatially resolved manner with diodes or with spectrometers in a frequency resolved manner. Alternatively or additionally, the sensor data set may include sensor data acquired for one or more process parameters, such as the focus position, focus diameter and/or distance of the laser machining head 101 from the workpiece 1, during the laser machining process. Correspondingly, the sensor unit 210 may include sensors for acquiring these process parameters, for example a capacitive or inductive distance sensor, an optical coherence tomography system, etc.

[0055] The system 200 for analyzing a machining result comprises an analysis unit 220. The analysis unit 220 is configured to determine a value of at least one physical property using a transfer function based on the at least one sensor data set acquired for the laser machining process carried out by the laser machining system 100. The analysis unit 220 is connected to the sensor unit 210 so that the analysis unit 220 can receive the sensor data sets acquired by the sensor unit 210.

[0056] According to an embodiment, the analysis unit 220 includes a processor for determining the value of a physical unit according to embodiments of the present invention. The transfer function is typically stored in a memory (not shown) of the analysis unit 220 or implemented as a circuit, for example as an FPGA. The transfer function is formed by a learned, i.e. pre-trained, neural network. The value of the at least one physical property is determined by applying the transfer function to the at least one sensor data set. The memory may be configured to store further data, for example the determined value. The analysis unit 220 may be connected to the control unit 120 of the laser machining system 100 in order to transmit the determined value to the control unit 120. According to an embodiment, the analysis unit 220 is combined with the control unit 120 (not shown). In other words, the functionality of the analysis unit 220 may be combined with that of the control unit 120 in a common processing unit.

[0057] The analysis unit 220 may further be configured to receive control data from the control unit 120 of the laser machining system 100 and also to use the control data for determining the value of the physical property. The control data may include, for example, the laser output power, the target distance of the machining head 101 from the surface of the workpiece 1, the feed direction and speed, each at a given point in time.

[0058] According to embodiments, the sensor unit 210 may include an image acquisition unit 211 configured to capture images of a surface of the workpiece 1 and/or of the machining area of the laser machining process and to transmit them to the analysis unit 220 as a sensor data set. According to an embodiment, the image acquisition unit 211 is arranged on or attached to the machining head 101. For example, the image acquisition unit 211 may be arranged downstream of the machining head 101 with respect to the machining direction 20. The image acquisition unit 211 may be oriented coaxially or at an angle to the laser beam 10. The image acquisition unit 211 may comprise a camera system or a stereo camera system, e.g. with reflected light LED lighting. According to the invention, the images correspond to a two-dimensional image of a section of the workpiece surface. In other words, the captured images represent a two-dimensional image of the workpiece surface. The images may be captured at a predetermined rate over a predetermined period of time.

[0059] The control unit 120 may further be configured to control the machining head 101 and/or the sensor unit 210 and/or the image acquisition unit 211.

[0060] FIG. 2 shows a method for analyzing a machining result of a laser machining process according to embodiments of the present invention. The method comprises the steps of: acquiring at least one sensor data set for the laser machining process (S1); and determining a value of at least one physical property of a machining result of the laser machining process based on the at least one sensor data set using a transfer function (S2), the transfer function being formed by a trained neural network. The laser machining system 100 described above with reference to FIG. 1 or the system 200 described above for analyzing a machining result are configured to carry out the method shown in FIG. 2.

[0061] According to embodiments, the at least one sensor data set includes measurement values of a radiation intensity of the thermal radiation emitted from the machining area of the laser machining process. According to other embodiments, further sensor data sets may be acquired and used to determine the value of the physical property. For example, the sensor data sets may include measurement values of a radiation intensity at different wavelengths, for example an intensity of emitted plasma radiation and/or of reflected laser radiation, also called “back-reflected radiation”. Furthermore, the sensor data sets may also include images of the machining area of the laser machining process. All of these sensor data sets may represent input data sets for the neural network. In addition, process-relevant input variables or control data, such as a predetermined laser power, a predetermined machining speed, a workpiece material and/or a workpiece thickness, may also be used as input data sets for the neural network.

[0062] According to embodiments of the present invention, the physical property under consideration, the value of which is determined or predicted by the method according to the invention, is the strength, in particular the tensile strength, of a welded joint between two workpieces joined by a laser welding process.

[0063] In order to determine a value of the strength, the aforementioned data are acquired or recorded at a predetermined sampling rate over a predetermined period of time, for example for the duration of the laser welding process. The size of the sensor data set therefore depends on the sampling rate and the duration of the laser welding process and thus also on the length of the weld seam to be produced by the laser welding process. The sensor data set acquired in this way is also called “time data series” or “time series” and may form an input vector or tensor of the neural network. When the process radiation is measured at different wavelengths or in different wavelength ranges, the correspondingly acquired sensor data sets may be combined into a multi-dimensional tensor. When images or image data are additionally acquired and added to the sensor data sets, a higher-dimensional tensor is created.

[0064] In order to teach the neural network, also called “training”, before the system according to the invention is put into operation or before the method according to the invention is carried out, exemplary training data sets are created for the neural network. For this purpose, a large number of machining processes, e.g. weldings, are carried out and the associated physical properties of the respective machining result are measured experimentally. For example, the intensities of a thermal radiation, a reflected laser radiation and/or a plasma radiation are measured during a laser machining process and acquired in at least one sensor data set for each welding. The at least one physical property of the machining result is then measured. The physical property of the machining result is preferably determined in a reference measurement system, for example in a conventional system for determining the tensile force or tensile strength. The corresponding measurement value of the physical property is assigned to each sensor data set in the training data sets.

[0065] In an example of quantifying the tensile strength, the intensities of the emitted process radiation, i.e. temperature radiation, back-reflected laser radiation and plasma radiation, are acquired for a large number of welding processes over a welding period of 0.5 s and at a sampling rate of 50 KHz, and a tensor of the dimensions 3 × 25000 is formed therefrom. Furthermore, the value of the tensile force at which the formed weld breaks is measured for each welding process. The measurement is carried out with a reference measurement system, for example. The tensile force at which the weld breaks is defined as the tensile strength of the weld. These tensile force values, typically in Newton, are assigned to the respective tensors in order to generate training data sets. The neural network, which is in particular configured as a deep neural network, for example with an architecture made up of convolutional layers, LSTM layers and/or fully connected layers, is then trained with this training data in order to later predict a value for the tensile strength of weld seams produced using this welding process.

[0066] When training the neural network, the sensor data sets or the time series are mapped to the physical property, for example the tensile strength. The objective function, also known as the “cost function”, is minimized using an optimization process such as backpropagation. After optimization of the objective function to zero, an assignment of a predicted or estimated value of the physical property, for example the tensile strength, to the actually measured value would form a straight line, as shown in FIG. 3. Each predicted or estimated value for the tensile force corresponds to the actual measured value. However, since such measurements are always subject to error, the curve shown in FIG. 3 is highly idealized.

[0067] After completing the training with a given variable of the objective function, a model containing the neural network and the parameters of the neural network is obtained. This model may be the transfer function according to embodiments of the present invention. During inference, i.e. when carrying out the method according to the invention, the acquired sensor data sets are mapped to a regression value or to a value of the physical property by the transfer function. The inference thus provides the predicted physical property directly, in the case described the tensile force at which the weld seam will break. This procedure may be carried out for all physical properties that can be determined by measuring the weld seam produced by the laser welding process. The only prerequisite for this is that the information about the measurement value to be predicted for the respective physical property is contained in the signals from the process.

[0068] Although the present invention has been illustrated above using examples of a welding process, the present invention is not limited thereto. The laser machining process may also be a laser cutting process or a laser soldering process. In order to assess the laser cutting process, corresponding physical properties, such as the roughness of a cut edge of a workpiece cut by the laser machining process, a burr or a burr height of a cut edge of a workpiece cut by the laser machining process, a steepness of the cutting front and a perpendicularity of a cut edge of a workpiece cut by the laser machining process may also be quantified according to the present invention to analyze the laser cutting process.

[0069] A user of the laser machining system according to embodiments of the present disclosure does not need to set any parameters. The basic training of the neural network is carried out before the system for analyzing the laser machining process is put into operation, using training data that contain the example data previously collected in the field and the values of the physical property of the machining result under consideration assigned thereto. In the case of minor changes to the laser machining process, transfer learning of the trained neural network may be carried out.

[0070] According to the invention, a regression of the at least one acquired sensor data set to a numerical value for the at least one physical property may thus be carried out by the trained neural network. The sensor data sets may form a multi-dimensional vector, typically consisting of time series data such as temperature radiation, plasma radiation and/or reflected laser radiation, and directly form an input tensor of the trained neural network. Therefore, an “end-to-end” mapping is preferably performed without previously extracting, calculating or parameterizing features. By considering or combining the input data in different ways, various physical quantities or properties can then be quantified by the transfer function, i.e. by the trained neural network. The trained neural network then directly outputs the regression result, i.e. the value of the physical property.

[0071] By mapping one or more sensor data sets to a value of at least one physical property and the resulting finely granular evaluation metrics, laser machining processes can be better analyzed and adjusted to material or environmental fluctuations. The present invention allows for knowledge to be accumulated on the basis of data aggregated during the production life cycle of a laser machining system and can thus provide an ever more accurate basis for decision-making over the course of a service life.

LIST OF REFERENCE SYMBOLS

[0072] Workpiece 1 [0073] Laser beam 10 [0074] Machining direction 20 [0075] Laser machining system 100 [0076] Laser machining head 101 [0077] Control unit 120 [0078] System for analyzing a machining result 200 [0079] Sensor unit 210 [0080] Image acquisition unit 211 [0081] Analysis unit 220