Computing system for predicting substrate temperatures during laser operation

Abstract

To provide a computing system for predicting substrate temperatures during simulated laser operation.SOLUTION: To calculate a first segment of laser operation, a computing system (22) outputs an updated geometry (36) of a substrate after a predicted ablation of material from the substrate via at least one laser pulse, and an updated temperature value (38) of the substrate after being subjected to the at least one laser pulse. To calculate a second segment of laser operation which outputs an updated temperature value (40) of the substrate after heat transfer through the substrate from the substrate to ambient air, the system (22) iteratively switches between an ablation stage portion and a thermodynamic stage portion (32B) through a final segment of laser operation.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates generally to laser operation and, more particularly, to model-based engineering (MBE) tools to aid in the development of laser ablation processes for materials. [Background technology]

[0002] Laser systems are commonly used for operations such as laser ablation, in which material is removed from a substrate by a laser. In one application, laser ablation is used to remove paint from a surface and / or prepare the surface for repainting. During this process, a layer of paint is removed by the energy of the laser. The laser can also be configured to remove impurities such as oxides from the surface and / or impart a desired surface roughness. New paint applied to a surface treated in this way is expected to have better application, adhesion, and longevity. Summary of the Invention [Problem to be solved by the invention]

[0003] However, in highly regulated industries such as aerospace, any modification to an already approved process or the development of an entirely new process often requires significant investment in the certification of the updated or new process by the appropriate regulatory agency. This can apply to updated laser technology, new materials, and modified operating settings. For example, a single laser system for a specific application may take several years to fully qualify and approve, requiring detailed analysis of the laser system operating on hundreds of fatigue test specimens. [Means for solving the problem]

[0004] To address the above-mentioned problems, according to one aspect of the present disclosure, a computing system for predicting a substrate temperature during simulated laser operation is provided herein. In this aspect, the computing system includes processing circuitry that retrieves parameters of a laser and a substrate subjected to laser operation and inputs the laser and substrate parameters into an ablation stage portion of a model to calculate a first segment of laser operation. As used herein, a "substrate" refers to an article or material subjected to laser operation, and may include one or more distinct layers and / or one or more coatings, or may be formed from a single homogeneous material. The first calculation outputs an updated geometry of the substrate after predicted ablation of material from the substrate by at least one laser pulse, and an updated temperature value of the substrate after being subjected to at least one laser pulse. The processing circuitry is further configured to input the updated geometry and updated temperature value into a thermodynamic stage portion of the model to calculate a second segment of laser operation, which outputs an updated temperature value of the substrate after heat transfer from the substrate to ambient air through the substrate. The processing circuitry is further configured to iteratively switch between the ablation stage portion and the thermodynamic stage portion throughout a final segment of laser operation to output a final predicted temperature value of the substrate.

[0005] Another aspect of the present disclosure relates to a method for predicting substrate temperature during simulated laser operation. In this aspect, the method includes retrieving parameters of a laser and parameters of a substrate subjected to laser operation and inputting the laser and substrate parameters into an ablation stage portion of a model to calculate a first segment of laser operation. The calculation outputs an updated geometry of the substrate after predicted ablation of material from the substrate by at least one laser pulse and an updated temperature value of the substrate after being subjected to at least one laser pulse. The method includes inputting the updated geometry and updated temperature value into a thermodynamic stage portion of the model to calculate a second segment of laser operation, outputting an updated temperature value of the substrate after heat transfer from the substrate to ambient air through the substrate. The method includes iteratively switching between the ablation stage portion and the thermodynamic stage portion throughout a final segment of laser operation to output a final predicted temperature value of the substrate.

[0006] Yet another aspect of the present disclosure relates to a computing system for predicting substrate temperatures during simulated laser operation. The computing system includes processing circuitry that retrieves parameters of each laser and parameters of multiple substrates separately subjected to laser operation and inputs the respective laser and substrate parameters into an ablation stage portion of a model to calculate a first segment of laser operation. The calculation outputs a respective updated geometric shape of each substrate after predicted ablation of material from the substrate by at least one laser pulse and a respective updated temperature value of each substrate after being subjected to at least one laser pulse. The processing circuitry is further configured to input the respective updated geometric shapes and respective updated temperature values ​​into a thermodynamic stage portion of the model to calculate a second segment of laser operation that outputs a respective updated temperature value of each substrate after heat transfer from the substrate to ambient air through the substrate. The processing circuitry is further configured to iteratively switch between the ablation stage portion and the thermodynamic stage portion throughout a final segment of laser operation to output a respective predicted final temperature value of the substrate. The processing circuitry is further configured to identify a respective maximum temperature that each substrate is predicted to experience during the entire laser operation, compare the respective maximum temperatures to a respective parameter of each substrate, and output a prediction of whether each substrate of the plurality of substrates will be damaged by the laser operation based on the comparison. [Brief explanation of the drawings]

[0007] [Figure 1] 1 illustrates an exemplary laser operation modeled by a computing system to predict substrate temperature during simulated laser operation, according to one embodiment of the present disclosure. [Figure 2] FIG. 2 is a schematic diagram of an example of a computing system used to model the laser operation of FIG. 1. [Figure 3] 1 is a flowchart of a method for predicting substrate temperature during simulated laser operation. [Figure 4] FIG. 3 is a schematic diagram of an exemplary computing environment in which the computing system of FIG. 2 may be implemented. DETAILED DESCRIPTION OF THE INVENTION

[0008] To address the above-mentioned issues, FIG. 1 illustrates an exemplary laser operation modeled by a computing system for predicting substrate temperature during simulated laser operation, according to one embodiment of the present disclosure. Here, a laser system 10 including one or more lasers applies laser operation to a substrate 12. For example, the laser operation may be paint removal, removal of another type of layer other than paint, surface preparation, drilling, etching, etc. If the purpose of the laser operation is to remove or process only the top surface, the substrate 12 may include a top layer 12A and a bottom layer 12B, as shown separated by dashed lines in FIG. 1. Examples of materials for the top layer 12A may include, but are not limited to, paint, ink, epoxy, ceramic coating, and various plastics.

[0009] Prior to laser operation, the substrate 12 may be substantially flat. As shown, the laser system 10 emits laser pulses at three locations during laser operation. Each laser pulse may leave a crater 14 in the top surface of the substrate 12 as material is ablated from the substrate 12, as indicated by the straight lines on either side of the laser beam 16. In general, the laser beam 16 may have any suitable profile, such as a super-Gaussian (rounded top-hat) profile, as may the temporal profile of the laser beam 16. As the laser beam 16 heats and converts the material of the substrate 12 into plasma, a cloud 18 of ash and vaporized residue may rise around the laser beam, which may be collected and safely filtered by a fume extractor. Any suitable type of laser may be modeled, such as an ultraviolet (UV) laser or a near-infrared (near-IR) laser.

[0010] In particular, as each crater 14 is formed, heat from the laser beam 16 is transferred to the remainder of the substrate 12, and as the laser system 10 moves along the next location, residual heat from the previous crater 14 continues to spread throughout the substrate 12 and dissipate into the surrounding air. The temperature profile of the crater 14 is shown in grayscale with a color gradient reproduced in FIG. 1 . Additionally, as shown in the right half of FIG. 1 , three-dimensional (3D) renderings of the modeled laser action disclosed herein may include a mesh 20 that follows the topography of the substrate 12 as it is modified by the laser system 10. Because flat surfaces will retain and dissipate heat differently than cratered surfaces, the mesh 20 may be updated in three dimensions throughout the modeled laser action to increase the accuracy of the final output. Alternatively, where preferred, the modeling may be simplified to output a two-dimensional (2D) rendering.

[0011] Referring now to Figure 2, a schematic diagram of an example computing system 22 used to model the laser operation of Figure 1 is provided. The computing system 22 for predicting substrate temperature during simulated laser operation includes at least one computing device 24 having processing circuitry 26 configured to execute instructions stored in memory 28 to perform various functions. At a basic level, the computing device 24 receives input parameters 30 from sources such as internal or external storage, user input, measurements, etc., performs calculations using a model 32, and outputs as output 34 any number of values, data, and / or renderings based on the predicted laser operation determined by the model 32.

[0012] More specifically, processing circuitry 24 is configured to retrieve laser parameters 30A and substrate parameters 30B subjected to laser operation and input the laser and substrate parameters 30A, 30B into an ablation stage portion 32A of model 32 to calculate a first segment of laser operation. Ablation stage portion 32A of model 32 may be simplified as a one-dimensional (1D) equation-based model coupling a dozen or more physical processes, such as photothermolysis and photodissociative bond breaking, depending on the material used. In this case, the depth of substrate 12 is discretized into segments in 1D as time also progresses in discrete steps. In some embodiments, the top layer 12A of the substrate is simplified as a polymer represented as a system of chemical moieties with two possible electronic states, and the set of these states is tracked in time as energy is deposited into substrate 12 from laser beam 16. There are additional laser-induced chemical modifications resulting in a modified polymer, the concentration of which is monitored in each 1D segment to indicate the onset of ablation, i.e., when material is separated and removed from the remainder of substrate 12. Note that in other embodiments, ablation stage portion 32A of model 32 may be extended to 2D or 3D as desired and / or constructed to accommodate different physical mechanisms for different materials and scenarios as desired.

[0013] Through the course of a single pulse of the laser beam 16 and the initiation of ablation occurring deeper in the substrate 12, the total ablation depth (crater depth) of that pulse is calculated. By incorporating parameters 30A such as spot size and distribution, the 1D results can be extended to hybrid 2D results of ablation depth and rate, or even 3D. The resulting temperature and ablation depth are highly dependent on the power (irradiance) of the laser system 10, in addition to other settings in the laser parameters 30A input into the model 32, employing a form of the Beer-Lambert law. For example, the laser parameters 30A may include one or more of the target ablation depth, frequency, power setting, efficiency, power delivery, spot diameter, spot area, pulse duration, pulse energy, peak power, spot fluence, irradiance, pulse overlap, line speed, line spacing, spot spacing, and / or dose. Using a combination of these parameters 30A, 30B, for the first segment of laser operation, the ablation stage portion 32A of the model may be configured with an updated geometry 36 of the substrate 12 after predicted ablation of material from the substrate by at least one laser pulse (e.g., a mathematical description of the mesh 20 with one crater 14), and updated temperature values ​​38 of the substrate 12 after being subjected to at least one laser pulse. Thus, further calculations can be performed based on the updated values ​​of both the heat and geometry after each ablation stage, increasing the accuracy of the model. As noted above, the model may describe ablation in 2D or 3D, and thus the updated geometry 36 of the substrate 12 may be 2D or 3D depending on design considerations.

[0014] In some embodiments, at least one laser pulse may be a single laser pulse, such that model 32 calculates the effects of laser operation one pulse at a time. This may allow model 32 to accurately calculate the temperature change between each pulse. However, multiple laser pulses may alternatively be grouped together. In some embodiments, such as for drilling holes, multiple laser pulses may be performed at the same location on substrate 12 rather than moving along the surface as shown in FIG. 1 . Furthermore, in the case of paint removal, substrate 12 may include a top layer 12A of paint and a bottom layer 12B of a metal alloy, and the ablated material may include paint. In this case, paint removal from various components, such as aircraft parts, can be performed carefully and thoroughly to precise specifications while avoiding damage to the metal alloy. In other embodiments, top layer 12A to be removed may also be resin formed on top of a composite bottom layer, and the substrate 12 parameters 30B utilized by model 32 may be adjusted accordingly. For example, top layer 12A may be a degraded resin to be removed. While the laser operation herein is described from a top-down perspective, with the laser operating downward on the top surface or top layer 12A, it will be understood that most substrates 12 can be flipped over so that the bottom surface or bottom layer 12B can additionally or alternatively be subjected to laser operation. Furthermore, the laser system 10 may be pointed in any feasible direction, rather than strictly downward. Alternatively, the laser operation may be for surface preparation of the substrate 12 prior to painting, to provide a clean and suitable surface for a long-lasting, smooth, and consistent paint layer.

[0015] Processing circuitry 26 may then be configured to input the updated geometry 36 and updated temperature values ​​38 into a thermodynamic stage portion 32B of model 32 to calculate a second segment of laser operation, which outputs an updated temperature value 40 of substrate 12 after heat transfer from substrate 12 to ambient air through substrate 12. For example, thermodynamic stage portion 32B may calculate heat transfer including natural convection, conduction, and radiation by using one or more heat transfer equations, among other calculations. Computing system 22 may be configured to receive user input to select which calculations to include as part of the setup. In some cases, the system may include one or more backside temperature sensors 42 (see FIG. 1 ) configured to sample the temperature on the bottom surface of substrate 12, and model 32 may be configured to output a predicted top surface temperature based on measurements from temperature sensors 42.

[0016] Processing circuitry 26 may further be configured to input laser and substrate parameters 30A, 30B into a thermodynamic stage portion 32B of model 32 so as to accurately account for thermodynamic effects between pulses of laser beam 16. Here, substrate 12 parameters 30B may include one or more of the initial geometry, substrate material composition, material thermodynamic properties, material mechanical properties, material phase transformation properties, and / or material glass transition properties, thereby enhancing the accuracy of model 32 by accounting for various effects. Preferably, substrate 12 parameters 30B include temperature-dependent properties of the substrate 12 material to enhance the accuracy of model calculations by accounting for the wide range of temperatures experienced during and after ablation. Typically, thermodynamic stage portion 32B of model 32 utilizes density, thermal conductivity, and specific heat, among other parameters of substrate 12. In some embodiments, thermodynamic stage portion 32B may also perform temperature-induced stress calculations that utilize the substrate 12's linear expansion coefficient, Poisson's ratio, and Young's modulus.

[0017] Processing circuitry 26 may then be configured to iteratively switch between ablation stage portion 32A and thermodynamic stage portion 32B through the final (n) segments of laser operation, outputting a final predicted substrate temperature value 44 after n iterations of switching through both portions 32A, 32B. For example, if the number of laser pulses calculated per ablation stage is 1 and thermodynamic stage portion 32B calculates inter-pulse or post-pulse heat transfer, then n=x for x pulses of laser operation, or the total number of segments of laser operation=2x. By dividing the modeling into smaller segments and iteratively switching between the two portions 32A, 32B, model 32 becomes a comprehensive model that can more accurately calculate changes occurring in each stage based on the changes just calculated in the previous stage, even if each stage models a different mechanism.

[0018] The final predicted temperature value 44 and intermediate values ​​calculated throughout the modeling process may be used in a variety of ways. For example, processing circuitry 26 may be further configured to output a maximum temperature 46 predicted to be experienced by substrate 12, which may be the final predicted temperature value 44 or a previously stored temperature value before heat loss reduces the temperature to the final predicted temperature value 44. Similarly, processing circuitry 26 may be further configured to output a predicted material removal rate 48 of material from substrate 12. The removal rate may be, for example, the rate at which material is ablated over time, which may be measured as the depth of material removed over time. The removal rate 48 may be incorporated into further calculations using additional logic 50 or another computing device 24 to provide recommendations to a user, control actual laser operation, or augment or modify laser operation with other parameters. Any of the simulation data in output 34, including the intermediate values, may be collected and stored to build a database of parameters that can be used to build a “digital twin” of actual devices and components. Such a digital twin can be used, for example, to avoid parameters predicted by the model 32 to cause problems in future design studies.

[0019] Computing system 22 may be configured to generate other items as output 34 of model 32 using additional logic 50. For example, processing circuitry 26 may be further configured to output a predicted structural effect 52 on the substrate 12 based on a comparison between the maximum temperature 46 of substrate 12 and parameters 30B. For example, one of parameters 30B may indicate a phase change, other structural change, or failure point of the material of substrate 12, such as a melt zone. By determining that maximum temperature 46 meets or exceeds this parameter during the simulation, optionally for a specified amount of time, processing circuitry 26 may be able to predict that substrate 12 has experienced a predicted structural effect 52, such as melting, warping, weakening, cracking, accelerated fatigue, or the like.

[0020] Based on at least the predicted structural effects 52, processing circuitry 26 may be configured to determine whether either the laser system 10 or the material passed or failed the simulated test, with a predetermined negative structural effect resulting in a fail. Thus, while a single test using one laser system 10 and one substrate 12 was described above, processing circuitry 26 may be configured to retrieve respective laser parameters 30A and parameters 30B for multiple substrates 12 separately subjected to laser operation, and input the respective parameters 30A, 30B to ablation stage portion 32A to output a respective updated geometry 36 for each substrate 12 and a respective updated temperature value 38 for each substrate 12. Similarly, processing circuitry 26 may be configured to input each updated geometry 36 and each updated temperature value 38 into thermodynamic stage portion 32B to output a respective updated temperature value 40 for each substrate 12, and to iteratively switch between ablation stage portion 32A and thermodynamic stage portion 32B through a final segment of laser operation to output a respective final predicted temperature value 44 for each substrate 12, as described above.

[0021] Thus, the processing circuitry may be configured to identify a respective maximum temperature 46 that each substrate 12 is predicted to experience during the entire laser operation, compare each maximum temperature 46 with each substrate 12's respective parameters 30B, and, based on the comparison, output a prediction 54 of whether each substrate 12 of the plurality of substrates 12 will be damaged by the laser operation. In this manner, various substrates 12 may be tested under simulation with the same laser system 10, and a user may be informed of whether any given substrate 12 is likely to succeed or fail when subjected to operation with a given laser system 10. Thus, a user may make more informed decisions when developing new systems, components, and materials, after which actual testing may be reserved for validation of the model 32 output 34, interpolation, directed testing according to the model 32's recommendations 56, etc. While testing multiple specimens of substrates 12 may typically require 350 specimens for full qualification and approval, use of model 32 can reduce actual testing to testing only 15-30 specimens, resulting in significant savings in cost, time, labor, and use and wear of laser system 10. Furthermore, instead of various substrates 12, multiple laser systems 10 with their respective parameters 30A may instead be similarly modeled, allowing new laser systems 10 to be investigated and corresponding recommendations 56 to be made. Additionally, output 34 may be output directly to a client device, an associated display device, internal or external storage, a fabrication system for controlling actual operation, etc. Thus, output 34 may be used manually by a user at the time of output or at a later date, or output 34 may be used automatically by computing system 22 without further user input.

[0022] FIG. 3 is a flowchart of a method 300 for predicting substrate temperature during simulated laser operation. At 302, the method 300 may include retrieving parameters of a laser and parameters of a substrate subjected to laser operation. At 304, the method 300 may include inputting the laser and substrate parameters into an ablation stage portion of the model to calculate a first segment of laser operation that outputs an updated geometry of the substrate after predicted ablation of material from the substrate by at least one laser pulse at 306 and an updated temperature value of the substrate after being subjected to at least one laser pulse at 308. Optionally, at 310, the method 300 may include inputting the laser and substrate parameters into a thermodynamic stage portion of the model. At 312, the method 300 may include inputting the updated geometry and updated temperature values ​​into the thermodynamic stage portion of the model to calculate a second segment of laser operation that outputs an updated temperature value of the substrate after heat transfer from the substrate to ambient air through the substrate. Thus, based on various parameters, each portion of the model may be able to consider different factors that may affect areas of other portions of the model. Thus, at 314, the method 300 may include iteratively switching between the ablation stage portion and the thermodynamic stage portion throughout the final segment of laser operation to output a final predicted temperature value for the substrate, whereby the influence of each portion of the model is taken into account in small increments as it progresses to the other portion.

[0023] As described above, the at least one laser pulse may be a single laser pulse, allowing the ablation stage portion to account for the ablation of a single crater, and then the thermodynamic stage portion to account for the heat transfer that occurs during ablation. Alternatively, multiple laser pulses may be grouped together. In some cases, the substrate may have a top layer of paint and a bottom layer of a metal alloy, and the ablated material may include the paint. In other cases, the laser operation may be surface preparation of the substrate before painting. Additional use cases are possible. Optionally, at 316, the method 300 may include outputting a maximum temperature predicted to be experienced by the substrate. The maximum temperature may be used in many different ways, such as monitoring physical changes in the substrate and predicting failure of laser operation. For example, at 318, the method 300 may include outputting a predicted structural effect on the substrate based on a comparison between the maximum temperature and a parameter of the substrate. The predicted structural effect may include, for example, melting, warping, weakening, cracking, or accelerated fatigue. At 320, the method 300 may include outputting a predicted material removal rate of the material from the substrate. The removal rate may be a useful metric that may be considered for applicability and scalability of the simulated laser operation for practical use. The method 300 may result in further predictions and outputs, such as material or laser setting recommendations.

[0024] In some embodiments, the methods and processes described herein may be coupled to a computing system of one or more computing devices. In particular, such methods and processes may be implemented as a computer application program or service, an application programming interface (API), a library, and / or other computer program product. Examples of such programs may include, but are not limited to, COMSOL and MATLAB.

[0025] 4 illustrates a schematic representation of a non-limiting embodiment of a computing system 400 capable of implementing one or more of the methods and processes described above. The computing system 400 is shown in simplified form. The computing system 400 may embody the computing system 1 described above and illustrated in FIG. 1. The components of the computing system 400 may be included in one or more personal computers, server computers, tablet computers, home entertainment computers, network computing devices, video game devices, mobile computing devices, mobile communication devices (e.g., smartphones), and / or other computing devices, as well as wearable computing devices such as smart watches and head-mounted augmented reality devices.

[0026] Computing system 400 includes a logical processor 402, a volatile memory 404, and a non-volatile storage device 406. Computing system 400 may optionally include a display subsystem 408, an input subsystem 410, a communication subsystem 412, and / or other components not shown in FIG.

[0027] Logical processor 402 comprises one or more physical devices that execute instructions. For example, a logical processor may be configured to execute instructions that are part of one or more applications, programs, routines, libraries, objects, components, data structures, or other logical constructs. Such instructions may be implemented to perform a task, implement a data type, transform the state of one or more components, achieve a technical effect, or otherwise lead to a desired result.

[0028] A logical processor may include one or more physical processors that execute software instructions. Additionally or alternatively, a logical processor may include one or more hardware logic circuits or firmware devices configured to execute hardware-implemented logic or firmware instructions. The processors of logical processor 402 may be single-core or multi-core, and the instructions executed thereon may be configured for sequential, parallel, and / or distributed processing. Individual components of a logical processor may optionally be distributed across two or more separate devices, which may be remotely located and / or configured for cooperative processing. Aspects of a logical processor may be virtualized and executed by remotely accessible networked computing devices configured in a cloud computing configuration. In such cases, it will be understood that these virtualized aspects execute on different physical logical processors of various different machines.

[0029] Non-volatile storage 406 includes one or more physical devices configured to hold instructions executable by a logical processor to implement the methods and processes described herein. When such methods and processes are implemented, the state of non-volatile storage 406 may be transformed, for example, to hold different data.

[0030] The non-volatile storage 406 may include removable and / or internal physical devices. The non-volatile storage 406 may include optical, semiconductor, and / or magnetic memory, or other mass storage technologies. The non-volatile storage 406 may include non-volatile, dynamic, static, read / write, read-only, sequential access, position-addressable, file-addressable, and / or content-addressable devices. It will be appreciated that the non-volatile storage 406 is configured to retain instructions even when power to the non-volatile storage 406 is interrupted.

[0031] Volatile memory 404 may include physical devices including random access memory. Volatile memory 404 is typically utilized by logical processor 402 to temporarily store information during the processing of software instructions. It will be appreciated that volatile memory 404 typically does not continue to store instructions when power to volatile memory 404 is interrupted.

[0032] Aspects of the logic processor 402, volatile memory 404, and non-volatile storage 406 may be integrated into one or more hardware logic components, which may include, for example, field programmable gate arrays (FPGAs), program specific and application specific integrated circuits (PASICs / ASICs), program specific and application specific standard products (PSSPs / ASSPs), systems on a chip (SOCs), and complex programmable logic devices (CPLDs).

[0033] The terms “module,” “program,” and “engine” may be used to describe an aspect of computing system 400 that is typically implemented in software by a processor to perform a particular function using a portion of volatile memory, the function including a transformation that specifically configures the processor to perform that function. Thus, a module, program, or engine may be instantiated by logical processor 402 using a portion of volatile memory 404 to execute instructions held by non-volatile storage 406. It will be understood that different modules, programs, and / or engines may be instantiated from the same application, service, code block, object, library, routine, API, function, etc. Similarly, the same module, program, and / or engine may be instantiated from different applications, services, code blocks, objects, routines, APIs, functions, etc. The terms “module,” “program,” and “engine” may encompass individual or groups of executable files, data files, libraries, drivers, scripts, database records, etc.

[0034] If included, the display subsystem 408 may be used to present a visual representation of the data maintained by the non-volatile storage device 406. The visual representation may take the form of a graphical user interface (GUI). As the methods and processes described herein modify the data maintained by the non-volatile storage device and thus transform the state of the non-volatile storage device, the state of the display subsystem 408 may be similarly transformed, so that the changes in the underlying data may be visually represented. The display subsystem 408 may include one or more display devices utilizing virtually any type of technology. Such display devices may be combined with the logic processor 402, the volatile memory 404, and / or the non-volatile storage device 406 in a shared enclosure, or such display devices may be peripheral display devices.

[0035] If included, input subsystem 410 may include or interface with one or more user input devices such as a keyboard, mouse, touchscreen, camera, or microphone.

[0036] If included, communications subsystem 412 may be configured to communicatively couple the various computing devices described herein with each other and with other devices. Communications subsystem 412 may include wired and / or wireless communication devices supporting one or more different communications protocols. By way of non-limiting example, communications subsystem may be configured for communication over wired or wireless local or wide area networks, broadband cellular networks, etc. In some embodiments, communications subsystem may enable computing system 400 to send and / or receive messages to and from other devices over a network, such as the Internet.

[0037] The present disclosure further includes the following features: One aspect provides a computing system for predicting a substrate temperature during simulated laser operation. The computing system includes processing circuitry that retrieves parameters of a laser and parameters of a substrate subjected to laser operation, inputs the laser and substrate parameters into an ablation stage portion of a model to calculate a first segment of laser operation that outputs an updated geometry of the substrate after predicted ablation of material from the substrate by at least one laser pulse and an updated temperature value of the substrate after being subjected to at least one laser pulse, inputs the updated geometry and updated temperature values ​​into a thermodynamic stage portion of the model to calculate a second segment of laser operation that outputs an updated temperature value of the substrate after heat transfer from the substrate to ambient air through the substrate, and iteratively switches between the ablation stage portion and the thermodynamic stage portion throughout a final segment of laser operation to output a final predicted temperature value of the substrate. In this aspect, additionally or alternatively, the at least one laser pulse is a single laser pulse. In this aspect, additionally or alternatively, the substrate has a top layer of paint and a bottom layer of metal alloy, and the ablated material includes paint. Additionally or alternatively, in this aspect, the laser operation is surface preparation of the substrate before painting. Additionally or alternatively, in this aspect, the processing circuitry is further configured to input laser and substrate parameters into a thermodynamic stage portion of the model. Additionally or alternatively, in this aspect, the processing circuitry is further configured to output a predicted maximum temperature experienced by the substrate. Additionally or alternatively, in this aspect, the processing circuitry is further configured to output a predicted structural effect on the substrate based on a comparison between the maximum substrate temperature and the parameters. Additionally or alternatively, in this aspect, the processing circuitry is further configured to output a predicted material removal rate of material from the substrate.In this aspect, additionally or alternatively, the laser parameters include one or more of target ablation depth, frequency, power setting, efficiency, power delivery to the top surface of the substrate, spot diameter, spot area, pulse duration, pulse energy, peak power, spot fluence, irradiance, pulse overlap, line speed, row spacing, spot spacing, and dose. In this aspect, additionally or alternatively, the substrate parameters include one or more of initial geometry, composition of the substrate material, thermodynamic properties of the material, mechanical properties of the material, and phase transformation properties of the material. In this aspect, additionally or alternatively, the substrate parameters include temperature-dependent properties of the substrate material.

[0038] Another aspect provides a method for predicting a substrate temperature during simulated laser operation. The method includes retrieving parameters of a laser and parameters of a substrate subjected to laser operation; inputting the laser and substrate parameters into an ablation stage portion of the model to calculate a first segment of laser operation, outputting an updated geometry of the substrate after predicted ablation of material from the substrate by at least one laser pulse and an updated temperature value of the substrate after being subjected to at least one laser pulse; inputting the updated geometry and updated temperature value into a thermodynamic stage portion of the model to calculate a second segment of laser operation, outputting an updated temperature value of the substrate after heat transfer from the substrate to ambient air through the substrate; and repeatedly switching between the ablation stage portion and the thermodynamic stage portion throughout a final segment of laser operation to output a final predicted temperature value of the substrate. Additionally or alternatively, in this aspect, the at least one laser pulse is a single laser pulse. Additionally or alternatively, in this aspect, the substrate has a top layer of paint and a bottom layer of metal alloy, and the ablated material includes the paint. Additionally or alternatively, in this aspect, the laser operation is surface preparation of the substrate before painting. Additionally or alternatively, in this aspect, the method further includes inputting laser and substrate parameters into a thermodynamic stage portion of the model. Additionally or alternatively, in this aspect, the method further includes outputting a predicted maximum temperature experienced by the substrate. Additionally or alternatively, in this aspect, the method further includes outputting a predicted structural effect on the substrate based on a comparison between the maximum substrate temperature and the parameters. Additionally or alternatively, in this aspect, the method further includes outputting a predicted material removal rate of material from the substrate.

[0039] Another aspect provides a computing system for predicting substrate temperatures during simulated laser operation, the computing system comprising processing circuitry that retrieves parameters of each of a laser and parameters of a plurality of substrates separately subjected to laser operation, inputs the respective parameters of the laser and the substrates into an ablation stage portion of the model to calculate a first segment of the laser operation that outputs a respective updated geometric shape of each substrate after predicted ablation of material from the substrate by at least one laser pulse and a respective updated temperature value of each substrate after being subjected to the at least one laser pulse, inputs the respective updated geometric shapes and the respective updated temperature values ​​into a thermodynamic stage portion of the model to calculate a second segment of the laser operation that outputs a respective updated temperature value of each substrate after heat transfer from the substrate to ambient air through the substrate, iteratively switches between the ablation stage portion and the thermodynamic stage portion throughout a final segment of the laser operation to output a respective predicted final temperature value for each substrate, identifies a respective maximum temperature predicted to be experienced by each substrate during the entire laser operation, compares the respective maximum temperature to the respective parameters of each substrate, and outputs a prediction of whether each of the plurality of substrates will be damaged by the laser operation based on the comparison. In this aspect, additionally or alternatively, each updated geometric shape is three-dimensional.

[0040] As used herein, "and / or" is defined as inclusive or ∨ as specified by the truth table below.

[0041] [Table 1]

[0042] It will be understood that the structures and / or techniques described herein are exemplary in nature and are susceptible to numerous variations, and therefore, these specific embodiments or examples should not be considered limiting. The particular routines or methods described herein may represent one or more of any number of processing strategies. Thus, various operations shown and / or described may be performed in the order shown and / or described, in other orders, in parallel, or omitted. Similarly, the order of the processes described above may be changed.

[0043] The subject matter of the present disclosure includes all novel and non-obvious combinations and subcombinations of the various processes, systems, and configurations, as well as other features, functions, operations, and / or properties disclosed herein, and all equivalents thereof. [Explanation of symbols]

[0044] 10 Laser System 12 PCB 12A Top layer 12B Bottom layer 14 Crater 16 Laser Beam 18 clouds 20 mesh 22 Computing Systems 24 Computing Devices 26 Processing circuit 28 memory 30 Input Parameters 30A Laser Parameters 30B Board Parameters 32 models 32A Ablation stage part 32B Thermodynamic stage part 34 Output 36 Updated Geometric Shapes 38 Updated Temperature Values 40 Updated Temperature Values 42 Backside temperature sensor 44 Final predicted temperature value 46 maximum temperature 48 Predicted Material Removal Rate 50 Additional Logic 52 Predicted structural effects 54 Predictions 56 Recommendations 300 ways 400 Computing Systems 402 logical processors 404 Volatile Memory 406 Non-volatile storage 408 Display Subsystem 410 Input Subsystem 412 Communication Subsystem

Claims

1. 1. A computing system (22) for predicting substrate temperature during simulated laser operation, said computing system (22) comprising: a processing circuit (26); the processing circuitry - retrieving the parameters (30A) of the laser and of the substrate (12) subjected to the operation of said laser (30B); inputting the parameters (30A, 30B) of the laser and the substrate (12) into an ablation stage portion (32A) of a model (32) to perform a first segment of laser operation, an updated geometry (36) of the substrate (12) after predicted ablation of material from the substrate (12) by at least one laser pulse; and an updated temperature value (38) of the substrate (12) after being subjected to at least one of the laser pulses; Compute a first segment, which outputs - inputting the updated geometry (36) and the updated temperature values ​​(38) into a thermodynamic stage portion (32B) of the model (32) to calculate a second segment of laser operation, which outputs an updated temperature value (40) of the substrate (12) after heat transfer from the substrate (12) through the substrate (12) to the ambient air; and, - repeatedly switching between the ablation stage portion (32A) and the thermodynamic stage portion (32B) throughout a final segment of laser operation to output a final predicted temperature value (44) of the substrate (12); A computing system (22) configured to:

2. The computing system (22) of claim 1, wherein at least one of the laser pulses is a single laser pulse.

3. 2. The computing system of claim 1, wherein the substrate has a top layer of paint and a bottom layer of metal alloy, and the ablated material comprises the paint.

4. The computing system (22) of claim 1, wherein the laser action is surface preparation of the substrate (12) before painting.

5. 2. The computing system (22) of claim 1, wherein the processing circuitry (26) is further configured to input the parameters (30A, 30B) of the laser and the substrate (12) into the thermodynamic stage portion (32B) of the model (32).

6. The computing system (22) of claim 1, wherein the processing circuitry (26) is further configured to output a maximum temperature (46) predicted to be experienced by the substrate (12).

7. 2. The computing system (22) of claim 1, wherein the processing circuitry (26) is further configured to output a predicted structural effect (52) on the substrate (12) based on a comparison between a maximum temperature (46) of the substrate (12) and the parameter (30B).

8. The computing system (22) of claim 1, wherein the processing circuitry (26) is further configured to output a predicted material removal rate (48) of material from the substrate (12).

9. 2. The computing system of claim 1, wherein the parameters of the laser include one or more of a target ablation depth, a frequency, a power setting, an efficiency, a power delivery to the top surface of the substrate, a spot diameter, a spot area, a pulse duration, a pulse energy, a peak power, a spot fluence, an irradiance, a pulse overlap, a line speed, a row spacing, a spot spacing, and a dose.

10. 2. The computing system of claim 1, wherein the parameters of the substrate include one or more of an initial geometric shape, a material composition of the substrate, a thermodynamic property of the material, a mechanical property of the material, and a phase transformation property of the material.

11. The computing system (22) of claim 1, wherein the parameters (30B) of the substrate (12) include temperature-dependent properties of a material of the substrate (12).

12. 1. A method (300) for predicting substrate temperature during simulated laser operation, said method (300) comprising: - retrieving (302) the parameters of the laser and of the substrate subjected to the operation of said laser; a first segment of laser operation by inputting the parameters of the laser and the substrate into an ablation stage portion of a model; an updated geometry of the substrate after predicted ablation of material from the substrate by at least one laser pulse (306); and an updated temperature value of the substrate after being subjected to at least one of the laser pulses (308); calculating (304) a first segment of laser operation, outputting: - inputting the updated geometry and the updated temperature values ​​into a thermodynamic stage portion of the model to calculate a second segment of laser operation (312), the second segment outputting an updated temperature value of the substrate after heat transfer from the substrate to ambient air through the substrate; - iteratively switching between the ablation stage portion and the thermodynamic stage portion through a final segment of laser operation to output a final predicted temperature value for the substrate (314); The method (300).

13. 13. The method (300) of claim 12, wherein at least one of the laser pulses is a single laser pulse.

14. 13. The method (300) of claim 12, wherein the substrate has a top layer of paint and a bottom layer of a metal alloy, and the ablated material comprises the paint.

15. 13. The method (300) of claim 12, wherein the laser action is surface preparation of the substrate before painting.

16. 13. The method (300) of claim 12, further comprising inputting (310) the parameters of the laser and the substrate into the thermodynamic stage portion of the model.

17. 13. The method (300) of claim 12, further comprising outputting (316) a maximum temperature that the substrate is expected to experience.

18. 13. The method (300) of claim 12, further comprising outputting (318) a predicted structural effect on the substrate based on a comparison between the maximum temperature of the substrate and the parameter.

19. The method (300) of claim 12, further comprising outputting (320) a predicted material removal rate for material from the substrate.

20. 1. A computing system (22) for predicting substrate temperature during simulated laser operation, said computing system (22) comprising: a processing circuit (26); the processing circuitry - retrieving the parameters (30A) of each laser and of a plurality of substrates (12) separately subjected to the operation of said laser (30B); inputting the respective parameters (30A, 30B) of the laser and the substrate (12) into an ablation stage portion (32A) of a model (32) to perform a first segment of laser operation, a respective updated geometric shape (36) of each substrate (12) after predicted ablation of material from said substrate (12) by at least one laser pulse; and - updated temperature values ​​(38) of each substrate (12) after being subjected to at least one said laser pulse; and calculating a first segment of laser operation that outputs - inputting the respective updated geometries (36) and the respective updated temperature values ​​(38) into a thermodynamic stage portion (32B) of the model (32) to calculate a second segment of laser operation, which outputs a respective updated temperature value (40) for each substrate (12) after heat transfer from the substrate (12) through the substrate (12) to ambient air; - repeatedly switching between the ablation stage portion (32A) and the thermodynamic stage portion (32B) throughout a final segment of laser operation to output a respective final predicted temperature value (44) for each substrate (12); - Identifying the respective maximum temperatures (46) that each substrate (12) is expected to experience during the entire laser operation; - comparing said respective maximum temperatures (46) with said respective parameters (30B) of each substrate (12); and - based on said comparison, outputting a prediction of whether each substrate (12) of said plurality of substrates (12) will be damaged by said laser action; A computing system (22) configured to:

21. 21. The computing system (22) of claim 20, wherein each of the updated geometries (36) is three-dimensional.

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