Multi-laser 3D printing beam spot verification method and system

Abstract

The invention discloses a multi-laser 3D printing beam spot verification method and system, and belongs to the technical field of laser processing control. The method comprises the following steps: acquiring multi-physical field signal data such as thermal response, stress, pressure and the like of a plurality of laser sources, and performing synchronous fusion to obtain comprehensive physical field data; extracting thermal response data of a laser beam spot area, and performing fusion analysis on the thermal response data and the comprehensive data to obtain position offset characteristic data; performing Fourier transform on the thermal response data, extracting frequency domain characteristic data, and further processing to obtain beam spot frequency characteristic data; generating verification standard data in combination with the self-adaptive thermal response threshold value; fusing external temperature, humidity and interference information to generate environment adaptability data, and obtaining optimized signal processing data; and finally, laser beam spot precision data is obtained. According to the scheme, the stability, the consistency and the intelligent response capability of the multi-laser system in a high-precision manufacturing scene are improved, the printing quality can be improved, the defect rate is reduced, and the service life of equipment is prolonged.

Description

Technical Field

[0001] The present application belongs to the field of laser processing control technology, and specifically relates to a multi-laser 3D printing beam spot calibration method and system. Background Art

[0002] In multi-laser 3D printing technology, the accuracy and stability of the laser beam spot directly determine the quality and performance of additively manufactured parts. With the growing demand for high-precision and high-efficiency manufacturing of complex metal components in fields such as aerospace and biomedicine, multi-laser collaborative printing has become a key technical direction. However, during dynamic scanning, multi-laser beam spots are easily affected by the coupling of multiple physical fields such as thermal stress, powder splashing, and substrate deformation, leading to problems such as beam spot position offset and uneven energy density distribution, which in turn cause printing defects (such as interlayer misalignment and increased porosity). Traditional single-laser beam spot calibration methods cannot meet the real-time and global accuracy requirements of multi-laser collaborative operations. There is an urgent need for a technology that can synchronously monitor multi-physical field signals and dynamically calibrate beam spot parameters to solve the core problems of low yield and poor process repeatability caused by beam spot misalignment in multi-laser 3D printing.

[0003] Nowadays, independent sensors are used to collect multi-physical field signals such as thermal response, stress, and pressure in a time-sharing manner. For example, infrared thermal imagers monitor the temperature field, and displacement sensors measure structural deformation. Subsequently, the collected signals are simply superimposed through post-processing algorithms, such as directly adding thermal imaging data and displacement data or weighted averaging. Finally, a fixed threshold judgment method is used, such as a preset temperature gradient threshold or stress upper limit value, to perform offline analysis on the superimposed data. If the threshold is exceeded, an alarm is triggered or parameter adjustment is made.

[0004] Since independent sensors collect data in time-sharing manner, resulting in data timing misalignment, it is difficult to accurately correlate transient phenomena such as thermal expansion and beam spot offset; beam spot position monitoring relies solely on optical image analysis, without integrating frequency domain characteristics and environmental adaptability parameters, thus missing key information such as beam spot vibration modes; fixed thresholds and static environmental models cannot adapt to dynamic working conditions, resulting in low real-time calibration accuracy; in addition, the essence of the technology is "data stacking analysis", without building a multi-physics field coupling model and closed-loop feedback mechanism, making it difficult to meet the stability requirements of long-term printing of complex components. Summary of the Invention

[0005] The embodiments of the present application provide a multi-laser 3D printing beam spot calibration method and system to solve the problems in the prior art that due to the time-sharing acquisition of data by independent sensors, the timing misalignment of data occurs, making it difficult to accurately correlate transient phenomena such as thermal expansion and beam spot offset; the beam spot position monitoring relies solely on optical image analysis, without integrating frequency domain characteristics and environmental adaptability parameters, and omitting key information such as beam spot vibration modes; the fixed threshold and static environmental model cannot adapt to dynamic working conditions, resulting in low real-time calibration accuracy; in addition, the technology is essentially "data stacking analysis", and does not construct a multi-physical field coupling model and closed-loop feedback mechanism, making it difficult to meet the stability requirements of long-term printing of complex components.

[0006] In a first aspect, an embodiment of the present application provides a multi-laser 3D printing beam spot calibration method, the method comprising: Acquire multi-physical field signal data from multiple laser sources, and synchronously collect and fuse the multi-physical field signal data to obtain integrated physical field data; wherein the multi-physical field signal data includes thermal response signals, stress signals, and pressure signals; Acquiring thermal response data of beam spot areas of multiple laser sources, fusing and analyzing the thermal response data with comprehensive physical field data to obtain multi-laser beam spot position offset characteristic data; Performing Fourier transform processing on the thermal response signal data of the multiple laser sources to obtain frequency domain characteristic data of the multiple laser sources, and performing Fourier transform processing on the multiple laser beam spot position offset characteristic data and the frequency domain characteristic data to obtain beam spot frequency characteristic data of the multiple laser sources; Acquiring an adaptive thermal response threshold, and combining the adaptive thermal response threshold, beam spot frequency characteristic data, and frequency domain characteristic data to obtain calibration standard data; Acquiring external temperature data, external humidity data, and external interference data, determining environmental adaptability data based on the external temperature data, external humidity data, and external interference data, and combining the environmental adaptability data with calibration standard data to obtain optimized signal processing data; Real-time laser beam spot accuracy data of multiple laser sources is acquired, and the real-time laser beam spot accuracy data is combined with optimized signal processing data to obtain calibrated multi-laser beam spot accuracy data.

[0007] In a second aspect, an embodiment of the present application provides a multi-laser 3D printing beam spot calibration system, the system comprising: A physical field fusion module is used to obtain multi-physical field signal data from multiple laser sources, and synchronously collect and fuse the multi-physical field signal data to obtain comprehensive physical field data; wherein the multi-physical field signal data includes thermal response signals, stress signals, and pressure signals; An offset identification module is used to obtain thermal response data of beam spot areas of multiple laser sources, fuse and analyze the thermal response data with comprehensive physical field data, and obtain multi-laser beam spot position offset characteristic data; a frequency domain extraction module, configured to perform Fourier transform processing on the thermal response signal data of the multiple laser sources to obtain frequency domain characteristic data of the multiple laser sources, and perform Fourier transform processing on the multiple laser beam spot position offset characteristic data and the frequency domain characteristic data to obtain beam spot frequency characteristic data of the multiple laser sources; a calibration standard generation module, configured to obtain an adaptive thermal response threshold, and combine the adaptive thermal response threshold, beam spot frequency characteristic data, and frequency domain characteristic data to obtain calibration standard data; an environmental adaptation module, configured to obtain external temperature data, external humidity data, and external interference data, determine environmental adaptability data based on the external temperature data, external humidity data, and external interference data, and combine the environmental adaptability data with calibration standard data to obtain optimized signal processing data; The precision calibration module is used to obtain real-time laser beam spot precision data of multiple laser sources, and combine the real-time laser beam spot precision data with optimized signal processing data to obtain calibrated multi-laser beam spot precision data.

[0008] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the first aspect.

[0009] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0010] In an embodiment of the present application, multi-physical field signal data of multiple laser sources are obtained, and the multi-physical field signal data are synchronously collected and fused to obtain comprehensive physical field data; wherein, the multi-physical field signal data includes a thermal response signal, a stress signal, and a pressure signal; thermal response data of the beam spot area of ​​multiple laser sources are obtained, and the thermal response data and the comprehensive physical field data are fused and analyzed to obtain multi-laser beam spot position offset characteristic data; Fourier transform processing is performed on the thermal response signal data of multiple laser sources to obtain frequency domain characteristic data of multiple laser sources, and Fourier transform processing is performed on the multi-laser beam spot position offset characteristic data and the frequency domain characteristic data , obtain the beam spot frequency characteristic data of multiple laser sources; obtain an adaptive thermal response threshold, combine the adaptive thermal response threshold, beam spot frequency characteristic data and frequency domain characteristic data to obtain calibration standard data; obtain external temperature data, external humidity data and external interference data, determine environmental adaptability data based on the external temperature data, external humidity data and external interference data, combine the environmental adaptability data with the calibration standard data to obtain optimized signal processing data; obtain real-time laser beam spot accuracy data of multiple laser sources, combine the real-time laser beam spot accuracy data with the optimized signal processing data to obtain calibrated multi-laser beam spot accuracy data. The above-mentioned multi-laser 3D printing beam spot calibration method improves the stability, consistency and intelligent response capability of the multi-laser system in high-precision manufacturing scenarios, which helps to improve printing quality, reduce defect rates and extend equipment life. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a flow chart of a multi-laser 3D printing beam spot calibration method provided in Example 1 of the present application; Figure 2 This is a flow chart of a multi-laser 3D printing beam spot calibration method provided in Example 2 of the present application; Figure 3 This is a schematic diagram of the structure of the multi-laser 3D printing beam spot calibration system provided in Example 3 of the present application; Figure 4 This is a schematic diagram of the structure of the electronic device provided in Example 4 of the present application. DETAILED DESCRIPTION

[0012] To further clarify the objectives, technical solutions, and advantages of this application, specific embodiments of the present application are described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are intended only to illustrate this application and are not intended to limit it. It should also be noted that, for ease of description, the drawings only illustrate portions relevant to this application, not all of them. Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts depict the various operations (or steps) as sequential processes, many of the operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process may terminate upon completion of its operations, but may also include additional steps not shown in the accompanying drawings. The process may correspond to a method, function, procedure, subroutine, subprogram, or the like.

[0013] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0014] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0015] In the following, in conjunction with the accompanying drawings, a multi-laser 3D printing beam spot calibration method and system provided by the embodiment of the present application are described in detail through specific embodiments and their application scenarios.

[0016] Example 1 Figure 1 This is a flow chart of the multi-laser 3D printing beam spot calibration method provided in Example 1 of this application. Figure 1 As shown, the specific steps include: S101, acquiring multi-physical field signal data from multiple laser sources, synchronously collecting and fusion processing the multi-physical field signal data to obtain comprehensive physical field data; wherein the multi-physical field signal data includes thermal response signals, stress signals and pressure signals.

[0017] A laser source may refer to a light source device that can emit laser light (with high directionality, monochromaticity, high brightness and coherence).

[0018] Multiphysics signal data can refer to multiple measurement signals from different physical properties or fields in an object or system, and is often used to analyze the coupled behavior of complex systems.

[0019] The integrated physical field data can be a data set obtained by synchronously collecting, aligning and fusing the above-mentioned multiple physical signals (heat, force, pressure, etc.).

[0020] Thermal response signals can refer to signals caused by temperature changes or heat conduction characteristics of the system after it is subjected to laser or other energy input, mainly including surface or internal temperature distribution, thermal infrared images or thermocouple data, deformation signals caused by thermal expansion, etc.

[0021] Stress signals can refer to the internal stress or strain data generated by a material under external action (such as laser shock or thermal expansion caused by heating), which can be manifested as: stress waveform sensor data (such as strain gauges, laser interferometers), structural deformation, stress concentration degree, and can reflect changes in the mechanical properties of the material.

[0022] A pressure signal can refer to a pressure change signal generated by external or internal forces in a system, such as a pressure change in a gas or liquid (measured using a pressure sensor), an internal pressure response within a closed structure, or a transient pressure response under laser shock (such as an explosive laser shock experiment).

[0023] During the laser excitation process, a variety of physical signal sensors are deployed to obtain corresponding multi-physical field signal data. Specifically including: Thermal response signal acquisition: Thermocouples, infrared thermal imagers, thermistors, or fiber optic temperature sensors are placed on or within the object to monitor the temperature rise caused by laser heating in real time. The thermal imager continuously captures the target surface temperature distribution at a high frame rate (e.g., 30–120 fps) to capture transient temperature changes.

[0024] Stress signal acquisition: Strain gauges (resistance strain gauges) or fiber Bragg grating (FBG) sensors are placed in key stress concentration areas of the structure to collect real-time strain responses caused by thermal expansion, impact loads, or localized material yielding. Strain data is collected synchronously using a high-precision data acquisition (DAQ) card, typically with a sampling rate exceeding 10kHz to capture dynamic stress waves.

[0025] Pressure Signal Acquisition: Miniature piezoelectric pressure sensors or MEMS pressure sensors are deployed in closed systems or gas-liquid interaction areas to capture pressure changes caused by local gas expansion, liquid disturbance, or shock wave reactions due to laser excitation. The pressure signal must share a clock source with the temperature and stress signal sampling system to ensure time consistency of the sampled data.

[0026] To achieve time-synchronized acquisition of these multi-physics field signals, a unified data acquisition and control platform was constructed. This platform controls all sensor acquisition modules through a unified clock and utilizes multi-channel high-speed acquisition cards (such as the NIPXI series) for parallel acquisition. Data synchronization is achieved through timestamps and triggering mechanisms (such as TTL trigger signals) to ensure that all sensors initiate acquisition simultaneously upon laser excitation. After acquisition, the maximum temperature rise, heating rate, and temperature gradient are extracted from the thermal response signal; the stress wave amplitude, strain rate, and dominant frequency component are extracted from the stress signal; and the pressure peak, waveform duration, and pulse characteristics are extracted from the pressure signal. Fusion strategies are then implemented, including feature-level fusion (concatenating the feature vectors of the three signal types according to time windows to construct a multidimensional feature matrix); or model fusion (such as using LSTM-CNN or Transformer) for nonlinear multimodal feature modeling and comprehensive judgment. Finally, a unified data structure is output, containing thermal, stress, and pressure fields and fused fields.

[0027] S102 , obtaining thermal response data of beam spot areas of multiple laser sources, fusing and analyzing the thermal response data with integrated physical field data to obtain multi-laser beam spot position offset characteristic data.

[0028] The beam spot area can refer to the high energy concentration area formed by the laser beam on the surface of the printing material or the processing area, which usually has clear spatial geometric characteristics, such as diameter, energy distribution profile, focal position, etc.

[0029] Thermal response data may refer to the temperature change data generated in the beam spot area during laser irradiation, reflecting the heating, conduction, and cooling behaviors of the material under different laser effects.

[0030] Multi-beam position offset characteristic data represents the offset characteristics of multiple laser beams relative to their theoretical paths or desired positions during actual operation. It reflects the combined effects of the system's geometric accuracy, thermal interference coupling, vibration errors, and other factors.

[0031] To acquire thermal response data from the beam spot regions of multiple laser sources, a high-resolution infrared thermal imager is first deployed above the print area. Its sampling frame rate must be synchronized with the laser scanning system (e.g., ≥100 Hz) to record the thermal diffusion process of the laser beam spot on the printed substrate surface in real time. Simultaneously, a distributed temperature sensor array, such as a thermocouple grid or fiber Bragg grating (FBG) array, is deployed on the print platform or build surface to precisely capture temperature changes at the beam spot center and its surrounding area, improving the accuracy of spatial thermal response measurements. The beam spot region of each laser source is located by associating the scanning path with control parameters (such as power, frequency, and speed). Within the thermal image sequence, the corresponding hot spot regions are extracted using temperature threshold segmentation and region growing methods, and the thermal evolution trajectory of each beam spot region is tracked over time. Simultaneous activation of different laser sources results in spatial overlap and nonlinear diffusion of the thermal field. Time-synchronized labeling and spatial mapping algorithms (such as feature point registration and perspective transformation) are required to align the infrared data with a predefined laser path coordinate system to establish a one-to-one mapping between the beam spot and the thermal response. Next, to fuse and analyze the thermal response data with the integrated physical field data, the thermal response data is first aligned with the stress and pressure data from strain gauges and pressure sensors by timestamp and uniformly mapped to the 3D printing model coordinate system. Interpolation or interpolation regression methods (such as Gaussian processes or cubic splines) are used to reconstruct the joint thermal-stress-pressure field distribution at each point. Based on this, a multi-physics feature fusion algorithm (such as principal component analysis (PCA) or a multi-source data coupling model based on a graph neural network) is employed to extract the coupled physical response characteristics of each laser beam spot region. This analysis then analyzes the beam spot trajectory deviation behavior caused by thermal coupling, non-uniform material response, or beam interference during the multi-laser collaborative printing process. Finally, by comparing the spatial differences between the actual thermal response trajectory and the expected laser path, multi-laser beam spot position deviation feature data is obtained. This data includes the deviation direction, deviation amplitude, deviation occurrence time, and thermal distribution characteristics of the deviation region, serving as the core input for subsequent calibration, compensation, and feedback control.

[0032] The training process of the 3D printing model is: Collect control parameters for each laser beam spot during multi-laser printing (laser power, scanning speed, path coordinates, emission angle, etc.). Collect multi-physics data on the substrate surface during printing, including thermal response (temperature field), stress data, and pressure data, using a sensor array and a high-resolution thermal imager. Collect geometric error and mechanical property measurement data (such as deformation and crack distribution) of the printed product to serve as model annotation data. De-noise the collected sensor data (using filtering, wavelet transform, etc.). Temporally align and interpolate the thermal, stress, and pressure field data, mapping them to a unified 3D mesh. Extract key features, such as local temperature gradients, maximum stress point locations, and thermal-stress coupling coefficients. Select an appropriate model structure, such as a multi-physics coupling network based on deep learning or a hybrid physics model combined with finite element simulation. Determine the inputs (laser parameters, process parameters, multi-physics field features) and outputs (beam spot position offset, stress field distribution, thermal field changes, etc.). Use the collected experimental and simulation data as the training set and employ supervised learning to train the model. Introduce regularization techniques and cross-validation to prevent overfitting. Optimize model parameters using a loss function (e.g., mean squared error) to ensure high consistency between predicted results and actual measurements. Use an independent test set to verify model accuracy and generalization. Compare the error between predicted beam offset and actual measurements to evaluate model performance. Integrate the trained model into the print control system for real-time beam offset prediction and correction. Periodically fine-tune and update the model through online feedback data collection to improve adaptability.

[0033] S103, performing Fourier transform processing on the thermal response signal data of the multiple laser sources to obtain frequency domain characteristic data of the multiple laser sources, and performing Fourier transform processing on the multiple laser beam spot position offset characteristic data and the frequency domain characteristic data to obtain beam spot frequency characteristic data of the multiple laser sources.

[0034] Frequency-domain signature data refers to the frequency-domain representation of the signal obtained by Fourier transforming the thermal response signals from multiple laser sources. It reflects the amplitude and phase information of each frequency component in the thermal response signal, revealing the periodic characteristics, frequency distribution, and dynamic patterns of thermal changes under the action of the laser beam spot. This helps identify oscillations, periodic fluctuations, or abnormal frequency components in the thermal response.

[0035] Beam frequency characteristic data refers to the frequency characteristics obtained by further Fourier transforming the multi-laser beam position offset characteristic data and the frequency domain characteristic data, reflecting the frequency pattern of the laser beam position changing over time. These characteristics describe the dynamic frequency response of the beam position offset, including the offset periodicity, amplitude, and variation trend. They can be used to analyze the stability, vibration mode, and offset type of the laser beam, providing a key basis for beam position correction and precision optimization.

[0036] For each laser source's thermal response signal data, a fast Fourier transform (FFT) algorithm is applied to transform the time-domain signal into frequency-domain signature data. The FFT decomposes the original temperature signal into a superposition of several sinusoidal waveforms, outputting the amplitude and phase information corresponding to each frequency component. To improve Fourier transform accuracy, the thermal response signal data is typically first detrended and weighted using a window function (such as a Hamming or Hanning window) to eliminate spectral leakage caused by edge effects. The resulting frequency-domain signature data for multiple laser sources contains information such as the dominant frequency, energy density concentration, harmonic distribution, and spectral width of the thermal response in each beam spot area. These characteristics reveal whether the thermal response signal data exhibits regular perturbations (such as repetitive temperature peaks) and whether it is affected by phenomena such as structural resonance, scan path overlap, or multi-beam interference. Subsequently, the matching multi-laser beam spot position offset signature data (typically acquired through image recognition or laser displacement sensors) is aligned to align its sampling time axis with the thermal response signal data. In order to further reveal the frequency coupling relationship between the thermal response signal data and the multi-laser beam position offset characteristic data, the two types of data are jointly processed using short-time Fourier transform (STFT) or continuous wavelet transform (CWT). For example, during the STFT processing process, a sliding time window is used to cut the multi-laser beam position offset characteristic data into multiple short time periods, and Fourier transforms are performed on each of them to extract the frequency characteristics in different time periods, which are then cross-compared with the corresponding frequency domain characteristic data. In this way, it is possible to find whether certain specific frequency components exist simultaneously in the thermal response signal data and the position offset characteristic data and have a strong correlation. Finally, through frequency matching and correlation analysis, the frequency characteristics that significantly affect the laser beam position offset and their manifestation in the thermal response signal data are extracted, and the beam spot frequency characteristic data of multiple laser sources are obtained.

[0037] On the basis of the above technical solution, optionally, Fourier transform processing is performed on the multi-laser beam spot position offset characteristic data and the frequency domain characteristic data to obtain beam spot frequency characteristic data of multiple laser sources, including: The frequency domain characteristic data and the multi-laser beam spot position offset characteristic data are processed using an adaptive frequency domain filtering technology to obtain optimized frequency domain characteristic data; Kalman filtering and wavelet transform are used to eliminate noise data of the optimized frequency domain characteristic data to obtain beam spot frequency characteristic data of multiple laser sources.

[0038] In this solution, adaptive frequency domain filtering technology can be a signal processing method that automatically adjusts filtering parameters based on the frequency characteristics of the input signal. It is often used to extract effective information from complex or dynamically changing frequency domain signals and suppress irrelevant frequency components or noise interference.

[0039] The Kalman filter is a recursive optimization algorithm used to estimate the state of a dynamic system in the presence of noise. It combines the system model predictions with sensor observations to calculate the optimal estimate.

[0040] Wavelet transform can be a signal analysis method with both time and frequency resolution capabilities. It can decompose the signal into local waveforms of different scales and is suitable for detecting transient changes, local anomalies or edge features in the signal.

[0041] During the processing process, the acquired frequency-domain feature data and the multi-laser beam position offset feature data are first synchronized in the time domain through frequency-time alignment to facilitate subsequent fusion analysis. Once synchronized, the spectral information of the two signal sources is processed using adaptive frequency-domain filtering. Specifically, power spectral density analysis is performed on both signal sets to identify the regions of spectral energy concentration and the dominant frequency components, and their correlation with the system's target frequencies (such as the laser scanning frequency and the beam offset oscillation frequency) is evaluated. Based on this, a dynamically adjustable bandpass filter window is constructed with a variable bandwidth and center frequency. The filter parameters are adjusted in real time using a least mean square (LMS) adaptive algorithm to retain frequency bands with high relevance for analysis while filtering out irrelevant frequency interference with low signal-to-noise ratio. Furthermore, frequency-domain feature entropy analysis and a distributed energy threshold judgment mechanism can be introduced during this process to further enhance the filter's ability to identify multi-source interference, resulting in optimized frequency-domain feature data with a clearer structure and greater representativeness. Subsequently, the optimized signal data is subjected to a combined Kalman filter and wavelet transform for noise removal and frequency-domain compensation. First, the signal is decomposed using wavelet multiscale decomposition (such as using the Daubechies wavelet or Symlet wavelet family) to separate different frequency subbands. Soft threshold compression is then applied to the high-frequency portion to suppress short-term burst noise and systematic glitches. Simultaneously, a Kalman filter is introduced into the low-frequency portion to predict the current subband frequency trend using a state transition equation. This is then combined with the current observations to perform an optimal estimate, dynamically correcting for signal trend drift and background interference. After wavelet-filtered signal reconstruction, a full-cycle smoothing process is performed using a Kalman smoother to enhance spectral structural continuity and suppress residual oscillations. Finally, key indicators such as the dominant frequency distribution, harmonic positions, and energy density concentration segments of the spectrum after multidimensional noise reduction are extracted to generate beam spot frequency characteristic data corresponding to multiple laser sources, providing key input for subsequent offset identification, frequency drift diagnosis, and control compensation strategies.

[0042] In this solution, environmental interference and system noise can be effectively eliminated, and the frequency characteristics that truly reflect the dynamic characteristics of the laser beam spot can be extracted, thereby improving the accuracy and stability of multi-laser source beam spot offset identification and providing high-quality data support for subsequent precision compensation and dynamic control.

[0043] S104 , obtaining an adaptive thermal response threshold, and combining the adaptive thermal response threshold, the beam spot frequency characteristic data, and the frequency domain characteristic data to obtain calibration standard data.

[0044] The adaptive thermal response threshold can be a dynamic reference standard value obtained by dynamically adjusting the real-time thermal response changes during the laser printing process, the thermal properties of the material, and environmental conditions (such as temperature, humidity, interference, etc.) to determine whether the thermal response is normal or deviated.

[0045] Calibration standard data can be a comprehensive benchmark data set used to determine whether the laser beam spot accuracy meets the requirements. It is composed of multiple indicators such as adaptive thermal response threshold, beam spot frequency characteristic data, and frequency domain characteristic data, reflecting the thermal stability, vibration characteristics, and position consistency of each beam spot in the multi-laser system.

[0046] During the execution of multiple laser source printing tasks, the system monitors the thermal response signal data of each beam spot in real time, recording the temperature changes over time and generating a continuous thermal response curve. For each beam spot thermal response curve, data preprocessing is first performed, including signal denoising, baseline drift correction, and outlier removal, to ensure the high reliability of the subsequently extracted features. Subsequently, by statistically analyzing key characteristic parameters of the thermal response curves during the current and past printing cycles, including average temperature rise, peak response time, cooling slope, and thermal response oscillation period, a sample distribution of the thermal response behavior of the beam spot under stable processing conditions is generated. Based on this distribution, combined with statistical analysis methods (such as sliding window averaging, empirical distribution function, and autoregressive moving average (ARMA)), the upper and lower limits of the thermal response for the current cycle are dynamically calculated, resulting in a time-dependent baseline thermal threshold range. To achieve dynamic adjustment for different materials and environmental conditions, auxiliary parameters such as externally collected ambient temperature disturbance data, material thermal diffusivity, and processing path complexity are used as correction factors and input into a pre-defined dynamic thermal response adjustment model. The model can use a numerical simulation model based on the heat conduction equation or a trained regression network to output an adaptive thermal response threshold suitable for the current conditions in the form of a dynamic threshold function: in, is the mean thermal response of the current cycle; is the standard deviation of thermal response; is the ambient temperature change; It is the preset material thermal characteristic parameters; The preset thermal response fluctuation sensitivity coefficient is used to adjust the system's response to the current thermal response standard deviation. The response strength is used to measure the system's tolerance to thermal fluctuations; The preset environmental disturbance compensation coefficient is used to adjust the adaptive threshold to the external ambient temperature change. Adaptability to dynamically compensate for thermal changes caused by air temperature, humidity or convection conditions; It is the preset material thermal characteristic weight coefficient, which is used to adjust the preset material thermal characteristic parameters. Contribution in the adaptive thermal response threshold calculation.

[0047] Next, the resulting adaptive thermal response threshold is fused with the frequency domain feature data and beam spot frequency feature data previously extracted through frequency domain analysis methods such as Fourier transform. Specifically, a fusion algorithm based on rule matching and Bayesian inference is used to compare whether the frequency domain dominant frequency exceeds the influence range of the adaptive threshold and identify abnormal characteristics such as resonance, laser cross-thermal coupling, and periodic thermal disturbances.

[0048] In this process, a multivariate feature vector set F = [f1, f2, ..., fn] is constructed, where each dimension represents a numerical indicator of a thermal frequency domain feature, such as frequency offset, main frequency energy, amplitude mutation rate, etc. A trained classifier (such as a support vector machine or random forest) is used to determine whether the thermal response feature is in a normal state based on this vector and annotate the corresponding beam spot frequency response pattern and offset type.

[0049] Ultimately, the system integrates adaptive thermal response thresholds, frequency domain feature data, and beam spot frequency feature data to form a complete calibration standard. This data structure includes each laser source's thermal response safety interval; dominant frequency range; offset trend prediction labels; and anomaly risk scores. This data is incorporated into the system's precision control logic in real time, serving as the standard for subsequent beam spot position corrections or anomaly alarm determinations, significantly improving the reliability and dynamic response of laser beam spot control.

[0050] Based on the above technical solution, optionally, obtaining an adaptive thermal response threshold includes: Power data of multiple laser sources, scanning speed data of multiple laser sources, material thermal conductivity data, material specific heat capacity data, material density data, and external temperature data are obtained, and an adaptive thermal response threshold is calculated based on the power data of multiple laser sources, scanning speed data of multiple laser sources, material thermal conductivity data, material specific heat capacity data, material density data, external temperature data, and a preset adaptive thermal response threshold calculation formula; wherein the preset adaptive thermal response threshold calculation formula is: in, Adaptive thermal response threshold; is the preset thermal response sensitivity coefficient; is the power data; is the thermal conductivity data of the material; is the material specific heat capacity data; is the material density data; is the scanning speed data; is the external temperature data; It is the preset ambient temperature suppression adjustment factor.

[0051] In this scenario, power refers to the energy emitted per unit time by each laser source, typically expressed in watts (W). It reflects the energy intensity of the laser when it impacts the material surface and is a key parameter affecting the material's temperature rise and thermal diffusion behavior.

[0052] Scanning speed refers to the speed at which the laser beam moves across the processing area, typically in millimeters per second (mm / s). It affects the energy density received per unit area, thereby determining heat accumulation and melt pool behavior.

[0053] The thermal conductivity of a material indicates its ability to conduct heat, measured in W / (m·K). The higher the thermal conductivity, the faster the material dissipates heat.

[0054] The specific heat capacity of a material indicates the amount of heat required to raise the unit temperature of a material per unit mass, expressed in J / (kg·K). It determines how quickly the material will rise in temperature after input of heat energy.

[0055] Material density data refers to the mass of a material per unit volume, expressed in kg / m³. In thermal response calculations, it is used to combine parameters such as specific heat capacity and volume to calculate the temperature change of a material.

[0056] External temperature data can refer to the ambient temperature in the material processing environment, usually in degrees Celsius (°C) or Kelvin (K). The external temperature affects the initial thermal field distribution and heat dissipation path.

[0057] To calculate the adaptive thermal response threshold, the laser system's integrated photodetector array first monitors the emission power of each laser channel in real time, acquiring power data for multiple laser sources in watts (W). Photodetectors utilize the property of converting incident light into electrical signals, enabling high-frequency sampling. These signals are then recorded as time-series energy data using analog-to-digital conversion circuits for computational purposes.

[0058] Next, the scanning speed data of multiple laser sources is obtained from the servo control encoder system. The system records the angular velocity and displacement trajectory of the scanning lens or galvanometer in real time based on the encoder, and converts it into the linear velocity of the beam spot on the processing surface (in mm / s) to reflect the scanning efficiency of the laser beam at different positions.

[0059] Then, based on the selected printing material (e.g., metal powder, polymer, or ceramic), the corresponding thermal conductivity data (in W / m·K), specific heat capacity data (in J / kg·K), and density data (in kg / m³) are called from the built-in material thermophysical property database. This data, derived from experimental data or material manuals (e.g., ASTM, NIST), is used to model thermal diffusion behavior.

[0060] For ambient temperature monitoring, a high-precision thermocouple sensor or infrared temperature probe installed near the print chamber or platform measures the surrounding air temperature in real time to obtain external temperature data (in °C), which is used to reflect the environmental heat conduction boundary conditions.

[0061] The adjustment coefficient α is used as the thermal response sensitivity coefficient to adjust the output amplitude of the thermal response threshold. It is usually determined by experimental calibration, that is, under typical material and process conditions, the actual maximum safe temperature rise is recorded and fitted with the theoretical value. It can also be normalized based on the material heat capacity density or optimized as a trainable parameter through historical data. As an ambient temperature suppression adjustment factor, it is used to control the exponential decay effect of ambient temperature on the thermal response threshold to prevent misjudgment caused by drastic temperature changes. Its value is generally determined through empirical fitting or dynamic feedback mechanism according to the ambient temperature range of the system design, and the common value range is hundreds to thousands. The reasonable setting of the two ensures that the thermal response threshold formula can accurately reflect the thermal dynamic characteristics of the laser processing process under different materials, processes and environmental conditions, and effectively assist in overheating warning and coupled interference control. It can be set through empirical fitting or dynamic feedback mechanism to control the exponential decay effect of ambient temperature on the thermal response threshold. The preset of the two combines thermal conductivity characteristics, material parameters and system operating conditions to ensure that the thermal response threshold is accurate and adaptive under multi-source interference and complex environments, thereby improving the thermal control accuracy and stability during laser processing.

[0062] This solution effectively reflects the actual thermal state of the material and environment during laser processing, improves the real-time adaptability and accuracy of the thermal response threshold, helps to adjust laser parameters in a timely manner, prevents overheating and thermal deformation, ensures processing quality and equipment safety, and improves the overall stability and reliability of the system.

[0063] S105, obtaining external temperature data, external humidity data and external interference data, determining environmental adaptability data based on the external temperature data, external humidity data and external interference data, combining the environmental adaptability data with calibration standard data to obtain optimized signal processing data.

[0064] The external temperature data may refer to real-time change data of the ambient air temperature of the printing environment collected by the temperature sensor when the multi-laser source system is running.

[0065] External humidity data can indicate the relative humidity RH in the printing environment, usually expressed in percentage (%).

[0066] External interference data can refer to disturbance information from an external non-ideal environment, including airflow disturbance (cooling air, external wind speed), vibration information (ground vibration, equipment resonance), electromagnetic interference (laser equipment or power supply fluctuations), and laser reflection / scattering anomalies (due to powder accumulation and wall interference).

[0067] The environmental adaptability data can be a multi-dimensional environmental stability evaluation index calculated based on the above-mentioned external temperature data, external humidity data and external interference data through a fusion algorithm (such as weighted average, fuzzy logic or neural network regression), reflecting the system's adaptability to current external conditions.

[0068] The optimized signal processing data can be the comprehensive laser control and calibration control reference data formed after filtering, compensation, modeling, prediction and other processing by fusing multiple sources of information such as environmental adaptability data, thermal response data, frequency domain characteristic data, threshold parameters, etc.

[0069] To acquire external temperature, humidity, and interference data, the system first deploys high-precision environmental sensing modules throughout the multi-laser processing area. These modules include thermocouples or infrared temperature sensors, industrial-grade humidity sensors (such as DHT22 and SHT35), and multi-source interference monitoring devices (such as MEMS accelerometers, airflow sensors, and EMC (electromagnetic interference) monitors. External temperature data is collected using high-sampling-rate thermoelectric sensors at the edges of the laser printing area, with a typical update rate exceeding 10Hz. These sensors also record both instantaneous temperature changes and historical fluctuations. External humidity data is continuously measured by the water vapor content in the air and converted to a relative humidity percentage, reflecting the potential impact of air on thermal conductivity and powder hygroscopicity. External interference data is collected using a multi-source heterogeneous sensor to detect non-thermal environmental interference, including the base vibration spectrum (collected by a triaxial accelerometer), wind flow disturbance amplitude (obtained by an airflow differential pressure sensor), and electromagnetic fluctuations (monitored by a radio frequency interference analysis module).

[0070] The system then uses a set of multivariate fusion models to jointly analyze the three aforementioned environmental factors to generate environmental adaptability data. This process begins with normalization and denoising of the raw data. For example, a sliding mean filter is used to remove transient jitter in temperature and humidity data, and a Kalman filter is used to smooth acceleration data. Principal component analysis (PCA) or environmental index calculations based on a weighted model are then performed. In this weighted model, the external temperature change rate, relative humidity fluctuation amplitude, and interference intensity indicators are assigned three dynamically adjusted weight coefficients, λ3, λ4, and λ5, respectively, to calculate a unified environmental adaptability score: E_adapt=λ3·ΔT_norm+λ4·ΔRH_norm+λ5·D_env Where E_adapt is the environmental adaptability score; ΔT_norm represents the standardized temperature fluctuation, ΔRH_norm represents the standardized humidity change, and D_env represents the interference intensity score. λ3 is a preset temperature sensitivity weight, indicating the impact of ambient temperature changes on the thermal stability of the laser process. λ4 is a preset humidity coupling weight, indicating the influence of humidity on laser-material interaction, powder moisture absorption and deformation, and other effects. λ5 is a preset interference robustness weight, indicating the combined impact of environmental disturbances (such as vibration, electromagnetic interference, and wind currents). Finally, the system integrates this environmental adaptability data with established calibration standard data using multidimensional feature fusion. Using a fuzzy rule system or BP neural network, the system assesses the impact on laser accuracy and outputs a correction parameter matrix. This matrix serves as the weight compensation input and is superimposed with the ideal frequency domain response and thermal threshold window recorded in the calibration standard data. The final optimized signal processing data is output as real-time input to the laser beam spot control system, improving the laser process system's tolerance to environmental disturbances and response stability.

[0071] S106 , acquiring real-time laser beam spot accuracy data of multiple laser sources, and combining the real-time laser beam spot accuracy data with the optimized signal processing data to obtain calibrated multi-laser beam spot accuracy data.

[0072] Real-time laser beam spot accuracy data can be the original data reflecting the spatial characteristics and stability of the current output spot of each laser source, obtained through an online monitoring system (such as a high-speed camera system, displacement sensor, laser interferometer, etc.) during the multi-laser 3D printing or processing process.

[0073] The multi-laser beam spot accuracy data can be the optimal accuracy value of the multi-laser beam spot used for control system correction, which is obtained by fusing the above-mentioned real-time data with the optimized signal processing data obtained by early modeling (such as thermal response prediction, interference compensation results, frequency domain characteristic trends, etc.), and then corrected by algorithms (such as Kalman filtering, Bayesian optimization, etc.).

[0074] High-frame-rate visual monitoring systems (such as industrial cameras or high-speed infrared cameras), laser displacement sensors, or laser interferometry equipment can be deployed on laser processing or multi-laser printing platforms. These devices collect, in real time, the behavioral characteristics of each laser beam spot within the processing area, including its position coordinates, size, energy density distribution, edge profile, and center drift trajectory. By processing the raw image or sensor data using image processing algorithms (such as edge detection, shape fitting, and sub-pixel feature extraction), the spatial geometric parameters and energy distribution characteristics of each laser beam spot at a specific point in time can be extracted, generating structured, real-time laser beam spot accuracy data.

[0075] This real-time laser beam accuracy data is then fused with the optimized signal processing data previously derived from system modeling. This signal processing data typically includes the theoretical thermal response characteristics of the laser beam spot, frequency domain interference patterns, historical offset trends, and environmental disturbance compensation factors. To fuse these two types of data, a time synchronization mechanism is employed during the data alignment phase (e.g., by aligning the timestamps of the two data sources using a trigger clock or encoding signal) to ensure that both sets of data have a consistent timing reference.

[0076] During the fusion processing phase, a multi-channel Kalman filter algorithm is used to filter and estimate the state of the precision data streams from each laser source. This algorithm dynamically corrects random errors caused by system delays, measurement noise, and external disturbances, thereby outputting a more stable laser beam precision state that approaches the true value. Furthermore, a wavelet transform can be introduced to decompose and suppress frequency anomalies or noise components, further enhancing the robustness of data fusion. Ultimately, the filtered and corrected output is the calibrated multi-laser beam precision data.

[0077] In an embodiment of the present application, multi-physical field signal data of multiple laser sources are obtained, and the multi-physical field signal data are synchronously collected and fused to obtain comprehensive physical field data; wherein the multi-physical field signal data include thermal response signals, stress signals, and pressure signals; thermal response data of beam spot areas of multiple laser sources are obtained, and the thermal response data and the comprehensive physical field data are fused and analyzed to obtain multi-laser beam spot position offset characteristic data; Fourier transform processing is performed on the thermal response signal data of the multiple laser sources to obtain frequency domain characteristic data of the multiple laser sources, and Fourier transform processing is performed on the multi-laser beam spot position offset characteristic data and the frequency domain characteristic data. Obtain beam spot frequency characteristic data of multiple laser sources; obtain an adaptive thermal response threshold, combine the adaptive thermal response threshold, beam spot frequency characteristic data, and frequency domain characteristic data to obtain calibration standard data; obtain external temperature data, external humidity data, and external interference data, determine environmental adaptability data based on the external temperature data, external humidity data, and external interference data, combine the environmental adaptability data with the calibration standard data to obtain optimized signal processing data; obtain real-time laser beam spot accuracy data of multiple laser sources, combine the real-time laser beam spot accuracy data with the optimized signal processing data, and obtain calibrated multi-laser beam spot accuracy data. The above-mentioned multi-laser 3D printing beam spot calibration method improves the stability, consistency, and intelligent response capabilities of the multi-laser system in high-precision manufacturing scenarios, which helps to improve printing quality, reduce defect rates, and extend equipment life.

[0078] Based on the above technical solution, optionally, after obtaining the calibrated multi-laser beam spot accuracy data, the method further includes: Acquire the real-time accuracy data of each laser beam spot, fuse and correct the real-time accuracy data and the optimized signal processing data using the Kalman filter algorithm to obtain the corrected multi-laser beam spot accuracy data; The corrected multi-laser beam spot accuracy data is input into a preset multi-laser beam spot interaction effect model to update the multi-laser beam spot accuracy data.

[0079] In this solution, real-time accuracy data refers to the specific performance indicators of each laser beam spot monitored in real time during laser 3D printing or multi-laser processing, such as the beam spot's position deviation, size change, shape distortion, and offset. This data is typically collected in real time using devices such as high-speed cameras, optical sensors, and displacement sensors, reflecting the actual state and accuracy of the current laser beam spot.

[0080] The Kalman filter is an optimal filtering algorithm based on recursive estimation, used to fuse measurement data from multiple sources, filter out noise and errors, and obtain more accurate state estimates. In this application, the Kalman filter effectively corrects and modifies the precision parameters of the laser beam spot by combining real-time precision data with optimized signal processing data. This enables dynamic data fusion and error compensation, improving overall measurement and control accuracy.

[0081] The pre-defined multi-laser beam interaction model can be a pre-established mathematical or physical model that describes the interactions and interference effects between multiple laser beams, including complex interaction mechanisms such as phase difference, energy overlap, and thermal coupling. Based on multi-dimensional data such as the power, wavelength, spatial position, and thermal response of the laser beams, this model can predict and adjust the dynamic behavior and accuracy of the beams in a multi-laser system, supporting subsequent calibration and control strategies.

[0082] Acquiring real-time precision data for each laser beam spot involves utilizing high-speed optical sensors, laser displacement sensors, and industrial cameras to collect key parameters such as the spatial position, size, shape, and offset of each laser beam spot in real time. During the acquisition process, a preprocessing module performs denoising, correction, and time synchronization on the raw data to ensure data accuracy and timeliness. Subsequently, this preprocessed real-time precision data, along with optimized signal processing data previously obtained through physical modeling, experimental calibration, or signal processing, is input into the Kalman filter algorithm module. The Kalman filter algorithm, based on recursive estimation, first predicts the current state of the laser beam spot based on the system dynamic model. This state is then updated based on observed data. Through repeated iterations, noise and measurement errors are eliminated, enabling real-time data fusion and correction, ultimately outputting corrected multi-laser beam spot precision data. This corrected precision data is then input into an established multi-laser beam spot interaction effect model, which comprehensively considers multi-physics interaction factors such as the relative spatial position, phase difference, interference effects, thermal coupling, and energy distribution between the laser beam spots. Based on the current correction data, the model dynamically calculates the interaction effects of laser beam spots, updates the precision parameters and position status of each beam spot, and realizes real-time optimization and adjustment of the overall accuracy of the multi-laser system.

[0083] The training process of the established multi-laser beam spot interaction effect model is as follows: A large amount of experimental data from a multi-laser beam system under different operating conditions was collected. This data covers multi-dimensional information such as the spatial position of the laser beams, optical power, wavelength, phase difference, temperature variation, scanning speed, and the thermophysical properties of the materials. High-precision sensors and a high-speed data acquisition system ensured the data's time synchronization and accuracy. Secondly, physical simulation software (such as finite element analysis and optical simulation tools) was used to numerically simulate the interference effects, thermal coupling, and energy distribution of the laser beams. This generated simulated data to supplement the experimental data and enhance the diversity and coverage of the training samples. The experimental and simulated data were then preprocessed, including denoising, normalization, feature extraction, and multi-physics signal fusion, to obtain a structured input feature set. Subsequently, an appropriate machine learning algorithm, such as a deep neural network, convolutional neural network, or graph neural network, was selected, and the network architecture was designed to effectively capture the complex nonlinear interactions between the laser beams. During model training, a supervised learning approach was employed, using historically measured laser beam accuracy data as labels and iteratively optimizing the model using loss functions (such as mean squared error and cross entropy). Model parameters were adjusted using a backpropagation algorithm to gradually improve prediction accuracy. Cross-validation and early stopping are used to avoid overfitting and ensure the model's generalization capabilities. After training, offline validation and online testing are performed to compare the model's predictions with actual measurement data, allowing adjustments to the model structure or training strategy to further improve the model's accuracy and robustness. Finally, the trained multi-laser beam interaction effect model is deployed to accurately simulate and predict the dynamic behavior of laser beams, supporting real-time precision optimization and system adaptive control.

[0084] This solution acquires real-time precision data for each laser beam spot and combines it with optimized signal processing data. Using the Kalman filter algorithm for fusion and correction, it effectively filters out noise and measurement errors, improving data accuracy and stability. This corrected precision data is fed into a pre-defined multi-laser beam interaction model to dynamically predict and correct the mutual influence of the laser beam spots. This significantly improves the accuracy and response speed of the overall system, ensuring the stability and processing quality of the collaborative operation of multiple laser sources.

[0085] Example 2 Figure 2 This is a flow chart of the multi-laser 3D printing beam spot calibration method provided in Example 2 of this application. Figure 2 As shown, the specific steps include: S201, acquiring multi-physical field signal data from multiple laser sources, synchronously collecting and fusing the multi-physical field signal data to obtain integrated physical field data; wherein the multi-physical field signal data includes thermal response signals, stress signals, and pressure signals.

[0086] S202 , obtaining thermal response data of beam spot areas of multiple laser sources, fusing and analyzing the thermal response data with integrated physical field data to obtain multi-laser beam spot position offset characteristic data.

[0087] S203, performing Fourier transform processing on the thermal response signal data of the multiple laser sources to obtain frequency domain characteristic data of the multiple laser sources, and performing Fourier transform processing on the multiple laser beam spot position offset characteristic data and the frequency domain characteristic data to obtain beam spot frequency characteristic data of the multiple laser sources.

[0088] S204 , obtaining an adaptive thermal response threshold, and combining the adaptive thermal response threshold, the beam spot frequency characteristic data, and the frequency domain characteristic data to obtain calibration standard data.

[0089] S205, obtaining external temperature data, external humidity data and external interference data, determining environmental adaptability data based on the external temperature data, external humidity data and external interference data, combining the environmental adaptability data with calibration standard data to obtain optimized signal processing data.

[0090] S206 , acquiring real-time laser beam spot accuracy data of multiple laser sources, and combining the real-time laser beam spot accuracy data with the optimized signal processing data to obtain calibrated multi-laser beam spot accuracy data.

[0091] S207 , acquiring real-time velocity data of each laser beam spot, and calculating the velocity error of each laser beam spot based on the real-time velocity data.

[0092] A laser beam spot refers to the area of ​​light formed when a laser beam is irradiated on a material surface. In applications such as laser processing, laser printing, or laser cladding, the size, shape, position, and energy distribution of the beam spot directly impact the processing quality and precision. Each laser source corresponds to one or more beam spot areas, and their behavior and precision are crucial to the coordination of multi-laser systems.

[0093] Real-time velocity data refers to the instantaneous velocity information collected by the system using displacement sensors, scanning control feedback, or image processing techniques as the laser beam spot moves over time. This data, typically expressed in millimeters per second (mm / s) or micrometers per second (μm / s), reflects the beam spot's movement speed during each print cycle or scan path, facilitating dynamic beam trajectory adjustment, error compensation, or vibration analysis.

[0094] Velocity error refers to the difference between the ideal (set) beam spot velocity and the actual real-time velocity. It typically reflects velocity deviations caused by system control, environmental disturbances, or mechanical lag. This error is a crucial basis for control compensation, dynamic adjustment, and trajectory correction.

[0095] During the printing process, the system uses high-precision displacement sensors (such as laser interferometers, encoders built into the scanning mirror, or visual tracking systems) deployed near the laser processing head to monitor the position of each laser beam spot within the work area over time. By continuously recording beam spot position information at multiple moments and combining it with a unified timestamp management mechanism within the system, the system can calculate the beam spot's movement speed over different time periods. This is known as real-time velocity data. Furthermore, the system pre-sets target speeds for each laser beam spot at different processing path segments. These target speeds are automatically generated based on process requirements, material properties, and path planning, and represent the ideal movement speeds for each laser beam spot. After receiving the real-time velocity data collected by the sensors, the control system compares it with the target velocity value at the corresponding time point. The difference between the two is the velocity error, which reflects the degree of deviation between actual and ideal motion.

[0096] S208 , acquiring real-time temperature change data, and inputting the real-time temperature change data into a preset thermal response model to obtain a thermal response coefficient.

[0097] Real-time temperature change data may refer to data on the temperature change over time of the material surface or beam spot area collected in real time by thermocouples, infrared thermal imagers, thermistors, thermopiles or other temperature sensing devices during the laser processing or printing process.

[0098] The preset thermal response model can be a mathematical or simulation model used to describe the heat conduction, heat diffusion and heat accumulation processes of the material under laser irradiation. It is usually established in advance based on the material properties (such as thermal conductivity, specific heat capacity, density) and used to predict the impact of temperature changes on the thermal behavior of the material.

[0099] The thermal response coefficient is a parameter used to quantify a material's sensitivity to laser thermal stimulation, reflecting the degree of thermal expansion, property change, or structural deformation that occurs with a unit temperature change. It can be a scalar, vector, or set of polynomial coefficients.

[0100] Infrared thermal imagers (such as shortwave infrared cameras), micro-thermocouples, or thermopile sensors can be placed in each laser beam spot area on the processing platform. These sensors continuously and frequently (e.g., 1-10 kHz) collect the temperature of the processing surface, thereby recording the temperature variation curve of the laser irradiation area over time in real time, thus obtaining real-time temperature change data. This data is usually expressed as a temperature time series for each laser source beam spot, in a format such as T1(t), T2(t), ..., T nThe raw temperature data is preprocessed, including denoising (e.g., median filtering), sampling rate unification, and time alignment, to ensure spatiotemporal consistency across sensors. This processed real-time temperature change data is then fed into a predefined thermal response model defined in the system. This thermal response model can take one of three typical forms: Analytical model: Based on the one-dimensional / two-dimensional Fourier heat conduction equation, considering laser power density, beam spot radius, initial material temperature boundary conditions, etc., calculates the material temperature rise rate per unit time; Finite element heat conduction model: Use software such as COMSOL and ANSYS to build a two-dimensional or three-dimensional thermal model, and simulate the heat diffusion behavior caused by laser irradiation based on input parameters such as material thermal conductivity, specific heat capacity, and density; Data-driven neural network model: The input is multi-channel time series temperature data, and the output is local thermal conductivity or temperature rise response index. The model can be obtained through supervised learning of historical processing samples.

[0101] When the model is running, the real-time temperature change data is input in a time series and combined with the current material's thermophysical parameters (such as thermal conductivity, density, and specific heat capacity). Based on the model's internal heat conduction mechanism or fitting relationship, the material's response degree under local unit temperature disturbance is solved, and the thermal response coefficient is then output.

[0102] The thermal response coefficient can be expressed as the local thermal diffusivity (the temperature propagation distance per unit time); the thermal inertia factor (characterizing the relationship between temperature rise and laser irradiation time); and an indicator of the sensitivity of temperature changes to material deformation or laser offset.

[0103] S209, obtain the current time data and the initial laser position of each laser beam spot, input the real-time speed data, speed error, thermal response coefficient, initial laser position and current time data into a preset beam spot position calculation formula, calculate the adjusted beam spot position of each laser beam spot, and transmit the adjusted beam spot position to the laser beam spot control system.

[0104] The current time data can refer to the system real-time timestamp of the moment when the beam spot position correction is obtained, which is usually in seconds (s), milliseconds (ms) or even microseconds (μs), and records the specific time point of the laser processing system within a certain processing cycle.

[0105] The initial laser position may refer to the reference coordinates of each laser beam spot in space set or measured by the system at the beginning of a certain printing / processing cycle, usually expressed as two-dimensional or three-dimensional coordinates.

[0106] Adjusting the beam spot position can be the current optimal target position of each laser beam spot calculated based on real-time speed data, speed error, thermal response coefficient and other factors to offset the effects of temperature drift, speed disturbance, thermal expansion and the like during the processing.

[0107] The laser beam control system can be a subsystem in the entire laser processing platform used to accurately control parameters such as the laser beam position, power, and scanning path. It consists of both hardware and software.

[0108] During the laser processing process, in order to achieve high-precision, multi-laser beam spot synchronous control, the system needs to obtain the current time data and the initial laser position of each laser beam spot, and input it together with the real-time perception parameters into the beam spot position calculation model to dynamically calculate the adjusted beam spot position and transmit it to the laser beam spot control system for real-time correction.

[0109] Specifically, the system first obtains the current time data from an internal high-precision synchronous clock module or embedded time synchronization controller. This data records the moment when the current laser beam spot control instruction takes effect with a high-resolution timestamp (e.g., nanosecond or microsecond level), ensuring strict timing consistency between all data sampling and control signals. At the same time, the system obtains the initial laser position of each laser beam spot through a laser path initialization program or displacement feedback mechanism during each printing cycle or starting processing segment. This initial position data is usually expressed in three-dimensional coordinate form and calibrated in combination with the preset trajectory of the laser scanning path. It comes from a laser position encoder, a high-resolution displacement sensor, or a beam spot repositioning algorithm based on image recognition.

[0110] After acquiring the aforementioned static and dynamic inputs, the system inputs real-time velocity data (real-time beam spot velocity derived from galvanometer feedback or estimated laser scanning trajectory), velocity error (calculated from the difference between actual and desired velocity), thermal response coefficient (derived from real-time temperature change data fed into a thermal response model), initial laser position, and current time data into a preset beam spot position calculation formula. This calculation formula incorporates nonlinear acceleration terms, temperature drift compensation terms, and time coupling terms to output the compensated adjusted beam spot position in real time—the precise coordinates to which the current laser beam spot should be moved—to offset offsets caused by factors such as temperature rise, inertia, and interference coupling.

[0111] The resulting adjusted beam spot position is then transmitted to the laser beam control system in the form of digital control instructions (such as G-code or control voltage vectors) via a system bus (e.g., EtherCAT, CAN, or FPGA channel). The control system interprets the input coordinate values ​​in real time and drives the scanning galvanometer system or motion platform actuator to perform micron-level position corrections, ensuring that each laser beam spot accurately moves within the processing area along the optimized path, significantly improving overall processing quality and topography consistency.

[0112] In this embodiment, by acquiring the speed and temperature change data of each laser beam spot in real time, combining the thermal response model and time synchronization information, the adjustment position of the beam spot is accurately calculated, and the adjustment result is fed back to the control system in real time, thereby realizing dynamic compensation and precise control of the laser beam spot, effectively reducing the positioning error caused by speed fluctuations and temperature changes, and improving the accuracy, stability and product quality of laser processing.

[0113] On the basis of the above technical solution, an optional, preset beam spot position calculation formula is: in, To adjust the beam spot position; is the initial laser position; It is real-time speed data; is the speed error; is the current time data; is the preset acceleration adjustment coefficient; It is the preset speed adjustment coefficient.

[0114] In this solution, in actual processing, multiple typical working conditions (such as different powers, scanning speeds, and material types) can be selected to record the deviation between the actual beam spot position and the ideal trajectory. The trend of the deviation over time can be compared to fit a nonlinear trend curve of the beam spot offset. Curve fitting tools (such as least squares method or polynomial fitting) can be used to determine the deviation. 、 value, so that the formula predicts The position is as close as possible to the measured trajectory. It reflects the influence of acceleration on position. It reflects the degree of correction of linear velocity drift.

[0115] Based on the above technical solution, optionally, after transmitting the adjusted beam spot position to the laser beam spot control system, the method further includes: After each preset update time interval, the real-time velocity data of each laser beam spot is reacquired, and the velocity error of each laser beam spot is calculated based on the real-time velocity data; Reacquiring real-time temperature change data, and inputting the real-time temperature change data into a preset thermal response model to obtain a thermal response coefficient; The current time data and the initial laser position of each laser beam spot are reacquired, and the real-time speed data, speed error, thermal response coefficient, initial laser position and current time data are input into the preset beam spot position calculation formula to calculate the adjusted beam spot position of each laser beam spot. The adjusted beam spot position is transmitted to the laser beam spot control system until the laser printing process is completed.

[0116] In this solution, the preset update time interval may refer to the time interval at which the system automatically triggers a dynamic parameter update and adjustment operation according to a fixed time period (eg, every 10 milliseconds, 50 milliseconds, or 1 second) during the laser printing process.

[0117] During the laser printing process, to ensure the laser beam spot maintains high-precision positioning and dynamically responds to complex factors such as thermal deformation, velocity deviation, and time lag, the system automatically initiates a closed-loop calibration update process every preset update interval (e.g., every 10ms, 20ms, or even shorter real-time period). This process continuously optimizes and corrects the laser beam spot position through high-frequency data acquisition, dynamic model calculation, and control command updates.

[0118] First, when a timer or real-time control thread in the control system triggers an update signal, the system immediately acquires real-time velocity data for the current laser beam spot from each laser scanning path. This data is collected in real time by a high-speed optical encoder, galvanometer feedback device, or position-velocity closed-loop control module in the motor driver, obtaining the actual velocity of each laser beam spot at the current sampling point in the XY axis or three-dimensional space. At this point, the previously stored target velocity curve (generated based on the print path plan) is also called to calculate the current velocity error of each beam spot—the deviation between the measured velocity and the theoretical velocity—to compensate for dynamic drift in trajectory tracking.

[0119] The system then simultaneously collects real-time temperature change data from thermocouple sensors, infrared temperature probes, or high-frame-rate thermal imagers placed in the print workspace, accurately reflecting subtle changes in the printing material and local ambient temperature over time. The acquired temperature change curve is input into the system's embedded thermal response model. This model is constructed based on heat conduction-diffusion theory or experimental fitting curves, taking into account the material's thermal conductivity, heat capacity, density, and other properties. The thermal response coefficient of each beam spot under current conditions is calculated, indicating the degree of positional deviation that may occur in the area where the beam spot is located under thermal disturbances.

[0120] Next, the control system collects the current system timestamp and the initial laser spot position of each laser spot (which can be obtained through initial displacement calibration or trajectory recording). This time information, the initial laser position of each laser spot, the real-time velocity, velocity error, and thermal response coefficient are input as variables into the preset beam spot position calculation formula. The output is the position coordinate to which each laser spot should be adjusted at the current sampling point, resulting in the adjusted beam spot position.

[0121] Finally, the system encodes these adjusted precise position coordinate data into position signals or motion control instructions in real time and transmits them to the laser beam control system, including the scanning galvanometer system, optical path drive platform, or direct-drive motor module. Based on the received instructions, the control system rapidly adjusts the projection position of the laser beam in space, achieving microsecond-level laser path correction and trajectory control. This process continues to loop in each update cycle until the entire laser printing process is completely completed, ensuring highly consistent beam spot position, minimal error, and optimal processing quality throughout the entire printing process.

[0122] In this solution, by periodically collecting multi-source data such as speed and temperature and dynamically correcting the laser beam spot position, the accuracy error caused by speed drift, heat accumulation and path deviation can be effectively suppressed, and high-precision trajectory tracking can be maintained.

[0123] Example 3 Figure 3 This is a schematic diagram of the structure of the multi-laser 3D printing beam spot calibration system provided in Example 3 of this application. Figure 3 As shown, specifically including: The physical field fusion module 301 is used to obtain multi-physical field signal data from multiple laser sources, and synchronously collect and fuse the multi-physical field signal data to obtain comprehensive physical field data; wherein the multi-physical field signal data includes thermal response signals, stress signals, and pressure signals; The offset identification module 302 is used to obtain thermal response data of the beam spot areas of multiple laser sources, and fuse and analyze the thermal response data with the comprehensive physical field data to obtain multiple laser beam spot position offset feature data; The frequency domain extraction module 303 is configured to perform Fourier transform processing on the thermal response signal data of the multiple laser sources to obtain frequency domain characteristic data of the multiple laser sources, and perform Fourier transform processing on the multiple laser beam spot position offset characteristic data and the frequency domain characteristic data to obtain beam spot frequency characteristic data of the multiple laser sources; The calibration standard generation module 304 is used to obtain an adaptive thermal response threshold, and combine the adaptive thermal response threshold, the beam spot frequency characteristic data, and the frequency domain characteristic data to obtain calibration standard data; An environmental adaptation module 305 is configured to obtain external temperature data, external humidity data, and external interference data, determine environmental adaptability data based on the external temperature data, external humidity data, and external interference data, and combine the environmental adaptability data with calibration standard data to obtain optimized signal processing data; The precision calibration module 306 is used to obtain real-time laser beam spot precision data of multiple laser sources, and combine the real-time laser beam spot precision data with the optimized signal processing data to obtain calibrated multi-laser beam spot precision data.

[0124] The multi-laser 3D printing beam spot calibration system provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0125] Example 4 like Figure 4 As shown, an embodiment of the present application further provides an electronic device 400, including a processor 401, a memory 402, and a program or instruction stored in the memory 402 and executable on the processor 401. When the program or instruction is executed by the processor 401, each process of the above-mentioned multi-laser 3D printing beam spot calibration method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0126] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0127] Example 5 An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned cable installation process based on the tension adaptive control system embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0128] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk.

[0129] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or system comprising the element. In addition, it should be noted that the scope of the methods and systems in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0130] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of this application.

[0131] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

[0132] The above are only preferred embodiments of the present application and the technical principles employed. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that are possible for those skilled in the art will not depart from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include more other equivalent embodiments without departing from the concept of the present application. The scope of the present application is determined by the scope of the claims.

Claims

1. A multi-laser 3D printing beam spot calibration method, characterized in that: The method comprises: Acquire multi-physical field signal data from multiple laser sources, and synchronously collect and fuse the multi-physical field signal data to obtain integrated physical field data; wherein the multi-physical field signal data includes thermal response signals, stress signals, and pressure signals; Acquiring thermal response data of beam spot areas of multiple laser sources, fusing and analyzing the thermal response data with comprehensive physical field data to obtain multi-laser beam spot position offset characteristic data; Performing Fourier transform processing on the thermal response signal data of the multiple laser sources to obtain frequency domain characteristic data of the multiple laser sources, and performing Fourier transform processing on the multiple laser beam spot position offset characteristic data and the frequency domain characteristic data to obtain beam spot frequency characteristic data of the multiple laser sources; Acquiring an adaptive thermal response threshold, and combining the adaptive thermal response threshold, beam spot frequency characteristic data, and frequency domain characteristic data to obtain calibration standard data; Acquiring external temperature data, external humidity data, and external interference data, determining environmental adaptability data based on the external temperature data, external humidity data, and external interference data, and combining the environmental adaptability data with calibration standard data to obtain optimized signal processing data; Real-time laser beam spot accuracy data of multiple laser sources is acquired, and the real-time laser beam spot accuracy data is combined with optimized signal processing data to obtain calibrated multi-laser beam spot accuracy data.

2. The method according to claim 1, characterized in that in, Gets the adaptive thermal response threshold, including: Power data of multiple laser sources, scanning speed data of multiple laser sources, material thermal conductivity data, material specific heat capacity data, material density data, and external temperature data are obtained, and an adaptive thermal response threshold is calculated based on the power data of multiple laser sources, scanning speed data of multiple laser sources, material thermal conductivity data, material specific heat capacity data, material density data, external temperature data, and a preset adaptive thermal response threshold calculation formula; wherein the preset adaptive thermal response threshold calculation formula is: in, Adaptive thermal response threshold; is the preset thermal response sensitivity coefficient; is the power data; is the thermal conductivity data of the material; is the material specific heat capacity data; is the material density data; is the scanning speed data; is the external temperature data; It is the preset ambient temperature suppression adjustment factor.

3. The method according to claim 1, characterized in that in, After obtaining the calibrated multi-laser beam spot accuracy data, the method further includes: Acquire the real-time accuracy data of each laser beam spot, fuse and correct the real-time accuracy data and the optimized signal processing data using the Kalman filter algorithm to obtain the corrected multi-laser beam spot accuracy data; The corrected multi-laser beam spot accuracy data is input into a preset multi-laser beam spot interaction effect model to update the multi-laser beam spot accuracy data.

4. The method according to claim 1, wherein in, Performing Fourier transform processing on the multiple laser beam spot position offset characteristic data and the frequency domain characteristic data to obtain beam spot frequency characteristic data of multiple laser sources, including: The frequency domain characteristic data and the multi-laser beam spot position offset characteristic data are processed using an adaptive frequency domain filtering technology to obtain optimized frequency domain characteristic data; Kalman filtering and wavelet transform are used to eliminate noise data of the optimized frequency domain characteristic data to obtain beam spot frequency characteristic data of multiple laser sources.

5. The method according to claim 1, wherein in, After obtaining the calibrated multi-laser beam spot accuracy data, the method further includes: Acquiring real-time velocity data of each laser beam spot, and calculating a velocity error of each laser beam spot based on the real-time velocity data; Acquiring real-time temperature change data, and inputting the real-time temperature change data into a preset thermal response model to obtain a thermal response coefficient; The current time data and the initial laser position of each laser beam spot are obtained, and the real-time speed data, speed error, thermal response coefficient, initial laser position and current time data are input into a preset beam spot position calculation formula to calculate the adjusted beam spot position of each laser beam spot, and the adjusted beam spot position is transmitted to the laser beam spot control system.

6. The method according to claim 5, characterized in that in, The preset beam spot position calculation formula is: in, To adjust the beam spot position; is the initial laser position; It is real-time speed data; is the speed error; is the current time data; is the preset acceleration adjustment coefficient; It is the preset speed adjustment coefficient.

7. The method according to claim 5, characterized in that in, After transmitting the adjusted beam spot position to the laser beam spot control system, the method further includes: After each preset update time interval, the real-time velocity data of each laser beam spot is reacquired, and the velocity error of each laser beam spot is calculated based on the real-time velocity data; Reacquiring real-time temperature change data, and inputting the real-time temperature change data into a preset thermal response model to obtain a thermal response coefficient; The current time data and the initial laser position of each laser beam spot are reacquired, and the real-time speed data, speed error, thermal response coefficient, initial laser position and current time data are input into the preset beam spot position calculation formula to calculate the adjusted beam spot position of each laser beam spot. The adjusted beam spot position is transmitted to the laser beam spot control system until the laser printing process is completed.

8. A multi-laser 3D printing beam spot calibration system, characterized in that: The system comprises: A physical field fusion module is used to obtain multi-physical field signal data from multiple laser sources, and synchronously collect and fuse the multi-physical field signal data to obtain comprehensive physical field data; wherein the multi-physical field signal data includes thermal response signals, stress signals, and pressure signals; An offset identification module is used to obtain thermal response data of beam spot areas of multiple laser sources, fuse and analyze the thermal response data with comprehensive physical field data, and obtain multi-laser beam spot position offset characteristic data; a frequency domain extraction module, configured to perform Fourier transform processing on the thermal response signal data of the multiple laser sources to obtain frequency domain characteristic data of the multiple laser sources, and perform Fourier transform processing on the multiple laser beam spot position offset characteristic data and the frequency domain characteristic data to obtain beam spot frequency characteristic data of the multiple laser sources; a calibration standard generation module, configured to obtain an adaptive thermal response threshold, and combine the adaptive thermal response threshold, beam spot frequency characteristic data, and frequency domain characteristic data to obtain calibration standard data; an environmental adaptation module, configured to obtain external temperature data, external humidity data, and external interference data, determine environmental adaptability data based on the external temperature data, external humidity data, and external interference data, and combine the environmental adaptability data with calibration standard data to obtain optimized signal processing data; The precision calibration module is used to obtain real-time laser beam spot precision data of multiple laser sources, and combine the real-time laser beam spot precision data with optimized signal processing data to obtain calibrated multi-laser beam spot precision data.

9. An electronic device, characterized in that: The invention comprises a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein when the program or instruction is executed by the processor, the steps of the multi-laser 3D printing beam spot calibration method according to any one of claims 1 to 7 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the multi-laser 3D printing beam spot calibration method according to any one of claims 1 to 7 are implemented.

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