11005 results about "Machining process" patented technology

The invention discloses a numerical control tool machining monitoring method. The method comprises the following steps that firstly, data collection and primary processing are conducted; 2, constructing a digital twinborn model; 3, adjusting a self-adaptive threshold value; 4, performing prediction maintenance and fault early warning; and 5, optimizing machining parameters in real time. Multi-dimensional data such as temperature, vibration, stress and cutting force are collected in real time through a multi-sensor system, high-frequency sampling and signal denoising technologies are combined, comprehensiveness and accuracy of monitoring data are ensured, and a foundation is laid for follow-up analysis; the virtual machining model is constructed through the digital twinning technology, real-time simulation and synchronization of the machining process are achieved, the current state of the tool can be comprehensively reflected, anomaly detection and pre-judgment are conducted in combination with historical data and real-time data, and the fault monitoring accuracy is improved.

The invention relates to the technical field of machining control, and discloses a forging part machining process control method and system based on digital twinning. The method comprises the following steps: acquiring temperature field and stress field data streams of large oil press equipment and forgings at multiple points in different areas; performing organization-defect association mode recognition on the data stream to generate a forging process database; a constitutive unit nesting method is adopted to construct a digital twinborn model, and a microstructure evolution unit is embedded in a macroscopic unit; performing nonlinear progressive simulation to obtain a critical deformation condition and a defect threshold value; and carrying out reverse solution on the process window to obtain an optimal process parameter combination. According to the method, the forging digital twin model is established through the constitutive unit nesting method, bidirectional coupling of macroscopic deformation and microstructure evolution is achieved, a stable process parameter combination is determined through the process window reverse solution technology, and therefore the quality stability and production efficiency of forged parts are improved.

The invention relates to the technical field of information, in particular to an error compensation method for multi-axis linkage machining of a mold blank hole system, which comprises the following steps: judging the time range of a tool changing window based on an error accumulation trend, if error accumulation reaches a preset threshold value, triggering a compensation action, otherwise, continuously monitoring error change; aiming at the complexity of a motion track, a nonlinear optimization algorithm is adopted, an execution path of a compensation action is optimized, and conflicts with a processing action are reduced; according to the optimized compensation path, the control parameters of the servo motor are adjusted in real time, and it is ensured that the compensation action is stably executed in the machining process; dynamically updating an error model through data of a main shaft temperature sensor and a tool wear monitoring device, and continuously optimizing a time point and an execution path of a compensation action; in the machining process, the error accumulation condition is monitored in real time, if the error exceeds the preset range, the compensation action is triggered again, and the machining precision is ensured.

The invention provides a numerical control machining model verification method and system combined with digital twinning, and the method comprises the steps: firstly obtaining a numerical control machining twinning data association set which is correspondingly formed by a physical machining process actual measurement data sequence and a virtual twinning model simulation data sequence according to a machining stage identifier; the actual measurement data sequence and the simulation data sequence both comprise processing action and material response feature sequences, and then carrying out feature space unification processing on the twin data association set to generate a verification feature corresponding set comprising actual measurement comprehensive features and simulation comprehensive features; performing deviation analysis on the verification feature corresponding set by using a pre-trained model deviation analysis model to obtain a feature matching and association influence relationship, and deducing and determining a specific deviation position of the virtual twin model in a processing action and material response modeling link based on an analysis result; and model optimization guidance information for defining the parameter adjustment direction and the structure correction content is generated, so that the precision and the reliability of the virtual twin model are effectively improved.

The invention relates to a shaft linkage curved surface component machining path real-time control method, system and device, and the method comprises the steps: obtaining geometric data and machining process parameters of a shaft linkage curved surface component, and carrying out the shaft linkage decomposition to obtain a multi-shaft linkage machining path; real-time operation parameters of machining equipment and tool workpiece contact data are obtained, deviation matching is conducted on the multi-axis linkage machining path, and a real-time axis control instruction set and an axis position compensation trend curve are obtained; performing path optimization on the shaft position compensation trend curve according to the real-time shaft control instruction group to obtain an initial shaft linkage adjustment scheme; performing curved surface precision evaluation on the tool workpiece contact data, and performing curved surface prediction in combination with the axis position compensation trend curve to obtain a curved surface quality prediction area; and performing coordinated optimization on the initial axis linkage adjustment scheme according to the curved surface quality prediction area to obtain an axis linkage optimization processing strategy. It can be guaranteed that efficient operation can be achieved in different machining states, and path deviation in the machining process is reduced.

The invention discloses a numerical control machine tool machining environment monitoring system, and belongs to the technical field of intelligent monitoring. Comprising the following modules: a multi-dimensional sensing monitoring module for realizing omnibearing data acquisition of an interaction state of a cutter and a workpiece in a machining process; the feature extraction module is used for converting the time sequence data into a key feature set for representing the state of the cutter; the wear type identification module is used for accurately identifying and classifying the wear type of the cutter based on a deep learning classifier and judging the specific wear type; the wear progress prediction module calls a corresponding special prediction model according to the identified specific wear type, quantifies the wear progress rate and estimates the residual life of the cutter; the decision support module is used for integrating the tool wear state and the prediction result, balancing the production efficiency, the machining quality and the tool cost, and providing optimization suggestions of parameter adjustment and tool changing opportunities; and the self-learning optimization module is used for continuously collecting actual production data to carry out model evaluation and incremental learning so as to realize multi-machine knowledge sharing.

The invention provides a calculation task scheduling method and system applied to a numerical control machining system, and the method comprises the steps: obtaining a task request set containing a machining process type and a real-time constraint identifier, extracting a processing complexity feature and a resource demand feature, calling a task priority evaluation model to generate a priority sequence, and carrying out the calculation of a task. Generating an initial scheduling scheme according to the sequence and current available resource information of the system, adaptively adjusting the initial scheduling scheme based on task execution state information fed back in real time, and generating a final scheduling scheme including a task execution sequence and resource allocation parameters, thereby comprehensively considering task complexity, resource requirements and real-time constraints, and improving the scheduling efficiency. Dynamic matching and optimal scheduling of tasks and resources are achieved, and the machining efficiency and the resource utilization rate of the numerical control machining system are effectively improved.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention relates to the technical field of model construction, in particular to a method and a system for constructing a sheet metal working three-dimensional process model. The method comprises the following steps: acquiring equipment specification data and material characteristic data; the method comprises the following steps: acquiring geometric parameters and motion parameters of sheet metal processing equipment by using a three-dimensional scanner, and integrating equipment specification data and material characteristic data to construct a sheet metal three-dimensional initial process model; analyzing a material plastic deformation characteristic of the material characteristic data and setting a processing parameter threshold range; the machining parameter threshold range is input into the three-dimensional initial process model for stage machining process simulation, the flange area strain rate, the dangerous section thinning rate and the rebound angle variable quantity are output, and stage machining process simulation comprises plate inflow stage simulation, forming stage simulation and shaping stage simulation. According to the method, three-dimensional scanning, comprehensive machining process simulation, topological optimization and a real-time error feedback mechanism are introduced, so that the construction accuracy of the sheet metal machining process model is improved.

The invention relates to the technical field of numerical control machining, in particular to a CAM post-optimization method and system for geometric error compensation of a multi-axis linkage numerical control machine tool. The method comprises the following steps: firstly, carrying out kinematics analysis and geometric error modeling on a target machine tool, and deploying the target machine tool in a CAM post-optimization module; then actual state parameters of the target machine tool in the machining process are collected in real time through a laser interferometer; a motion instruction of a target machine tool and a detection result of a laser interferometer are synchronously obtained through a CAM post-optimization module, and then geometric errors of a linear axis and a rotating axis in the operation process of the target machine tool are calculated in real time through a kinematic model and a geometric error model. According to the method, different linear axis compensation modes are adopted for different instructions; and a cutter shaft attitude vector error and a cutter cutting point position error are calculated in combination with inverse kinematics, and are corrected step by step. The problem that the error compensation precision of a controller in an existing numerical control machine tool is insufficient can be solved.

The invention discloses a numerical control machining equipment state monitoring system based on big data analysis, and relates to the technical field of intelligent manufacturing. The method is used for solving the problems of correlation analysis and real-time feedback between tool wear and machining quality in the machining process. A time sequence correlation feature vector is extracted through a vibration signal, an acoustic emission signal and main shaft axial micro-displacement data, a machining quality degradation index is calculated in combination with a nonlinear regression model, and the machining quality is monitored in real time. And on the basis of adaptive feature weight adjustment of the types of the processing materials, feature fusion during processing of different materials is enhanced, and the precision of quality evaluation is improved. And establishing a nonlinear mapping relation between the tool wear and the surface roughness by utilizing a gradient lifting tree model, and realizing accurate prediction of the tool wear and the machining quality. And finally, through a closed-loop dynamic adjustment mechanism, the cutting speed and the feeding amount are automatically adjusted according to the machining quality degradation index and the fault positioning result, and the stability of the machining process and the product quality are ensured.

The invention relates to the field of machining parameter optimization, in particular to a machining parameter self-adaptive optimization method and device based on workpiece recognition, and the method comprises the steps: obtaining the multi-source feature information of a to-be-machined workpiece when a machining instruction is received; matching a preset process knowledge base according to the multi-source feature information, and generating initial processing parameters and process target data; dynamic state data in the machining process are obtained in real time, wherein the dynamic state data comprise change parameters of the workpiece and feedback parameters of machining equipment; based on the dynamic state data, whether deviation exists between the dynamic state data and the process target data is analyzed; and if deviation exists between the dynamic state data and the preset process target, adjusting parameters are generated according to the dynamic state data. By obtaining workpiece change parameters and equipment feedback parameters in real time, the machining state can be dynamically monitored, and deviation can be found and recognized in time. Through comparison with preset process target data, existence of deviation can be accurately identified, and then necessary adjustment measures are taken.

The invention relates to an automatic control method and system for milling cutter machining, and the method comprises the steps: obtaining geometric feature data and technological parameter information of a milling cutter, and carrying out the machining analysis of the geometric feature data based on the technological parameter information, and obtaining the corresponding original machining flow information; acquiring machining flow sensing data of the milling cutter, and performing state association on the machining flow sensing data and the original machining flow information to obtain a corresponding real-time machining state group; real-time cutting parameters of the milling cutter are obtained, machining prediction is conducted on the original machining process information according to the real-time cutting parameters, and a corresponding machining quality trend is obtained; performing intelligent control optimization on the real-time processing state group and the processing quality trend to obtain a corresponding initial control scheme; and performing global parameter optimization on the initial control scheme according to the compensation control parameters to obtain a corresponding global processing optimization scheme. According to the invention, the real-time processing state group and the processing quality trend can be intelligently controlled and optimized.

The invention belongs to the field of machining, and relates to a machining process quality online monitoring and feedback adjusting system and method. The system comprises a multi-modal data acquisition module used for acquiring a mechanical vibration signal and a high-frequency micro-damage signal; the feature preprocessing and cross-modal fusion module is used for carrying out time domain, frequency domain and time-frequency domain feature extraction on the collected mechanical vibration signals and high-frequency micro-damage signals, carrying out edge, texture and shape feature extraction on workpiece machining surface image signals, and introducing a cross-modal attention mechanism to achieve weighted fusion of heterogeneous signals. And finally generating a fusion feature vector with high discrimination. The machine tool / cutter state and workpiece quality intelligent analysis module is used for constructing an intelligent analysis model for the equipment operation state and the workpiece machining quality; inputting the fusion feature vector into an intelligent analysis model to obtain a quality evaluation result; and the quality judgment and feedback adjustment module is used for adopting a dynamic threshold adjustment mechanism to realize adaptive comparison with a process standard according to a quality evaluation result.

The invention provides a numerical control machine tool dynamic precision control method and system based on multi-axis linkage error compensation, and relates to the technical field of mechanical manufacturing, and the method comprises the steps: collecting shaft system motion parameters and real-time cutting load parameters of a multi-axis numerical control machine tool in a linkage machining process in real time; shafting motion parameters comprise displacement, speed and acceleration of each feed shaft, and cutting load parameters are obtained through inversion of main shaft current and servo driving torque; calculating a dynamic following error of each feed shaft in a linkage state based on the shaft system motion parameters; and carrying out load coupling correction on the dynamic following error by fusing real-time cutting load parameters to obtain an initial multi-axis linkage dynamic error. According to the invention, real-time precision control in the high-dynamic machining process can be realized.

The invention discloses a control method and equipment suitable for a rotary turn-over clamp of a numerical control milling center, and relates to the technical field of numerical control machining. The clamping force is optimized through the multi-source sensing and digital twinning technology, and workpiece deformation is prevented; thermal compensation and an error transfer chain model are integrated, the turnover angle is accurately corrected, and error accumulation is restrained; real-time comparison, traceability and self-adaptive adjustment in the machining process are achieved through digital twin pre-verification and multi-sensor closed-loop monitoring; the problems of out-of-tolerance of precision and batch consistency caused by unstable clamping, thermal deformation and error transmission in multi-surface milling of high-precision parts are solved, and the precision, reliability and process adaptability of complex part machining are remarkably improved.

The invention discloses a system and a method for optimizing and improving the performance of a manufacturing and processing material based on aluminum alloy parts, and relates to the technical field, the system comprises the following components: a data acquisition and storage module, a machine learning model construction module, a user interaction module and a scheme generation and optimization module; related data of an aluminum alloy material are widely collected through the data acquisition and storage module, training is carried out based on a deep learning algorithm by utilizing the machine learning model building module, an aluminum alloy material component-process-performance relation model is built, a user only needs to input target performance parameters through the user interaction module, and the aluminum alloy material component-process-performance relation model is built. The system can generate aluminum alloy material components and machining process schemes meeting the requirements through reverse calculation, optimization screening is carried out, the period of material performance optimization is greatly shortened through the process, and optimization efficiency and accuracy are improved.

The invention discloses a hard and brittle material ultra-precision turning damage monitoring method based on multi-sensor fusion, and relates to the technical field of precision / ultra-precision machining state monitoring, a multi-sensor signal acquisition device is built on an ultra-precision turning machine tool to capture different types of sensor signals generated in the turning process in real time; performing signal preprocessing operation of minimum entropy deconvolution filtering on the acquired sensor signal; feature extraction is carried out on the signals subjected to minimum entropy deconvolution filtering, and 95 time domain and frequency domain statistical features reflecting turning damage are extracted; by integrating data from different sensors, the precision and reliability of monitoring are remarkably improved, more comprehensive and more accurate information can be obtained by capturing and fusing acoustic characteristics, cutting force and mechanical vibration information in the machining process, so that the abrasion degree of a tool and the damage condition of a workpiece are judged more accurately, and the machining accuracy is improved. And false alarm and missing alarm can be reduced, and the monitoring reliability can be improved.

The invention relates to the technical field of numerical control machining monitoring, in particular to a tooth deviation high-precision monitoring system in a tooth cutting machining process, which comprises an online measurement module, a dynamic compensation module, a deviation calculation module and a result output module which are in signal connection with a numerical control system of a tooth cutting machine tool, the online measurement module, the dynamic compensation module, the deviation calculation module and the result output module are integrated on the processing element; the processing element is used for obtaining workpiece design parameters and constructing a theoretical workpiece model, the online measurement module is used for collecting tooth surface point cloud data in real time in the machining process, the dynamic compensation module is used for correcting vibration errors and temperature errors of the point cloud data, and the deviation calculation module is used for quantifying tooth surface deviation values. And the result output module is used for generating an early warning signal and a dynamic correction instruction and executing system self-calibration. The device and a numerical control system form a closed loop of machining, monitoring, correction and remachining, and finally high-precision real-time monitoring and dynamic correction of tooth deviation are achieved.

The invention relates to a large-model-driven adaptive closed-loop optimization method for machining process parameters of an aviation complex thin-wall component. The method comprises the following steps: acquiring multi-source heterogeneous machining data and quality detection data of the aviation complex component; constructing and training an aviation complex component processing quality prediction model and a multi-target process parameter optimization model; packaging the trained model into a micro service and deploying the micro service to a DeepSeek large model container platform; a DeepSeek large model is used as a controller, and closed-loop calling of the micro-service is achieved; an online self-adaptive optimization process is formed; an analysis report is automatically generated based on an optimization result; and optimizing the processing quality prediction model by using the new data increment and designing an automatic rollback strategy. According to the method, deep learning and a large model technology are fused, accurate prediction of the machining quality and efficient regulation and control of the technological parameters are achieved, and meanwhile the manufacturing efficiency and quality of the aviation complex component are effectively improved.

The invention provides a collaborative tool servo diamond turning method and system based on track pre-filtering, and the method comprises the steps: S1, integrating a fast tool shaft to a machine tool shaft in the cutting depth direction, and constructing a master-slave collaborative tool servo system; wherein the machine tool shaft serves as a main servo shaft of the master-slave cooperative tool servo system, and a low-frequency tool path obtained after reference tool path pre-filtering is executed; the fast cutter shaft is used as a slave servo shaft of the master-slave collaborative cutter servo system, and the deviation between the actual displacement of the master servo shaft and the reference cutter path is compensated in real time; s2, implementing a collaborative tool servo machining process based on a master-slave collaborative tool servo system; and S3, after machining is completed, surface contour data are obtained, and quantitative evaluation is conducted on a machining result through surface shape errors and surface roughness indexes.

The invention discloses a laser cutting method for cutting complex shapes, and particularly relates to the field of laser cutting. A three-dimensional curved surface model of a to-be-cut workpiece is obtained, and a surface normal tensor field is constructed; extracting a driving point set based on the curvature derivative and generating a sliding path network; constructing a laser power density mapping model in combination with the thermal diffusion response model and the thermophysical parameters of the material; thermal feedback images are collected in the cutting process, an energy offset function is constructed, laser focus displacement and the scanning rate are dynamically adjusted through fuzzy control, and closed-loop control over power, paths and thermal feedback is achieved; according to the method, self-adaptive adjustment of laser power distribution, curved surface adaptation of a cutting path and thermal field real-time compensation in the machining process can be achieved under a complex geometric structure, the cutting precision, stability and energy efficiency are effectively improved, and the method is suitable for laser machining application of high-requirement complex structures such as aerospace, mold manufacturing and medical instruments.

The utility model relates to a bag opening machine. A sewing device, a laser cutting device, a bag opening forming device and an accessory bearing device are arranged on a working table. The bag opening forming device comprises a folding mechanism and a material fixing mechanism, the folding mechanism is arranged above the material fixing mechanism, and the bag opening forming device can move on the working table; according to the pocket opening sewing processing technology, pocket opening cloth pieces are positioned on a pocket opening forming device, and the steps of cutting, folding, accessory installing, sewing, discharging and the like are sequentially carried out. The folding mechanism and the material fixing mechanism are integrated into a whole, and compared with the prior art, accessories such as bag eyebrows, bag cloth or zippers are not prone to deviation and displacement in the machining process, and the product quality stability is higher.

The invention provides a titanium alloy thin-wall part high-precision milling method and system based on dynamic compensation, and the method comprises the following steps: constructing a multi-physics field coupling model based on the constitutive relation and milling process parameters of a titanium alloy material; according to the method, a multi-physical field coupling model represented by dynamic parameters is constructed, structural feature recognition and coupling weight dynamic allocation are combined, and the model is iteratively optimized through a detection result after processing, so that the problem that a traditional prediction model is disjointed from an actual working condition is solved; by building a real-time monitoring and feedback system and combining a dynamic compensation closed-loop mechanism, non-interruption linkage of monitoring-adjustment in the machining process is achieved, and the problem that the hysteresis of a traditional compensation method is remarkable is solved; the initial compensation amount is optimized through a multi-objective genetic algorithm, a self-adaptive processing path strategy is adopted, dynamic parameter adjustment of structure adaptation is combined, a traditional single compensation strategy is replaced, and the problem of dynamic rigidity change in the complex curved surface and material removal process can be flexibly solved.

The invention provides a mold machining deviation dynamic correction method, system and equipment based on real-time monitoring, and relates to the technical field of industrial machining control. The method comprises the steps that a target machining plan of a target mold is read; performing collaborative analysis on the target design parameters and the target process requirements to obtain a real-time workpiece preset model; dynamically monitoring the machining process of the target mold; according to the real-time state parameters of the target mold, a real-time workpiece actual model is constructed; comparing the real-time workpiece actual model with a real-time workpiece preset model to obtain a model comparison deviation; performing correction optimization to obtain an optimal correction strategy; and carrying out dynamic processing correction on the target mold. According to the method and the device, the technical problem that the machining precision and the quality of the mold are affected due to the fact that the dynamic error is difficult to accurately recognize because the preset model is difficult to capture and correct the dynamic change generated in the machining process in time is solved, and the machining precision and the quality of the mold are improved by recognizing the dynamic error and determining the optimal correction strategy.

The invention discloses a numerical control machine tool efficient cutting method based on real-time surface roughness prediction. Firstly, acquisition and storage of internal information of an external sensor and a numerical control system machine tool are realized through data acquisition software developed by a numerical control machine tool type; the surface roughness of the part is predicted through data preprocessing, multi-source data feature extraction and fusion and a prediction model; and in the part machining process, data are collected and input into the model for real-time prediction and machining parameter adjustment, so that the cutting efficiency is improved on the premise of ensuring certain quality of the part, and the optimal parameters are stored in the knowledge base. The defects that the quality is uncertain in the part production process of the numerical control machine tool and manpower needs to be consumed for surface roughness detection after production is completed are effectively overcome, the knowledge base can provide optimal cutting parameters for production of various parts, and the method has great benefits on improving the production efficiency on the premise of ensuring certain quality of the parts.

The invention relates to the technical field of arc discharge control, in particular to an arc discharge machine control method based on multi-parameter collaborative optimization, which comprises the following steps of: generating a first regulation and control instruction by adopting a fuzzy PID (Proportion Integration Differentiation) algorithm based on a deviation value between a dynamic parameter feedback signal and a preset threshold value, the axial feeding speed of the servo motor, the power output curve of the arc generator and the coolant spraying time sequence are synchronously adjusted, so that the arc energy density gradient and the workpiece heat conduction rate form dynamic balance, the mechanical resonance frequency in the machining process is monitored through the vibration spectrum analysis unit, and when it is detected that the resonance intensity exceeds a safety threshold value, the coolant spraying time sequence is started. And a second regulation and control instruction is generated by triggering a frequency compensation algorithm, and the driving frequency of the servo motor is corrected in real time, so that the control precision is improved, the arc discharge process is more stable, the response speed is increased, the arc discharge time is shortened, the adaptability is enhanced, the requirements of different materials and processes can be met, the energy consumption is reduced, and the production efficiency is improved.

The invention discloses a milling path automatic generation method based on reinforcement learning, and relates to the technical field of machine tool machining, and the milling path automatic generation method comprises the following steps: step 1, defining action output; step 2, defining state input including a cutter state, a global observation space and a local observation space; 3, defining a reward function; value feedback given to the intelligent agent by the environment according to the current state and action is determined through the reward function; 4, setting deep neural networks including a feature extractor, a strategy network and a value network; 5, constructing a process autonomous generation model and a machining process world model; step 6, strategy training; step 7, outputting a strategy, and autonomously completing a machining process flow; by means of the method, the problems that in the prior art, manual experience dependence is too heavy, local optimization limitation is remarkable, and physical simulation efficiency is insufficient are solved.

The invention relates to the technical field of cycloidal gear machining control, in particular to a speed reducer cycloidal gear efficient machining method and device based on a machining machine tool, and the method comprises the steps that machining data in the cycloidal gear machining process are obtained and preprocessed; preliminarily judging whether a flutter phenomenon occurs or not by analyzing the periodic change of the cutting depth and the periodic fluctuation of the cutting force under different temperature changes; vibration, current and cutting force data are combined to evaluate the chatter occurrence possibility; classifying the processing data through clustering analysis by utilizing the chatter occurrence possibility so as to screen out high-quality processing data and flaw processing data; and a dragonfly optimization algorithm is adopted, initial position distribution of the algorithm is optimized through the high-quality machining data and the defect machining data, and the optimal cutting speed is solved to serve as the cutting speed in the current cycloidal gear machining process. According to the method, the flutter phenomenon is comprehensively analyzed through the multi-dimensional machining data, so that the overall machining efficiency in the current cycloidal gear machining process is improved.

The invention relates to the technical field of solar cell processing, in particular to a laser light-transmitting scribing control method and system for a solar thin-film cell. The control method comprises the steps that a large-size substrate is divided into a plurality of galvanometer view fields through a dynamic flight continuous machining mode, a continuous snake-shaped machining path is generated, the laser machining platform is driven to continuously move at the constant speed, and the galvanometer scanning system is synchronously triggered to start lineation in a splicing area in advance. According to the method, a time domain coupling equation of galvanometer scanning frequency-platform movement speed-laser pulse sequence is established, and dynamic compensation of an optical path and self-adaptive adjustment of energy density are achieved in the continuous machining process. Based on a view field expansion algorithm of a nonlinear kinematics model, the phase deviation of a scanning vector and a motion vector is effectively inhibited, and the geometric continuity of a splicing interface in ultra-long stroke machining is guaranteed.