Circuit board precision machining method, system and equipment and storage medium

Abstract

The invention relates to the technical field of circuit board processing, and discloses a circuit board precision processing method, system and device and a storage medium, and the method comprises the steps: carrying out the heat conduction characteristic detection of a plurality of circuit boards of first ceramic substrates with different thicknesses, and obtaining the relation data of the thickness and the heat conductivity; performing thermal diffusion velocity coupling calculation on the second ceramic substrate with the target thickness to obtain a coupling control relation model; laser pulse power decline processing is carried out based on the coupling control relation model, and pulse power decline control parameters are obtained; performing multi-parameter collaborative optimization based on the pulse power decline control parameters to obtain an optimal pulse parameter combination; and real-time temperature feedback regulation and control are conducted on the heat affected zone of the second ceramic substrate in the laser cutting process based on the optimal pulse parameter combination, a pulse sequence control instruction is output, the pulse parameters can be dynamically adjusted according to the actual machining state in the laser cutting process, and the machining precision of the circuit board is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit board processing, and in particular to a circuit board precision processing method, system, equipment and storage medium. Background Art

[0002] Ceramic substrate materials such as aluminum nitride have a thermal conductivity of up to 180W / m·K, significantly superior to the 0.3W / m·K thermal conductivity of common FR4-PCB substrates, effectively solving the heat dissipation issues of high-power devices. However, the unique material properties of ceramic substrates, such as high hardness, extreme brittleness, and ultra-high thermal conductivity, present unique technical challenges during precision machining.

[0003] The instantaneous heat conduction rate of ceramic substrates is 600 times that of ordinary PCB materials. This extremely high thermal diffusion characteristic causes traditional laser cutting technology to encounter serious technical bottlenecks during processing. When the laser acts on the surface of the ceramic substrate, the heat generated by the laser diffuses rapidly to the surrounding area, forming a heat-affected zone far beyond the expected range and generating severe thermal stress gradients within the substrate. This leads to low machining precision and high processing losses. Summary of the Invention

[0004] The present invention provides a circuit board precision machining method, system, equipment and storage medium. The present invention can dynamically adjust pulse parameters according to the actual machining state during laser cutting, thereby improving the machining accuracy of the circuit board.

[0005] In a first aspect, the present invention provides a method for precision machining of a circuit board, the method comprising: Conducting thermal conductivity testing on a plurality of circuit boards having first ceramic substrates of different thicknesses to obtain thickness-thermal conductivity relationship data; performing a coupled thermal diffusion rate calculation on a second ceramic substrate of target thickness according to the thickness-thermal conductivity relationship data to obtain a coupled control relationship model; Performing laser pulse power reduction processing based on the coupling control relationship model to obtain a pulse power reduction control parameter; Perform multi-parameter collaborative optimization based on the pulse power decrement control parameters to obtain an optimal pulse parameter combination; Based on the optimal pulse parameter combination, real-time temperature feedback control is performed on the heat-affected zone of the second ceramic substrate during the laser cutting process, and a pulse sequence control instruction is output.

[0006] In conjunction with the first aspect, in a first implementation of the first aspect of the present invention, the thermal conductivity characteristics of the circuit boards having first ceramic substrates of different thicknesses are tested to obtain thickness-thermal conductivity relationship data, including: Performing surface temperature distribution detection on a plurality of first ceramic substrates with different thicknesses in the circuit board to obtain real-time temperature distribution data of each first ceramic substrate; Calculating an actual thermal conductivity value of each first ceramic substrate according to the real-time temperature distribution data, and performing a layered heat conduction analysis based on the actual thermal conductivity value of each first ceramic substrate to obtain a thickness-layered heat conduction characteristic of each first ceramic substrate; According to the thickness-layered heat conduction characteristics of each first ceramic substrate, a thermal conductivity relationship analysis is performed on first ceramic substrates of different thicknesses to obtain thickness-thermal conductivity relationship data.

[0007] In combination with the first aspect, in a second implementation of the first aspect of the present invention, performing a coupled thermal diffusion rate calculation on a second ceramic substrate of target thickness based on the thickness-thermal conductivity relationship data to obtain a coupled control relationship model includes: Performing thermal diffusion modeling on first ceramic substrates of different thicknesses according to the thickness-thermal conductivity relationship data to obtain a thermal diffusion prediction model; Calculating the thermal diffusion rate of a second ceramic substrate of target thickness based on the thermal diffusion prediction model to obtain a thermal diffusion rate value of the second ceramic substrate; The coupling calculation between the thermal diffusion rate and the pulse power is performed according to the thermal diffusion rate value of the second ceramic substrate to obtain coupling control relationship parameters, and coupling control relationship modeling is performed based on the coupling control relationship parameters to obtain a coupling control relationship model.

[0008] In combination with the first aspect, in a third implementation of the first aspect of the present invention, performing laser pulse power reduction processing based on the coupling control relationship model to obtain the pulse power reduction control parameter includes: performing instantaneous temperature field distribution analysis on the second ceramic substrate based on the coupling control relationship model to obtain instantaneous temperature field distribution data; Performing power reduction calculation according to the instantaneous temperature field distribution data to obtain a power reduction parameter, and monitoring the temperature gradient of the laser action point based on the power reduction parameter to obtain a power reduction trigger condition; The pulse interval is optimized according to the power reduction triggering condition to obtain the pulse power reduction control parameter.

[0009] In combination with the first aspect, in a fourth implementation of the first aspect of the present invention, performing instantaneous temperature field distribution analysis on the second ceramic substrate based on the coupling control relationship model to obtain instantaneous temperature field distribution data includes: Extracting heat conduction parameters of the second ceramic substrate based on the coupling control relationship model to obtain a thermal diffusion coefficient and a heat conduction enhancement factor; Establishing an instantaneous temperature field equation of the second ceramic substrate according to the thermal diffusion coefficient and the thermal conductivity enhancement factor; Calculating the temperature distribution of the laser action area based on the instantaneous temperature field equation to obtain laser center point temperature data and radial temperature gradient data; Temperature field distribution is integrated according to the laser center point temperature data and the radial temperature gradient data to obtain instantaneous temperature field distribution data.

[0010] In combination with the first aspect, in a fifth implementation of the first aspect of the present invention, performing multi-parameter collaborative optimization based on the pulse power decrement control parameter to obtain an optimal pulse parameter combination includes: performing heat conduction velocity difference compensation calculation on the second ceramic substrate based on the pulse power decreasing control parameter to obtain a heat conduction compensation factor; Performing a weighted coupling analysis of a thickness correction factor and a heat conduction enhancement factor according to the heat conduction compensation factor to obtain a coupling control function; Dynamically generate a pulse power peak value and an attenuation coefficient corresponding to the thickness of the second ceramic substrate based on the coupling control function to obtain thickness adaptation pulse sequence parameters; Dynamic fine-tuning of parameter weights of thermal diffusion characteristics of the ceramic substrate is performed according to the thickness adaptation pulse sequence parameters to generate an optimal pulse parameter combination.

[0011] In combination with the first aspect, in a sixth implementation of the first aspect of the present invention, performing real-time temperature feedback control on the heat-affected zone of the second ceramic substrate during the laser cutting process based on the optimal pulse parameter combination and outputting a pulse sequence control instruction include: Based on the optimal pulse parameter combination, the instantaneous temperature field boundary of the second ceramic substrate during the laser cutting process is tracked in real time to obtain heat-affected zone boundary expansion data; Performing high-speed thermal diffusion compensation calculation on ceramic materials based on the heat-affected zone boundary expansion data to obtain thermal diffusion compensation correction parameters; Dynamically attenuate and control the pulse power density based on the thermal diffusion compensation correction parameter to obtain the heat-affected zone boundary control parameter; Thermal stress microcrack suppression verification and pulse sequence optimization are performed according to the heat-affected zone boundary control parameters, and a pulse sequence control instruction is output.

[0012] In a second aspect, the present invention provides a circuit board precision processing system, the circuit board precision processing system comprising: A detection module, configured to detect the thermal conductivity characteristics of a plurality of circuit boards having first ceramic substrates of different thicknesses, and obtain data on the relationship between thickness and thermal conductivity; a calculation module, configured to perform a coupled calculation of the thermal diffusion rate of the second ceramic substrate of target thickness according to the relationship data between the thickness and the thermal conductivity, and obtain a coupled control relationship model; a processing module, configured to perform laser pulse power reduction processing based on the coupling control relationship model to obtain a pulse power reduction control parameter; An optimization module, configured to perform multi-parameter collaborative optimization based on the pulse power decrement control parameters to obtain an optimal pulse parameter combination; An output module is used to perform real-time temperature feedback control on the heat-affected zone of the second ceramic substrate during the laser cutting process based on the optimal pulse parameter combination, and output a pulse sequence control instruction.

[0013] The third aspect of the present invention provides a computer device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned circuit board precision processing method.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, enable the computer to execute the above-mentioned circuit board precision processing method.

[0015] In the technical solution provided by the present invention, by establishing a dynamic control mechanism of pulse parameters based on the thermal conductivity characteristics of ceramic materials, the laser pulse power can be adjusted in real time according to the actual thermal diffusion rate of the ceramic substrate, effectively suppressing the rapid expansion of the heat-affected zone and achieving precise control of the boundary of the heat-affected zone. Through the pulse power decrement control algorithm and the real-time temperature feedback control mechanism, the thermal stress gradient formation process inside the ceramic substrate can be effectively controlled, the occurrence of stress concentration can be prevented, and the thermal stress microcrack defects unique to the ceramic substrate can be completely eliminated. By establishing a multi-dimensional parameter collaborative optimization system covering the thickness characteristics, thermal conductivity differences and real-time temperature feedback of the ceramic substrate, the optimal pulse parameter combination can be dynamically generated according to the characteristic differences of ceramic substrates of different specifications, significantly improving the adaptability of processing parameters and the stability of processing quality. By establishing a coupling control mechanism of the thermal conduction velocity difference compensation factor and the thermal conduction enhancement factor, compensation and control can be carried out specifically for the essential differences between ceramic materials and ordinary PCB materials, solving the technical limitation that traditional laser cutting technology cannot adapt to the special thermal conduction characteristics of ceramic materials. By establishing an adaptive pulse sequence generation system based on real-time temperature feedback, the pulse parameters can be dynamically adjusted according to the actual processing status during the laser cutting process. By establishing a thickness correction factor and a layered heat conduction modeling mechanism, specialized processing can be performed for the differences in thermal conductivity characteristics of ceramic substrates of different thicknesses. The heat-affected zone control technology of the present invention, based on dynamic regulation of pulse parameters, can significantly reduce the scope of the heat-affected zone and eliminate microcrack defects. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 Schematic diagram of the steps of the circuit board precision processing method according to an embodiment of the present invention; Figure 2 Schematic diagram of the structure of the circuit board precision processing system in an embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] Embodiments of the present invention provide a method, system, device, and storage medium for precision processing of circuit boards. The terms "first," "second," "third," "fourth," and so on (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0019] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of the circuit board precision processing method in the embodiment of the present invention includes: Step S1: testing the thermal conductivity characteristics of a plurality of circuit boards having first ceramic substrates of different thicknesses to obtain data on the relationship between thickness and thermal conductivity; It is understandable that the execution subject of the present invention can be a circuit board precision processing system, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0020] Specifically, multiple first ceramic substrates of varying thicknesses are placed on a standard laser preheating platform or in an isothermal environment. A high-speed infrared thermal imager and a micro-thermocouple array positioned on the substrate surfaces are used to synchronously collect data. The temperature changes of each ceramic substrate during heating are continuously monitored, generating high-resolution, real-time temperature distribution data. This temperature distribution data covers not only the laser action center but also the surrounding heat diffusion boundary, reflecting the lateral and longitudinal heat conduction behavior within the ceramic substrates of varying thicknesses. Based on this real-time temperature distribution data, the time-temperature curves of each sampling point are input into a pre-defined inversion solution module. Through numerical fitting and heat transfer model solution, the actual thermal conductivity of each first ceramic substrate is inferred. Given the anisotropic microstructure of ceramic materials, the thermal conductivity is expressed as a tensor with a specific resolution or using a layered model to more realistically characterize the interlayer variability in thermal conductivity within the ceramic material. Each ceramic substrate is divided into several microlayers based on the measured thickness, and the thermal conductivity intensity is calculated within each microlayer to generate thermal conductivity data with a layered distribution structure. This step generates a set of thickness-layered thermal conductivity characteristic parameters for each first ceramic substrate, describing the heat flow characteristics of each layer from the surface to the base. Based on the thickness-layered thermal conductivity characteristics of each first ceramic substrate, a functional relationship between thickness and thermal conductivity is established by comparing the evolution of thermal conductivity distribution for substrates of different thicknesses. A mathematical model is constructed, using thickness as the independent variable and thermal conductivity as the dependent variable. Curve fitting is performed using methods such as least squares, interpolation, or polynomial regression to generate thickness-thermal conductivity relationship data that expresses the changing trends in the thermal conductivity of ceramic materials at different thicknesses.

[0021] Step S2, performing a coupled calculation of the thermal diffusion rate of the second ceramic substrate of target thickness according to the relationship data between thickness and thermal conductivity, to obtain a coupled control relationship model; Specifically, based on data on the relationship between thickness and thermal conductivity, thermal diffusion modeling is performed for multiple first ceramic substrates of varying thicknesses. This modeling process, based on static thermal conductivity data and combined with thermophysical parameters such as the ceramic material's density and specific heat capacity, utilizes the thermal diffusion coefficient formula from classical thermal diffusion theory for layer-by-layer calculations. Furthermore, the governing equations for time-space thermal diffusion in three-dimensional coordinates are introduced, and a thermal diffusion prediction model is constructed using finite difference or finite element numerical solution methods. This model dynamically predicts the heat flow propagation behavior per unit time for ceramic materials of varying thicknesses. By simulating the evolution of temperature gradients under laser action, a thermal diffusion trend expression sensitive to thickness characteristics is derived, forming a thickness-thermal diffusion rate mapping function system. Based on this, the thickness of a second ceramic substrate of the target thickness is substituted as an input parameter into the thermal diffusion prediction model. The thermal diffusion rate is calculated based on the target material's properties, such as thermal conductivity, density, and specific heat capacity, to obtain the thermal diffusion rate value for the substrate at the moment of laser action. To effectively control the range of laser heat transfer, this thermal diffusion rate value is coupled with the pulsed laser power. By establishing a physical model for the relationship between the heat-affected zone radius and laser power, substituting the heat diffusion rate term into it and inversely calculating the pulse power attenuation parameters required under the heat diffusion rate conditions, combined with the target heat-affected zone radius threshold, the time constant and intensity of the pulse energy decay are determined. The coupling control relationship is modeled based on the coupling control relationship parameters to form a coupling control relationship model. This model structures the causal mapping relationship between the heat diffusion rate and the laser pulse power attenuation curve, considers the nonlinear effect of ceramic thickness on thermal diffusion behavior, and incorporates compensation mechanisms such as thickness correction factors and diffusion rate factors, enabling it to accurately reflect the response mechanism of laser processing behavior under different material structures.

[0022] Step S3, performing laser pulse power reduction processing based on the coupling control relationship model to obtain a pulse power reduction control parameter; Specifically, the instantaneous temperature field distribution of the target second ceramic substrate is analyzed based on a coupled control relationship model. This analysis is based on the coupling between the material's thermal diffusivity and laser power, and considers the heat transfer and diffusion mechanisms of the laser beam on and within the ceramic surface. Temperature response data are collected during the initial laser cutting phase using a high-frequency infrared thermal imager and a temperature sensor array. The model is then used to numerically solve the temperature distribution induced by the local heat source. This data describes the temperature evolution of the ceramic substrate per unit time at the laser center and its surrounding area. This data includes the peak temperature at the laser center and the spatial gradient during outward diffusion, quantifying the dynamic coupling effect between the laser thermal energy and the thermal diffusion of the ceramic material. Power decay is calculated based on this instantaneous temperature field distribution data. The power decay parameters, including the decay rate coefficient and the initial power normalization coefficient, together determine the decay trajectory of the pulse power over time. The decay calculation is based on an exponential function model, where the decay coefficient is inferred from the maximum temperature gradient and thermal diffusion rate within the temperature field. Combined with the critical temperature of the material for thermal damage, high-risk overheating areas are identified. Simultaneously, the temperature gradient at the laser impact point is monitored in real time. A measurement path is established around the outer ring of the laser center, and the rate of temperature change per unit length is extracted and compared with a set gradient threshold. For example, a temperature change rate exceeding 1000K / mm triggers a power reduction mechanism. This threshold serves as the power reduction trigger condition. This trigger condition ensures that laser output power is promptly reduced in the event of drastic temperature fluctuations or insufficient heat diffusion, thereby preventing crack propagation or material ablation caused by heat accumulation. Pulse interval optimization is performed based on the power reduction trigger condition. By analyzing the current power reduction function and the heat diffusion rate, the next laser pulse should not be emitted until heat diffusion is complete, preventing the pulse stacking effect from expanding the heat-affected zone. This optimization process estimates the heat transfer timescale to determine a reasonable minimum pulse interval. This time period is combined with the power reduction curve to generate the pulse power reduction control parameters, which include the form of the reduction function, the power update frequency, the minimum interval time, and the temperature trigger point.

[0023] Step S4: performing multi-parameter collaborative optimization based on the pulse power decrement control parameters to obtain an optimal pulse parameter combination; Specifically, based on the pulse power decrement control parameter, the response difference between the thermal conductivity velocity of the second ceramic substrate and the standard thermal diffusion model is compensated. By comparing the thermal diffusion velocity of the actual material with that of a reference ceramic sample or a baseline thermal diffusion velocity of an FR4 substrate, a function expressing the thermal conductivity difference is constructed. This function is then used to infer a dynamic thermal conductivity compensation factor, which is used to correct thermal response deviations in the laser energy control model. Based on the thermal conductivity compensation factor, a thickness correction factor and a thermal conductivity enhancement factor are introduced. By analyzing the coupling relationship between these two parameters and performing a weight distribution analysis, the core coupling control function required for the multi-parameter collaborative model is constructed. This function captures the physical influence of ceramic substrate thickness on thermal diffusion and simultaneously incorporates the nonlinear regulation effect of the material's microscopic thermophysical properties on laser energy dissipation. This enables the control system to demonstrate comprehensive responsiveness to the material's multi-scale characteristics during parameter calculation. Based on the generated coupling control function, dynamic matching of pulse energy output schemes is performed for ceramic substrates of varying thicknesses, including quantitative calculation of the pulse power peak and construction of a temporal distribution model of the corresponding attenuation coefficient. For example, for thinner ceramic substrates, a lower peak pulse power and a relatively fast power decay rate are generated to suppress the expansion of the heat-affected zone caused by transient high heat flux. For thicker ceramic materials, the initial pulse energy is appropriately increased while the energy decay rate is slowed to achieve a balanced heating strategy for materials with high thermal inertia. The dynamic generation of thickness-adaptive pulse sequence parameters ensures that the laser cutting system can flexibly adjust the energy release pattern according to the thickness variations of the ceramic material, maintaining optimal temperature control efficiency within the material's thermal response range during each laser cycle. Based on the thickness-adaptive pulse sequence parameters, the thermal diffusion characteristics of the ceramic substrate are modeled, and control parameters are dynamically fine-tuned based on real-time temperature feedback data. Specifically, the weight coefficients of various thermal response factors, including thermal conductivity contribution, diffusion velocity sensitivity, and pulse response delay, are adjusted to generate an optimal pulse parameter combination that combines material adaptability with feedback loop characteristics. This parameter combination includes the peak pulse power and decay coefficient, as well as the pulse interval, width adjustment factor, and trigger sensitivity threshold, forming a multi-parameter control vector.

[0024] Step S5: Based on the optimal pulse parameter combination, real-time temperature feedback control is performed on the heat-affected zone of the second ceramic substrate during the laser cutting process, and a pulse sequence control instruction is output.

[0025] Specifically, a dynamic thermal response analysis of the laser cutting process is performed based on an optimal pulse parameter combination. Based on this, a real-time tracking mechanism for the instantaneous temperature field boundary is constructed. This mechanism uses a high-frequency infrared thermal imaging sensor array as its core monitoring device. Combined with a two-dimensional coordinate grid of the laser active area and its surroundings, it performs time-series sampling and boundary gradient extraction on each frame of temperature distribution data, thereby capturing the actual expansion behavior of the heat-affected zone boundary within milliseconds. During the tracking process, the geometric expansion range of the boundary is recorded, and the rate of change of the boundary temperature gradient is estimated in real time to generate heat-affected zone boundary expansion data. This data serves as core feedback input into the control algorithm module to assess whether the current laser pulse output triggers the risk of excessive heat conduction or stress concentration. Based on this heat-affected zone boundary expansion data, a compensation calculation is performed on the thermal diffusion behavior of ceramic materials under high-speed laser action. By comparing the real-time measured boundary expansion rate with a theoretical thermal diffusion velocity model, the leading or lagging state of thermal diffusion is identified, and the thermal diffusion compensation correction parameters are calculated accordingly. This correction parameter accounts for complex factors such as the actual thermal inertia of the material, the laser beam imaging diameter, and the internal thermal conduction path of the ceramic. It then adjusts the dynamic expression of heat diffusion during the laser cycle, thereby limiting the uncontrolled expansion of the heat-affected zone (HAZ) while maintaining processing efficiency. Based on the thermal diffusion compensation correction parameter, the pulse power density is dynamically attenuated. By adjusting the power density decay rate over time and the power distribution gradient within the local area, the spatial distribution of the laser energy density is corrected. The HAZ boundary control parameter is derived as a multi-dimensional control indicator, including a power decay function, a temperature gradient threshold, and a local energy limiting coefficient. Thermal stress microcrack suppression is verified using the HAZ boundary control parameter. The stress concentration factor along the scanning path is compared and analyzed with surface microcrack monitoring data. During the verification process, the thermal stress field distribution and its temporal evolution are simulated based on the current temperature distribution and power density state to determine whether there are critical stress points that could lead to localized fracture of the ceramic structure. If microcrack initiation trends or crack-sensitive areas are detected, the optimized pulse sequence scheduling mechanism is immediately invoked to mitigate crack propagation paths by reducing peak power, increasing pulse intervals, or adjusting waveform width. Based on the dynamic response analysis and verification results, a pulse sequence control instruction is output, which includes the energy characteristic parameters of the current pulse and the predictive control strategy for the subsequent pulse cycles.

[0026] In a specific embodiment, the process of executing step S1 may specifically include the following steps: Performing surface temperature distribution detection on a plurality of first ceramic substrates with different thicknesses in the circuit board to obtain real-time temperature distribution data of each first ceramic substrate; Calculating an actual thermal conductivity value of each first ceramic substrate according to the real-time temperature distribution data, and performing a layered heat conduction analysis based on the actual thermal conductivity value of each first ceramic substrate to obtain a thickness-layered heat conduction characteristic of each first ceramic substrate; According to the thickness-layered heat conduction characteristics of each first ceramic substrate, a thermal conductivity relationship analysis is performed on first ceramic substrates of different thicknesses to obtain thickness-thermal conductivity relationship data.

[0027] Specifically, dynamic surface temperature monitoring is performed on multiple first ceramic substrates of varying thicknesses. Two-dimensional temperature distribution data is acquired in real time on the surface and adjacent areas of the material under the action of a laser beam or a standard heat source. This process leverages the non-contact temperature acquisition capabilities of a high-frame-rate infrared thermal imager, supplemented by a multi-channel thermocouple array positioned at the center and edges of the laser incident point. This enhances the depth and accuracy of the overall data structure, resulting in a temporal-spatial image of the temperature evolution of each substrate under given thermal excitation conditions. Based on this temperature distribution data, data inversion and parameter extraction are performed. By solving the fundamental heat conduction equation and incorporating the density and specific heat capacity of the ceramic material, finite element or finite difference numerical methods are used to infer the thermal diffusivity, further determining the dynamic value of the actual thermal conductivity. To enhance the reliability of the results, a minimum deviation optimization function is introduced to perform multi-point fitting and residual convergence analysis on the calculated results, ensuring that the thermal conductivity parameters obtained for each first ceramic substrate at its specific thickness are reproducible and engineering-relevant. Because ceramic materials inherently exhibit significant interlayer heterogeneity in thermal conductivity due to variations in microstructural compactness and grain orientation at different thickness levels, a layered modeling approach is employed across the thickness. Each ceramic substrate is divided into several thin layers of equal thickness. Thermal response fitting and thermal conductivity estimation are performed independently for each layer to construct a thickness-based layered thermal conductivity model incorporating multi-layer thermal conductivity parameters. Based on the layered thermal conductivity characteristics of multiple ceramic substrates of varying thicknesses, cross-thickness data alignment and function induction are performed. The layered thermal conductivity data for each sample is visualized with thickness as the abscissa and thermal conductivity as the ordinate, and all thickness levels are normalized to eliminate the influence of scale inconsistency on the analysis results. Curve fitting and regression modeling are then used to extract the mathematical relationship between different thicknesses and thermal conductivity. For example, power function fitting, log-linear regression, or polynomial regression are used to construct models for the original thermal conductivity point set. Residual analysis is then used to evaluate the fit of each function type, ultimately resulting in a data model for the thickness-thermal conductivity relationship that is universally applicable in engineering applications. This model reflects the nonlinear attenuation trend of the thermal conductivity of the material as the thickness increases due to loose lattice packing, stress release or enhanced thermal interface impedance.

[0028] In a specific embodiment, the process of executing step S2 may specifically include the following steps: Performing thermal diffusion modeling on first ceramic substrates of different thicknesses according to the thickness-thermal conductivity relationship data to obtain a thermal diffusion prediction model; Calculating the thermal diffusion rate of a second ceramic substrate of target thickness based on the thermal diffusion prediction model to obtain a thermal diffusion rate value of the second ceramic substrate; The coupling calculation between the thermal diffusion rate and the pulse power is performed according to the thermal diffusion rate value of the second ceramic substrate to obtain coupling control relationship parameters, and coupling control relationship modeling is performed based on the coupling control relationship parameters to obtain a coupling control relationship model.

[0029] Specifically, the physical modeling parameters are based on data on the relationship between thickness and thermal conductivity. This data, derived from the real-time response of multiple ceramic substrates of varying thicknesses during thermal stimulation, captures the actual thermal conductivity of each ceramic substrate as its thickness changes. By establishing a one-to-one mapping model between thermal conductivity values ​​and corresponding thicknesses and incorporating the density and specific heat capacity of the material itself, the corresponding thermal diffusion capacity of each thickness is calculated. Based on this data, a predictive model for thermal diffusion behavior is developed for ceramic substrates of varying thicknesses. This model, based on calculation rules for temperature trends in spatial and temporal dimensions and combined with the actual laser action radius and energy density, sets boundary conditions and an initial temperature distribution within the simulation model, thereby constructing a family of predictive functions for thermal diffusion behavior across multiple thicknesses. Based on this thermal diffusion prediction model, the thermal diffusion rate of a second ceramic substrate of the target thickness is calculated. By substituting the target thickness into the thickness-thermal diffusion relationship model and combining it with the thermophysical properties of the ceramic material, the actual diffusion rate under laser thermal stimulation is determined. This velocity reflects the rate of heat transfer from the laser center to the material's edge. A larger value indicates faster diffusion, meaning the laser-heated area has a wider thermal conductivity range within a short period of time. In practical control, this requires the system to release and regulate pulse energy at a faster pace to avoid localized overheating or energy accumulation that could lead to uncontrolled heat-affected zones (HAZs). The thermal diffusion velocity of the second ceramic substrate is coupled with the temporal variation of the pulsed laser power. A dynamic matching relationship between the thermal diffusion velocity and laser power is established, analyzing the material's thermal diffusion behavior over a given timeframe. Combined with parameters such as the initial laser pulse power, the energy release period, and the thermal damage threshold, a set of control factors describing this relationship, known as the coupled control relationship parameters, are derived. For example, to control the HAZ within 12 microns, the system inversely calculates the maximum power output allowed at this diffusion velocity. Taking into account the approximately 600-fold difference in thermal diffusion capacity between ceramic and FR4 materials, a compensation factor is introduced to modify the power release strategy. This allows the pulsed laser to decay its output energy at a steeper rate as the material's thermal response increases, thereby preventing excessive heat conduction and the growth of microcracks. The coupling control relationship is modeled based on the coupling control relationship parameters to obtain a coupling control relationship model. The model structure adopts a multi-factor weight nesting mechanism, allowing the control strategy to be dynamically switched according to changes in material thickness during the laser processing process. It also automatically matches the power deceleration rate and pulse period according to the current processing state, thereby achieving adaptive adjustment capabilities for laser power control.

[0030] In a specific embodiment, the process of executing step S3 may specifically include the following steps: performing instantaneous temperature field distribution analysis on the second ceramic substrate based on the coupling control relationship model to obtain instantaneous temperature field distribution data; Performing power reduction calculation according to the instantaneous temperature field distribution data to obtain a power reduction parameter, and monitoring the temperature gradient of the laser action point based on the power reduction parameter to obtain a power reduction trigger condition; The pulse interval is optimized according to the power reduction triggering condition to obtain the pulse power reduction control parameter.

[0031] Specifically, based on a coupled control relationship model, a dynamic feedback mechanism is constructed that integrates real-time temperature field analysis and laser power control. This mechanism responds to the ceramic substrate's transient response to laser thermal stimulation and continuously monitors and adjusts the temperature during the temperature diffusion process, ensuring that the laser power consistently matches the ceramic material's thermal diffusion capacity and temperature rise threshold. This effectively controls the heat-affected zone (HAZ) within a specified range and prevents crack propagation or overheating. During the initial system operation, the laser device applies a pulsed laser heat source to the second ceramic substrate according to the coupled control relationship model. Simultaneously, a temperature measurement system consisting of a high-speed infrared imaging device and an array of thermocouples arranged around the laser focal point continuously tracks the temperature field in the laser active area at a sampling frequency of 10kHz, acquiring instantaneous temperature field distribution data. This data records the peak temperature at the laser center and the temperature gradient extension structure within its periphery, forming a two-dimensional or three-dimensional thermal distribution image. The system then digitally solves this temperature distribution and extracts physical features, identifying the temperature variation trend along the time axis, the spatial diffusion boundary, and the temperature decay rate from the laser center to the outer ring. Power decrement calculations are performed based on instantaneous temperature field distribution data. Real-time temperature feedback is used to determine whether the current heat diffusion rate matches the laser heat input, and the appropriate power decrement parameters are calculated accordingly. The peak temperature at the laser impact point and its rate of change are evaluated, and dynamically compared against a set heat-affected zone target value (e.g., 12 microns) based on the thermal diffusion capacity and thermal conductivity compensation factor corresponding to the current ceramic substrate thickness. If the temperature expansion boundary exhibits a nonlinear acceleration trend or the central region temperature approaches the thermal damage threshold of the ceramic material (e.g., 1800K), the system initiates a power decrement calculation module. Based on the current temperature distribution width, temperature rise slope, and material diffusion rate, the system infers the optimal power decrement rate, resulting in a decreasing energy output trend for the laser pulse. This decrement trend is not set linearly over time, but rather modulates the curve shape based on the nonlinear characteristics of the material's thermal response. For example, a higher energy decrement gradient is set during periods of peak temperature acceleration at the boundary of the temperature field, while a slower decrement occurs during periods of relatively stable temperature or slower heat diffusion. This ensures energy output control that is perfectly aligned with the ceramic's thermal diffusion behavior. The temperature gradient at the laser point of application is monitored based on the power reduction parameter, and the triggering condition for power reduction is determined based on this monitoring result. By establishing multiple radial temperature measurement paths from the laser center to the periphery, the temperature values ​​along each path are subjected to derivative calculations to determine the rate of temperature change per unit distance, which is the temperature gradient value. If the system detects that this gradient value exceeds a preset threshold (e.g., 1000K per millimeter), it is determined that the laser heat input has caused excessive local heat accumulation. At this time, the power reduction mechanism is immediately triggered and a signal is sent to the control unit to force a switch to energy reduction mode.Based on the power decrement trigger conditions and the thermal diffusion timescale of the ceramic material, the pulse interval is optimized to prevent consecutive laser pulses from hitting the same area before thermal diffusion is complete, which could cause thermal overlap and regional stress concentration. The goal of pulse interval optimization is to ensure that after each pulse, the temperature gradients between the surface and mid-layers of the material decay to a safe level before the next pulse is applied. To this end, the system incorporates a pulse interval calculation model. This model uses the target heat-affected zone boundary distance, the material's thermal diffusion capacity, and the current power decrement trend as key variables to calculate the minimum delay between each pulse cycle. Dynamic fine-tuning is performed based on the real-time temperature recovery curve to synchronize laser pulse emission with thermal diffusion. The output of this optimization model is the pulse power decrement control parameters, which include multi-dimensional control variables such as decrement rate, pulse interval period, trigger threshold, and power adjustment step size. The system uses these parameters to adjust the laser's emission behavior in real time, ensuring that the pulse energy is consistently coupled to the thermal diffusion state of the ceramic substrate throughout the machining process. This ensures controlled convergence of the heat-affected zone and minimizes microcracks and thermal delamination caused by heat accumulation.

[0032] In a specific embodiment, the step of performing instantaneous temperature field distribution analysis on the second ceramic substrate based on the coupling control relationship model to obtain instantaneous temperature field distribution data may specifically include the following steps: Extracting heat conduction parameters of the second ceramic substrate based on the coupling control relationship model to obtain a thermal diffusion coefficient and a heat conduction enhancement factor; Establishing an instantaneous temperature field equation of the second ceramic substrate according to the thermal diffusion coefficient and the thermal conductivity enhancement factor; Calculating the temperature distribution of the laser action area based on the instantaneous temperature field equation to obtain laser center point temperature data and radial temperature gradient data; Temperature field distribution is integrated according to the laser center point temperature data and the radial temperature gradient data to obtain instantaneous temperature field distribution data.

[0033] Specifically, based on a coupled control relationship model, the thermal diffusion behavior of the second ceramic substrate is analyzed. Combined with acquired thickness-thermal conductivity data and real-time thermal response test results, key thermal conductivity parameters of the target ceramic material under the current processing environment are extracted. The thermal diffusivity coefficient is derived from the material's three physical properties: thermal conductivity, density, and specific heat capacity. This coefficient is inversely solved by comparing the experimental temperature rise data with the simulation results, ensuring its physical significance in regions with significant spatial variations. The thermal conductivity enhancement factor (TEF) is constructed by comparing the thermal response differences between the target ceramic substrate and traditional materials (such as FR4) under high heat flux. Properties such as the ceramic microstructure's high thermal conductivity path, close lattice packing, and low phonon scattering are converted into a gain factor, which serves as an incremental adjustment to the base thermal diffusion rate. Based on the TDF and TEF, the transient temperature field equation for the second ceramic substrate is established. This equation reflects the unsteady, nonlinear, and locally intense heat input characteristics, necessitating a partial time-varying and spatially inhomogeneous representation in the mathematical modeling. To improve adaptability to temperature variations in the material's edge transition region, the ceramic substrate is divided into multiple layers of thermally responsive units. Independent thermal conductivity response rates and diffusion direction characteristics are set for each region. The power density distribution function of the laser heat source, along with variables such as laser spot diameter, pulse width, and irradiation time, is also incorporated to develop a temperature field prediction model that incorporates time, radial position, depth, and material response coupling coefficients. This model reflects the peak temperature rise in the central high heat flux region and expresses the evolution of the temperature gradient during radial and depth diffusion of laser heat energy, which is limited by the ceramic's thermal diffusion capacity. Based on the aforementioned transient temperature field equations, a multi-dimensional temperature distribution is calculated for the laser-exposed region. During the calculation, multiple sampling annuli are established radially around the laser focus. The system combines actual laser irradiation parameters with the substrate's thermal response coefficient to calculate temperature variation data at the laser center in real time, including peak temperature, heating rate, and maximum stabilization time. This data is then used as a benchmark to extrapolate the radial temperature decay. At each sampling radius, the corresponding temperature gradient is calculated using spatial discretization and temperature derivatives, forming a set of radial temperature gradient data. These data reflect the ability of laser energy to transfer within the material and indirectly indicate whether the current power parameters match the material's diffusion capacity. Especially in ceramic materials with rapid thermal diffusion, the laser center temperature is extremely sensitive to changes, and the radial temperature gradient exhibits asymmetric expansion characteristics. The system needs to adjust the prediction results based on real-time temperature change trends to improve the model's response to sudden thermal imbalances. The laser center point temperature data and radial temperature gradient data are fused and spatially integrated to form instantaneous temperature field distribution data with global thermal state characteristics.This integration process utilizes multi-channel temperature data merging and interpolation techniques. By jointly reconstructing local temperature data in all directions along the radial, tangential, and depth directions, a temperature distribution map with high temporal efficiency and spatial resolution is constructed over the laser loading cycle. The integrated results include heat accumulation in the central high-temperature zone and describe the edge heat diffusion path, hotspot migration trends, and the spatial evolution of potential heat concentration areas.

[0034] In a specific embodiment, the process of executing step S4 may specifically include the following steps: performing heat conduction velocity difference compensation calculation on the second ceramic substrate based on the pulse power decreasing control parameter to obtain a heat conduction compensation factor; Performing a weighted coupling analysis of a thickness correction factor and a heat conduction enhancement factor according to the heat conduction compensation factor to obtain a coupling control function; Dynamically generate a pulse power peak value and an attenuation coefficient corresponding to the thickness of the second ceramic substrate based on the coupling control function to obtain thickness adaptation pulse sequence parameters; Dynamic fine-tuning of parameter weights of thermal diffusion characteristics of the ceramic substrate is performed according to the thickness adaptation pulse sequence parameters to generate an optimal pulse parameter combination.

[0035] Specifically, based on the pulse power decrement control parameter, compensation for thermal conduction rate differences is calculated based on the thermal response differences between the second ceramic substrate during processing and a standard reference model. Because different thicknesses and microstructures cause ceramic materials to exhibit different thermal diffusion rates and surface thermal gradients under laser irradiation, the system compares the measured temperature response data with a pre-established thermal diffusion prediction model, extracting the differences in heat conduction depth per unit time, temperature rise rate, and boundary heat transfer rate. This factor then calculates a thermal conduction compensation factor. This factor reflects the multiple difference in thermal diffusion capacity between the second ceramic substrate and the standard sample and reflects the correction factor for thermal inertia in the processing response. Based on the calculated thermal conduction compensation factor, a thickness correction factor and a thermal conduction enhancement factor are introduced. Through weighted coupling analysis between the three, a coupling control function is constructed. In this step, the actual thickness of the second ceramic substrate is calculated as the ratio of the reference thickness to the reference thickness to form the thickness correction factor. This factor, combined with the thermal conduction enhancement factor, which reflects the material's high thermal conductivity path capability, forms a multivariate weighted set. The weight distribution ratios of the three factors are set based on processing requirements and process tolerances. For example, the weight of the heat conduction compensation factor is set to 0.4, the weight of the thickness correction factor is set to 0.3, and the weight of the heat conduction enhancement factor is set to 0.3. Based on a comprehensive analysis of the contributions of these factors to heat-affected zone boundary control, pulse energy release accuracy, and crack suppression, a high-dimensional coupling control function is formed through weighted function coupling. This function, serving as the upper-level control term for the pulse control parameter generation logic, possesses multivariable drive and adaptive adjustment capabilities, enabling adaptive energy strategy matching across thicknesses and material systems. The coupling control function is applied to the automatic generation of thickness adaptation parameters. Based on the comprehensive response output by this function, the required pulse power peak and power attenuation coefficient for the second ceramic substrate are dynamically set, forming a set of thickness-adapted pulse sequence parameters that closely match the physical behavior of the ceramic. These sequence parameters include peak power and attenuation slope, as well as multi-dimensional control indicators such as pulse period, interval time, and temperature control delay. They offer both global power distribution controllability and local energy response adaptability. Dynamic fine-tuning of the parameter weights for the thermal diffusion characteristics of the ceramic substrate is performed based on the thickness-adapted pulse sequence parameters. Based on the actual thermal diffusion characteristics of the ceramic substrate, the weight distribution and numerical settings of each control parameter are fine-grainedly adjusted in real time to generate the optimal pulse parameter combination. During this process, the system obtains real-time information on the temperature evolution, heat-affected boundary changes, and crack risk signals of the second ceramic substrate after laser irradiation, and inputs this feedback data into the fine-tuning module.This module determines whether the current output is in the optimal state by comparing and calculating with the previously set coupling control function and parameter generation template. If the heat diffusion rate is found to be faster than expected, the system automatically increases the power attenuation rate or shortens the pulse duration. If it is detected that the heat accumulation trend is enhanced or the local thermal gradient is abnormally increased, the pulse power peak is automatically reduced or the pulse interval period is extended to ensure that the boundary of the heat-affected zone is always within 12 microns.

[0036] In a specific embodiment, the process of executing step S5 may specifically include the following steps: Based on the optimal pulse parameter combination, the instantaneous temperature field boundary of the second ceramic substrate during the laser cutting process is tracked in real time to obtain heat-affected zone boundary expansion data; Performing high-speed thermal diffusion compensation calculation on ceramic materials based on the heat-affected zone boundary expansion data to obtain thermal diffusion compensation correction parameters; Dynamically attenuate and control the pulse power density based on the thermal diffusion compensation correction parameter to obtain the heat-affected zone boundary control parameter; Thermal stress microcrack suppression verification and pulse sequence optimization are performed according to the heat-affected zone boundary control parameters, and a pulse sequence control instruction is output.

[0037] Specifically, a high-precision real-time temperature field boundary tracking mechanism is established on the surface of a second ceramic substrate during laser irradiation driven by an optimal pulse parameter combination. The system utilizes a high-speed infrared thermal imaging array coupled with a high-frequency thermocouple sensor module. Temperature measurement points are positioned radially around the laser's center of action and its surroundings. This system continuously samples millisecond-level temperature field changes. A differential algorithm and boundary gradient detection logic are then used to extract the actual expansion behavior of the heat diffusion edge. Each frame of boundary data is overlaid and compared to determine whether the lateral and longitudinal boundaries of the heat-affected zone (HAZ) are expanding, converging, or experiencing irregular perturbations. The instantaneous temperature field boundary trajectory of the laser-irradiated area is then established in real time, generating HAZ boundary expansion data. This data, including quantitative indicators such as the heat diffusion radius, boundary growth rate, and edge temperature gradient, serves as a dynamic feedback source for calibrating the heat diffusion model and laser power control strategy. The HAZ boundary expansion data is then compared with a pre-set heat diffusion prediction model to determine the degree of deviation between the actual heat diffusion behavior and the theoretical diffusion rate. This deviation is then used to perform high-speed thermal diffusion compensation calculations for the ceramic material. Because ceramic materials exhibit rapid thermal diffusion, uneven heat conduction paths, and sensitive boundary response during laser cutting, the current thermal diffusion trend is corrected within each pulse cycle. The system compares the target thermal diffusion radius (e.g., controlled within 12 microns) with the current actual expansion value, and then calculates a thermal diffusion compensation correction parameter based on parameters such as the ceramic material's thermal conductivity, thermal diffusivity, and specific heat capacity. This parameter is used to correct for overestimation or underestimation of thermal conduction in the laser power release strategy. The generation of this correction parameter takes into account the asymmetry and temporal fluctuations of thermal diffusion behavior, as well as its coupled response to the laser frequency, ensuring that the compensation mechanism fully reflects the material's true thermal conduction state under multivariable interaction conditions. Based on this thermal diffusion compensation correction parameter, the laser pulse power density is dynamically attenuated and controlled, establishing a pulse energy scheduling model that varies with time, space, and thermal behavior. The model adjusts the laser power density distribution curve in real time based on the degree of coupling between the current power peak and the thermal diffusion rate. It quickly reduces the power density when the thermal diffusion rate is higher than expected, and appropriately maintains the energy to stabilize the boundary thermal field when the thermal diffusion trend slows down, thereby limiting the expansion of the heat-affected zone in any direction to within the set range. At the same time, the system redistributes the cross-sectional energy distribution of the laser pulse so that the energy density at the center of the laser is reduced as much as possible without affecting the penetration depth, while the edge energy maintains the symmetry of the thermal field, thereby avoiding crack offset caused by uneven edge thermal field. The system integrates the thermal diffusion correction coefficient, current pulse parameters, and thermal field boundary response data to output the heat-affected zone boundary control parameters, which include control items such as real-time attenuation slope, energy distribution adjustment value, and boundary stability factor. The thermal stress microcrack suppression is verified based on the heat-affected zone boundary control parameters, and the verification results are fed back to the pulse sequence optimization module for strategy update.During the suppression verification process, the internal stress distribution map of the current laser irradiation area is predicted through the thermal field stress evolution analysis model, and based on the comparison of the maximum temperature gradient, the temperature change rate and the critical fracture strength of the material, it is determined whether there are potential microcrack generation points. If multiple nodes exceeding the material stress tolerance are found around the laser irradiation area, the system marks the current strategy as a high risk of cracks and automatically triggers a pulse power peak reduction or pulse interval extension instruction; if the thermal stress distribution is within a stable range, the system continues to run along the current parameter trajectory, but at the same time updates the predicted pulse structure of the next cycle to adapt to changes in the thermal diffusion state of the material. The system combines the current thermal diffusion trend, stress state, historical laser output records and microcrack prediction data to uniformly optimize the energy, frequency, duration and waveform of multiple subsequent pulse cycles, forming a pulse sequence control instruction with predictive and crack suppression control capabilities, and directly sends it down through the data link with the laser controller to achieve real-time online closed-loop adjustment.

[0038] The above describes the circuit board precision processing method according to the embodiment of the present invention. The following describes the circuit board precision processing system according to the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a circuit board precision processing system includes: A detection module, configured to detect the thermal conductivity characteristics of a plurality of circuit boards having first ceramic substrates of different thicknesses, and obtain data on the relationship between thickness and thermal conductivity; a calculation module, configured to perform a coupled calculation of the thermal diffusion rate of the second ceramic substrate of target thickness according to the relationship data between the thickness and the thermal conductivity, and obtain a coupled control relationship model; a processing module, configured to perform laser pulse power reduction processing based on the coupling control relationship model to obtain a pulse power reduction control parameter; An optimization module, configured to perform multi-parameter collaborative optimization based on the pulse power decrement control parameters to obtain an optimal pulse parameter combination; An output module is used to perform real-time temperature feedback control on the heat-affected zone of the second ceramic substrate during the laser cutting process based on the optimal pulse parameter combination, and output a pulse sequence control instruction.

[0039] Through the collaborative efforts of these components and the establishment of a dynamic pulse parameter control mechanism based on the thermal conductivity characteristics of ceramic materials, the laser pulse power can be adjusted in real time according to the actual thermal diffusion rate of the ceramic substrate, effectively suppressing the rapid expansion of the heat-affected zone (HAZ) and achieving precise control of the HAZ boundary. A pulse power decrement control algorithm and a real-time temperature feedback control mechanism effectively control the formation of thermal stress gradients within the ceramic substrate, preventing stress concentration and completely eliminating the unique thermal stress microcracks that characterize ceramic substrates. By establishing a multi-dimensional parameter collaborative optimization system that incorporates ceramic substrate thickness characteristics, thermal conductivity differences, and real-time temperature feedback, the optimal pulse parameter combination is dynamically generated based on the characteristic differences of ceramic substrates of different specifications, significantly improving the adaptability of processing parameters and the stability of processing quality. By establishing a coupled control mechanism for the thermal conductivity difference compensation factor and the thermal conductivity enhancement factor, the system can compensate for the inherent differences between ceramic materials and conventional PCB materials, overcoming the technical limitations of traditional laser cutting technology that cannot adapt to the unique thermal conductivity characteristics of ceramic materials. By establishing an adaptive pulse sequence generation system based on real-time temperature feedback, the pulse parameters can be dynamically adjusted according to the actual processing status during the laser cutting process. By establishing a thickness correction factor and a layered heat conduction modeling mechanism, specialized processing can be performed for the differences in thermal conductivity characteristics of ceramic substrates of different thicknesses. The heat-affected zone control technology of the present invention, based on dynamic regulation of pulse parameters, can significantly reduce the scope of the heat-affected zone and eliminate microcrack defects.

[0040] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, memory, display screen, input system, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0041] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0042] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0043] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0044] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0045] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0046] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A circuit board precision processing method, characterized in that: include: Conducting thermal conductivity testing on a plurality of circuit boards having first ceramic substrates of different thicknesses to obtain thickness-thermal conductivity relationship data; performing a coupled thermal diffusion rate calculation on a second ceramic substrate of target thickness according to the thickness-thermal conductivity relationship data to obtain a coupled control relationship model; Performing laser pulse power reduction processing based on the coupling control relationship model to obtain a pulse power reduction control parameter; Perform multi-parameter collaborative optimization based on the pulse power decrement control parameters to obtain an optimal pulse parameter combination; Based on the optimal pulse parameter combination, real-time temperature feedback control is performed on the heat-affected zone of the second ceramic substrate during the laser cutting process, and a pulse sequence control instruction is output.

2. The circuit board precision processing method according to claim 1, characterized in that: The thermal conductivity characteristics of the circuit boards having the first ceramic substrates of different thicknesses are tested to obtain thickness-thermal conductivity relationship data, including: Performing surface temperature distribution detection on a plurality of first ceramic substrates with different thicknesses in the circuit board to obtain real-time temperature distribution data of each first ceramic substrate; Calculating an actual thermal conductivity value of each first ceramic substrate according to the real-time temperature distribution data, and performing a layered heat conduction analysis based on the actual thermal conductivity value of each first ceramic substrate to obtain a thickness-layered heat conduction characteristic of each first ceramic substrate; According to the thickness-layered heat conduction characteristics of each first ceramic substrate, a thermal conductivity relationship analysis is performed on first ceramic substrates of different thicknesses to obtain thickness-thermal conductivity relationship data.

3. The circuit board precision processing method according to claim 1, characterized in that: The method of performing a coupled thermal diffusion rate calculation on a second ceramic substrate of target thickness according to the thickness-thermal conductivity relationship data to obtain a coupled control relationship model includes: Performing thermal diffusion modeling on first ceramic substrates of different thicknesses according to the thickness-thermal conductivity relationship data to obtain a thermal diffusion prediction model; Calculating the thermal diffusion rate of a second ceramic substrate of target thickness based on the thermal diffusion prediction model to obtain a thermal diffusion rate value of the second ceramic substrate; The coupling calculation between the thermal diffusion rate and the pulse power is performed according to the thermal diffusion rate value of the second ceramic substrate to obtain coupling control relationship parameters, and coupling control relationship modeling is performed based on the coupling control relationship parameters to obtain a coupling control relationship model.

4. The circuit board precision processing method according to claim 1, characterized in that: The laser pulse power reduction processing is performed based on the coupling control relationship model to obtain the pulse power reduction control parameter, including: performing instantaneous temperature field distribution analysis on the second ceramic substrate based on the coupling control relationship model to obtain instantaneous temperature field distribution data; Performing power reduction calculation according to the instantaneous temperature field distribution data to obtain a power reduction parameter, and monitoring the temperature gradient of the laser action point based on the power reduction parameter to obtain a power reduction trigger condition; The pulse interval is optimized according to the power reduction triggering condition to obtain the pulse power reduction control parameter.

5. The circuit board precision processing method according to claim 4, characterized in that: The performing instantaneous temperature field distribution analysis on the second ceramic substrate based on the coupling control relationship model to obtain instantaneous temperature field distribution data includes: Extracting heat conduction parameters of the second ceramic substrate based on the coupling control relationship model to obtain a thermal diffusion coefficient and a heat conduction enhancement factor; Establishing an instantaneous temperature field equation of the second ceramic substrate according to the thermal diffusion coefficient and the thermal conductivity enhancement factor; Calculating the temperature distribution of the laser action area based on the instantaneous temperature field equation to obtain laser center point temperature data and radial temperature gradient data; Temperature field distribution is integrated according to the laser center point temperature data and the radial temperature gradient data to obtain instantaneous temperature field distribution data.

6. The circuit board precision processing method according to claim 1, characterized in that: The multi-parameter collaborative optimization based on the pulse power decrement control parameter to obtain the optimal pulse parameter combination includes: performing heat conduction velocity difference compensation calculation on the second ceramic substrate based on the pulse power decreasing control parameter to obtain a heat conduction compensation factor; Performing a weighted coupling analysis of a thickness correction factor and a heat conduction enhancement factor according to the heat conduction compensation factor to obtain a coupling control function; Dynamically generate a pulse power peak value and an attenuation coefficient corresponding to the thickness of the second ceramic substrate based on the coupling control function to obtain thickness adaptation pulse sequence parameters; Dynamic fine-tuning of parameter weights of thermal diffusion characteristics of the ceramic substrate is performed according to the thickness adaptation pulse sequence parameters to generate an optimal pulse parameter combination.

7. The circuit board precision processing method according to claim 1, characterized in that: The method of performing real-time temperature feedback control on the heat-affected zone of the second ceramic substrate during the laser cutting process based on the optimal pulse parameter combination and outputting a pulse sequence control instruction includes: Based on the optimal pulse parameter combination, the instantaneous temperature field boundary of the second ceramic substrate during the laser cutting process is tracked in real time to obtain heat-affected zone boundary expansion data; Performing high-speed thermal diffusion compensation calculation on ceramic materials based on the heat-affected zone boundary expansion data to obtain thermal diffusion compensation correction parameters; Dynamically attenuate and control the pulse power density based on the thermal diffusion compensation correction parameter to obtain the heat-affected zone boundary control parameter; Thermal stress microcrack suppression verification and pulse sequence optimization are performed according to the heat-affected zone boundary control parameters, and a pulse sequence control instruction is output.

8. A circuit board precision processing system, characterized in that: Used to perform the circuit board precision machining method according to any one of claims 1 to 7, the circuit board precision machining system comprising: A detection module, configured to detect the thermal conductivity characteristics of a plurality of circuit boards having first ceramic substrates of different thicknesses, and obtain data on the relationship between thickness and thermal conductivity; a calculation module, configured to perform a coupled calculation of the thermal diffusion rate of the second ceramic substrate of target thickness according to the relationship data between the thickness and the thermal conductivity, and obtain a coupled control relationship model; a processing module, configured to perform laser pulse power reduction processing based on the coupling control relationship model to obtain a pulse power reduction control parameter; An optimization module, configured to perform multi-parameter collaborative optimization based on the pulse power decrement control parameters to obtain an optimal pulse parameter combination; An output module is used to perform real-time temperature feedback control on the heat-affected zone of the second ceramic substrate during the laser cutting process based on the optimal pulse parameter combination, and output a pulse sequence control instruction.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the circuit board precision processing method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the processor is caused to execute the circuit board precision processing method according to any one of claims 1 to 7.

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