Automatic control method and system for mobile phone card holder laser cutting equipment

Abstract

The invention provides an automatic control method and system for mobile phone card holder laser cutting equipment, relates to the technical field of laser cutting, is applied to laser cutting of a thin metal plate of a mobile phone card holder, and comprises the following steps that residual stress three-dimensional distribution information and mechanical property information of a stress concentration area in the thin metal plate are obtained; generating material state characteristics of the thin metal plate; based on the material state characteristics, temperature deformation information of the thin metal plate is obtained, and the deformation trend of the thin metal plate is calculated in combination with the temperature deformation information and preset cutting path information; according to the deformation trend, an adjusting instruction used for adjusting the cutting parameters is generated, and the adjusting instruction is executed after being subjected to smooth processing; wherein the cutting parameters comprise the cutting speed, the laser power, the auxiliary gas pressure and the focus position. The precision and quality consistency of mobile phone card holder laser cutting are improved, the service life of key optical mechanical parts is effectively prolonged, and the cutting yield is increased.

Description

Technical Field

[0001] This application relates to the field of laser cutting technology, and in particular to an automated control method and system for laser cutting equipment for mobile phone SIM card trays. Background Technology

[0002] The laser cutting process for mobile phone SIM card trays requires extremely high precision, necessitating the machining of complex contours on thin metal sheets. However, existing automated control systems reveal deficiencies in cutting path planning and real-time adaptive capabilities when faced with dynamic deformation caused by the microscopic inhomogeneities of thin metal sheets, and the nonlinear parameter adjustments made by operators to compensate for deformation. This not only affects the consistency of processing quality but also accelerates the wear and tear of critical optomechanical components.

[0003] Specifically, the path planning of existing systems is based on the idealized assumption that the material's physical properties (such as internal stress distribution, thermal conductivity, and coefficient of thermal expansion) are uniform and stable. However, actual thin metal sheets exhibit differences in grain structure and alloy element distribution at the microscopic level, leading to fluctuations in local stress and thermophysical properties. When lasers cut such materials, especially in areas with fine contours, the non-uniform input of laser heat can induce local stress release, causing warping or shrinkage of the sheet at the tens of micrometer level. This microscopic deformation causes a shift in the relative position between the laser focus and the sheet surface, reducing energy coupling efficiency and preventing the laser beam from effectively cutting at the optimal position. This can result in incompletely melted residues in the cut kerf or irregular slag and burrs at the cut edge.

[0004] To compensate for defects, operators must manually adjust parameters such as laser power and cutting speed based on experience. However, such real-time, non-linear manual intervention commands exceed the response capability of control algorithms (such as PID) optimized for smooth, predictable operating conditions, leading to motion jitter in the servo system and overshoot or undershoot of the laser power. This control instability disturbs the auxiliary gas flow field at the cutting head nozzle, affecting the molten material removal effect and subjecting the focusing lens to unexpected thermal stress shocks, accelerating nozzle ablation and lens coating degradation, creating a vicious cycle of continuously declining cutting quality. This results in the inadequacy of existing automated control systems for mobile phone SIM card tray laser cutting equipment in terms of cutting path planning and real-time adaptive capabilities when facing dynamic deformation caused by the microscopic inhomogeneities of thin metal sheets, frequent non-linear parameter adjustments by operators to compensate for these deformations, and the resulting system control stability issues. Consequently, this leads to poor processing quality consistency, accelerated wear of key optical and mechanical components, and a decrease in cutting yield. Summary of the Invention

[0005] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes an automated control method and system for laser cutting equipment for mobile phone SIM card trays. The aim is to solve the technical problems of insufficient cutting path planning and real-time adaptive capabilities in existing automated control systems for laser cutting equipment for mobile phone SIM card trays when facing dynamic deformation caused by the microscopic inhomogeneity of thin metal sheets, frequent nonlinear parameter adjustments by operators to compensate for these deformations, and the resulting system control stability issues. This leads to poor processing quality consistency, accelerated wear of key optical and mechanical components, and decreased cutting yield.

[0006] In a first aspect, this application provides an automated control method for laser cutting equipment for mobile phone SIM card trays, applied to the laser cutting of thin metal sheets for mobile phone SIM card trays, the method comprising the following steps:

[0007] Obtain the three-dimensional distribution information of residual stress and mechanical property information of stress concentration areas inside the thin metal sheet, and generate the material state characteristics of the thin metal sheet;

[0008] Based on the material state characteristics, the temperature deformation information of the thin metal sheet is obtained, and the deformation trend of the thin metal sheet is calculated by combining the temperature deformation information and the preset cutting path information.

[0009] Based on the deformation trend, adjustment instructions for adjusting the cutting parameters are generated, and the adjustment instructions are smoothed before execution; wherein, the cutting parameters include cutting speed, laser power, auxiliary gas pressure and focal position.

[0010] According to some embodiments of this application, the step of obtaining the three-dimensional distribution information of residual stress and mechanical property information of the stress concentration region inside the thin metal sheet, and generating the material state characteristics of the thin metal sheet, includes:

[0011] The residual stress three-dimensional distribution information and mechanical property information of the stress concentration region inside the thin metal sheet are obtained, and micropulse laser is applied to the thin metal sheet according to the residual stress three-dimensional distribution information and mechanical property information to induce the reconstruction of the microstructure of the stress concentration region of the thin metal sheet, so as to optimize the mechanical properties of the thin metal sheet.

[0012] The residual stress three-dimensional distribution information and mechanical property information of the stress concentration area inside the reconstructed thin metal sheet are obtained to generate the material state characteristics of the thin metal sheet.

[0013] According to some embodiments of this application, during the process of applying micropulse laser to a thin metal sheet:

[0014] The strain information of the thin metal plate under the action of micropulse laser is continuously monitored, and the operating parameters of the micropulse laser are dynamically adjusted according to the strain information.

[0015] According to some embodiments of this application, the strain information includes transient thermal stress change information, local phase transition information, and local temperature information of the thin metal plate under the action of micropulse laser; the operating parameters include the pulse sequence and energy density of the micropulse laser.

[0016] According to some embodiments of this application, the step of dynamically adjusting the operating parameters of the micropulse laser based on the strain information includes:

[0017] The pulse sequence is adjusted based on the transient thermal stress change information and the local phase transition information;

[0018] The energy density is adjusted based on the local temperature information.

[0019] According to some embodiments of this application, after the step of reconstructing the microstructure of the stress concentration region of the thin metal sheet to optimize the mechanical properties of the thin metal sheet, and before the step of obtaining the three-dimensional distribution information of residual stress and mechanical property information of the stress concentration region inside the reconstructed thin metal sheet, the method further includes the following steps:

[0020] Spectral analysis is performed on the thin metal sheet treated with the micropulse laser to evaluate the reconstruction effect of the microstructure of the thin metal sheet. If the reconstruction effect does not reach the preset state, the micropulse laser is applied to the thin metal sheet again.

[0021] According to some embodiments of this application, the step of performing spectral analysis on the thin metal sheet treated with the micropulse laser to evaluate the reconstruction effect of the microstructure of the thin metal sheet, and if the reconstruction effect does not reach a preset state, then applying the micropulse laser to the thin metal sheet again includes:

[0022] The thin metal sheet treated with the micropulse laser was subjected to spectral analysis to obtain the spectral analysis results.

[0023] Based on the spectral analysis results, identify whether there are micro-regions in the thin metal sheet that have not reached the preset state, so as to evaluate the reconstruction effect of the microstructure of the thin metal sheet;

[0024] If the reconstruction effect does not reach the preset state, then record the spatial coordinates and spectral characteristics of the micro-region;

[0025] The operating parameters of the micropulse laser are adjusted based on the spatial coordinates and spectral characteristics.

[0026] The adjusted micropulse laser is applied again to the thin metal sheet.

[0027] According to some embodiments of this application, the deformation trend includes predicting the dynamic deformation perpendicular to the surface of the thin metal sheet that will occur due to laser thermal effect at the point on the cutting path where the thin metal sheet is about to be cut; wherein the compensation amount of the adjustment command for the focal position is opposite to the direction of the dynamic deformation and equal to the amplitude of the dynamic deformation.

[0028] According to some embodiments of this application, the step of generating adjustment instructions for adjusting cutting parameters based on the deformation trend, and then smoothing the adjustment instructions before execution includes:

[0029] The initial adjustment amount is calculated based on the deformation trend.

[0030] A coupling relationship model is established between the cutting speed, laser power, auxiliary gas pressure, and focal position. This coupling relationship model is used to describe the influence of the adjustment of the cutting parameters on the cutting effect.

[0031] The initial adjustment amount is input into the coupling relationship model, and decoupling calculation is performed to generate an adjustment instruction that can counteract the negative interference effect between parameters of the cutting parameter;

[0032] The adjustment command is executed after being smoothed out.

[0033] Secondly, this application also provides an automated control system for a mobile phone SIM card tray laser cutting equipment, comprising:

[0034] The information acquisition module is used to acquire the three-dimensional distribution information of residual stress and mechanical property information of stress concentration areas inside the thin metal sheet, and generate the material state characteristics of the thin metal sheet.

[0035] The state acquisition module is used to acquire the temperature deformation information of the thin metal sheet based on the material state characteristics;

[0036] The deformation calculation module is used to calculate the deformation trend of the thin metal sheet by combining temperature deformation information and preset cutting path information.

[0037] The instruction generation module is used to generate adjustment instructions for adjusting the cutting parameters based on the deformation trend.

[0038] The instruction execution module is used to perform smoothing processing on the adjustment instruction before execution.

[0039] The technical solution according to the embodiments of this application has at least the following beneficial effects: The automated control method for laser cutting equipment for mobile phone SIM cards proposed in this application achieves intelligent perception, prediction, and adaptive control of the laser cutting process through a series of collaborative steps. This effectively offsets the impact of microscopic inhomogeneities in thin metal sheets on cutting quality and avoids cutting defects caused by deformation. Simultaneously, because the system can automatically predict and compensate for deformation, it reduces reliance on manual operator intervention, thereby avoiding control instability issues that may arise from manual intervention. This improves the automation level and stability of the cutting process and overcomes the shortcomings of existing technologies in cutting path planning and real-time adaptive capabilities. This not only improves the precision and quality consistency of laser cutting of mobile phone SIM cards but also effectively extends the service life of key optical and mechanical components and improves the cutting yield.

[0040] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0041] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0042] Figure 1 This is a flowchart illustrating an automated control method for a laser cutting device for a mobile phone SIM card tray, provided as an embodiment of this application.

[0043] Figure 2 This is a schematic diagram of the architecture of an automated control system for a mobile phone SIM card tray laser cutting equipment, provided as an embodiment of this application. Detailed Implementation

[0044] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0045] It should be noted that in the embodiments of this application, "at least one" means one or more, and "more than one" means two or more.

[0046] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0047] Traditional automated control methods for laser cutting equipment for mobile phone SIM card trays reveal deficiencies in cutting path planning and real-time adaptive capabilities when faced with dynamic deformation caused by the microscopic inhomogeneities of thin metal sheets, and the frequent, nonlinear parameter adjustments made by operators to compensate for these deformations. This not only affects the consistency of processing quality but also accelerates the wear and tear of critical optomechanical components. Specifically, existing systems are often based on the assumption of ideal uniformity, leading to non-uniform release of localized stress within the thin metal sheet by laser thermal effects during actual cutting, causing minute deformations that affect cutting accuracy and quality. Simultaneously, manual intervention by the operator can cause instability in the control system, accelerating component wear and ultimately resulting in a significant decrease in cutting yield.

[0048] For this, please refer to Figure 1 This application proposes an automated control method for laser cutting equipment for mobile phone SIM card trays, applied to the laser cutting of thin metal sheets for mobile phone SIM card trays. The method includes the following steps:

[0049] S110, Obtain the three-dimensional distribution information of residual stress and mechanical property information of stress concentration areas inside the thin metal sheet, and generate the material state characteristics of the thin metal sheet;

[0050] S120, Based on the material state characteristics, obtain the temperature deformation information of the thin metal sheet, and combine the temperature deformation information with the preset cutting path information to calculate the deformation trend of the thin metal sheet.

[0051] S130, Based on the deformation trend, an adjustment command for adjusting the cutting parameters is generated, and the adjustment command is smoothed before execution; wherein, the cutting parameters include cutting speed, laser power, auxiliary gas pressure and focal position.

[0052] This application effectively solves the problems of cutting accuracy and stability caused by microscopic inhomogeneity of materials and human intervention in the prior art by obtaining the material state characteristics of thin metal sheets, calculating deformation trends and dynamically adjusting cutting parameters, thus significantly improving processing quality and equipment life.

[0053] It should be noted that, in order to better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0054] "Thin metal sheet" refers to the metal material used to manufacture mobile phone SIM card trays. It is usually thin and requires extremely high precision and stability in laser cutting.

[0055] "Stress concentration areas" refer to regions within thin metal sheets where stress distribution is uneven due to manufacturing processes, material defects, or other reasons, resulting in localized stress values ​​far exceeding the average level. These areas are more prone to deformation during laser cutting.

[0056] "Residual stress three-dimensional distribution information" refers to detailed data on the stress distribution in three dimensions within a thin metal sheet, reflecting the internal stress state of the material under no external load.

[0057] "Mechanical property information" includes the elastic modulus, yield strength, tensile strength, and coefficient of thermal expansion of thin metal sheets. These parameters determine the material's response when heated or stressed.

[0058] "Material state characteristics" is a quantitative description of the current physical state of a thin metal sheet after integrating the three-dimensional distribution information of residual stress and mechanical property information, which is used for subsequent deformation prediction.

[0059] "Temperature deformation information" refers to the dimensional and shape changes of thin metal sheets caused by the temperature rise due to the thermal effect of laser during laser cutting.

[0060] "Cut path information" is a pre-defined laser cutting trajectory, including the cutting geometry, start point, end point, etc.

[0061] "Deformation trend" refers to the prediction of the direction and magnitude of dynamic deformation that will occur in thin metal sheets during laser cutting, based on their material state characteristics, temperature deformation information, and cutting path information.

[0062] "Cutting parameters" refer to the key process parameters that affect the laser cutting effect, including cutting speed, laser power, assist gas pressure, and focal point position. Precise control of these parameters is crucial to ensuring cutting quality.

[0063] The "adjustment command" is a command calculated based on the deformation trend and used to correct the cutting parameters in real time.

[0064] "Smoothing" refers to filtering or interpolating the adjustment commands to eliminate abrupt changes and burrs in the commands, ensuring the smoothness of the cutting equipment's movement and laser output.

[0065] First, by acquiring the three-dimensional distribution information of residual stress and mechanical properties in stress concentration regions within the thin metal sheet, the system can comprehensively understand the intrinsic physical state of the sheet and generate its material state characteristics. This step is the foundation for subsequent deformation prediction, overcoming the limitations of traditional systems' assumptions about material homogeneity and enabling the system to perceive microscopic differences in the material. For example, the three-dimensional distribution information of residual stress and mechanical properties in stress concentration regions within the thin metal sheet can be obtained through the following methods.

[0066] One approach involves scanning the thin metal sheet using X-ray diffraction or neutron diffraction to obtain the three-dimensional distribution of residual stress within it. Simultaneously, conventional mechanical testing methods such as tensile testing and hardness testing are used to acquire mechanical properties such as the elastic modulus and yield strength of the thin metal sheet. This raw data is then input into a data processing unit, where it is fused and analyzed using a pre-defined algorithm to generate the material state characteristics of the thin metal sheet. Another approach employs ultrasonic testing technology. By measuring the propagation speed and attenuation of ultrasonic waves within the thin metal sheet, the distribution of residual stress within it can be indirectly inferred. Combined with batch mechanical performance reports provided by the material supplier, the mechanical properties of the thin metal sheet are obtained. This information is then input into a machine learning-based material state characteristic generation model. This model, trained on a large amount of data from different material states, can automatically identify and generate the material state characteristics of the thin metal sheet.

[0067] Secondly, based on the generated material state characteristics, the system can acquire temperature deformation information of the thin metal sheet under laser thermal action. Combined with preset cutting path information, the system uses advanced predictive models (such as finite element analysis or machine learning models) to calculate the possible deformation trend of the thin metal sheet during the cutting process. This prediction of deformation trend is one of the core innovations of this application, enabling the system to predict its direction and magnitude before deformation actually occurs, thus providing forward-looking guidance for subsequent parameter adjustments. For example, temperature deformation information and deformation trend can be acquired and calculated in the following ways.

[0068] One approach involves using an infrared thermal imager to monitor the temperature distribution in the cutting area of ​​the thin metal sheet in real time during laser cutting, acquiring temperature deformation information. Simultaneously, the preset cutting path information is input into finite element analysis (FEA) software. This software combines previously generated material state characteristics with the real-time temperature deformation information, using thermo-mechanical coupling simulation calculations to predict the dynamic deformation perpendicular to the surface of the thin metal sheet at the point on the cutting path that will occur due to laser thermal effects, thus calculating the deformation trend of the thin metal sheet. Another approach is to use a laser displacement sensor or optical interferometer to measure the minute deformations in the cutting area of ​​the thin metal sheet in real time during laser cutting, directly acquiring temperature deformation information. This real-time deformation data, along with the preset cutting path information and material state characteristics, is input into a neural network-based deformation prediction model. This model, trained with a large amount of actual cutting data and simulation data, can quickly and accurately predict the possible deformation trends of the thin metal sheet during subsequent cutting processes.

[0069] Finally, based on the calculated deformation trend, the system can intelligently generate adjustment commands for adjusting the cutting parameters. These cutting parameters include cutting speed, laser power, auxiliary gas pressure, and focal position. For example, when it is predicted that the thin metal sheet will warp upwards, the system will generate a command to adjust the focal position downwards to compensate; when it is predicted that changes in material properties may lead to poor cutting, the system will adjust the laser power and cutting speed. To ensure that these adjustment commands can be executed smoothly by the cutting equipment and to avoid motion jitter and unstable laser output caused by sudden changes in commands, the system also performs smoothing processing on the adjustment commands. The smoothed commands are then executed, thereby achieving real-time, adaptive, and high-precision control of the laser cutting process. For example, adjustment commands can be generated and smoothed in the following ways.

[0070] One approach involves generating an adjustment command based on the calculated deformation trend, such as predicting that the thin metal sheet will warp upwards in a certain area. This command instructs the laser focus position to move downwards by a corresponding distance to compensate. Simultaneously, the cutting speed, laser power, and auxiliary gas pressure are adjusted according to the magnitude and rate of the deformation trend to optimize the cutting effect. These initial adjustment commands are then input into a digital filter for smoothing, such as using moving average filtering or Kalman filtering, to eliminate high-frequency noise and abrupt changes in the commands, ensuring smooth operation of the cutting equipment and avoiding jitter. Another approach is to establish a decision model based on fuzzy logic or an expert system. This model takes the deformation trend as input and generates comprehensive adjustment commands for cutting speed, laser power, auxiliary gas pressure, and focus position based on a pre-defined rule base and empirical knowledge. For example, when a large deformation of the thin metal sheet is predicted, the model might simultaneously suggest reducing the cutting speed, increasing the laser power, and adjusting the focus position. These commands are then smoothed using a spline interpolation algorithm to generate continuous and smooth parameter adjustment curves, ensuring the stability and consistency of the cutting process.

[0071] The automated control method for laser cutting equipment for mobile phone SIM card trays proposed in this application achieves intelligent sensing, prediction, and adaptive control of the laser cutting process through a series of collaborative steps. This effectively offsets the impact of microscopic inhomogeneities in thin metal sheets on cutting quality and avoids cutting defects caused by deformation. Simultaneously, because the system can automatically predict and compensate for deformation, it reduces reliance on manual operator intervention, thereby avoiding control instability issues that may arise from manual intervention. This improves the automation level and stability of the cutting process and overcomes the shortcomings of existing technologies in cutting path planning and real-time adaptive capabilities. This not only enhances the precision and quality consistency of laser cutting of mobile phone SIM card trays but also effectively extends the service life of key optical and mechanical components, increasing the cutting yield.

[0072] Specifically, the preferred steps for obtaining the three-dimensional distribution information of residual stress and mechanical property information in the stress concentration region inside the thin metal sheet, and generating the material state characteristics of the thin metal sheet, include:

[0073] The residual stress three-dimensional distribution information and mechanical property information of the stress concentration region inside the thin metal sheet are obtained, and micropulse laser is applied to the thin metal sheet according to the residual stress three-dimensional distribution information and mechanical property information to induce the reconstruction of the microstructure of the stress concentration region of the thin metal sheet, so as to optimize the mechanical properties of the thin metal sheet.

[0074] The residual stress three-dimensional distribution information and mechanical property information of the stress concentration area inside the reconstructed thin metal sheet are obtained to generate the material state characteristics of the thin metal sheet.

[0075] First, the three-dimensional distribution of residual stress and mechanical properties of the thin metal sheet before treatment are acquired. Based on this information, stress concentration regions within the thin metal sheet can be identified. Then, micropulse lasers are applied to these stress concentration regions. Micropulse lasers refer to lasers with extremely short pulse widths (e.g., picosecond or femtosecond levels) and high peak power, whose energy can be highly concentrated in tiny areas, thereby inducing microstructural reconstruction within the material without causing macroscopic thermal damage. This microstructural reconstruction can include grain refinement, dislocation density reduction, phase transformation induction, or the generation of local compressive stress, with the aim of effectively alleviating residual stress and optimizing the mechanical properties of the thin metal sheet, such as improving its toughness, reducing brittleness, or enhancing fatigue resistance. After the micropulse laser treatment is completed, the three-dimensional distribution of residual stress and mechanical properties of the stress concentration regions within the reconstructed thin metal sheet are acquired again. This updated information is used to generate the material state characteristics of the thin metal sheet.

[0076] This application's solution improves the intrinsic mechanical properties of thin metal sheets by reconstructing the microstructure of stress concentration areas using micropulse lasers before generating material state characteristics. Specifically, the precise energy input of the micropulse laser effectively eliminates or alleviates residual stress within the material and optimizes its microstructure, resulting in a more uniform and controllable response to thermal stress during subsequent laser cutting. Consequently, the deformation trend of the thin metal sheet becomes more stable and predictable during laser cutting, reducing deformation uncertainties caused by initial material defects and providing a more reliable basis for subsequent cutting parameter adjustments.

[0077] This application further proposes a preferred embodiment during the application of micropulse laser to a thin metal sheet:

[0078] The strain information of the thin metal plate under the action of micropulse laser is continuously monitored, and the operating parameters of the micropulse laser are dynamically adjusted according to the strain information.

[0079] Specifically, continuous monitoring refers to acquiring strain data of the thin metal sheet in real time through sensors or other detection devices throughout the entire micropulse laser processing process. Strain information can be understood as the physical quantities such as deformation and stress changes that occur inside or on the surface of the thin metal sheet when subjected to micropulse laser energy. Its purpose is to obtain the immediate response of the thin metal sheet to the micropulse laser. Dynamically adjusting the operating parameters of the micropulse laser based on the strain information means that, based on the real-time monitored strain information, the control system corrects the operating parameters of the micropulse laser, such as output power, pulse frequency, pulse width, and scanning speed, in real time. Its purpose is to ensure that the micropulse laser processing effect is always at its optimal state, avoiding over-processing or under-processing.

[0080] In practical applications, when performing micropulse laser processing on thin metal sheets, high-precision strain sensor arrays or optical measurement systems can be used to collect strain data on the surface or interior of the thin metal sheet in real time. This strain data is transmitted to a central control unit. This control unit has a built-in preset strain threshold model and parameter adjustment algorithm. When the monitored strain information deviates from the preset ideal range, such as when the strain value is too large or too small, the control unit will immediately calculate the corresponding adjustment amount of the micropulse laser operating parameters according to the algorithm. For example, if the strain is too large, it may mean that the laser energy is too high, and the control unit will instruct the laser to reduce the laser power or shorten the pulse width; if the strain is too small, it may be necessary to increase the laser power or extend the pulse duration. The adjusted operating parameters are fed back to the micropulse laser in real time, causing it to change its output characteristics in a very short time, thereby achieving precise and dynamic control of the microstructure reconstruction process of the thin metal sheet.

[0081] This application's solution addresses the problem of poor microstructure reconstruction that can result from traditional fixed-parameter processing by introducing a continuous monitoring mechanism for the strain information of thin metal sheets under micropulse laser irradiation and dynamically adjusting the micropulse laser's operating parameters accordingly. Because the strain response of the thin metal sheet can be sensed in real time and the laser parameters adjusted accordingly, the energy input of the micropulse laser matches the actual needs of the material, effectively inducing microstructure reconstruction and avoiding material damage or low processing efficiency caused by parameter mismatch.

[0082] Through the above technical solution, this application can achieve precise control over the microstructure reconstruction process of thin metal sheets. Compared with micropulse laser processing with fixed parameters, this solution significantly improves the accuracy and efficiency of microstructure reconstruction, ensuring the stability and consistency of the mechanical property optimization of thin metal sheets.

[0083] In some embodiments of this application, the strain information preferably includes transient thermal stress change information, local phase transition information, and local temperature information of the thin metal plate under the action of micropulse laser; the operating parameters include the pulse sequence and energy density of the micropulse laser.

[0084] Transient thermal stress change information refers to the instantaneous stress fluctuations in thin metal sheets caused by rapid local heating and cooling under micropulse laser energy input. This information can be acquired in real time using high-speed strain sensors or optical measurement systems, aiming to reflect the dynamic response of the internal stress field of the material. Local phase transformation information refers to the transformation of the microcrystalline structure or microstructure of the thin metal sheet under the action of micropulse laser, such as the transformation from austenite to martensite, grain refinement, etc. This information can be monitored using in-situ X-ray diffraction, Raman spectroscopy, or acoustic emission techniques, aiming to reveal the degree and area of ​​changes in the internal structure of the material. Local temperature information refers to the real-time temperature value of a specific area of ​​the thin metal sheet under the action of micropulse laser. This information can be measured non-contactly or contactlessly using infrared thermal imagers or thermocouple arrays, aiming to accurately grasp the laser energy absorption and heat diffusion.

[0085] The pulse sequence of the micropulse laser in the operating parameters refers to the combination of parameters such as the arrangement of laser pulses, pulse width, pulse interval, and repetition frequency. By adjusting the pulse sequence, the distribution of laser energy in time and space can be controlled, thereby affecting the heating rate, cooling rate, and stress wave propagation of the material. Energy density refers to the energy carried by a laser pulse per unit area. By adjusting the energy density, the intensity of the laser's effect on the material can be directly controlled, thus affecting the depth and range of melting, evaporation, phase transition, and stress-induced reconstruction of the material.

[0086] The solution proposed in this application clarifies the specific components of strain information (transient thermal stress change information, local phase transition information, and local temperature information) and the specific content of operating parameters (pulse sequence and energy density of micropulse laser), thus providing a more refined basis and a more direct control method for the dynamic adjustment during the application of micropulse laser to thin metal plates.

[0087] In some embodiments of this application described above, the step of dynamically adjusting the operating parameters of the micropulse laser based on strain information preferably includes:

[0088] The pulse sequence is adjusted based on the transient thermal stress change information and the local phase transition information;

[0089] The energy density is adjusted based on the local temperature information.

[0090] Specifically, the steps described above for dynamically adjusting the operating parameters of the micropulse laser based on strain information can be understood as associating and adjusting different types of strain information with different operating parameters of the micropulse laser. Specifically, the pulse sequence is adjusted based on transient thermal stress change information and local phase transition information. Transient thermal stress change information reflects the instantaneous thermal stress state generated in the thin metal sheet under laser irradiation, while local phase transition information indicates changes in the internal crystal structure or microstructure of the material. These two types of information are directly related to the microstructure reconstruction process of the material and the optimization of its mechanical properties. The pulse sequence, such as pulse duration, pulse interval, and pulse repetition frequency, has a decisive influence on the deposition method and thermal diffusion process of laser energy within the material, thus enabling precise control of the evolution of the microstructure. By adjusting the pulse sequence based on transient thermal stress change information and local phase transition information, it is possible to ensure that the laser energy input matches the microscopic response of the material, avoiding poor reconstruction results caused by overheating or insufficient energy. Furthermore, the energy density is adjusted based on local temperature information. Local temperature information directly reflects the thermal state of the thin metal sheet under micropulse laser irradiation. Energy density, or the intensity of laser energy per unit area, is a key parameter for directly controlling the local temperature rise of a material. By dynamically adjusting the energy density based on local temperature information, the temperature field in the laser-affected area can be precisely controlled, ensuring that while achieving microstructure reconstruction, overheating damage or unnecessary deformation of the material is avoided, thereby maintaining or optimizing the overall mechanical properties of thin metal sheets.

[0091] This application's solution addresses the lack of precision inherent in traditional dynamic adjustments by precisely matching different types of strain information with micropulse laser operating parameters. This targeted parameter adjustment strategy allows the micropulse laser to more accurately target stress concentration areas in thin metal sheets, effectively inducing microstructure reconstruction and significantly improving the material's mechanical properties. Compared to general dynamic adjustments, this solution avoids problems such as overheating, under-processing, or uneven processing caused by improper parameter adjustments, ensuring the uniformity and effectiveness of microstructure reconstruction. Ultimately, this improves the quality and efficiency of laser cutting for SIM card trays and extends the equipment's lifespan.

[0092] In some preferred embodiments of this application, after the step of reconstructing the microstructure of the stress concentration region of the thin metal sheet to optimize the mechanical properties of the thin metal sheet, and before the step of obtaining the three-dimensional distribution information of residual stress and mechanical property information of the stress concentration region inside the reconstructed thin metal sheet, the method further includes the following steps:

[0093] Spectral analysis is performed on the thin metal sheet treated with the micropulse laser to evaluate the reconstruction effect of the microstructure of the thin metal sheet. If the reconstruction effect does not reach the preset state, the micropulse laser is applied to the thin metal sheet again.

[0094] Specifically, spectral analysis is a non-contact material characterization technique that reveals microscopic information such as chemical composition, crystal structure, phase transitions, and stress states of materials by analyzing spectral information generated by the interaction between materials and electromagnetic waves. Here, spectral analysis is used to evaluate the reconstruction effect of the microstructure of thin metal sheets, that is, to determine whether the microstructure of stress concentration areas within the thin metal sheet has changed as expected after micropulse laser treatment, and whether the goal of optimizing mechanical properties has been achieved.

[0095] Here, "preset state" refers to the ideal microstructure state determined experimentally or theoretically that meets the requirements of subsequent laser cutting processes. For example, a target grain size, crystal orientation, or residual stress distribution range can be preset according to the material type and cutting requirements. If the spectral analysis results show that the microstructure reconstruction effect of the thin metal sheet does not reach the preset state, it indicates that the initial micropulse laser treatment has failed to fully achieve the expected optimization goal. In this case, to ensure the reliability of material performance, micropulse laser will be applied to the thin metal sheet again, in order to further induce microstructure reconstruction through repeated processing or adjustment of processing parameters until the preset state is reached.

[0096] The specific implementation method is as follows: First, a thin metal sheet of a mobile phone SIM card tray to be cut undergoes preliminary micro-pulse laser treatment to induce the reconstruction of the microstructure in its stress concentration areas. Then, a Raman spectrometer is used to scan the stress concentration areas of the treated thin metal sheet to obtain its Raman spectral data. The obtained Raman spectrum is compared with a pre-established standard Raman spectrum representing the ideal reconstruction effect. For example, if the standard spectrum shows obvious lattice stress relaxation characteristics at a certain characteristic peak position, while the actual measured spectrum shows a shift or insufficient intensity of that characteristic peak position, it is determined that the microstructure reconstruction effect has not reached the preset state. At this time, the control system will restart the micro-pulse laser based on the spectral analysis results to perform a second micro-pulse laser treatment on the thin metal sheet. This process can be repeated until the Raman spectral analysis results show that the microstructure reconstruction effect of the thin metal sheet has reached or exceeded the preset standard, thereby ensuring that the mechanical properties of the material have been fully optimized before entering the laser cutting stage.

[0097] This application's solution effectively addresses the uncertainty in microstructure reconstruction effects that may exist in basic solutions by introducing spectral analysis and a feedback-based micropulse laser reapplication mechanism. Specifically, spectral analysis, as a precise characterization method, can monitor the microstructure changes of thin metal sheets after micropulse laser application in real-time or near real-time. When the microstructure reconstruction effect is detected to be below the preset state, this solution can promptly identify and trigger the reapplication of micropulse laser. It is precisely this closed-loop quality control mechanism that makes the mechanical property optimization process of thin metal sheets controllable and reliable. Through iterative processing, it ensures that the material state characteristics of the thin metal sheet are fully optimized and meet the requirements before entering the subsequent cutting stage, thus providing accurate and reliable basic data for subsequent temperature deformation calculations and cutting parameter adjustments.

[0098] In a further embodiment of this application, spectral analysis is performed on the thin metal sheet treated with micropulse laser to evaluate the reconstruction effect of the microstructure of the thin metal sheet. If the reconstruction effect does not reach a preset state, the step of applying the micropulse laser to the thin metal sheet again preferably includes:

[0099] The thin metal sheet treated with the micropulse laser was subjected to spectral analysis to obtain the spectral analysis results.

[0100] Based on the spectral analysis results, identify whether there are micro-regions in the thin metal sheet that have not reached the preset state, so as to evaluate the reconstruction effect of the microstructure of the thin metal sheet;

[0101] If the reconstruction effect does not reach the preset state, then record the spatial coordinates and spectral characteristics of the micro-region;

[0102] The operating parameters of the micropulse laser are adjusted based on the spatial coordinates and spectral characteristics.

[0103] The adjusted micropulse laser is applied again to the thin metal sheet.

[0104] Specifically, spectral analysis is performed on thin metal sheets treated with micropulse lasers to obtain spectral analysis results. The aim is to acquire microstructural information about the thin metal sheets, such as grain size, grain boundary distribution, and phase composition, to reflect their mechanical properties. Spectral analysis can be understood as using techniques such as laser-induced breakdown spectroscopy (LIBS), Raman spectroscopy, or X-ray diffraction (XRD) to perform non-contact detection on the surface or interior of thin metal sheets, with the purpose of obtaining microstructural characteristic data.

[0105] Based on the spectral analysis results, the presence of microscopic regions in the thin metal sheet that have not reached a preset state is identified to evaluate the reconstruction effect of the microstructure of the thin metal sheet. Specifically, by comparing the spectral analysis data with the preset ideal microstructure spectral characteristics, it is possible to accurately determine which regions have insufficient microstructure reconstruction or defects.

[0106] If the reconstruction effect does not reach the preset state, the spatial coordinates and spectral characteristics of the micro-region are recorded. The spatial coordinates are used to accurately locate the area requiring further processing, while the spectral characteristics provide specific information about the microstructure of that area, such as stress distribution and lattice distortion, aiming to provide data support for subsequent precise intervention.

[0107] Based on this, the operating parameters of the micropulse laser are adjusted according to the spatial coordinates and spectral characteristics. For example, the pulse sequence, energy density, action time, or scanning path of the micropulse laser can be dynamically adjusted according to the stress concentration or lattice defect type in the substandard area to achieve targeted optimization of specific micro-regions.

[0108] Suppose that after initial micro-pulse laser processing of the thin metal sheet for a mobile phone SIM card tray, Raman spectroscopy analysis reveals that the lattice distortion in a specific region of the sheet (e.g., the region with coordinates X1, Y1, Z1) remains high, failing to meet the preset stress release standard. In this case, the system records the spatial coordinates X1, Y1, Z1 of this region and its corresponding Raman spectral characteristics (e.g., the shift of a specific peak and its full width at half maximum). Based on this information, the control system intelligently adjusts the operating parameters of the micro-pulse laser, for example, adjusting the pulse sequence to denser, shorter pulses and appropriately increasing the energy density in this region to enhance the reconstruction of the local lattice. Subsequently, the micro-pulse laser precisely applies the adjusted micro-pulse laser to the X1, Y1, Z1 region again until the Raman spectral characteristics of this region reach the preset standard, indicating that its microstructure has been sufficiently optimized.

[0109] The proposed solution first performs a detailed spectral analysis on the thin metal sheet to accurately identify specific regions with insufficient microstructure reconstruction and obtain their spatial coordinates and specific microscopic features. Based on this detailed information, the operating parameters of the micropulse laser, such as the pulse sequence and energy density, can be adjusted to precisely target the substandard microscopic regions. This localized and parameterized adjustment allows the micropulse laser to induce secondary reconstruction of the target region's microstructure in a more efficient and precise manner, effectively solving the problem of over- or under-processing that may result from repeatedly applying micropulse lasers to the entire surface, ensuring comprehensive and uniform optimization of the mechanical properties of the thin metal sheet.

[0110] It should be noted that the deformation trend preferably includes predicting the dynamic deformation perpendicular to the surface of the thin metal sheet that will occur at the point on the cutting path where the thin metal sheet is about to be cut due to the laser thermal effect; wherein, the compensation amount of the adjustment command for the focal position is opposite to the direction of the dynamic deformation and equal to the amplitude of the dynamic deformation.

[0111] Specifically, during laser cutting of thin metal sheets, the sheet heats up locally due to the input of laser energy, generating thermal stress and causing deformation. This deformation is not static but dynamically changes as the laser cutting point moves. The deformation trend referred to in this application is the prediction of this dynamic deformation, specifically the prediction of the displacement or deformation perpendicular to the surface of the thin metal sheet at the point on the cutting path that will be cut by the laser due to laser thermal effects. This dynamic deformation perpendicular to the surface of the thin metal sheet is a key factor affecting cutting quality, especially cutting depth and kerf width.

[0112] To effectively counteract the impact of this dynamic deformation on cutting quality, the focal position in the cutting parameters needs to be adjusted in real time. The adjustment command's compensation amount for the focal position is designed to be opposite to the direction of the dynamic deformation and equal to its amplitude. This means that if the thin metal sheet deforms upwards by X micrometers at a certain point, the focal position adjustment command will compensate downwards by X micrometers to ensure that the laser focal point always falls precisely on the preset cutting plane of the thin metal sheet, thereby maintaining a stable cutting effect.

[0113] This application's solution precisely defines the deformation trend as dynamic deformation perpendicular to the surface of the thin metal sheet, and ensures that the compensation amount at the focal point is opposite in direction and equal in magnitude to this dynamic deformation. This effectively locks the laser focus on the actual cutting surface of the thin metal sheet. When the thin metal sheet undergoes dynamic deformation due to laser thermal effects, such as bending upwards or downwards, traditional fixed-focus cutting methods cause the laser focus to deviate from the optimal position, thus affecting cutting efficiency and quality. Through the above technical solution, this application can more accurately describe and predict the actual deformation of the thin metal sheet during laser cutting, especially the dynamic deformation perpendicular to the surface of the thin metal sheet. Therefore, more precise focal point adjustment commands can be generated, effectively counteracting the focal point shift caused by thermal deformation, significantly improving cutting accuracy and stability, reducing cutting defects, and ultimately improving the laser cutting quality and yield of thin metal sheets such as mobile phone SIM card trays.

[0114] In a further embodiment of this application, the step of generating adjustment instructions for adjusting cutting parameters based on the deformation trend, and then smoothing the adjustment instructions before execution, preferably includes:

[0115] The initial adjustment amount is calculated based on the deformation trend.

[0116] A coupling relationship model is established between the cutting speed, laser power, auxiliary gas pressure, and focal position. This coupling relationship model is used to describe the influence of the adjustment of the cutting parameters on the cutting effect.

[0117] The initial adjustment amount is input into the coupling relationship model, and decoupling calculation is performed to generate an adjustment instruction that can counteract the negative interference effect between parameters of the cutting parameter;

[0118] The adjustment command is executed after being smoothed out.

[0119] Specifically, the initial adjustment amount calculated based on the deformation trend refers to the preliminary determination of the adjustment range required for each cutting parameter based on the calculated deformation trend of the thin metal sheet. For example, if the deformation trend shows that the thin metal sheet bulges upward in a certain area, the focal position may need to be adjusted downward by an initial amount to ensure that the laser focal point is always at the optimal cutting position.

[0120] The establishment of a coupling relationship model between cutting speed, laser power, auxiliary gas pressure, and focal position can be understood as constructing a mathematical model through extensive experimental data collection, simulation analysis, or machine learning algorithms. This model can quantitatively describe the impact of a change in one cutting parameter (e.g., laser power) on other cutting parameters (e.g., cutting speed, focal position) and the final cutting effect (e.g., kerf width, heat-affected zone, slag adhesion, etc.). Its purpose is to reveal the intrinsic correlation between different cutting parameters and their comprehensive effect on cutting quality.

[0121] In practical applications, inputting the initial adjustment amount into the coupling relationship model for decoupling calculation refers to using the established coupling relationship model to process the initially determined initial adjustment amount, thereby eliminating or reducing potential negative interactions between adjustments of different cutting parameters. For example, through multivariate control algorithms, optimization algorithms, or adaptive control strategies, the coupled initial adjustment amount can be transformed into a set of independent or minimally influential adjustment commands. The aim is to ensure that the adjustments of various parameters can work collaboratively, avoiding poor adjustment results or new problems caused by interactions between parameters.

[0122] Therefore, generating adjustment instructions that can counteract the negative interference effects between the cutting parameters refers to the final set of adjustment instructions obtained after decoupling calculations. This instruction set can not only accurately respond to the deformation trend of the thin metal sheet, but also effectively avoid the decline or instability in cutting quality caused by the coupling between parameters, thereby achieving more precise and stable cutting control.

[0123] Suppose that during the cutting of a thin metal sheet, localized thermal stress causes tiny protrusions on its surface. To maintain optimal cutting results, both the laser focus position and laser power need to be adjusted simultaneously. If the focus position is simply moved downwards based on the deformation trend, while simultaneously increasing the laser power to ensure cutting penetration, without considering the coupling relationship between the two, the following may occur: adjusting the focus position may alter the energy distribution of the laser within the material, while increasing the laser power further exacerbates the localized thermal effect. The combined effect could lead to an excessively wide kerf, an enlarged heat-affected zone, or even new deformations.

[0124] To address this, this application implements the following: First, based on the deformation trend of the thin metal sheet, the initial adjustment amounts for the focal position and laser power are calculated. Next, the system invokes a pre-established coupling relationship model. This model may be trained using extensive experimental data; for example, it describes the impact of a 10-micrometer adjustment of the focal position on the cutting speed and laser power requirements under specific materials and thicknesses, and the impact of a 10-watt increase in laser power on the focal depth and heat-affected zone. Then, the initial adjustment amounts are input into this coupling relationship model, and calculations are performed using a decoupling algorithm (e.g., an optimization algorithm based on Kalman filtering or a neural network). The decoupling calculation results in a set of finely adjusted instructions, such as adjusting the focal position downwards by 8 micrometers, increasing the laser power by 12 watts, and simultaneously fine-tuning the auxiliary gas pressure by 0.1 MPa. These optimized instructions ensure that while compensating for the deformation of the thin metal sheet, they also minimize the negative interference effects that may arise between the focal position and laser power adjustments, thereby ensuring that the kerf quality and heat-affected zone remain within a controllable range, ultimately achieving stable and high-quality cutting.

[0125] The proposed solution effectively addresses the potential negative interference issues in traditional methods by introducing a coupling relationship model and decoupling calculations. This ensures that the adjustments of various cutting parameters are coordinated and effective, avoiding a chain reaction of negative effects on other parameters caused by the adjustment of a single parameter. This guarantees the stability of the cutting process and the consistency of cutting quality.

[0126] Please refer to Figure 2 This application also proposes an automated control system 200 for a mobile phone SIM card tray laser cutting equipment, comprising:

[0127] The information acquisition module 210 is used to acquire the three-dimensional distribution information of residual stress and mechanical property information of stress concentration areas inside the thin metal sheet, and generate the material state characteristics of the thin metal sheet.

[0128] The state acquisition module 220 is used to acquire the temperature deformation information of the thin metal sheet based on the material state characteristics;

[0129] The deformation calculation module 230 is used to calculate the deformation trend of the thin metal sheet by combining temperature deformation information and preset cutting path information.

[0130] The instruction generation module 240 is used to generate adjustment instructions for adjusting the cutting parameters based on the deformation trend.

[0131] The instruction execution module 250 is used to perform smoothing processing on the adjustment instruction before execution.

[0132] This system, through its modular design, achieves automated and intelligent control of the laser cutting process for thin metal sheets. The information acquisition module is responsible for sensing the material's internal state. The state acquisition module and deformation calculation module work together to predict the material's dynamic response during the cutting process. The instruction generation and execution modules are responsible for adjusting the cutting parameters in real time and smoothly based on the prediction results. This effectively solves the limitations of traditional systems when facing microscopic inhomogeneities and dynamic deformations of materials, significantly improving cutting accuracy and stability.

[0133] Specifically, the information acquisition module 210 can be understood as a functional unit configured to perform the aforementioned information acquisition and feature generation functions. Specifically, the information acquisition module can consist of one or more sensors, a data acquisition unit, and a data processing unit. For example, the sensor can be an X-ray diffractometer or an ultrasonic testing device used to collect raw data; the data acquisition unit is responsible for converting the sensor data into processable digital signals; the data processing unit has a built-in preset algorithm or machine learning model for analyzing and fusing the collected data, and ultimately generating the material state characteristics of the thin metal sheet. As one implementation method, the information acquisition module can be a standalone hardware unit that communicates with the main controller through a standard interface; alternatively, its functions can be integrated into the software layer of the main controller, implemented by calling specific drivers and algorithm libraries.

[0134] The status acquisition module 220 can be composed of measuring equipment such as an infrared thermal imager, a laser displacement sensor or an optical interferometer and its supporting data processing circuit. It is configured to monitor and acquire the temperature distribution or minute deformation data of the thin metal sheet under the action of laser in real time.

[0135] The deformation prediction module 230 can be an independent computing unit with built-in finite element analysis (FEA) software or a neural network-based deformation prediction model. It is configured to receive material state characteristics, temperature deformation information and cutting path information, and perform complex simulation calculations or model inference to predict the dynamic deformation trend of thin metal sheets.

[0136] The instruction generation module 240 can be a decision-making unit with a built-in decision model based on fuzzy logic, expert system or optimization algorithm. It is configured to calculate the initial adjustment amount according to the deformation trend, establish a coupling relationship model between cutting parameters, perform decoupling calculation, and thus generate adjustment instructions that can offset the negative interference effect between parameters.

[0137] The instruction execution module 250 can be a motion controller or a laser controller, which is configured to receive adjustment instructions output by the instruction generation module and perform smoothing processing such as digital filtering (e.g., moving average filtering or Kalman filtering) or spline interpolation on them to ensure the smoothness of the instructions. The smoothed instructions are then sent to the laser, motion platform and auxiliary gas system to achieve precise control of cutting speed, laser power, auxiliary gas pressure and focal position.

[0138] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0139] The foregoing has provided a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. An automated control method for laser cutting equipment for mobile phone SIM card trays, applied to the laser cutting of thin metal sheets for mobile phone SIM card trays, characterized in that, The method includes the following steps: Obtain the three-dimensional distribution information of residual stress and mechanical property information of stress concentration areas inside the thin metal sheet, and generate the material state characteristics of the thin metal sheet; Based on the material state characteristics, the temperature deformation information of the thin metal sheet is obtained, and the deformation trend of the thin metal sheet is calculated by combining the temperature deformation information and the preset cutting path information. Based on the deformation trend, adjustment instructions for adjusting the cutting parameters are generated, and the adjustment instructions are smoothed before execution; wherein, the cutting parameters include cutting speed, laser power, auxiliary gas pressure and focal position.

2. The automated control method for a mobile phone SIM card tray laser cutting equipment according to claim 1, characterized in that, The step of obtaining the three-dimensional distribution information of residual stress and mechanical property information of stress concentration areas inside the thin metal sheet, and generating the material state characteristics of the thin metal sheet, includes: The residual stress three-dimensional distribution information and mechanical property information of the stress concentration region inside the thin metal sheet are obtained, and micropulse laser is applied to the thin metal sheet according to the residual stress three-dimensional distribution information and mechanical property information to induce the reconstruction of the microstructure of the stress concentration region of the thin metal sheet, so as to optimize the mechanical properties of the thin metal sheet. The residual stress three-dimensional distribution information and mechanical property information of the stress concentration area inside the reconstructed thin metal sheet are obtained to generate the material state characteristics of the thin metal sheet.

3. The automated control method for a laser cutting device for a mobile phone SIM card tray according to claim 2, characterized in that, During the application of micropulse laser to thin metal sheets: The strain information of the thin metal plate under the action of micropulse laser is continuously monitored, and the operating parameters of the micropulse laser are dynamically adjusted according to the strain information.

4. The automated control method for a mobile phone SIM card tray laser cutting equipment according to claim 3, characterized in that, The strain information includes transient thermal stress change information, local phase transition information, and local temperature information of the thin metal plate under the action of micropulse laser. The operating parameters include the pulse sequence and energy density of the micropulse laser.

5. The automated control method for a laser cutting device for a mobile phone SIM card tray according to claim 4, characterized in that, The step of dynamically adjusting the operating parameters of the micropulse laser based on the strain information includes: The pulse sequence is adjusted based on the transient thermal stress change information and the local phase transition information; The energy density is adjusted based on the local temperature information.

6. The automated control method for a mobile phone SIM card tray laser cutting equipment according to claim 2, characterized in that, After the step of reconstructing the microstructure of the stress concentration region inducing the thin metal sheet to optimize the mechanical properties of the thin metal sheet, and before the step of obtaining the three-dimensional distribution information of residual stress and mechanical property information of the stress concentration region inside the reconstructed thin metal sheet, the method further includes the following steps: Spectral analysis is performed on the thin metal sheet treated with the micropulse laser to evaluate the reconstruction effect of the microstructure of the thin metal sheet. If the reconstruction effect does not reach the preset state, the micropulse laser is applied to the thin metal sheet again.

7. The automated control method for a laser cutting equipment for mobile phone SIM card trays according to claim 6, characterized in that, The step of performing spectral analysis on the thin metal sheet treated with the micropulse laser to evaluate the reconstruction effect of the microstructure of the thin metal sheet, and if the reconstruction effect does not reach the preset state, then applying the micropulse laser to the thin metal sheet again includes: The thin metal sheet treated with the micropulse laser was subjected to spectral analysis to obtain the spectral analysis results. Based on the spectral analysis results, identify whether there are micro-regions in the thin metal sheet that have not reached the preset state, so as to evaluate the reconstruction effect of the microstructure of the thin metal sheet; If the reconstruction effect does not reach the preset state, then record the spatial coordinates and spectral characteristics of the micro-region; The operating parameters of the micropulse laser are adjusted based on the spatial coordinates and spectral characteristics. The adjusted micropulse laser is applied again to the thin metal sheet.

8. The automated control method for a mobile phone SIM card tray laser cutting equipment according to claim 1, characterized in that, The deformation trend includes predicting the dynamic deformation perpendicular to the surface of the thin metal sheet that will occur at the point on the cutting path where the thin metal sheet will be cut due to laser thermal action; wherein, the compensation amount of the adjustment command for the focal position is opposite to the direction of the dynamic deformation and equal to the amplitude of the dynamic deformation.

9. The automated control method for a laser cutting device for a mobile phone SIM card tray according to claim 1, characterized in that, The step of generating adjustment instructions for adjusting cutting parameters based on the deformation trend, and then smoothing the adjustment instructions before execution includes: The initial adjustment amount is calculated based on the deformation trend. A coupling relationship model is established between the cutting speed, laser power, auxiliary gas pressure, and focal position. This coupling relationship model is used to describe the influence of the adjustment of the cutting parameters on the cutting effect. The initial adjustment amount is input into the coupling relationship model, and decoupling calculation is performed to generate an adjustment instruction that can counteract the negative interference effect between parameters of the cutting parameter; The adjustment command is executed after being smoothed out.

10. An automated control system for a mobile phone SIM card tray laser cutting equipment, characterized in that, include: The information acquisition module is used to acquire the three-dimensional distribution information of residual stress and mechanical property information of stress concentration areas inside the thin metal sheet, and generate the material state characteristics of the thin metal sheet. The state acquisition module is used to acquire the temperature deformation information of the thin metal sheet based on the material state characteristics; The deformation calculation module is used to calculate the deformation trend of the thin metal sheet by combining temperature deformation information and preset cutting path information. The instruction generation module is used to generate adjustment instructions for adjusting the cutting parameters based on the deformation trend. The instruction execution module is used to perform smoothing processing on the adjustment instruction before execution.

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