Intelligent cutting device and cutting method for titanium alloy structural part

Abstract

The intelligent cutting method comprises the steps that the temperature gradient change rate is analyzed according to temperature distribution data, a key area where an oxidation reaction is likely to happen is determined in combination with geometrical characteristics, and target monitoring area data are obtained; obtaining reflection spectrum information of the cutting area and comparison spectrum information of the adjacent non-cutting area according to the target monitoring area data, and obtaining spectrum characteristic data; performing space-time collaborative analysis on the spectral feature data, extracting a spectral feature change value, and combining the spectral feature change value with the diffusion rate of the oxidation reaction to obtain oxidation state prediction data; and according to the oxidation state prediction data, the cutting power is adjusted in different regions, the flow field parameters of the protective gas are dynamically compensated, and cutting of the titanium alloy structural part is executed. According to the technical scheme, through spectrum monitoring and space-time collaborative analysis, dynamic prediction and accurate regulation and control of the oxidation reaction are achieved, the problem that laser energy coupling is not stable due to the surface oxidation reaction is solved, and the cutting quality is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of precision machining of titanium alloy structural parts, and in particular to an intelligent cutting device and a cutting method for titanium alloy structural parts. Background Art

[0002] Titanium alloy structural parts are widely used in high-end manufacturing fields such as aerospace, military equipment and medical devices due to their excellent specific strength, corrosion resistance and biocompatibility. Such structural parts often need to meet complex shape and size requirements in practical applications, so precise cutting is required. In some fields, titanium alloy structural parts not only require high dimensional accuracy, but also need to ensure the quality of the cutting surface to ensure its reliability and service life in harsh working environments.

[0003] At present, high-energy laser cutting has become the main method for processing titanium alloy structural parts due to its advantages such as high processing efficiency and good incision quality. However, in the actual cutting process, due to the low thermal conductivity and high chemical activity of titanium alloy, it is easy to accumulate heat rapidly in local areas during the processing, and trigger a violent oxidation reaction, resulting in the rapid formation of an oxide film on the surface of the workpiece. The formation of this oxide film not only changes the optical properties of the material surface, but also causes dynamic fluctuations in the energy coupling efficiency between the laser and the workpiece during the laser cutting process. In traditional cutting methods, the processing technology usually relies on pre-set fixed parameters, such as laser power, cutting speed, focal length, etc. These parameters are often average values ​​obtained through a large number of experiments in an idealized or laboratory environment, and do not fully consider the dynamic characteristics of local temperature changes, environmental interference, and the oxidation reaction of titanium alloy itself during the actual processing process. At the same time, the traditional process mainly adopts an open-loop control mode, lacks real-time feedback and adjustment means, so it is difficult to deal with the unstable energy transmission problem caused by the change of the oxide film between the laser and the workpiece surface, which ultimately leads to hidden problems such as uneven cutting edges, depth deviations, and microscopic defects. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem of unstable laser energy coupling caused by dynamic reflectivity changes caused by surface oxidation reactions in the existing high-energy laser cutting technology of titanium alloy structural parts.

[0005] A first aspect of the present invention provides an intelligent cutting method for a titanium alloy structural part, the intelligent cutting method for a titanium alloy structural part comprising: Analyzing the rate of change of the temperature gradient according to the temperature distribution data on the surface of the titanium alloy structural part, and determining the key area prone to oxidation reaction in combination with the geometric characteristics of the titanium alloy structural part, to obtain the target monitoring area data; According to the target monitoring area data, obtaining the reflection spectrum information of the cutting area of ​​the titanium alloy structural part and the comparison spectrum information of the adjacent uncut area to obtain the spectrum characteristic data; Performing spatiotemporal coordinated analysis on the spectral characteristic data, extracting spectral intensity change values, peak shape change values ​​and peak position drift values, and combining the spatial diffusion rate and time evolution data of the oxidation reaction to obtain oxidation state prediction data; According to the oxidation state prediction data, the cutting power is adjusted in different regions, and the flow field parameters of the protective gas are dynamically compensated to execute the cutting of the titanium alloy structural part.

[0006] Optionally, the process of acquiring the temperature distribution data on the surface of the titanium alloy structural part includes: According to the cutting trajectory of the titanium alloy structural part, the cutting path is divided into straight segments, corner segments and curve segments, and the heat accumulation characteristics of different types of segments are determined according to the thermal conductivity parameters of the titanium alloy to obtain the heat accumulation characteristic marking data; According to the heat accumulation characteristic mark data, an initial heat conduction prediction zone is established along the cutting track, wherein the width and shape of the initial heat conduction prediction zone are adapted to the heat accumulation characteristics of different cutting sections, and prediction zone contour data is obtained; Setting a temperature monitoring point array based on the predicted band profile data, collecting real-time temperature of the temperature monitoring point array, calculating the heat diffusion direction and rate according to the temperature change trend, and obtaining heat conduction characteristic data; According to the heat conduction characteristic data, the temperature anomaly points are dynamically spatially clustered, and the boundaries of the clustering areas are adjusted in combination with the heat diffusion direction and rate to generate temperature distribution data on the surface of the titanium alloy structural part.

[0007] Optionally, the temperature gradient change rate is analyzed according to the temperature distribution data on the surface of the titanium alloy structural part, and the key area prone to oxidation reaction is determined in combination with the geometric characteristics of the titanium alloy structural part to obtain the target monitoring area data, including: Performing gradient scanning on the temperature distribution data on the surface of the titanium alloy structural part, calculating the rate of change of the temperature gradient, and performing heat partitioning on the surface of the titanium alloy structural part according to the rate of change to obtain heat distribution characteristic data; Performing geometric feature analysis on the surface profile of the titanium alloy structural part, identifying the corner transition area, the groove convergence area and the thickness mutation area, and determining the heat accumulation risk of each characteristic area in combination with the heat distribution characteristic data to obtain regional risk characteristic data; According to the regional risk characteristic data, combined with the thermal conductivity parameters of titanium alloy, the heat transfer rate between different characteristic regions is calculated, and the heat interaction relationship between adjacent regions is analyzed to obtain the heat transfer chain data; Based on the heat transfer chain data and in combination with the oxidation activity parameters of the titanium alloy, the oxidation reaction probability of each area is calculated, and the chain propagation law of the oxidation reaction is analyzed to obtain the target monitoring area data.

[0008] Optionally, the geometric feature analysis is performed on the surface profile of the titanium alloy structural part to identify the corner transition area, the groove convergence area and the thickness mutation area, and the heat accumulation risk of each characteristic area is determined in combination with the heat distribution characteristic data to obtain the regional risk characteristic data, including: Performing curvature analysis on the surface profile of the titanium alloy structural part, calculating the curvature value of each point on the surface, identifying the geometric feature mutation point according to the change rate of the curvature value, and obtaining feature point distribution data; According to the characteristic point distribution data, the connectivity analysis of adjacent characteristic points is performed to divide the corner transition area, the groove convergence area and the thickness mutation area, and the area and depth parameters of each area are calculated to obtain characteristic area parameter data; Cross-mapping the characteristic area parameter data with the heat distribution characteristic data, calculating the heat density value in each characteristic area, and analyzing the flow trend of heat in the characteristic area to obtain heat accumulation pattern data; According to the heat accumulation pattern data, combined with the specific heat capacity parameters of titanium alloy, the heat storage capacity and heat diffusion coefficient of each characteristic area are calculated to obtain the regional risk characteristic data.

[0009] Optionally, cross-mapping the characteristic region parameter data with the heat distribution characteristic data, calculating the heat density value in each characteristic region, and analyzing the flow trend of heat in the characteristic region to obtain heat accumulation pattern data includes: Performing weight calculation on the regional area and depth parameters in the characteristic regional parameter data to generate a regional shape coefficient, and correcting each regional shape coefficient according to the thermal conductivity of the titanium alloy to obtain regional characteristic weight data; According to the regional characteristic weight data, the heat distribution characteristic data is divided into regions, and the heat density gradient and heat flow direction in each region are calculated to obtain heat flow distribution data; Performing a time series analysis on the heat flux distribution data, calculating the rate of change of heat density in each region, and analyzing the retention time of heat in the region in combination with the thermal diffusion coefficient of the titanium alloy to obtain heat retention characteristic data; According to the heat retention characteristic data, the heat accumulation rate and diffusion pattern of each area are analyzed, and the heat accumulation risk index is calculated in combination with the critical oxidation temperature of the titanium alloy to obtain the heat accumulation pattern data.

[0010] Optionally, the step of acquiring the reflection spectrum information of the cut area of ​​the titanium alloy structural part and the contrast spectrum information of the adjacent uncut area according to the target monitoring area data to obtain the spectrum characteristic data includes: Performing regional stratification processing on the target monitoring area data, dividing the surface of the titanium alloy structural part into a core cutting area, an edge transition area and a reference comparison area, and determining the acquisition frequency band of each area according to the spectral characteristic band of the titanium alloy to obtain spectral acquisition area data; According to the spectral acquisition area data, a multi-band spectral scan is performed on each area to identify the characteristic spectral peaks of the titanium alloy oxidation reaction, and the spectral reflection intensity of different acquisition frequency bands is calculated to obtain multi-band spectral data; Performing spatial distribution analysis on the multi-band spectral data, calculating the spectral intensity gradient between adjacent acquisition points, and analyzing the spatial migration law of characteristic spectral peaks to obtain spectral distribution characteristic data; According to the spectral distribution characteristic data, the spectral characteristics of the cut area and the adjacent uncut area are compared and analyzed, the spectral difference coefficient is calculated, and the oxidation degree index is calculated in combination with the oxidation spectral characteristics of the titanium alloy to obtain the spectral characteristic data.

[0011] Optionally, the spectral acquisition region data is used to perform a multi-band spectral scan on each region, identify characteristic spectral peaks of the titanium alloy oxidation reaction, and calculate the spectral reflection intensity of different acquisition frequency bands to obtain multi-band spectral data, including: Performing multi-band scanning on each area in the spectrum collection area data, setting a scanning band group based on the oxidation reaction characteristics of the titanium alloy, and classifying and collecting spectral information of different bands to obtain band spectral data; Characteristic peaks are identified on the spectrum data of the bands, the peak positions and peak intensities of the spectrum of the titanium alloy oxidation reaction in different bands are calculated, and the evolution characteristics of the spectrum peaks are analyzed according to the growth law of the oxide film to obtain the spectrum peak characteristic data; According to the spectral peak characteristic data, the spectral reflection intensity of different acquisition frequency bands is calculated, and the intensity correction is performed in combination with the energy coupling characteristics of the titanium alloy oxide film to obtain multi-band spectral data.

[0012] Optionally, the spectral feature data is subjected to a spatiotemporal coordinated analysis to extract spectral intensity change values, peak shape change values ​​and peak position drift values, and the oxidation state prediction data is obtained by combining the spatial diffusion rate and time evolution data of the oxidation reaction, including: The spectral characteristic data are processed in time series segments, characteristic change values ​​of each time period are calculated according to the change rates of spectral intensity, peak shape and peak position, and the change trend is analyzed in combination with the oxidation kinetic parameters of the titanium alloy to obtain time series characteristic data; Performing spatial mapping analysis on the time series characteristic data, calculating the spectral characteristic difference values ​​between adjacent regions, and determining the propagation direction and rate of the oxidation reaction according to the oxidation diffusion characteristics of the titanium alloy to obtain spatial propagation data; According to the spatial propagation data, the diffusion process of the oxidation reaction is analyzed in time and space, the oxidation reaction activity index of different regions is calculated, and the reaction chain effect is analyzed in combination with the critical oxidation temperature of the titanium alloy to obtain the oxidation state prediction data.

[0013] Optionally, the cutting power is adjusted in different regions according to the oxidation state prediction data, and the flow field parameters of the shielding gas are dynamically compensated to execute the cutting of the titanium alloy structural part, including: Performing partition mapping on the oxidation state prediction data and the cutting path of the titanium alloy structural part, determining the energy coupling efficiency threshold and critical oxidation temperature of each region, calculating the attenuation coefficient of the oxide layer thickness to the laser energy, and obtaining the energy compensation parameter; Based on the energy compensation parameters, the cutting power correction values ​​of the corner transition segment, the straight segment and the curved segment are calculated respectively, and the power adjustment step is calculated according to the oxidation activity of the titanium alloy at different temperatures to obtain the cutting parameter data; The cutting parameter data is analyzed, the flow field deformation of the protective gas in the high temperature area is calculated, the pressure distribution characteristics of the airflow disturbance area are determined, and the gas flow direction and flow rate are adjusted according to the temperature field distribution in the cutting area to execute the cutting of the titanium alloy structural part.

[0014] A second aspect of the present invention provides an intelligent cutting device for a titanium alloy structural part, the intelligent cutting device for a titanium alloy structural part comprising: A thermal field prediction module is used to analyze the rate of change of the temperature gradient according to the temperature distribution data on the surface of the titanium alloy structural part, and determine the key area prone to oxidation reaction in combination with the geometric characteristics of the titanium alloy structural part to obtain the target monitoring area data; A spectrum acquisition module is used to obtain the reflection spectrum information of the cutting area of ​​the titanium alloy structural part and the contrast spectrum information of the adjacent uncut area according to the target monitoring area data, so as to obtain spectrum characteristic data; An oxidation analysis module is used to perform spatiotemporal coordinated analysis on the spectral feature data, extract spectral intensity change values, peak shape change values ​​and peak position drift values, and obtain oxidation state prediction data by combining the spatial diffusion rate and time evolution data of the oxidation reaction; The parameter control module is used to adjust the cutting power in different regions according to the oxidation state prediction data, and dynamically compensate the flow field parameters of the protective gas to execute the cutting of the titanium alloy structural part.

[0015] The technical solution provided in the embodiment of the present application performs a preliminary scan and analysis of the workpiece based on the temperature distribution data and geometric features of the workpiece surface to identify key areas prone to oxidation reactions. In this process, the temperature data of each point on the workpiece surface is obtained through thermal imaging technology, and the temperature gradient change rate is calculated, and the area of ​​local heat accumulation is determined in combination with the shape information of the workpiece (such as corners, grooves, and thickness mutation areas). Since the low thermal conductivity of titanium alloys makes the local temperature rise more obvious, and the high chemical activity easily promotes the occurrence of oxidation reactions, therefore, through this pre-screening step, areas with higher oxidation risks can be locked before cutting, providing a clear target for subsequent real-time monitoring.

[0016] Next, in the identified key monitoring area, reflectance spectral data of the cut area and adjacent uncut areas are collected using multi-band spectral acquisition technology. The focus here is on comparative analysis. By collecting two sets of data at the same time, the reflectivity changes caused by the formation of the oxide film can be captured more accurately. Since the formation of the oxide film changes the optical properties of the material surface, and these changes vary between different areas, comparative acquisition not only helps to eliminate environmental interference, but also highlights the subtle changes in spectral characteristics caused by oxidation. This step provides a direct and accurate physical signal for the subsequent dynamic monitoring of the oxidation state.

[0017] Next, the collected spectral feature data is subjected to a spatiotemporal coordinated analysis. In this process, the system not only focuses on changes in spectral intensity, but also performs statistics and analysis on multidimensional features such as spectral peak shape and peak position drift. At the same time, combining time series data and spatial diffusion information, the evolution trend of the oxidation reaction in the key area is modeled to predict the growth direction, diffusion rate and possible chain reaction of the oxide film. The key here is to quantify and track the dynamic change process so that the fluctuation of the oxidation state during the cutting process can be reflected in real time and form a set of indicator data for subsequent regulation. Through this meticulous spatiotemporal data analysis, the system can identify the signs of intensified oxidation reactions in advance, providing a timely basis for laser energy regulation.

[0018] Finally, according to the predicted oxidation state data, the laser cutting parameters and the protective gas flow field are adaptively adjusted in different regions and dynamically. In specific operations, the system will adjust the output power of the laser according to the oxidation risk in different regions, and manage the cutting path in detail; in areas with higher oxidation risks, the energy input is appropriately reduced, and by changing the flow rate and direction of the protective gas, a stronger local gas barrier is formed to inhibit the rapid development of the oxidation reaction. The adjustment here is hierarchical: at the macro level, the overall cutting parameters are adjusted, at the meso level, differentiated settings are made for regional characteristics, and at the micro level, fine energy compensation and airflow control are performed. This linkage adjustment method ensures that the transmission of laser energy throughout the cutting process is more stable, and even if the reflectivity fluctuates due to surface oxidation reactions, the consistency of energy coupling can be maintained through real-time compensation.

[0019] On the whole, this application fundamentally solves the problem of dynamic reflectivity changes caused by surface oxidation reactions of titanium alloys by identifying key oxidation risk areas in advance, then using real-time, regional spectral monitoring to capture surface oxidation dynamics, and finally by graded regulation of laser power and airflow parameters. Specifically, the comprehensive analysis of temperature data and geometric features enables the oxidation location to be accurately locked; the real-time acquisition and comparison of spectral data clearly presents the reflectivity changes during the oxidation process; spatiotemporal collaborative analysis converts these changes into predictable data indicators; and the final adaptive regulation can adjust the cutting process parameters in time according to actual conditions, so that the energy coupling between the laser and the workpiece surface remains stable, thereby ensuring cutting quality and accuracy. Through these four closely linked steps, the problem of unstable energy coupling caused by surface oxidation reactions during high-energy laser cutting of titanium alloys can be effectively solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0021] Figure 1 A schematic diagram of an embodiment of an intelligent cutting method for a titanium alloy structural part according to an embodiment of the present invention; Figure 2 A schematic diagram of an embodiment of an intelligent cutting device for a titanium alloy structural part in an embodiment of the present invention; Figure 3 Schematic diagram of an embodiment of an intelligent cutting device for titanium alloy structural parts in an embodiment of the present invention.

[0022] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0025] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, which must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0026] An embodiment of the present application provides an intelligent cutting method for a titanium alloy structural part. Figure 1 A flow chart of a method for intelligent cutting of titanium alloy structural parts provided in one embodiment of the present application. In this embodiment, the method includes: See also Figure 1 , analyzing the rate of change of the temperature gradient according to the temperature distribution data on the surface of the titanium alloy structural part, and determining the key area prone to oxidation reaction in combination with the geometric characteristics of the titanium alloy structural part, and obtaining the target monitoring area data; In one embodiment of the present invention, the process of acquiring the temperature distribution data on the surface of the titanium alloy structural part includes: According to the cutting trajectory of the titanium alloy structural part, the cutting path is divided into straight segments, corner segments and curve segments, and the heat accumulation characteristics of different types of segments are determined according to the thermal conductivity parameters of the titanium alloy to obtain the heat accumulation characteristic marking data; According to the heat accumulation characteristic mark data, an initial heat conduction prediction zone is established along the cutting track, wherein the width and shape of the initial heat conduction prediction zone are adapted to the heat accumulation characteristics of different cutting sections, and prediction zone contour data is obtained; Setting a temperature monitoring point array based on the predicted band profile data, collecting real-time temperature of the temperature monitoring point array, calculating the heat diffusion direction and rate according to the temperature change trend, and obtaining heat conduction characteristic data; According to the heat conduction characteristic data, the temperature anomaly points are dynamically spatially clustered, and the boundaries of the clustering areas are adjusted in combination with the heat diffusion direction and rate to generate temperature distribution data on the surface of the titanium alloy structural part.

[0027] The following is a specific description of the steps involved in the above embodiment: First, according to the cutting trajectory of the titanium alloy structural part, the cutting path is divided in detail, and the path is divided into straight segments, corner segments and curve segments. This process is achieved by digital processing of the workpiece CAD data or high-precision scanned images, using conventional design software to automatically identify the straight and curved parts, and judge the corner area according to the geometric shape. Subsequently, combined with the known thermal conductivity parameters of titanium alloys, the heat accumulation characteristics of different types of segments are quantitatively analyzed. The straight segment is expected to have low heat accumulation due to its stable direction and uniform heat flow distribution; while the corner segment and the curved segment have a high degree of heat accumulation due to the sharp change in direction, and the local heat is difficult to diffuse rapidly. This step uses standard digital image processing technology and mathematical models to analyze the cutting trajectory and generate heat accumulation characteristic labeling data for each segment; in some alternatives, manual labeling or simplified models can be used for preliminary judgment. This step realizes an accurate quantitative description of the heat behavior of each area in the cutting path, provides data support for the subsequent delineation of the heat conduction area, and clarifies the risk distribution of heat accumulation.

[0028] In the second step, according to the heat accumulation characteristic marking data obtained above, an initial heat conduction prediction zone is established along the cutting trajectory. The specific method is to use computer simulation methods to determine the width and shape of the prediction zone according to the heat accumulation characteristics of each cutting segment: the prediction zone corresponding to the straight segment is narrower, while the corner segment and the curved segment require a wider or asymmetrically distributed prediction zone to adapt to the local heat conduction characteristics. Common finite element analysis software is used to simulate the heat conduction in the cutting area, and the thermal conductivity coefficient of the titanium alloy is combined with the geometric data to generate a set of prediction zone contour data, which details the influence range of heat conduction in each area. As an alternative, an analytical heat conduction model can also be used for rough estimation. The prediction zone contour data obtained in this step provides an accurate spatial reference for the layout of temperature monitoring points, ensuring that the key monitoring areas cover the actual range of heat flow diffusion.

[0029] In the third step, based on the prediction zone contour data obtained in the second step, an array of temperature monitoring points is set, and real-time temperature acquisition is performed on the array. The specific method is to arrange conventional thermocouples or standard infrared temperature sensors according to certain rules in the prediction zone, or to use a high-resolution infrared thermal imager to continuously scan the specified area. The sensor outputs temperature data in real time through the data acquisition system, and uses digital signal processing technology to calculate the temperature change trend. Further, based on the temperature gradient and time series, the numerical differential method is used to solve the heat diffusion direction and diffusion rate to form detailed heat conduction characteristic data. Alternative solutions include simplified analysis using fixed sampling points and regional average temperature values. This step realizes real-time monitoring of the dynamic changes in the surface temperature of the workpiece, provides an accurate data basis for reflecting the heat conduction characteristics, and ensures that the temperature acquisition coverage area is consistent with the prediction zone.

[0030] In the fourth step, the temperature anomaly points are dynamically spatially clustered according to the heat conduction characteristic data obtained in the third step. A density-based spatial clustering algorithm is used to aggregate the collection points in the temperature data that are higher than the set threshold into one or more regions, and then the boundaries of each clustering region are automatically adjusted in combination with the heat diffusion direction and rate calculated above to more accurately reflect the actual heat diffusion situation. During the implementation process, the commonly used algorithm generates the adjusted contour of the temperature anomaly area by processing the sensor coordinates and temperature values; image segmentation technology can be used to process the infrared thermal map to obtain a similar effect. The temperature distribution data on the surface of the titanium alloy structural part generated in this step accurately depicts the local heat accumulation and diffusion, and provides an accurate thermal state map for subsequent process control. Through this method, the temperature anomaly area is accurately located and the boundary is optimized, ensuring that the heat conduction characteristics are fully reflected in space, thereby providing a basis for energy compensation during laser cutting.

[0031] In one embodiment of the present invention, the temperature gradient change rate is analyzed according to the temperature distribution data on the surface of the titanium alloy structural part, and the key area prone to oxidation reaction is determined in combination with the geometric characteristics of the titanium alloy structural part to obtain the target monitoring area data, including: Performing gradient scanning on the temperature distribution data on the surface of the titanium alloy structural part, calculating the rate of change of the temperature gradient, and performing heat partitioning on the surface of the titanium alloy structural part according to the rate of change to obtain heat distribution characteristic data; Performing geometric feature analysis on the surface profile of the titanium alloy structural part, identifying the corner transition area, the groove convergence area and the thickness mutation area, and determining the heat accumulation risk of each characteristic area in combination with the heat distribution characteristic data to obtain regional risk characteristic data; According to the regional risk characteristic data, combined with the thermal conductivity parameters of titanium alloy, the heat transfer rate between different characteristic regions is calculated, and the heat interaction relationship between adjacent regions is analyzed to obtain the heat transfer chain data; Based on the heat transfer chain data and in combination with the oxidation activity parameters of the titanium alloy, the oxidation reaction probability of each area is calculated, and the chain propagation law of the oxidation reaction is analyzed to obtain the target monitoring area data.

[0032] The following is a specific description of the steps involved in the above embodiment: the first step is to perform a gradient scan on the temperature distribution data on the surface of the titanium alloy structural part. First, an infrared thermal imager or a thermocouple array is used to collect the temperature data of each point on the titanium alloy surface, and the temperature distribution image is discretized by image processing software to form a digital matrix. Subsequently, the temperature matrix is ​​gradient calculated by numerical differentiation to obtain the numerical data of the temperature change rate at each position; for example, the gradient value of each pixel point is calculated using a two-dimensional difference formula, where each parameter in the formula represents the amount of temperature change in the horizontal and vertical directions. The calculated gradient change rate data will be heat partitioned according to the set threshold, that is, the surface will be divided into high, medium and low heat accumulation areas according to the size of the temperature gradient value; an optional solution is to perform a gradient scan using an edge detection-based algorithm. This step obtains detailed heat distribution characteristic data by quantitatively describing the local heat flow changes, providing a numerical basis for subsequent risk assessment of the region.

[0033] In the second step, the geometric features of the surface contour of the titanium alloy structural parts are analyzed. Using common edge detection and curvature analysis algorithms, the contour of the digitized image of the titanium alloy surface is extracted to identify the corner transition area, the groove convergence area and the thickness mutation area; for example, by calculating the curvature value of each point on the surface, when the curvature value exceeds the preset threshold, the position is identified as a geometric mutation point. The extracted geometric features are combined with the heat distribution feature data obtained in the first step, and the heat accumulation risk of each characteristic area is quantitatively evaluated based on the distribution of heat density in each area; an alternative is to use morphological filtering for regional identification. The regional risk feature data generated in this step clearly indicates the strength of heat accumulation in each geometric feature area on the workpiece surface, reflecting the risk differences in heat accumulation in different areas, and providing a basis for regional division for subsequent heat transfer analysis.

[0034] The third step is to calculate the heat transfer rate between different characteristic regions based on the regional risk characteristic data and the thermal conductivity parameters of titanium alloy. The heat flux density between each region is calculated using the classical heat conduction law, where the heat flux density q is calculated as follows: q=-k·(ΔT / Δx), where q represents the local heat flux density (unit: W / m²), k represents the thermal conductivity of titanium alloy (unit: W / (m·K)), ΔT is the temperature difference between adjacent regions (unit: K), and Δx is the distance between two regions (unit: m). The heat transfer rate between regions is obtained using the temperature difference and spatial distance in each region in the regional risk characteristic data. Further, the heat interaction relationship between adjacent regions is analyzed, and the heat transfer process is modeled by numerical simulation or finite element analysis software to generate heat transfer chain data describing the heat flow transfer path and its intensity; an optional option is to use a model based on statistical distribution to estimate the heat transfer rate. This step achieves a quantitative description of the heat transfer dynamics between regions of the workpiece, provides detailed data for identifying heat flow interactions, and ensures that the heat transfer chain data truly reflects the actual heat flow evolution.

[0035] The fourth step is to calculate the oxidation reaction probability of each region based on the heat transfer chain data and the oxidation activity parameters of titanium alloy, and analyze the chain propagation law of the oxidation reaction. According to the kinetic theory of oxidation reaction, a mathematical model similar to the Arrhenius equation can be used to calculate the local oxidation reaction probability P, and the formula is: ; Where P is the probability of oxidation reaction, A is the pre-exponential factor (unit is related to the reaction rate), is the activation energy of the oxidation reaction (unit: J / mol), R is the gas constant (about 8.314 J / (mol·K)), and T is the absolute temperature in the region (unit: K). Combined with the heat transfer chain data obtained in the third step, the influence of temperature changes and heat flow distribution in each region on the oxidation reaction is analyzed, and an oxidation reaction propagation model between regions is established to obtain the target monitoring area data. This model quantitatively calculates the oxidation risk of each region in an actual laser cutting environment by comprehensively considering heat transfer and temperature factors; alternatives include data-driven prediction models, but the use of kinetic formulas can more directly reflect the oxidation activity of the material.

[0036] In one embodiment of the present invention, the geometric feature analysis is performed on the surface profile of the titanium alloy structural part to identify the corner transition area, the groove convergence area and the thickness mutation area, and the heat accumulation risk of each characteristic area is determined in combination with the heat distribution characteristic data to obtain the regional risk characteristic data, including: Performing curvature analysis on the surface profile of the titanium alloy structural part, calculating the curvature value of each point on the surface, identifying the geometric feature mutation point according to the change rate of the curvature value, and obtaining feature point distribution data; According to the characteristic point distribution data, the connectivity analysis of adjacent characteristic points is performed to divide the corner transition area, the groove convergence area and the thickness mutation area, and the area and depth parameters of each area are calculated to obtain characteristic area parameter data; Cross-mapping the characteristic area parameter data with the heat distribution characteristic data, calculating the heat density value in each characteristic area, and analyzing the flow trend of heat in the characteristic area to obtain heat accumulation pattern data; According to the heat accumulation pattern data, combined with the specific heat capacity parameters of titanium alloy, the heat storage capacity and heat diffusion coefficient of each characteristic area are calculated to obtain the regional risk characteristic data.

[0037] The following is a specific description of the steps involved in the above embodiment: The first step is to collect and analyze the curvature of the workpiece surface using digital image processing technology. First, a high-resolution image of the surface of the titanium alloy structural part is obtained by an industrial camera or laser scanner, and the edge of the workpiece surface is extracted using image processing software to form digital contour data. Then, the curvature value of each point of the contour is calculated using a discrete differential method or a local polynomial fitting technique, and the geometric feature mutation point is determined based on the rate of change of the curvature value of adjacent sampling points. The spatial coordinates of the mutation point are recorded to form feature point distribution data. This method ensures the accurate capture of geometric change information on the workpiece surface and provides a detailed geometric basis for subsequent area division.

[0038] In the second step, based on the feature point distribution data obtained in the first step, the connectivity analysis of adjacent feature points is performed to divide different geometric feature areas. The connected component analysis algorithm (such as the findContours method based on OpenCV) is used to group adjacent feature points in the image, and then the corner transition area, groove convergence area and thickness mutation area are identified. In each divided area, the area is obtained by extracting the area boundary and using the polygon area calculation formula (such as the area calculation of continuous boundary points). At the same time, the local thickness data in the area is obtained with the help of a laser rangefinder or a three-dimensional scanner to obtain the depth parameter. The generated feature area parameter data contains the area and depth information of each area. Such a quantitative description can accurately reflect the geometric characteristics of each area on the workpiece surface.

[0039] In the third step, the characteristic area parameter data obtained in the second step is cross-mapped with the heat distribution characteristic data, and then the heat density value in each characteristic area is calculated and the heat flow trend is analyzed. First, based on the temperature data collected by the infrared thermal imager or thermocouple array, the temperature distribution is aligned with the coordinate system of the workpiece surface, the temperature monitoring points in each characteristic area are counted, and the average temperature or cumulative temperature value in the area is calculated, and then divided by the area of ​​the area to obtain the heat density value. Combined with the time series temperature data, the vector field analysis method is used to describe the flow direction and rate of heat in the area, and then the heat accumulation pattern data is constructed. This method achieves accurate matching of geometric areas and local heat data, and truly reflects the distribution and dynamic changes of heat in each area.

[0040] The fourth step is to quantitatively calculate the heat storage capacity and heat diffusion coefficient of each characteristic area based on the heat accumulation pattern data obtained in the third step and the thermophysical parameters of titanium alloy, so as to form regional risk characteristic data. First, the area and thickness data of each characteristic area are obtained by digital image processing and three-dimensional scanning, which are recorded as (area, in m²) and (Region thickness, unit: m). Using the known density of titanium alloy (Unit: kg / m³), calculate the mass of each area, the formula is: ; in Represents the mass of the characteristic area (unit: kg).

[0041] Next, based on the temperature variation within the region (Unit: K) and the specific heat capacity of titanium alloy (Unit: J / (kg·K)), calculate heat storage capacity The formula is: ; This formula quantitatively describes the amount of heat stored in each area. At the same time, according to the thermal conductivity of titanium alloy (Unit: W / (m·K)), using the thermal diffusion relationship: ; Calculate the thermal diffusion coefficient , reflecting the rate of heat diffusion in the area. By comprehensively evaluating the heat density, heat storage capacity and diffusion coefficient of each area, the generated regional risk characteristic data can accurately describe the risk of heat accumulation and diffusion in each characteristic area during the laser cutting process, thereby providing a quantitative basis for the precise control of process parameters.

[0042] In one embodiment of the present invention, the characteristic area parameter data is cross-mapped with the heat distribution characteristic data, the heat density value in each characteristic area is calculated, and the flow trend of heat in the characteristic area is analyzed to obtain the heat accumulation pattern data, including: Performing weight calculation on the regional area and depth parameters in the characteristic regional parameter data to generate a regional shape coefficient, and correcting each regional shape coefficient according to the thermal conductivity of the titanium alloy to obtain regional characteristic weight data; According to the regional characteristic weight data, the heat distribution characteristic data is divided into regions, and the heat density gradient and heat flow direction in each region are calculated to obtain heat flow distribution data; Performing a time series analysis on the heat flux distribution data, calculating the rate of change of heat density in each region, and analyzing the retention time of heat in the region in combination with the thermal diffusion coefficient of the titanium alloy to obtain heat retention characteristic data; According to the heat retention characteristic data, the heat accumulation rate and diffusion pattern of each area are analyzed, and the heat accumulation risk index is calculated in combination with the critical oxidation temperature of the titanium alloy to obtain the heat accumulation pattern data.

[0043] The following is a detailed description of the steps involved in the above embodiment: Step 1: weight calculation is performed using the area and depth parameters in the feature area parameter data. First, the area data of each feature area (denoted as , in square meters) and regional depth data (denoted as , in meters). The regional shape factor is generated by using a calculation method based on the ratio of area to depth. and The normalized coefficients are combined into a dimensionless coefficient that quantitatively describes the geometric shape of the region. Subsequently, the regional shape coefficient is corrected according to the thermal conductivity of titanium alloy. Here, the conventional material heat conduction model is used to convert the known thermal conductivity of titanium alloy (denoted as , in W / (m·K)) as a correction factor, adjust the shape factor to reflect the ease of heat flow conduction in the region, thereby generating regional characteristic weight data. This method integrates the material thermophysical parameters while ensuring that the data accurately quantifies the regional geometric characteristics, and can better predict local heat conduction behavior. Alternative solutions can use other normalization methods, but the method combined with thermal conductivity correction has a clearer physical meaning.

[0044] In the second step, the heat distribution characteristic data is divided into regions according to the regional characteristic weight data, and the heat density gradient and heat flow direction in each region are calculated. The spatial segmentation algorithm is used to divide the overall heat distribution map into several sub-regions according to the regional characteristic weight data, and each sub-region corresponds to a region with relatively uniform geometric characteristics. Subsequently, the heat density gradient is obtained by discrete gradient calculation of the temperature field data in each sub-region, that is, a quantitative description of the temperature change within a unit distance; at the same time, the gradient direction of the temperature field is statistically analyzed using the vector field analysis method to determine the main flow direction of heat in the region. This step uses common image segmentation and gradient calculation algorithms to convert continuous temperature data into spatial data reflecting heat flow characteristics, effectively revealing the dynamic distribution of local heat transfer. An alternative solution is to use the optical flow method, but gradient analysis and vector field statistics are more intuitive in terms of numerical accuracy.

[0045] The third step is to perform a time series analysis on the heat flux distribution data to calculate the rate of change of heat density in each area, and analyze the residence time of heat in the area in combination with the thermal diffusion coefficient of titanium alloy. Use a thermal sensor or infrared thermal imager to collect temperature data in each sub-area in real time, perform numerical differentiation calculation on the time series data, and obtain the rate of change of heat density, that is, the magnitude of change of heat density per unit time. At the same time, the obtained rate of change is compared with the thermal diffusion coefficient of titanium alloy (denoted as , in m² / s, according to the formula Calculate, where is the density, The heat conduction theory is used to evaluate the residence time of heat in each area. This analysis method can quantitatively describe the evolution of heat in the area over time and provide dynamic data support for subsequent risk assessment. Alternative solutions include numerical simulation, but direct time series analysis can more timely reflect the thermal dynamics in the actual processing environment.

[0046] The fourth step is to analyze the heat accumulation rate and diffusion pattern of each region based on the heat retention characteristic data, and calculate the heat accumulation risk index in combination with the critical oxidation temperature of titanium alloy. For each sub-region, the heat accumulation rate is calculated based on the above time series analysis results, and the heat diffusion pattern in the region is combined to describe the heat diffusion pattern in space. Using the critical oxidation temperature of titanium alloy (denoted as ) as a reference, compare the actual peak temperature with By comparing, a heat accumulation risk index is calculated by setting a weighted formula, which quantitatively describes the risk level of heat exceeding the safety threshold in the area. The generated heat accumulation pattern data can reflect the possibility of oxidation reaction caused by heat accumulation in each characteristic area during the laser cutting process, thereby providing a quantitative risk basis for real-time regulation of the cutting process. An alternative solution is to use a risk prediction model based on machine learning, but the weighted calculation method combined with the critical oxidation temperature is more physically based in theory.

[0047] Please continue reading Figure 1 , according to the target monitoring area data, obtaining the reflection spectrum information of the cutting area of ​​the titanium alloy structural part and the contrast spectrum information of the adjacent uncut area to obtain the spectrum characteristic data; In one embodiment of the present invention, the reflection spectrum information of the cutting area of ​​the titanium alloy structural part and the contrast spectrum information of the adjacent uncut area are obtained according to the target monitoring area data to obtain the spectrum characteristic data, including: Performing regional stratification processing on the target monitoring area data, dividing the surface of the titanium alloy structural part into a core cutting area, an edge transition area and a reference comparison area, and determining the acquisition frequency band of each area according to the spectral characteristic band of the titanium alloy to obtain spectral acquisition area data; According to the spectral acquisition area data, a multi-band spectral scan is performed on each area to identify the characteristic spectral peaks of the titanium alloy oxidation reaction, and the spectral reflection intensity of different acquisition frequency bands is calculated to obtain multi-band spectral data; Performing spatial distribution analysis on the multi-band spectral data, calculating the spectral intensity gradient between adjacent acquisition points, and analyzing the spatial migration law of characteristic spectral peaks to obtain spectral distribution characteristic data; According to the spectral distribution characteristic data, the spectral characteristics of the cut area and the adjacent uncut area are compared and analyzed, the spectral difference coefficient is calculated, and the oxidation degree index is calculated in combination with the oxidation spectral characteristics of the titanium alloy to obtain the spectral characteristic data.

[0048] The following is a specific description of the steps involved in the above embodiment: the first step is to use regional stratification processing based on the target monitoring area data to divide the surface of the titanium alloy structural part into a core cutting area, an edge transition area and a reference comparison area. Specifically, the infrared thermal imaging data or the digital image processing results are used to divide the workpiece surface into three hierarchical areas according to the temperature distribution and cutting requirements, wherein the core cutting area is the key area for laser cutting, the edge transition area is the area where the temperature and oxidation reaction change more slowly, and the reference comparison area is the area not affected by cutting. The digital image is processed using common image segmentation algorithms (such as threshold segmentation and regional growth algorithms), and the regional information pre-defined in the workpiece design drawing is combined to achieve accurate division. Subsequently, according to the characteristic spectral bands displayed by the titanium alloy during the oxidation process, the frequency bands required to be collected in each area are determined. For example, the core area uses a narrower band to cover the high oxidation sensitive signal, while the reference area uses a wider band for comparison. This step obtains accurate spectral acquisition area data through regional stratification and acquisition frequency band setting, providing clear spatial and frequency information for subsequent multi-band scanning; alternative solutions can use manual assisted regional division, but the automatic stratification method has data processing consistency and high efficiency.

[0049] In the second step, based on the spectral acquisition area data obtained in the first step, multi-band spectral scanning is performed on each area. A conventional programmable fiber optic spectrometer is used to scan each preset area separately to collect multi-band spectral data including ultraviolet, visible light and near infrared. The instrument records the reflection spectrum in each band according to the set acquisition frequency band, and the data output is the spectral reflection intensity of different acquisition frequency bands. The system's built-in algorithm detects typical spectral peaks that appear in the oxidation reaction of titanium alloys through characteristic spectral peak recognition technology, such as abnormal reflectivity at specific wavelengths caused by the formation of oxide film. This method can accurately capture different spectral information through multi-band scanning, and realize quantitative recording of the spectral characteristics of the oxidation reaction in the area; the alternative is single-band acquisition, but multi-band scanning can more comprehensively reflect the dynamic changes of the oxidation reaction.

[0050] The third step is to perform spatial distribution analysis on the multi-band spectral data obtained in the second step. Using digital signal processing technology, the spectral reflectance intensity data of each acquisition point are arranged according to their spatial coordinates, and the spectral intensity gradient between adjacent acquisition points is calculated, that is, the rate of change of the spectral signal in the local area is quantitatively described; at the same time, the spatial position migration law of the characteristic spectral peak is analyzed, and the direction and amplitude of the spectral peak movement are determined by statistical methods, thereby reflecting the evolution characteristics of the local oxidation reaction in spatial distribution. Commonly used image processing algorithms (such as gradient operators and edge detection) are used to calculate and analyze the spatial changes of spectral data to obtain spectral distribution characteristic data reflecting the spatial distribution of heat or oxidation effects; alternatives can use clustering methods based on local statistical characteristics, but gradient and migration law analysis more intuitively describes the trend of spectral changes.

[0051] The fourth step is to compare and analyze the spectral characteristics of the cutting area and the adjacent uncut area based on the spectral distribution characteristic data obtained in the third step. The specific operation is to calculate the point-by-point or regional average value of the spectral data of the cutting area and the reference comparison area, and then determine the spectral difference coefficient through data comparison. This coefficient quantitatively reflects the amplitude of spectral changes caused by the oxidation reaction in the cutting area. Subsequently, combined with the typical spectral characteristics of the titanium alloy oxidation reaction, the oxidation degree index is calculated based on the preset threshold and oxidation spectral characteristic parameters to quantify the formation of oxide film in the cutting area. This comparative analysis provides objective data reflecting the surface oxidation of the material during the laser cutting process through clear spectral differences and oxidation indices, which facilitates the dynamic adjustment of subsequent cutting parameters; alternatives can use data fitting or statistical test methods, but direct comparison and index calculation can more intuitively reveal the degree of oxidation.

[0052] In one embodiment of the present invention, the spectral acquisition area data is used to perform a multi-band spectral scan on each area, identify the characteristic spectral peaks of the titanium alloy oxidation reaction, and calculate the spectral reflection intensity of different acquisition frequency bands to obtain multi-band spectral data, including: Performing multi-band scanning on each area in the spectrum collection area data, setting a scanning band group based on the oxidation reaction characteristics of the titanium alloy, and classifying and collecting spectral information of different bands to obtain band spectral data; Characteristic peaks are identified on the spectrum data of the bands, the peak positions and peak intensities of the spectrum of the titanium alloy oxidation reaction in different bands are calculated, and the evolution characteristics of the spectrum peaks are analyzed according to the growth law of the oxide film to obtain the spectrum peak characteristic data; According to the spectral peak characteristic data, the spectral reflection intensity of different acquisition frequency bands is calculated, and the intensity correction is performed in combination with the energy coupling characteristics of the titanium alloy oxide film to obtain multi-band spectral data.

[0053] The following is a specific description of the steps involved in the above embodiment: the first step is to use a conventional programmable fiber optic spectrometer to perform multi-band scanning on each area in the spectral acquisition area data. The specific method is to set the scanning band group in advance according to the typical spectral characteristics exhibited by titanium alloy during the oxidation reaction process. These bands include ultraviolet, visible light and near-infrared regions, which can fully cover the characteristic signals appearing in the oxidation process. The instrument implements classified acquisition of each target area according to the preset band group, records the spectral information of different bands in each area separately, and outputs the band spectrum data. The digital signal processing system stores the collected data by region and band, while ensuring the time and space synchronization of each data channel. This method uses common spectral acquisition equipment, and can be replaced by manually setting the scanning band or using an automatic band adjustment algorithm, but multi-band classification acquisition can more comprehensively reflect the characteristics of the oxidation reaction, ensuring that the output band spectrum data can cover the information of each key band in the material oxidation process, thereby providing an accurate basis for the original spectral data.

[0054] The second step is to identify the characteristic peaks of the obtained band spectrum data. Digital signal processing technology and spectral data analysis algorithm are used to automatically extract the peak position and peak intensity of the reflection spectrum in each band data. The built-in spectral analysis software of the instrument identifies the characteristic spectral peaks that appear during the oxidation reaction of titanium alloy according to the set threshold and signal-to-noise ratio standard. Subsequently, the evolution trend of the spectral peak is tracked in time and space in combination with the growth law of the oxide film, the movement of the spectral peak position and the change of intensity are recorded, and the spectral peak characteristic data is output. This process can be implemented by a commonly used peak detection algorithm, or graphic processing or pattern recognition technology can be used to assist in the judgment of the data. The implementation of this step ensures the quantitative description of the key spectral features in the oxidation process, so that the spectral peak characteristic data accurately reflects the dynamic changes of the oxidation reaction state, and provides a clear basis for subsequent data correction.

[0055] The third step is to quantitatively calculate the original spectral reflection intensity of each acquisition frequency band by analyzing the previously obtained spectral peak characteristic data, and perform intensity correction on the data in combination with the energy coupling characteristics of the titanium alloy oxide film. The specific method is: under laboratory conditions, multi-band spectral measurements are performed on standard titanium alloy samples in different oxidation states, reflection intensity data in each band is collected, and the corresponding oxidation state parameters are recorded. Using these experimental data, a correction model is constructed by regression analysis or curve fitting method. The model establishes a mapping relationship between the original reflection intensity of each band and the influence of the oxide film on the laser energy coupling, thereby determining the correction factor of each band. In the actual processing process, the reflection intensity in the original spectral data of each region is corrected according to the corresponding correction factor applied to its acquisition band to generate adjusted multi-band spectral data. This correction process ensures that the output spectral data can truly reflect the influence of the change of the oxidation state of the titanium alloy surface on the energy coupling during laser cutting, and provides an accurate and reliable spectral basis for subsequent oxidation state analysis and process control.

[0056] Please continue reading Figure 1 , performing spatiotemporal coordinated analysis on the spectral characteristic data, extracting spectral intensity change values, peak shape change values ​​and peak position drift values, and combining the spatial diffusion rate and time evolution data of the oxidation reaction to obtain oxidation state prediction data; In one embodiment of the present invention, the spectral feature data is subjected to a spatiotemporal coordinated analysis to extract spectral intensity change values, peak shape change values, and peak position drift values, and the oxidation state prediction data is obtained by combining the spatial diffusion rate and time evolution data of the oxidation reaction, including: The spectral characteristic data are processed in time series segments, characteristic change values ​​of each time period are calculated according to the change rates of spectral intensity, peak shape and peak position, and the change trend is analyzed in combination with the oxidation kinetic parameters of the titanium alloy to obtain time series characteristic data; Performing spatial mapping analysis on the time series characteristic data, calculating the spectral characteristic difference values ​​between adjacent regions, and determining the propagation direction and rate of the oxidation reaction according to the oxidation diffusion characteristics of the titanium alloy to obtain spatial propagation data; According to the spatial propagation data, the diffusion process of the oxidation reaction is analyzed in time and space, the oxidation reaction activity index of different regions is calculated, and the reaction chain effect is analyzed in combination with the critical oxidation temperature of the titanium alloy to obtain the oxidation state prediction data.

[0057] The following is a specific description of the steps involved in the above embodiment: The first step is to perform time-series segmentation processing on the spectral feature data. Using the continuous spectral data obtained from the spectral acquisition area, the acquired data is segmented on the time axis through digital signal processing software, and each segment corresponds to a spectral record within a certain period of time. A method based on data smoothing and differential analysis is used to quantitatively calculate the spectral intensity, peak shape, and peak position changes in each segment, thereby generating a characteristic change value for each time period. At the same time, combined with the oxidation kinetic parameters of titanium alloys (such as oxidation reaction rate constants, activation energy and other material parameters), the characteristic change values ​​of each time period are trend analyzed to form time-series feature data that describes the dynamic evolution of the oxidation process. This step uses digital signal processing and statistical analysis methods to achieve quantitative division and change trend description of continuous spectral data in the time domain, ensuring that the data can reflect the real-time dynamics of the oxidation reaction during laser cutting.

[0058] The second step is to perform spatial mapping analysis on the time series feature data. Using the aforementioned time series feature data, by associating the spectral feature value corresponding to each time period with the specific position on the workpiece surface, the spatial mapping algorithm is used to compare and calculate the spectral features of each position in the acquisition area to obtain the spectral feature difference value between adjacent areas. According to the oxidation diffusion characteristics of titanium alloys, by comparing the feature differences between different regions, the propagation direction and rate of the oxidation reaction on the workpiece surface are determined. This method uses image processing and data comparison technology to accurately correspond time series data to spatial coordinates. The output spatial propagation data can intuitively reflect the diffusion path and diffusion rate of the oxidation reaction in the local area, providing a quantitative basis for subsequent dynamic regulation.

[0059] The third step is to conduct spatiotemporal coupling analysis based on the spatial propagation data. The spatial propagation data is combined with the corresponding time series information, and a comprehensive data analysis method is used to couple the oxidation reaction diffusion process in each area of ​​the workpiece surface, and the oxidation reaction activity index of each area is calculated. This activity index reflects the intensity and diffusion trend of the oxidation reaction in each area. When analyzing, the critical oxidation temperature of the titanium alloy is combined to compare the actual temperature in the area with the critical oxidation temperature of the material, thereby quantifying the reaction chain effect. The oxidation state prediction data finally generated provides an accurate description of the oxidation risk for real-time control during the laser cutting process, and can guide the adjustment of process parameters to ensure the stable transmission of laser energy during the cutting process.

[0060] Please continue reading Figure 1 According to the oxidation state prediction data, the cutting power is adjusted in different regions, and the flow field parameters of the shielding gas are dynamically compensated to execute the cutting of the titanium alloy structural part.

[0061] In one embodiment of the present invention, the cutting power is adjusted in different regions according to the oxidation state prediction data, and the flow field parameters of the shielding gas are dynamically compensated to perform the cutting of the titanium alloy structural part, including: Performing partition mapping on the oxidation state prediction data and the cutting path of the titanium alloy structural part, determining the energy coupling efficiency threshold and critical oxidation temperature of each region, calculating the attenuation coefficient of the oxide layer thickness to the laser energy, and obtaining the energy compensation parameter; Based on the energy compensation parameters, the cutting power correction values ​​of the corner transition segment, the straight segment and the curved segment are calculated respectively, and the power adjustment step is calculated according to the oxidation activity of the titanium alloy at different temperatures to obtain the cutting parameter data; The cutting parameter data is analyzed, the flow field deformation of the protective gas in the high temperature area is calculated, the pressure distribution characteristics of the airflow disturbance area are determined, and the gas flow direction and flow rate are adjusted according to the temperature field distribution in the cutting area to execute the cutting of the titanium alloy structural part.

[0062] The following is a specific description of the steps involved in the above embodiment: The first step is to perform partition mapping of the oxidation state prediction data and the cutting path of the titanium alloy structural part. Using the digitized cutting path data and the real-time collected oxidation state data, regional mapping is achieved through image processing software, and the entire cutting path is divided into several independent areas. In each area, the threshold of the laser energy coupling efficiency and the critical temperature of the material in the oxidation reaction are determined based on the monitoring data. Conventional optical or infrared microscopes are used to detect the surface of each area to obtain the oxide layer thickness information, and the degree of attenuation of the laser energy by the unit thickness oxide layer is calculated in combination with the quantitative relationship between the oxide layer and the laser energy attenuation previously determined by experiments. The oxide layer thickness is multiplied by the attenuation degree to obtain the energy compensation parameters of each area. This method achieves a quantitative description of the laser energy attenuation caused by oxidation in different areas on the cutting path through precise mapping and empirical data correction.

[0063] In the second step, based on the energy compensation parameters obtained, the cutting power correction values ​​are calculated for the corner transition segment, straight segment and curve segment respectively. First, the cutting path is divided into three segment types according to the geometric characteristics, and then the laser power of each area is dynamically adjusted and calculated by combining the energy compensation parameters of each area with the laser cutting data obtained by the pre-experimental calibration. Using standard digital signal processing technology, the actual laser power is compared with the theoretical correction value, and a power adjustment step is determined as the minimum adjustment unit of the laser power based on the oxidation activity of titanium alloy under different temperature conditions. The final output cutting parameter data includes the laser power correction value required for each cutting segment and the corresponding adjustment step, so as to achieve precise control of the laser energy in different geometric segments.

[0064] The third step is to adjust the state of the protective gas flow field in the high-temperature area after parsing the cutting parameter data. Using high-precision pressure sensors and infrared thermal imagers, the temperature and airflow data in the cutting area are collected in real time. The deformation of the protective gas flow field in the high-temperature area is quantified through digital signal processing technology to obtain the pressure distribution characteristics of the disturbance area. Subsequently, these pressure and temperature data are compared to determine the location and degree of the airflow disturbance area, and the flow direction and flow rate of the protective gas (such as nitrogen or argon) supply system are adjusted based on this information to ensure that a stable gas barrier is maintained during the cutting process. This method directly uses real-time sensor data and known physical associations to achieve dynamic regulation of the protective gas flow field, thereby ensuring the stability of energy transfer between the laser and the workpiece surface, and ensuring high precision and consistency of the cutting process.

[0065] The above describes the intelligent cutting method of the titanium alloy structural member in the embodiment of the present invention. The following describes the intelligent cutting device of the titanium alloy structural member in the embodiment of the present invention. Figure 2 , an embodiment of the intelligent cutting device for titanium alloy structural parts in the embodiment of the present invention includes: The thermal field prediction module 101 is used to analyze the change rate of the temperature gradient according to the temperature distribution data on the surface of the titanium alloy structural part, and determine the key area prone to oxidation reaction in combination with the geometric characteristics of the titanium alloy structural part to obtain the target monitoring area data; The spectrum acquisition module 102 is used to obtain the reflection spectrum information of the cutting area of ​​the titanium alloy structural part and the contrast spectrum information of the adjacent uncut area according to the target monitoring area data, so as to obtain the spectrum characteristic data; The oxidation analysis module 103 is used to perform spatiotemporal coordinated analysis on the spectral feature data, extract spectral intensity change values, peak shape change values ​​and peak position drift values, and obtain oxidation state prediction data by combining the spatial diffusion rate and time evolution data of the oxidation reaction; The parameter control module 104 is used to adjust the cutting power in different regions according to the oxidation state prediction data, and dynamically compensate the flow field parameters of the shielding gas to execute the cutting of the titanium alloy structural part.

[0066] above Figure 2 The intelligent cutting device for titanium alloy structural parts in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The intelligent cutting device for titanium alloy structural parts in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0067] Figure 31 is a schematic diagram of the structure of an intelligent cutting device for titanium alloy structural parts provided by an embodiment of the present invention. The intelligent cutting device 200 for titanium alloy structural parts may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 210 (for example, one or more processors) and a memory 220, and one or more storage media 230 (for example, one or more mass storage device terminals) storing application programs 233 or data 232. Among them, the memory 220 and the storage medium 230 can be short-term storage or permanent storage. The program stored in the storage medium 230 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the intelligent cutting device 200 for titanium alloy structural parts. Furthermore, the processor 210 may be configured to communicate with the storage medium 230, and execute a series of instruction operations in the storage medium 230 on the intelligent cutting device 200 for titanium alloy structural parts to implement the steps of the intelligent cutting method for titanium alloy structural parts described above.

[0068] The intelligent cutting device 200 for titanium alloy structural parts may also include one or more power supplies 240, one or more wired or wireless network interfaces 250, one or more input and output interfaces 260, and / or one or more operating systems 231, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It can be understood by those skilled in the art that Figure 3 The structure of the intelligent cutting device for titanium alloy structural parts shown does not constitute a limitation on the intelligent cutting device for titanium alloy structural parts provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0069] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the intelligent cutting method for titanium alloy structural parts.

[0070] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0071] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0072] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. An intelligent cutting method for titanium alloy structural parts, characterized in that: include: Analyzing the rate of change of the temperature gradient according to the temperature distribution data on the surface of the titanium alloy structural part, and determining the key area prone to oxidation reaction in combination with the geometric characteristics of the titanium alloy structural part, to obtain the target monitoring area data; According to the target monitoring area data, obtaining the reflection spectrum information of the cutting area of ​​the titanium alloy structural part and the comparison spectrum information of the adjacent uncut area to obtain the spectrum characteristic data; Performing spatiotemporal coordinated analysis on the spectral characteristic data, extracting spectral intensity change values, peak shape change values ​​and peak position drift values, and combining the spatial diffusion rate and time evolution data of the oxidation reaction to obtain oxidation state prediction data; According to the oxidation state prediction data, the cutting power is adjusted in different regions, and the flow field parameters of the protective gas are dynamically compensated to execute the cutting of the titanium alloy structural part.

2. The intelligent cutting method for titanium alloy structural parts according to claim 1, characterized in that: The process of acquiring the temperature distribution data on the surface of the titanium alloy structural part includes: According to the cutting trajectory of the titanium alloy structural part, the cutting path is divided into straight segments, corner segments and curve segments, and the heat accumulation characteristics of different types of segments are determined according to the thermal conductivity parameters of the titanium alloy to obtain the heat accumulation characteristic marking data; According to the heat accumulation characteristic mark data, an initial heat conduction prediction zone is established along the cutting track, wherein the width and shape of the initial heat conduction prediction zone are adapted to the heat accumulation characteristics of different cutting sections, and prediction zone contour data is obtained; Setting a temperature monitoring point array based on the predicted band profile data, collecting real-time temperature of the temperature monitoring point array, calculating the heat diffusion direction and rate according to the temperature change trend, and obtaining heat conduction characteristic data; According to the heat conduction characteristic data, the temperature anomaly points are dynamically spatially clustered, and the boundaries of the clustering areas are adjusted in combination with the heat diffusion direction and rate to generate temperature distribution data on the surface of the titanium alloy structural part.

3. The intelligent cutting method for titanium alloy structural parts according to claim 1, characterized in that: The method of analyzing the rate of change of the temperature gradient according to the temperature distribution data on the surface of the titanium alloy structural part and determining the key area prone to oxidation reaction in combination with the geometric characteristics of the titanium alloy structural part to obtain the target monitoring area data includes: Performing gradient scanning on the temperature distribution data on the surface of the titanium alloy structural part, calculating the rate of change of the temperature gradient, and performing heat partitioning on the surface of the titanium alloy structural part according to the rate of change to obtain heat distribution characteristic data; Performing geometric feature analysis on the surface profile of the titanium alloy structural part, identifying the corner transition area, the groove convergence area and the thickness mutation area, and determining the heat accumulation risk of each characteristic area in combination with the heat distribution characteristic data to obtain regional risk characteristic data; According to the regional risk characteristic data, combined with the thermal conductivity parameters of titanium alloy, the heat transfer rate between different characteristic regions is calculated, and the heat interaction relationship between adjacent regions is analyzed to obtain the heat transfer chain data; Based on the heat transfer chain data and in combination with the oxidation activity parameters of the titanium alloy, the oxidation reaction probability of each area is calculated, and the chain propagation law of the oxidation reaction is analyzed to obtain the target monitoring area data.

4. The intelligent cutting method for titanium alloy structural parts according to claim 3 is characterized in that: The geometric feature analysis is performed on the surface profile of the titanium alloy structural part to identify the corner transition area, the groove convergence area and the thickness mutation area, and the heat accumulation risk of each characteristic area is determined in combination with the heat distribution characteristic data to obtain the regional risk characteristic data, including: Performing curvature analysis on the surface profile of the titanium alloy structural part, calculating the curvature value of each point on the surface, identifying the geometric feature mutation point according to the change rate of the curvature value, and obtaining feature point distribution data; According to the characteristic point distribution data, the connectivity analysis of adjacent characteristic points is performed to divide the corner transition area, the groove convergence area and the thickness mutation area, and the area and depth parameters of each area are calculated to obtain characteristic area parameter data; Cross-mapping the characteristic area parameter data with the heat distribution characteristic data, calculating the heat density value in each characteristic area, and analyzing the flow trend of heat in the characteristic area to obtain heat accumulation pattern data; According to the heat accumulation pattern data, combined with the specific heat capacity parameters of titanium alloy, the heat storage capacity and heat diffusion coefficient of each characteristic area are calculated to obtain the regional risk characteristic data.

5. The intelligent cutting method for titanium alloy structural parts according to claim 4, characterized in that: The cross-mapping of the characteristic area parameter data with the heat distribution characteristic data, calculating the heat density value in each characteristic area, and analyzing the flow trend of heat in the characteristic area to obtain heat accumulation pattern data includes: Performing weight calculation on the regional area and depth parameters in the characteristic regional parameter data to generate a regional shape coefficient, and correcting each regional shape coefficient according to the thermal conductivity of the titanium alloy to obtain regional characteristic weight data; According to the regional characteristic weight data, the heat distribution characteristic data is divided into regions, and the heat density gradient and heat flow direction in each region are calculated to obtain heat flow distribution data; Performing a time series analysis on the heat flux distribution data, calculating the rate of change of heat density in each region, and analyzing the retention time of heat in the region in combination with the thermal diffusion coefficient of the titanium alloy to obtain heat retention characteristic data; According to the heat retention characteristic data, the heat accumulation rate and diffusion pattern of each region are analyzed, and the heat accumulation risk index is calculated in combination with the critical oxidation temperature of the titanium alloy to obtain the heat accumulation pattern data.

6. The intelligent cutting method for titanium alloy structural parts according to claim 1, characterized in that: The step of obtaining the reflection spectrum information of the cutting area of ​​the titanium alloy structural part and the contrast spectrum information of the adjacent uncut area according to the target monitoring area data to obtain the spectrum characteristic data includes: Performing regional stratification processing on the target monitoring area data, dividing the surface of the titanium alloy structural part into a core cutting area, an edge transition area and a reference comparison area, and determining the acquisition frequency band of each area according to the spectral characteristic band of the titanium alloy to obtain spectral acquisition area data; According to the spectral acquisition area data, a multi-band spectral scan is performed on each area to identify the characteristic spectral peaks of the titanium alloy oxidation reaction, and the spectral reflection intensity of different acquisition frequency bands is calculated to obtain multi-band spectral data; Performing spatial distribution analysis on the multi-band spectral data, calculating the spectral intensity gradient between adjacent acquisition points, and analyzing the spatial migration law of characteristic spectral peaks to obtain spectral distribution characteristic data; According to the spectral distribution characteristic data, the spectral characteristics of the cut area and the adjacent uncut area are compared and analyzed, the spectral difference coefficient is calculated, and the oxidation degree index is calculated in combination with the oxidation spectral characteristics of the titanium alloy to obtain the spectral characteristic data.

7. The intelligent cutting method for titanium alloy structural parts according to claim 6, characterized in that: According to the spectral acquisition area data, a multi-band spectral scan is performed on each area to identify the characteristic spectral peaks of the titanium alloy oxidation reaction, and the spectral reflection intensity of different acquisition frequency bands is calculated to obtain multi-band spectral data, including: Performing multi-band scanning on each area in the spectrum collection area data, setting a scanning band group based on the oxidation reaction characteristics of the titanium alloy, and classifying and collecting spectral information of different bands to obtain band spectral data; Characteristic peaks are identified on the spectrum data of the bands, the peak positions and peak intensities of the spectrum of the titanium alloy oxidation reaction in different bands are calculated, and the evolution characteristics of the spectrum peaks are analyzed according to the growth law of the oxide film to obtain the spectrum peak characteristic data; According to the spectral peak characteristic data, the spectral reflection intensity of different acquisition frequency bands is calculated, and the intensity correction is performed in combination with the energy coupling characteristics of the titanium alloy oxide film to obtain multi-band spectral data.

8. The intelligent cutting method for titanium alloy structural parts according to claim 1, characterized in that: The spectral characteristic data is subjected to a spatiotemporal coordinated analysis to extract spectral intensity change values, peak shape change values ​​and peak position drift values, and the oxidation state prediction data is obtained by combining the spatial diffusion rate and time evolution data of the oxidation reaction, including: The spectral characteristic data are processed in time series segments, characteristic change values ​​of each time period are calculated according to the change rates of spectral intensity, peak shape and peak position, and the change trend is analyzed in combination with the oxidation kinetic parameters of the titanium alloy to obtain time series characteristic data; Performing spatial mapping analysis on the time series characteristic data, calculating the spectral characteristic difference values ​​between adjacent regions, and determining the propagation direction and rate of the oxidation reaction according to the oxidation diffusion characteristics of the titanium alloy to obtain spatial propagation data; According to the spatial propagation data, the diffusion process of the oxidation reaction is analyzed in time and space, the oxidation reaction activity index of different regions is calculated, and the reaction chain effect is analyzed in combination with the critical oxidation temperature of the titanium alloy to obtain the oxidation state prediction data.

9. The intelligent cutting method for titanium alloy structural parts according to claim 1, characterized in that: The method of adjusting the cutting power by region according to the oxidation state prediction data and dynamically compensating the flow field parameters of the shielding gas to cut the titanium alloy structural part comprises: Performing partition mapping on the oxidation state prediction data and the cutting path of the titanium alloy structural part, determining the energy coupling efficiency threshold and critical oxidation temperature of each region, calculating the attenuation coefficient of the oxide layer thickness to the laser energy, and obtaining the energy compensation parameter; Based on the energy compensation parameters, the cutting power correction values ​​of the corner transition segment, the straight segment and the curved segment are calculated respectively, and the power adjustment step is calculated according to the oxidation activity of the titanium alloy at different temperatures to obtain the cutting parameter data; The cutting parameter data is analyzed, the flow field deformation of the protective gas in the high temperature area is calculated, the pressure distribution characteristics of the airflow disturbance area are determined, and the gas flow direction and flow rate are adjusted according to the temperature field distribution in the cutting area to execute the cutting of the titanium alloy structural part.

10. An intelligent cutting device for titanium alloy structural parts, characterized in that: The intelligent cutting device for titanium alloy structural parts adopts the intelligent cutting method for titanium alloy structural parts according to any one of claims 1 to 9, and the intelligent cutting device for titanium alloy structural parts comprises: A thermal field prediction module is used to analyze the rate of change of the temperature gradient according to the temperature distribution data on the surface of the titanium alloy structural part, and determine the key area prone to oxidation reaction in combination with the geometric characteristics of the titanium alloy structural part to obtain the target monitoring area data; A spectrum acquisition module is used to obtain the reflection spectrum information of the cutting area of ​​the titanium alloy structural part and the contrast spectrum information of the adjacent uncut area according to the target monitoring area data, so as to obtain spectrum characteristic data; An oxidation analysis module is used to perform spatiotemporal coordinated analysis on the spectral feature data, extract spectral intensity change values, peak shape change values ​​and peak position drift values, and obtain oxidation state prediction data by combining the spatial diffusion rate and time evolution data of the oxidation reaction; The parameter control module is used to adjust the cutting power in different regions according to the oxidation state prediction data, and dynamically compensate the flow field parameters of the protective gas to execute the cutting of the titanium alloy structural part.

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