Device and system for providing optical real-time information relating to a process by means of an optical neural network, and method for providing the device

Abstract

Exemplary embodiments according to the present invention comprise an optical device for providing optical real-time information relating to a process, wherein the optical device is designed to detect an optical input signal which is emitted and / or reflected from the process and wherein the optical device has an optical neural network which is designed to provide the optical real-time information relating to the process based on the optical input signal. The invention also relates to a system for providing optical real-time information, and to a method for providing the device.

Description

[0001] Device and system for providing optical real-time information regarding a process by means of an optical neural network, and method for providing the device

[0002] Description

[0003] Technical area

[0004] Embodiments according to the present invention relate to devices and systems for providing optical real-time information regarding a process by means of an optical neural network, as well as methods for providing such devices.

[0005] Embodiments further include optical process monitoring with diffractive neural networks, optical process monitoring with diffractive deep neural networks and / or optical image processing with diffractive deep neural networks.

[0006] Background of the invention

[0007] In laser material processing and laser metrology, systems are used to monitor the status of the process during processing. This process monitoring is often based on a system consisting of illumination, observation optics, and a detector (CCD chip). The detector generates an image of the process zone, which is then evaluated in the connected computer using image processing methods. Based on this evaluation, conclusions can be drawn about the quality of the process and the processing process can be controlled. Modern approaches to process the image data (in-line) and adjust the process parameters. However, this has so far only been done in the electronic processing unit that controls the processing system.

[0008] State of the art

[0009] Such and very similar approaches can also be found in patents or utility models or corresponding applications (for example US65974491 , CN000216680796U,

[0010] US5517420A, CA2467221A1, CN000201052570 Y). The disadvantages of the current technology include the latency (the time between data acquisition and output of the control parameters) and the required hardware for image processing (often high-performance computers with multi-GPUs). The hardware is expensive, large (server racks), and consumes a lot of energy during operation. This energy consumption can be a hindrance for some mobile applications. The resources required for the high-performance computer scale with the number of sensors used.

[0011] Thus, there is a need for an improved process monitoring concept. Therefore, the present invention is based on the object of providing a process monitoring concept that enables an improved compromise between the complexity of required components, energy consumption, and the quality of process monitoring.

[0012] This problem is solved by the subject matter of the independent patent claims. Further developments according to the invention are defined in the subclaims.

[0013] Summary of the invention

[0014] Embodiments according to the present invention comprise an optical device for providing optical real-time information regarding a process, wherein the optical device is configured to detect an optical input signal emitted and / or reflected by the process, and wherein the optical device comprises an optical neural network configured to provide the optical real-time information regarding the process based on the optical input signal.

[0015] Embodiments are based on the idea of ​​carrying out a process evaluation using an optical neural network. In general, the optical neural network can be a diffractive neural network, for example. A diffractive neural network can, for example, have a sequence of several diffractive elements that are designed to modulate radiation, for example the optical input signal or an optical signal derived therefrom, in phase and amplitude. The inventors have recognized that a process evaluation can take place in real time using an optical neural network, so that the device can provide real-time information about the process. Real-time information here means, for example, information whose generation, from acquisition to provision, takes place at the speed of light, i.e., for example,A delay resulting from the distance traveled from the point of emission of the optical input signal at the process to the point of emission of the real-time information at the device, based on the speed of light. Real-time information can therefore, for example, be information whose provision or processing is limited only by the speed of light.

[0016] Furthermore, the inventors have recognized that this can lead to significant energy savings. A corresponding process evaluation can thus be performed entirely optically using the optical neural network, for example, so that no energy needs to be provided for electronic evaluation. Alternatively, only a particularly computationally intensive part of the evaluation can be performed optically. Optionally, however, any part of the evaluation can also be performed optically. In all cases, energy can be saved compared to electronic evaluation, thus enabling, for example, mobile process monitoring.

[0017] It should be noted that, for example, with regard to production machines, high mobility may be less relevant under certain circumstances or even normally. However, embodiments in this regard may have the advantage that they can be designed very compactly, i.e., with a small volume, and / or can enable a compact, e.g., spatially small, design of such a production machine. Furthermore, according to embodiments, no (large, i.e., powerful) high-performance computer on the machine or access to cloud computing with the corresponding infrastructure is required.

[0018] Furthermore, costs for process monitoring can be saved because less powerful computing units can be used for electronic evaluation due to the possible optical preprocessing.

[0019] The optical input signal can, for example, be a signal emitted and / or reflected by the process. This means that an evaluation can, for example, take place solely on the basis of light generated by the process itself. Alternatively, light generated independently of the process, e.g. ambient light or light from a specially designed light source, can be reflected by the process, e.g. a workpiece that is being measured or processed, and serve as the input signal. The process can therefore, for example, be a processing and / or measurement of a workpiece (e.g. a material processing process on the workpiece and / or a measuring process on the workpiece using a laser), in which a signal emitted or reflected by the workpiece is used as the optical input signal.

[0020] In particular, the optical device can, for example, be designed to directly use such radiation emitted or reflected by the workpiece, i.e., for example, without further electronic recording or processing.

[0021] In other words, the input signal can be provided, for example, with process light (e.g. thermal radiation), with ambient light and / or with special external illumination (e.g. coherent illumination and / or structured illumination).

[0022] Embodiments can be used, for example, for general process monitoring or measurement technology (e.g., waterjet cutting, tape laying with IR irradiation, etc.) and, in particular, for process monitoring or measurement technology in laser-based processes (e.g., laser beam welding, drilling, additive manufacturing, LIBS, laser triangulation, etc.). As previously explained, one inventive idea is to use optical neural networks for process monitoring, for example, in the aforementioned application areas.

[0023] In particular, specific embodiments comprise a novel component and method for capturing and evaluating process images. A diffractive neural network (DNN, or generally an optical neural network) can be used to evaluate the light coming from the process zone (i.e., emitted and / or reflected light) directly in the processing optics and thus output an intensity pattern (e.g., instead of or in addition to 2D image data) in which the results of the evaluation or new control and / or regulation signals can be encoded. Compared to conventional methods (e.g., evaluation in an electronic processing unit), embodiments offer the advantage that data processing occurs at the speed of light. This is based on the inventors' finding that the duration of image processing, derivation of the quality characteristics, and the new control signals are crucial for the speed of the regulation.The image analysis can thus be available to a control unit, for example, virtually instantly. Embodiments can thus avoid or shorten the longer computing times in the computer (e.g., in the case of electronic further processing of the real-time information). According to embodiments, the optical device is further configured to capture the optical input signal directly from the process in order to detect the optical input signal emitted and / or reflected by the process.

[0024] For example, the device can be integrated directly into process-related elements, e.g., in contrast to an "external" evaluation of an electronically captured image on a computer remote from the process. For example, the optical device or parts of the optical device, such as the optical neural network, can be integrated into the processing optics of a corresponding process.

[0025] The input signal can therefore be obtained directly from the process, for example, without being transmitted via a waveguide. For example, the optical device can be designed to be placed in the same fluid in which the process is carried out, in order to capture the optical input signal directly from the fluid.

[0026] The inventors have recognized that an optical device according to the invention can be implemented with minimal space requirements and can thus be integrated into process-essential (i.e., simply put, existing) elements. This saves space and prevents or reduces the influence of potential error sources due to signal transmission (e.g., signal losses in electrical lines, electromagnetic interference on lines).

[0027] According to embodiments, the optical device further comprises an optical device configured to receive the optical input signal and to provide the optical neural network, based on the input signal, with an optical signal for determining the real-time information. Optionally, the optical device may comprise at least one of a lens, a beam splitter, a mirror, a wavelength filter, and / or an aperture.

[0028] The inventors have recognized that the optical input signal for the optical neural network can be processed using an optical device, for example, to enable an improved classification result. Furthermore, the use of optical elements enables a degree of freedom in the integration of the optical device, since the optical input signal can be guided to an advantageous installation location of the optical device (for example, using mirrors). According to embodiments, the process comprises beam shaping of a process beam using processing optics, and the optical device is configured to receive the optical input signal via the processing optics and / or via individual optical sub-elements of the processing optics.

[0029] The inventive procedures are not limited to specific processes and corresponding process beams. The process beam can be, for example, a laser beam or an electron beam (e-beam). The processing optics can comprise any combination of optical elements, such as one or more optical mirrors, lenses, and / or prisms.

[0030] Furthermore, it should be noted that the processing optics or parts thereof can also act as an optical device or can be used as such.

[0031] The inventors realized that a processing optics system, or even individual optical components of such an optics system, can be used jointly by both the process and process monitoring. This allows for savings in components and installation space.

[0032] According to embodiments, the optical device comprises a beam splitter, wherein the beam splitter is configured to receive the optical input signal via the processing optics and / or via individual optical sub-elements of the processing optics. Furthermore, the optical device is configured to separate the optical input signal from the process beam by means of the beam splitter and to provide the optical input signal to the optical neural network by means of the beam splitter. Separating the beams can, for example, comprise forwarding them in different directions.

[0033] The inventors have recognized that this can enable, for example, coaxial process monitoring. A corresponding processing optics can thus, for example, shape the process beam and direct it onto a workpiece. The radiation emitted and / or reflected by the workpiece can be guided as an optical input signal through the same processing optics (or at least the same parts thereof). A beam path can be adjusted using the beam splitter so that the optical input signal is provided to the optical neural network and, for example, is not directed into an area where the process beam is generated.

[0034] Specifically, in so-called coaxial process monitoring, the sensor technology, such as a device according to the invention, can be integrated directly into the processing head, which simultaneously influences the processing laser. The trick here is that both beams (the process beam, such as a processing laser beam, and the optical input signal, such as a measuring beam from the process zone) partially pass through the same optical elements and are separated away from the process zone by one or more beam splitters. Thus, according to the invention, both parts (sensor technology and application) can interact with each other. This enables efficient component integration.

[0035] According to embodiments, the optical neural network comprises at least one of a diffractive optical element, a spatial light modulator, and / or a meta-optic system. For example, a configuration for an optical device according to the invention and / or the optical neural network (ONN) can comprise a free combination of at least two diffractive optical elements (DOEs) and / or spatial light modulators (SLMs) (or one each, or, for example, only one or the other, or only several DOEs or only several SLMs), classical optical components (e.g., lenses, mirrors, apertures, wavelength filters, etc.), and / or classical (computer-based) AI methods (->ONN replaces, for example, some computationally intensive layers).

[0036] According to embodiments, the process is a controlled and / or regulated process and the optical neural network is configured to determine a control signal and / or a regulation signal based on the optical input signal and to encode the control signal and / or the regulation signal in the optical real-time information.

[0037] This allows control and / or regulation information to be provided in real time. In particular, this enables control and / or regulation for very fast processes, for example, with high settling time requirements. Such control can be implemented entirely analogically, for example, based on detection of the coded information without AD conversion. For example, the amplitude or phase of the detection signal can be used directly for control and / or regulation.

[0038] According to embodiments, the optical neural network is designed to determine a process parameter based on the optical input signal and to encode the process parameter in the optical real-time information. A process parameter is, for example, a variable relevant to the process. In the context of machining a workpiece, a process parameter can, for example, describe the quality of a machined workpiece. Furthermore, a measurement result using a process beam can also form a process parameter. For example, in a light section process, a laser can provide the process beam, which is reflected by a surface so that the reflected signal forms the optical input signal. The process parameter can, for example, comprise information about a height measurement value. The inventors have recognized that, according to the invention, process evaluations can be provided at high speed and with low energy consumption.

[0039] It should be noted that a process according to embodiments does not have to be controlled or regulated in all cases. In this regard, embodiments particularly include and / or address applications in which quality characteristics (as an example of process parameters) are, for example, "only" monitored. These can be, for example, the formation of spatter or defects in a weld seam. Accordingly, relevant information can be encoded into the optical real-time information.

[0040] According to embodiments, the optical device is configured to modify a phase and / or amplitude of the optical input signal to provide the optical real-time information in the form of a light spot, a line beam, and / or a two-dimensional light array.

[0041] Providing real-time information as a light point enables easy-to-read information display. Using a line beam, for example, a scalar value can be displayed as a process parameter or control and / or regulation information through a center of gravity of the line (e.g., in the interval [0, 1], so that, for example, a center of gravity on one side of the line corresponds to a 0 and on the opposite side to a 1, with corresponding intermediate values). Using a two-dimensional light array, information can be displayed two-dimensionally; coding can be achieved, for example, using patterns or centers of gravity.

[0042] It should be noted that an optical input signal, e.g., in the form of an input light field, can also have exactly one phase and amplitude, so that the device can be configured to modify the phase and / or amplitude of the optical input signal. Furthermore, embodiments can also address or process input signals that exhibit partial coherence through multiple superimposed fields with different phases. According to embodiments, the optical neural network has at least one static optical element and at least one adaptable optical element, and the adaptable optical element is configured to modify the processing of the optical input signal in the optical neural network.

[0043] This allows, for example, transfer learning methods to be applied, whereby the static elements are generated according to, for example, a generic pre-training, and the dynamic elements are adapted application-specifically for a second training session. Furthermore, further adjustments can be made even during the device's lifetime.

[0044] According to embodiments, the optical device further comprises an optical filter configured to wavelength-selectively filter the optical input signal to provide a filtered optical input signal to the optical neural network.

[0045] This makes it possible, for example, to select wavelengths that allow for particularly meaningful process analysis.

[0046] According to embodiments, the optical device further comprises a light source, wherein the light source is configured to illuminate the process in order to generate the optical input signal that is emitted and / or reflected by the process. Such a light source can, for example, also be coaxially integrated into process elements.

[0047] According to embodiments, the optical neural network is configured to process light having a specific optical property and the light source is configured to provide the light having the specific optical property.

[0048] In other words, the light source and the optical neural network can be coordinated. The optical property can, for example, form a specific wavelength. This can improve the accuracy and / or efficiency of the neural network.

[0049] According to embodiments, the optical device comprises a beam splitter, the beam splitter or a further beam splitter, and the optical device is designed to separate the light of the light source from the optical input signal by means of the beam splitter or the further beam splitter and to provide the optical input signal to the optical neural network by means of the beam splitter or the further beam splitter.

[0050] As previously explained in the context of processing optics, an integration, e.g. coaxial, of the light source and the optical neural network can thus be carried out, for example, together with a process beam source, e.g. in a processing head or in a processing optics.

[0051] It should be noted that with regard to illumination (or illumination latitude), according to embodiments, wavelengths between 10 pm (thermal radiation) and 400 nm (visible light) can be used. As previously explained, external illumination (e.g., via the light source) can be coaxial.

[0052] An advantage of embodiments in wavelength ranges above UV (ultraviolet) light (i.e., for example, for wavelengths greater than 400 nm) can be, for example, a simple, or, for example, simpler, production of the optical elements (e.g., DOEs and / or SLMs) for the optical neural network.

[0053] According to embodiments, the optical device is designed to be supplied with energy for providing the optical real-time information exclusively by means of the optical input signal. This also allows mobile applications and applications with high energy consumption requirements to be addressed.

[0054] Embodiments according to the present invention further comprise an optical system comprising an optical device according to any of the embodiments disclosed herein and a detector (e.g., CCD chip) configured to detect the optical real-time information and to provide an electrical signal based on the optical real-time information, wherein the detector comprises at least one of a photodiode, a line detector, and / or an area detector.

[0055] Thus, a processed or pre-processed electrical signal can be provided at high speed due to the optical processing in the optical neural network.

[0056] According to embodiments, the optical system further comprises a processing device which is designed to control and / or regulate the process based on the electrical signal of the detector and / or to provide information relating to the process based on the electrical signal of the detector. The processing device can operate digitally, for example, or, in the case of very fast control loops, analogically. The control and / or regulation of the process can in particular comprise control and / or regulation of a process system. For example, a system comprising a laser can be controlled and / or regulated. The control and / or regulation can in this case comprise, for example, an adjustment of a feed rate, a material feed and / or an adjustment of or with regard to process gases.

[0057] According to embodiments, the process is a material processing process and / or a measurement process (e.g., a laser measurement process, e.g., laser-induced plasma spectroscopy, LIBS) using a laser, and the processing device is configured to control and / or regulate the laser based on the electrical signal from the detector. Furthermore, other parameters in a machine of the process (e.g., a machine that includes the laser), e.g., axes, scanners, and shielding gas supply, can also be controlled and / or regulated.

[0058] Embodiments according to the present invention further include methods for providing an optical device (for example, one of the previously discussed optical devices), wherein the optical device comprises an optical neural network configured to provide real-time optical information regarding the process based on an optical input signal emitted and / or reflected by a process. The optical neural network comprises at least one first optical element and at least one adjustable optical element.

[0059] The method comprises simulative pre-training of a virtual model of the optical neural network with a first set of training data, wherein the at least one first optical element and the at least one adaptable optical element are depicted in the virtual model.

[0060] The method further comprises generating the optical neural network based on the pre-trained model, adapting the at least one adaptable optical element in the virtual pre-trained model of the optical neural network based on simulative training of the virtual pre-trained model with a second set of training data, and adapting the at least one adaptable optical element of the optical neural network according to the adapted virtual model of the optical neural network in order to provide the optical device. The inventors have recognized that, by using adaptable optical elements, transfer learning methods can be applied to optical neural networks. For example, generic pre-training can therefore be used, for example.by means of at least one first optical element, so that application-specific "fine-tuning" can be achieved using the adaptable elements. The training can be carried out efficiently with computer support.

[0061] However, it should be noted that, according to exemplary embodiments, training can be performed purely on the computer using images, without the use of adaptive or dynamic elements. Thus, conventional training methods (from applications in image recognition) can also be used for training.

[0062] Furthermore, it should be noted that, in general, according to exemplary embodiments, the at least one first optical element and the at least one adjustable optical element can be arranged in any desired order in the optical neural network. Along a beam path emanating from the process, the at least one first optical element can therefore be arranged, for example, before or after the at least one adjustable optical element. The adjustable optical element can, for example, form a frontmost or a rearmost optical element of the optical neural network. Thus, for example, the first optical element (e.g., in the sense of the frontmost with respect to a beam path emanating from the process) can be adjustable, and all optical elements behind it can be static. Conversely, for example, the frontmost optical elements can be static, and only the rearmost optical element can be adjustable or dynamic.

[0063] According to embodiments, the at least one first optical element is a static or an adjustable (e.g., dynamic) optical element. Thus, arrangements with purely adjustable optical elements are also possible.

[0064] According to embodiments, the method further comprises generating the first and / or second set of training data using a camera that captures the optical input signal via an optical device and using the optical device to provide the optical neural network, based on the optical input signal, with an optical signal for determining the real-time information.

[0065] The inventors have recognized that—in simple terms—dual use of such an optical device can enable improved training efficiency, since both the training data and the optical input signal can be provided by the same optical device. This can counteract errors due to differing optical properties (e.g., component tolerances) when using two different optical devices. It should be noted that the optical device can also be formed by a sub-element of a processing optics.

[0066] Fiourenkurzbeschreibung

[0067] Examples according to the present disclosure are explained in more detail below with reference to the accompanying figures. With regard to the schematic figures shown, it should be noted that the illustrated functional blocks are to be understood both as elements or features of the device according to the disclosure and as corresponding method steps of the method according to the disclosure, and corresponding method steps of the method according to the disclosure can also be derived therefrom. They show:

[0068] Fig. 1 is a schematic representation of an optical device for providing optical real-time information regarding a process according to embodiments of the present invention

[0069] Fig. 2 is a schematic representation of an optical device with additional, optional features according to embodiments of the present invention;

[0070] Figs. 3a)-c) schematic views of embodiments according to the present invention, wherein the process comprises beam shaping of a process beam by means of processing optics;

[0071] Fig. 4 is a schematic view of a system according to embodiments of the present invention;

[0072] Fig. 5 is a schematic view of an optical device according to embodiments with an adjustable optical element;

[0073] Fig. 6 is a schematic block diagram of a method according to the invention for providing an optical device; and Fig. 7 is a schematic view of a system according to embodiments with further optional features.

[0074] Detailed description of the examples according to the figures

[0075] Before exemplary embodiments of the present invention are explained in more detail below with reference to the drawings, it is pointed out that identical, functionally equivalent or equivalent elements, objects and / or structures in the different figures are provided with the same or similar reference numerals, so that the description of these elements shown in different exemplary embodiments is interchangeable or can be applied to one another.

[0076] Fig. 1 shows a schematic representation of an optical device for providing optical real-time information regarding a process according to embodiments of the present invention. The optical device 100 comprises an optical neural network 110.

[0077] The optical input signal 1 11 can, for example, be emitted by a process (e.g. in the case of thermal emission) and / or reflected (e.g. in the case of machining or measuring a workpiece with a laser, whereby laser radiation is reflected away from the workpiece).

[0078] The optical device 100 is accordingly configured to detect the optical input signal. The optical neural network 110 is configured to provide real-time optical information 112 regarding the process based on the optical input signal.

[0079] Fig. 2 shows a schematic representation of an optical device with additional, optional features according to embodiments of the present invention. Fig. 2 shows device 200 comprising an optical neural network 210, an optional filter 220, an optional optical device 230, and an optional light source 240. As explained with reference to Fig. 1, the optical neural network 210 is configured to provide optical real-time information 212 regarding a process P.

[0080] As shown in Fig. 2, the optical device 200 may optionally be configured to capture the optical input signal 201 emitted and / or reflected by the process P, directly from the process.

[0081] Fig. 2 shows an example of an embodiment in which the filter 220 is arranged upstream (signal path originating from the process P) of the optical device 230. However, according to embodiments, the optical device 230 can also be arranged downstream of the filter 220. The filter 220 is designed, for example, to filter the optical input signal 201 emitted and / or reflected by the process P, for example, in a wavelength-selective manner. Thus, wavelengths that contribute little or nothing to the process classification can be filtered out.

[0082] For example, for processes from which no radiation, or insufficient radiation (to determine real-time information) is emitted, a device 200 according to the invention can have a light source 240. The light source can be used to illuminate or irradiate the process, so that light 241 can cause a reflected signal 201, which can form the input signal. However, it should be noted that a corresponding input signal 201 can also comprise a superposition of process radiation, such as thermal radiation, and reflected radiation from an illumination 240, or even reflected radiation from a process beam, such as a processing laser. The light source can of course also be used for processes that emit radiation on their own, for example to add information in a different wavelength range to the input signal.

[0083] Optionally, for example, the optical diffractive neural network 210 can be configured to process light with a specific optical property, for example, a specific wavelength, and the light source can be configured accordingly to provide the light with that specific optical property (e.g., wavelength range). Accordingly, the filter can be configured to transmit only light with the specific optical property.

[0084] In this case, an advantage of using the filter 220 may be that a simple, i.e., inexpensive, light source 240 with a broad spectrum can be used, whereby the filter can filter out wavelengths that are irrelevant or carry little information for the process classification.

[0085] Thus, a filtered input signal 221 is provided to the optical device 230. The optical device, in turn, is configured to receive the input signal 201, for example, in a further processed form of the filtered input signal 221, and to supply it to the optical neural network 210 as an optical signal 231.

[0086] The optical device may, for example, comprise at least one of a lens, a beam splitter, a mirror and / or an aperture. The optical neural network may further comprise, for example, at least one of a diffractive optical element, a spatial light modulator and / or a meta-optic.

[0087] The optical real-time information can be provided, for example, in the form of a light spot, a line beam, and / or a two-dimensional light array, or can include similar representations. For this purpose, the optical neural network 210 can be configured to modify a phase and / or amplitude of the optical input signal, thus in particular optionally also the optical signal 231 and / or the filtered input signal 221.

[0088] For example, without a light source 240, an optical device 200 can optionally be powered exclusively by means of the optical input signal 201 to provide the optical real-time information 212.

[0089] As a further optional feature, the real-time information 212 can contain a control signal and / or a regulation signal determined by the optical neural network 210. This allows, for example, a process P to be controlled and / or regulated. Alternatively or additionally, a process parameter determined by the optical neural network 210 can be encoded in the optical real-time information 212.

[0090] Figs. 3 a)-c) show schematic views of embodiments according to the present invention, wherein the process comprises beam shaping of a process beam using processing optics. Figures 3 a) to c) show optical neural networks (ONN), process beam sources L, optical sub-elements O, beam splitter T, light source B, and workpieces W. The process beam source can be, for example, a laser.

[0091] Fig. 3a) shows a schematic view of a typical coaxial structure, for example, according to exemplary embodiments. A process beam is shaped by means of processing optics 320, comprising a first optical sub-element 322 and a second optical sub-element 324, and guided to a workpiece W. The optical device 330, comprising the optical neural network ONN, 332, is designed to receive the optical input signal 340 via the optical sub-element 324 of the processing optics 320. For this purpose, the device 330 comprises, as an additional, optional feature, a beam splitter T, 332. The beam splitter 350 is designed to separate the process beam 310 from the reflected input signal 340 in order to provide the optical input signal to the optical neural network.

[0092] Such an arrangement enables coaxial integration of the optical device into the processing optics of the process. It should be noted that the processing optics can also comprise only a single optical sub-element. These optical sub-elements can be, for example, conventional optical components such as lenses, apertures, or even protective glass. It should also be mentioned again that the processing optics, or sub-elements thereof, can serve as the optical device of the device.

[0093] Fig. 3 b) shows a schematic view of another configuration according to embodiments, wherein the device 300 includes an optional light source 334 and a further beam splitter 336. As previously explained, the light source 334 is configured to illuminate the process in order to generate the optical input signal 340, which is emitted and / or reflected by the process.

[0094] It should be noted that, in general, according to embodiments, the input signal can comprise both light emitted by the process itself and light generated and reflected by the light source. The light source 334 then generates the input signal 340 by providing the portion of the input signal not emitted by the process itself.

[0095] By means of the beam splitter 332, as shown in Fig. 3b), the process beam 310 can be separated or split from the optical input signal. In turn, by means of the beam splitter 336, the light 335 from the light source 334 can be separated from the optical input signal, so that the workpiece W is illuminated and the optical input signal 340 can be provided to the optical neural network 332.

[0096] Fig. 3b) shows a schematic view of a structure according to exemplary embodiments in which, in addition to the process beam, for example, the processing or measuring beam, the optional additional illumination can also be coaxially integrated. It should be noted that, according to exemplary embodiments, only one of the beam splitters can be present at a time. In other words, an illumination with a beam splitter can be used not only in combination with a beam splitter for the process beam. Furthermore, a beam splitter, for example with several optical sub-elements, can also separate both the process beam and the light from the light source from the optical input signal.

[0097] Furthermore, it should be noted that the processing optics, e.g., an illumination optics with which the processing beam can be shaped, does not necessarily have to consist of only a single element. According to exemplary embodiments, several lenses, mirrors, and other individual optics can be combined to achieve the desired beam shaping. Thus, only some of the elements of the processing optics may be relevant for the ONN measurement beam, for example, 340.

[0098] Fig. 3c) shows a schematic view of a further structure according to embodiments, wherein the process comprises beam shaping of a process beam by means of a processing optics 320 (which, for example, may in turn comprise a plurality of sub-elements) and wherein the optical device 320 is designed to receive the optical input signal 320 via the processing optics.

[0099] An advantage of the arrangements according to Fig. 3a) and 3b) compared to an arrangement 3c) can be, for example, that smaller optical components can be used for the processing optics.

[0100] However, an arrangement according to Fig. 3c) can, for example, address a special case in which both beams are guided through a protective glass located behind the last beam-shaping element in the beam path. This protective glass, for example, separates the process zone from the surrounding area and prevents contamination there. However, such a protective glass can also form an optical element or optical sub-element of a processing optics or an optical device.

[0101] The arrangements according to Figs. 3a)-c) enable the acquisition of feedback from the process and / or component without the risk of destroying optical elements of the neural network, for example spatial light modulators at high power, as they are usually required in the material processing process.

[0102] Fig. 4 shows a schematic view of a system according to embodiments of the present invention. System 400 includes an optical device 410 and a detector 420 configured to detect real-time optical information 412 and provide an electrical signal 421 based on the real-time optical information. The optical input signal from process P is designated by reference numeral 411.

[0103] In particular, the device 410 may comprise optional features explained above, both individually and in combination.

[0104] As a further optional feature, system 400 includes a processing device 430 configured to control and / or regulate process P based on electrical signal 421 from detector 420. For this purpose, an actuating intervention 431 in process P is shown as an optional feature. Alternatively or additionally, processing device 430 can be configured to provide information regarding the process based on the electrical signal from the detector. In this case, actuating intervention 431 can then be omitted, for example, and the information regarding the process can be provided by processing device 430 instead.

[0105] Specifically, the process may be, for example, a material processing process and / or a measuring process using a laser, wherein the processing device 430 may be configured accordingly to control and / or regulate the laser based on the electrical signal 421 of the detector.

[0106] Fig. 5 shows a schematic view of an optical device according to embodiments with an adaptable optical element. Device 500 comprises an optical neural network 510 and, as a further optional feature, an optical device 520. The optical device is configured to provide an optical signal 502 for the optical neural network 510 based on the optical input signal 501, wherein the optical neural network 510 is in turn configured to provide the real-time information 503.

[0107] As an optional feature, the optical diffractive neural network comprises at least one static optical element 512 and at least one adjustable optical element 514. The adjustable optical element is designed to modify the processing of the optical input signal 501 in the optical diffractive neural network. In other words, the adjustable optical element can be an adjustable or, for example, dynamic optical element, which can therefore be modified. A method according to the invention for providing an optical device, for example, a device 500 (see Fig. 5, optionally without optical device 520), is discussed below.

[0108] Fig. 6 shows a schematic block diagram of such an inventive method for providing an optical device, wherein the optical device, e.g. 500, has an optical neural network, e.g. 510, which is designed to provide, based on an optical input signal, e.g. 501, which is emitted and / or reflected by a process, optical real-time information, e.g. 503, relating to the process, wherein the optical neural network has at least one first optical element, e.g. 512, and at least one adaptable optical element, e.g. 514.

[0109] The method 600 comprises simulative pre-training 610 of a virtual model of the optical neural network with a first set of training data, wherein the at least one first optical element, e.g. 512, and the at least one adaptable optical element, e.g. 514, are mapped in the virtual model. The method 600 further comprises generating the optical neural network, e.g. 510, based on the pre-trained model, adapting the at least one adaptable optical element in the virtual pre-trained model of the optical neural network based on simulative training of the virtual pre-trained model with a second set of training data, and adapting the at least one adaptable optical element, e.g. 514, of the optical neural network according to the adapted virtual model of the optical neural network in order to provide the optical device, e.g. 500.

[0110] The one or more first optical elements, e.g., 512 from Fig. 500, can optionally be static or adjustable, e.g., dynamic optical elements. A combination of static and adjustable elements is also possible.

[0111] According to embodiments, a portion of the optical elements of the optical neural network can be adjusted based on a first training session, for example, fixedly adjusted, for example, using static optical elements, and a second portion of the optical elements of the network, which are adaptable and / or adjustable, can be readjusted based on a second training session. Thus, for example, transfer learning methods can be applied. However, it should be noted again that the training according to embodiments can be carried out purely on the computer using images, and an arrangement according to the invention cannot have or require any dynamic elements. Thus, known training methods (for example, for applications in image recognition) can also be used to provide a device according to the invention.

[0112] Furthermore, the first and / or second set of training data can optionally be generated using the same optical device. The inventors have recognized that training quality can be improved if the training data is generated using the same optical device that is used to provide the optical signal from the optical input signal for the neural network.

[0113] In this way, imaging errors, e.g. when using different optics for generating the training data compared to the “field use” of the optical device, can be prevented or reduced.

[0114] An example sequence of training an ONN according to the invention can therefore include the following:

[0115] 1 . Optional installation of external lighting if required / advantageous

[0116] 2. For example, take traditional camera images for training data. Depending on the process requirements, the camera can be used solely as a sensor, but also with a lens, or in a coaxial beam path through the processing optics. This lens can then optionally remain in the setup (e.g., as an optical device for the optical device), or the integration of the ONN could optionally also be coaxial.

[0117] 3. Labeling, e.g., classification, of the (e.g., electronic) training data: For simple features such as the size of the melt pool, this can optionally be automated, e.g., using established image processing methods; for more complex data, this can be done manually. Training data can optionally be artificially upscaled, e.g., by mirroring / rotating, if appropriate.

[0118] 4. Training an ONN with the labeled training data on the computer: The complexity of the ONN can be determined based on the complexity of the task, for example. A pre-trained ONN on, for example, the image-net dataset can be used with transfer learning to map only the last layers, e.g., 1-2, using customizable optical elements, e.g., SLMs (which can or even must be retrained).

[0119] -> Advantage of rapid adaptation in serial production

[0120] 5. Production of the required first optical elements, e.g. DOEs (e.g. externally, e.g. using known state-of-the-art methods) and, optionally, complete integration of the system (e.g. with additional optical components, e.g. depending on further application properties) into the process setup, e.g. coaxial.

[0121] In the following, embodiments are explained again in other words, e.g. with further optional features, and further embodiments are discussed.

[0122] Examples of implementations are not limited to process monitoring in laser material processing. In general, image data from manufacturing processes or other areas (e.g., facial recognition, environmental analysis, autonomous driving, etc.) can be evaluated extremely quickly using compact sensors.

[0123] For this purpose, embodiments comprise a novel component and method for capturing and evaluating process images. An optical neural network, e.g. in the form of a diffractive neural network (DNN), is used to evaluate the light coming from the process zone, e.g. directly in the processing optics, and thus output an intensity pattern (e.g. instead of 2D image data) in which the results of the evaluation or new control signals can be encoded. Compared to conventional methods (e.g. evaluation or exclusive evaluation in an electronic processing unit), embodiments offer the advantage that the data processing, or at least part of the data processing, can take place at the speed of light. The image analysis is therefore available to the control unit, for example, practically instantaneously. Embodiments can thus avoid the longer computing times in the computer.

[0124] As explained above, embodiments according to the invention solve disadvantages in the prior art, for example, among other things, by carrying out the process image analysis in an optical neural network (e.g. 110, 210, 310, 332). The information carrier for the (optical) calculation is thus, for example, the light collected from the process zone (e.g. in the form of the optical input signal 111, 201, 340, 411, 501). The light can thus pass through an optical system and, for example, undergo modifications (e.g. in phase and / or amplitude), which can occur analogously to the processing of information in a (digital) neural network. One difference is, for example, that the information is processed at the speed of light and, apart from the optical elements used (e.g. 512, 514) for phase and amplitude manipulation, no further (electronic) hardware is required for the calculation.Depending on the type and number of output variables of the network, detectors (e.g. 420) with individual pixels, line arrays or area detectors can be used.

[0125] According to embodiments, image acquisition and image processing are combined in an optical system, which can consist, for example, of a sequence of conventional optics (e.g., lenses, beam splitters, mirrors, etc., e.g., an optical device 230, 520 and / or processing optics 320), diffractive optical elements, DOEs (e.g., 512), and also spatial light modulators (SLMs) (e.g., 514) such as liquid crystal-based technology or micromirror arrays. The optical neural network, e.g., in the form of a diffractive optical neural network, DNN, can consist, for example, of a sequence of phase or amplitude masks and can be implemented both statically and dynamically in reflection or transmission with diffractive optical elements (DOEs), SLMs, or meta-optics. Embodiments are not limited to specific diffractive neural networks.According to embodiments, various concepts of a DNN and various forms of implementation can be used. According to embodiments, the DNN can, for example, be integrated directly into the observation optics (see, for example, Fig. 3a)-c)). The evaluation can be carried out, for example, with a CCD chip or, depending on the application, with line detectors or individual photodiodes. The choice of detectors can depend, for example, on the number and type (scalar or binary) of the DNN's output parameters. The optical system or optical device can optionally further include one or more wavelength-selective filters (e.g., 220) in order to, for example, analyze only the light from the targeted illumination (e.g., 240) of the process. The illumination of the process can optionally be carried out with a coherent source. In other words, the light source can, for example, be a coherent light source, for example in the form of a laser.

[0126] If the optical neural network, e.g., a DNN, contains one or more dynamic elements, such as an SLM, the sensor's function can be modified, e.g., over time. This adaptation can be used to train the DNN, for example, to adapt it to the environmental conditions of the process or to modify the evaluation algorithm, e.g., in the event of changed quality requirements or changes to the process (e.g., material, product type, etc.).

[0127] The following discusses, among other things, further inventive advantages of embodiments over conventional solutions. Embodiments can enable extremely fast image analysis in process monitoring at the speed of light and can make an electronic computing unit that would otherwise be used for image processing (e.g., including the analysis of neural networks) obsolete. The electronic computing unit generally also includes special hardware, such as graphic processing units (GPUs) for fast image analysis. A corresponding sensor, e.g., an optical device or a system according to embodiments, for process monitoring can be built very small, compact, and cost-effectively. A sensor unit can, for example, comprise at least one or more DOEs and a detector, e.g., a photodiode (which, for example, detects whether a process is running within defined quality criteria).These sensors do not require a connected or integrated electronic processing unit. The sensors can then be connected directly to the process control system, for example. This allows process parameters such as process speed, material feed, or laser power to be changed directly without going through a processing unit.

[0128] Since the optical neural network can perform the image analysis calculations using the process light itself, no additional energy is required. In other words, no additional energy can be consumed or needs to be consumed. The energy consumption of the entire sensor, e.g., a system according to the exemplary embodiments, relates, for example, solely or primarily to the detector analysis and, optionally, the illumination used.

[0129] Embodiments can thus particularly include laser processing machines and OEMs (Original Equipment Manufacturers) in the field of sensor technology, or address such fields of application.

[0130] Embodiments further include or enable or address applications such as: analysis of manufacturing processes, environments, objects, for example one based on the evaluation of image data by neural networks or for which such evaluation is too slow or too inefficient.

[0131] Fig. 7 shows a schematic view of a system according to embodiments with further optional features. The system 700 comprises an optical device 710, e.g., a sensor, with diffractive optical elements 712, an optional optical device 714, which comprises, e.g., an optical element, and an optional light source in the form of the illumination source 716. The system 700 further comprises a detector 720. As a further optional feature, the system 700 comprises a processing device in the form of a system controller 730, as well as signal lines 731.

[0132] Furthermore, Fig. 7 shows a processing head 740, for example a laser processing head, as well as a workpiece 750.

[0133] The workpiece 750 can thus be processed by the processing head 740, for example, using a laser beam 741. A signal from the laser beam can be reflected from the workpiece toward the device 710, which can form the optical input signal 701 or at least a portion of the optical input signal 701. Additionally, the input signal 701 can include radiation components emitted by the workpiece itself, for example, due to thermal radiation. As a further option, the light source 716 can illuminate the workpiece 750 and thus provide a further portion of the input signal 701 based on further reflections. Such illumination can also be provided, for example, by ambient light (e.g., daylight or room lighting).

[0134] It should be noted again here that any combination can be used to generate the optical input signal. For example, radiation reflected by the laser can form the input signal alone (without the use of an additional light source 716, e.g., in the form of a laser). In a process such as water jet cutting, for example, the light source can be used solely to generate the input signal (since there is no reflection of laser radiation from a processing laser). In all cases, reflected room light, e.g., daylight, can form the input signal or part thereof. In other processing operations, the input signal 701 can, in turn, be formed solely by thermal radiation from the workpiece. However, any combination of these input signal-generating options can of course also be used according to the invention.

[0135] In other words, according to embodiments, the scenery can be made visible, both by active illumination and by ambient light, or the thermal emissions from the process are analyzed.

[0136] Based on the input signal 701, which could, for example, be further filtered, e.g., wavelength-selectively, an optical signal 702 is fed to the optical neural network, which, for example, comprises elements 712, on the basis of which the real-time information 703 is provided. This can be detected by detector 720 and forwarded in electrical form to the system controller 730.

[0137] It should be noted that the detector 720 can be integrated into the device 710 or, for example, simply in a common housing. However, an external arrangement is also possible. Based on the detected real-time information, further evaluation can then be performed in the system controller 730 and / or closed-loop or closed-loop control of the processing head 740 (e.g., with regard to speed, power).

[0138] For other applications, the processing head can be a cutting head, for example.

[0139] All lists of materials, environmental influences, electrical properties and optical properties listed herein are to be considered exemplary and not exhaustive.

[0140] Although some aspects have been described in the context of a device, it should be understood that these aspects also represent a description of the corresponding method, so that a block or component of a device can also be understood as a corresponding method step or as a feature of a method step. Analogously, aspects described in the context of or as a method step also represent a description of a corresponding block, detail, or feature of a corresponding device. Some or all of the method steps may be performed by (or using) a hardware apparatus, such as a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, some or more of the key method steps may be performed by such an apparatus.

[0141] Depending on specific implementation requirements, embodiments of the invention may be implemented in hardware or software. The implementation may be performed using a digital storage medium, such as a floppy disk, a DVD, a Blu-ray Disc, a CD, a ROM, a PROM, an EPROM, an EEPROM, or a FLASH memory, a hard disk, or other magnetic or optical storage device storing electronically readable control signals that can interact or cooperate with a programmable computer system to perform the respective method. Therefore, the digital storage medium may be computer-readable.

[0142] Some embodiments according to the invention thus comprise a data carrier having electronically readable control signals capable of interacting with a programmable computer system such that one of the methods described herein is carried out.

[0143] In general, embodiments of the present invention may be implemented as a computer program product having a program code, wherein the program code is effective to perform one of the methods when the computer program product is run on a computer.

[0144] The program code can, for example, also be stored on a machine-readable medium.

[0145] Other embodiments include the computer program for performing one of the methods described herein, wherein the computer program is stored on a machine-readable carrier.

[0146] In other words, an embodiment of the method according to the invention is thus a computer program which has a program code for carrying out one of the methods described herein when the computer program runs on a computer.

[0147] A further embodiment of the method according to the invention is thus a data carrier (or a digital storage medium or a computer-readable medium) on which the computer program for performing one of the methods described herein is recorded. The data carrier, the digital storage medium, or the computer-readable medium is typically physical and / or non-perishable or non-transient.

[0148] A further embodiment of the method according to the invention is thus a data stream or a sequence of signals that represents the computer program for carrying out one of the methods described herein. The data stream or the sequence of signals can be configured, for example, to be transferred via a data communication connection, for example, via the Internet.

[0149] A further embodiment comprises a processing device, for example a computer or a programmable logic device, which is configured or adapted to carry out one of the methods described herein.

[0150] A further embodiment comprises a computer on which the computer program for performing one of the methods described herein is installed.

[0151] A further embodiment according to the invention comprises a device or system designed to transmit a computer program for performing at least one of the methods described herein to a recipient. The transmission can be electronic or optical, for example. The recipient can be, for example, a computer, a mobile device, a storage device, or a similar device. The device or system can, for example, comprise a file server for transmitting the computer program to the recipient.

[0152] In some embodiments, a programmable logic device (e.g., a field-programmable gate array, an FPGA) may be used to perform some or all of the functionalities of the methods described herein. In some embodiments, a field-programmable gate array may cooperate with a microprocessor to perform any of the methods described herein. In general, in some embodiments, the methods are performed by any hardware device. This may be general-purpose hardware such as a computer processor (CPU) or method-specific hardware such as an ASIC.

[0153] The devices described herein may be implemented, for example, using a hardware apparatus, or using a computer, or using a combination of a hardware apparatus and a computer.

[0154] The devices described herein, or any components of the devices described herein, may be implemented at least partially in hardware and / or software (computer program). The methods described herein may be implemented, for example, using a hardware device, or using a computer, or using a combination of a hardware device and a computer. The methods described herein, or any components of the methods described herein, may be executed at least partially by hardware and / or by software.

[0155] The above-described embodiments are merely illustrative of the principles of the present invention. It is understood that modifications and variations of the arrangements and details described herein will be apparent to others skilled in the art. Therefore, it is intended that the invention be limited only by the scope of the following claims and not by the specific details presented in the description and explanation of the embodiments herein.

[0156] References

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Claims

Patent claims 1. An optical device (100, 200, 330, 410, 500, 710) for providing optical real-time information (112, 212, 412, 503, 703) relating to a process; wherein the optical device is configured to detect an optical input signal (111, 201, 340, 411, 501, 701) which is emitted and / or reflected by the process; and wherein the optical device comprises an optical neural network (110, 210, 310, 332, 510) configured to provide the optical real-time information relating to the process based on the optical input signal.

2. Optical device (100, 200, 330, 410, 500, 710) according to claim 1, wherein the optical device is designed to capture the optical input signal directly from the process in order to detect the optical input signal (111, 201, 340, 411, 501, 701) which is emitted and / or reflected by the process.

3. Optical device (100, 200, 330, 410, 500, 710) according to one of the preceding claims, further comprising: an optical device (230, 520, 714) which is designed to receive the optical input signal (111, 201, 340, 411, 501, 701) and to provide the optical neural network (110, 210, 310, 332, 510), based on the input signal, with an optical signal (231, 502, 702) for determining the real-time information (112, 212, 412, 503, 703).

4. The optical device (100, 200, 330, 410, 500, 710) of claim 3, wherein the optical device (230, 520, 714) comprises at least one of a lens, a beam splitter, a mirror, a wavelength filter, and / or an aperture.

5. Optical device according to one of the preceding claims, wherein the process comprises beam shaping of a process beam (310, 741) by means of processing optics (320); and wherein the optical device is designed to receive the optical input signal (111, 201, 340, 411, 501, 701) via the processing optics (320) and / or via individual optical sub-elements (322, 324, 326) of the processing optics.

6. The optical device (100, 200, 330, 410, 500, 710) according to claim 5, wherein the optical device comprises a beam splitter (332); wherein the beam splitter is configured to receive the optical input signal (111, 201, 340, 411, 501, 701) via the processing optics (320) and / or via individual optical sub-elements (322, 324, 326) of the processing optics; and wherein the optical device is configured to separate the optical input signal from the process beam (310, 741) by means of the beam splitter; and wherein the optical device is configured to provide the optical input signal to the optical neural network (110, 210, 310, 332, 510) by means of the beam splitter.

7. Optical device (100, 200, 330, 410, 500, 710) according to one of the preceding claims, wherein the optical neural network (110, 210, 310, 332, 510) comprises at least one of a diffractive optical element, a spatial light modulator and / or a meta-optic.

8. Optical device (100, 200, 330, 410, 500, 710) according to one of the preceding claims, wherein the process is a controlled and / or regulated process; and wherein the optical neural network (110, 210, 310, 332, 510) is designed to determine a control signal and / or a regulation signal based on the optical input signal (111, 201, 340, 411, 501, 701), and to to encode the control signal and / or the regulation signal in the optical real-time information (1 12, 212, 412, 503, 703).

9. Optical device (100, 200, 330, 410, 500, 710) according to one of the preceding claims, wherein the optical neural network (110, 210, 310, 332, 510) is configured to determine a process parameter based on the optical input signal and to encode the process parameter in the optical real-time information (112, 212, 412, 503, 703).

10. Optical device (100, 200, 330, 410, 500, 710) according to one of the preceding claims, wherein the optical device is designed to modify a phase and / or amplitude of the optical input signal (111, 201, 340, 411, 501, 701) in order to provide the optical real-time information (112, 212, 412, 503, 703) in the form of a light spot, a line beam and / or a two-dimensional light array.

11. Optical device (100, 200, 330, 410, 500, 710) according to one of the preceding claims, wherein the optical neural network (110, 210, 310, 332, 510) comprises at least one static optical element (512, 712) and at least one adjustable optical element (514); and wherein the adjustable optical element is configured to change the processing of the optical input signal (111, 201, 340, 411, 501, 701) in the optical neural network.

12. Optical device (100, 200, 330, 410, 500, 710) according to one of the preceding claims, further comprising: an optical filter (220) which is designed to filter the optical input signal (111, 201, 340, 411, 501, 701) in a wavelength-selective manner in order to assign the optical neural network (110, 210, 310, 332, 510) to provide a filtered optical input signal (221).

13. Optical device (100, 200, 330, 410, 500, 710) according to one of the preceding claims; further comprising: a light source (240, 334, 716), wherein the light source is configured to illuminate the process in order to generate the optical input signal (111, 201, 340, 411, 501, 701) which is emitted and / or reflected by the process.

14. The optical device (100, 200, 330, 410, 500, 710) according to claim 13, wherein the optical neural network (110, 210, 310, 332, 510) is configured to process light having a specific optical property; and wherein the light source (240, 334, 716) is configured to provide the light having the specific optical property.

15. Optical device (100, 200, 330, 410, 500, 710) according to one of claims 13 or 14, wherein the optical device comprises a beam splitter (336), the beam splitter (332) or a further beam splitter (336); wherein the optical device is designed to separate the light of the light source (240, 334, 716) from the optical input signal (111, 201, 340, 411, 501, 701) by means of the beam splitter or the further beam splitter; and wherein the optical device is designed to provide the optical input signal to the optical neural network (110, 210, 310, 332, 510) by means of the beam splitter or the further beam splitter.

16. Optical device (100, 200, 330, 410, 500, 710) according to one of claims 1 to 13, wherein the optical device is designed to be supplied with energy exclusively by means of the optical input signal (11 1 , 201, 340, 41 1 , 501, 701) for Provision of optical real-time information (112, 212, 412, 503, 703).

17. An optical system (400, 700) comprising: an optical device (100, 200, 330, 410, 500, 710) according to any one of the preceding claims; a detector (320, 720) configured to detect the optical real-time information (112, 212, 412, 503, 703) and to provide an electrical signal (421) based on the optical real-time information; wherein the detector comprises at least one of a photodiode, a line detector, and / or an area detector.

18. The optical system (400, 700) according to claim 17, further comprising: a processing device (430, 730) configured to control and / or regulate the process based on the electrical signal (421) of the detector (320, 720); and / or to provide information regarding the process based on the electrical signal of the detector.

19. Optical system (400, 700) according to claim 18, wherein the process is a material processing process and / or a measuring process using a laser and wherein the processing device (430, 730) is designed to control and / or regulate the laser based on the electrical signal (421) of the detector (320, 720).

20. A method (600) for providing an optical device, wherein the optical device (100, 200, 330, 410, 500, 710) comprises an optical neural network (110, 210, 310, 332, 510) configured to, based on an optical input signal (111, 201, 340, 411, 501, 701 ) which is emitted and / or reflected by a process, to provide optical real-time information (1 12, 212, 412, 503, 703) relating to the process; wherein the optical neural network comprises at least one first optical element (512, 712) and at least one adjustable optical element (514); wherein the method comprises the following features: Simulatively pre-training (610) a virtual model of the optical neural network with a first set of training data, wherein the at least one first optical element and the at least one adjustable optical element are depicted in the virtual model; and Generating (620) the optical neural network based on the pre-trained model; Adapting (630) the at least one adaptable optical element in the virtual pre-trained model of the optical neural network based on simulative training of the virtual pre-trained model with a second set of training data; and Adapting (640) the at least one adaptable optical element of the optical neural network according to the adapted virtual model of the optical neural network to provide the optical device.

21. The method (600) of claim 20, wherein the at least one first optical element is a static or an adjustable optical element.

22. The method (600) according to any one of claims 20 or 21, further comprising: Generating the first and / or second set of training data using a camera that captures the optical input signal (111, 201, 340, 411, 501, 701) via an optical device (230, 320, 520, 714); and Using the optical device to provide the optical neural network (110, 210, 310, 332, 510) with an optical signal for determining the real-time information (112, 212, 412, 503, 703) based on the optical input signal.

23. Method (600) according to one of claims 20 to 21, further comprising: Generating the first and / or second set of training data using a camera that captures the optical input signal (111, 201, 340, 411, 501, 701) via an optical device (230, 320, 520, 714); and Using the same optical device to provide the optical neural network (110, 210, 310, 332, 510) with an optical signal for determining the real-time information (112, 212, 412, 503, 703) based on the optical input signal.

24. An optical device (100, 200, 330, 410, 500, 710) for providing optical real-time information (112, 212, 412, 503, 703) relating to a process; wherein the process is a material processing process and / or a measuring process using a laser in which a workpiece is measured or machined; wherein the optical device is designed to detect an optical input signal (111, 201, 340, 411, 501, 701) which is emitted and / or reflected by the process; and wherein the optical device comprises an optical neural network (110, 210, 310, 332, 510) which is designed to provide the optical real-time information relating to the process based on the optical input signal.

25. An optical device (100, 200, 330, 410, 500, 710) for providing real-time optical information (112, 212, 412, 503, 703) relating to a process; wherein the process is a material processing process and / or a measuring process using a laser; and wherein the optical device is configured to detect an optical input signal (111, 201, 340, 411, 501, 701) emitted and / or reflected by the process; and wherein the optical device comprises an optical neural network (110, 210, 310, 332, 510) configured to provide the optical real-time information relating to the process based on the optical input signal; wherein the optical device is configured to capture the optical input signal directly from the process in order to detect the optical input signal (111, 201, 340, 411, 501, 701) emitted and / or reflected by the process.

26. The optical device (100, 200, 330, 410, 500, 710) according to claim 25, wherein the process is a controlled and / or regulated process; and wherein the optical neural network (110, 210, 310, 332, 510) is configured to determine a control signal and / or a regulation signal for the material processing process and / or the measuring process based on the optical input signal (111, 201, 340, 411, 501, 701), and to encode the control signal and / or the regulation signal in the optical real-time information (112, 212, 412, 503, 703).

27. An optical device (100, 200, 330, 410, 500, 710) for providing optical real-time information (112, 212, 412, 503, 703) relating to a process; wherein the process is a material processing process and / or a measuring process using a laser; and wherein the optical device is configured to detect an optical input signal (111, 201, 340, 411, 501, 701) emitted and / or reflected by the process; and wherein the optical device comprises an optical neural network (110, 210, 310, 332, 510) configured to provide the optical real-time information relating to the process based on the optical input signal; wherein the process comprises beam shaping of a process beam (310, 741) by means of processing optics (320); and wherein the processing optics are designed to shape the process beam and direct it onto a workpiece; wherein the optical device has a beam splitter (332); wherein the beam splitter is designed to receive the optical input signal (111, 201, 340, 411, 501, 701) in the form of radiation emitted and / or reflected from the workpiece, via the processing optics (320) and / or via individual optical sub-elements (322, 324, 326) of the processing optics; and wherein the optical device is designed to separate the optical input signal from the process beam (310, 741) by means of the beam splitter; and wherein the optical device is designed to provide the optical input signal to the optical neural network (110, 210, 310, 332, 510) by means of the beam splitter.

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