A high-precision complex surface processing method for titanium alloy materials

Abstract

The present invention relates to the technical field of titanium alloy processing, specifically a method for processing high-precision complex curved surfaces of titanium alloy materials, comprising the following steps: collecting temperature data and vibration data in the processing of titanium alloy materials, performing time series analysis and frequency domain analysis on the data, identifying key frequency domain features, and comparing historical wear data, evaluating the degree of wear of the tool, and generating wear feature identification data. In the present invention, by performing time series and frequency domain analysis on the temperature and vibration data in the processing of titanium alloy materials, the degree of wear of the tool can be more accurately evaluated, data support is provided for the adjustment of tool parameters, and the use efficiency and durability of the tool are optimized. In particular, in the processing of high-precision complex curved surfaces, the processing quality can be effectively controlled, and real-time image monitoring and dynamic cutting parameter adjustment further ensure the adaptability and feedback speed of the processing process, allowing instant adjustment to ensure the processing accuracy and quality of the titanium alloy material.

Description

Technical Field

[0001] The invention relates to the technical field of titanium alloy processing, and in particular to a method for processing a titanium alloy material with high precision and complex curved surfaces. Background Art

[0002] The field of titanium alloy processing technology focuses on the mechanical processing of titanium and its alloys. The material is widely used in aerospace, biomedicine, chemical industry and high-performance racing fields due to its excellent mechanical properties and corrosion resistance. Titanium alloy has high strength, low density and excellent high temperature resistance, but it also faces problems such as difficult processing, fast tool wear and high processing costs. Titanium alloy processing technology includes traditional mechanical processing methods such as milling, turning and drilling, as well as electron beam processing, laser processing and chemical processing.

[0003] Among them, the high-precision complex surface processing method of titanium alloy materials involves the use of mechanical processing technology to process titanium alloy materials to form surfaces with high precision and complex geometric shapes. It is usually used in aerospace components, medical devices such as joint replacements and dental implants. These application fields have very high requirements on the accuracy and complexity of materials. By precisely controlling the trajectory and parameters of the cutting tool, and using special fixtures and high-performance cutting fluids, titanium alloy complex surface parts can be effectively processed to meet specific functional and performance requirements.

[0004] When processing complex geometric shapes, traditional processing technology has inaccurate tool trajectory and parameter control, resulting in insufficient processing precision. There is a lack of real-time data analysis and instant adjustment mechanism. Once the tool deviates, the workpiece will be scrapped, increasing the waste of materials and time. The technical shortcomings not only increase production costs, but also limit the use of titanium alloys in more high-demand application fields. The problems of rapid tool wear and difficulty in predicting tool life in traditional technology further increase the cost of use and the difficulty of maintenance. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a high-precision complex curved surface processing method for titanium alloy materials.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for high-precision complex curved surface processing of titanium alloy material, comprising the following steps:

[0007] S1: Collect temperature data and vibration data during titanium alloy material processing, perform time series analysis and frequency domain analysis on the data, identify key frequency domain features, compare historical wear data, evaluate the wear degree of the tool, and generate wear feature identification data;

[0008] S2: Based on the wear feature identification data, adjust tool parameters, including cutting speed and feed speed, record and analyze tool performance data, optimize the microstructure and processing efficiency of the tool by adjusting the heat treatment process, and generate structural optimization indicators;

[0009] S3: Based on the structural optimization index, using machine vision technology, monitor the real-time image of the titanium alloy material surface processing, identify the changes in the tool trajectory and the material contact surface, determine whether the cutting parameters need to be adjusted, and generate adjustment feedback judgment records;

[0010] S4: According to the adjustment feedback judgment record, the tool and processing parameters are readjusted, the tool performance and material processing information are monitored again, and it is verified whether the surface processing quality of the titanium alloy material meets the design requirements, and a qualified titanium alloy quality result is generated.

[0011] The present invention is improved in that the step of identifying the key frequency domain features is specifically as follows:

[0012] S111: Collect temperature and vibration data during titanium alloy material processing, adjust the data scale, and use the formula:

[0013]

[0014] Get time Normalized data ,in, Indicates time The original data points, is the data mean, is the standard deviation, is the coefficient that adjusts the normalized intensity, is a constant;

[0015] S112: Perform fast Fourier transform on the normalized data, identify the frequency components in the data, and perform frequency domain transformation using the formula:

[0016]

[0017] Get frequency domain features ,in, It's time The normalized data, Represents the total number of data points, is the frequency adjustment factor, is a frequency variable, is an imaginary unit, is the time index;

[0018] S113: Based on the frequency domain features, identify key features through thresholds, and use a judgment formula:

[0019]

[0020] Get the key frequency domain features ,in, represents the amplitude of the frequency domain feature, is the amplitude threshold, is the coefficient for adjusting the recognition sensitivity.

[0021] The present invention is improved in that the step of acquiring the wear feature identification data is specifically as follows:

[0022] S121: Based on the historical wear data, including vibration and load data, a statistical average of the historical data is calculated using the formula:

[0023]

[0024] Get the historical average ,in, Representative Historical data points, is the number of data points;

[0025] S122: Based on the historical average value, in the current processing cycle, the same tool performance data is monitored and recorded in real time, and the real-time data is processed using the formula:

[0026]

[0027] Calculate real-time average , It is Real-time data points, is the number of data points;

[0028] S123: Based on the historical average value and the real-time average value, the absolute difference is used to evaluate the degree of wear, using the formula:

[0029]

[0030] Get wear difference data ,in, is the historical average, It is the real-time average value.

[0031] The present invention is improved in that the step of adjusting the tool parameters is specifically as follows:

[0032] S211: Based on the wear feature identification data, the cutting force and surface roughness are analyzed, and the temperature and tool usage time are combined to adopt the formula:

[0033]

[0034] Get the performance degradation factor ,in, and is the weight coefficient, and Indicates the new and old cutting forces, and Indicates the roughness of the new and old surfaces. and It is the coefficient to adjust the temperature change and usage time. and are the old and new temperature values, is the new usage duration;

[0035] S212: Based on the performance degradation factor, the cutting speed and feed speed are adjusted using the formula:

[0036]

[0037] and

[0038]

[0039] Get the adjusted cutting speed and feed speed ,in, and is the adjustment factor, and are the original cutting speed and feed rate, is the performance degradation factor.

[0040] The present invention is improved in that the steps of obtaining the structural optimization index are specifically as follows:

[0041] S221: Obtain the microstructure data of the tool, and calculate the average value of the grain size in combination with the grain size, using the formula:

[0042]

[0043] Get the average grain size ,in, It is The observed grain size, is the sample size;

[0044] S222: Based on the average grain size, the heat treatment temperature is adjusted to optimize the microstructure using the formula:

[0045]

[0046] Get the adjusted heat treatment temperature ,in, is the temperature regulation coefficient, is the base temperature, is the average grain size;

[0047] S223: Testing the adjusted heat treatment temperature, collecting performance data, using the formula:

[0048]

[0049] Calculate the improvement index ,in, and These are the performance data before and after adjustment.

[0050] The present invention is improved in that the real-time image monitoring step is specifically as follows:

[0051] S311: Based on the structural optimization index, a camera is set to continuously capture real-time images during the surface processing of the titanium alloy material, and each frame of the image is analyzed in real time using the formula:

[0052]

[0053] Get the total size of the image , used to evaluate the lighting conditions and image quality of the processing area, where It is The brightness value of the pixel, is the total number of pixels in the image;

[0054] S312: Based on the total measurement of the image, edge detection and contour tracking technology are applied to analyze the real-time image, and key visual features of the contact surface between the tool trajectory and the material are automatically identified, using the formula:

[0055]

[0056] Calculate trajectory contrast , which is used to quantify the visual contrast between the tool path and the material interface, where It is The brightness value of the pixel, and Indicates the maximum and minimum brightness values ​​in the image.

[0057] The present invention is improved in that the step of obtaining the adjustment feedback judgment record is specifically as follows:

[0058] S321: According to the real-time image, the change of the contact surface between the tool and the material is identified, using the formula:

[0059]

[0060] Get the change index ,in, Indicates The visual parameters of the frame, is the average visual parameter, is the number of frames analyzed;

[0061] S322: Based on the change index, determine whether the cutting parameters need to be adjusted. If the change index exceeds a threshold, record feedback information that needs to be adjusted, using the judgment formula:

[0062]

[0063] Get adjustment feedback judgment record , where 1 means adjustment is required and 0 means no adjustment is required. is the threshold value, is the change index.

[0064] The present invention is improved in that the steps for obtaining the qualified quality result of the titanium alloy are specifically as follows:

[0065] S411: According to the adjustment feedback judgment record, recalculate and set the tool parameters, using the formula:

[0066]

[0067] and

[0068]

[0069] Get the adjusted cutting speed and feed speed ,in, and Indicates the current cutting speed and feed speed. is the speed proportional parameter adjusted based on feedback, It is the feed ratio parameter adjusted based on feedback;

[0070] S412: Collect tool performance data and material processing quality after readjustment, evaluate the adjustment effect, and use the formula:

[0071]

[0072] Get average processing quality ,in, Representative Quality indicators of each measuring point, It is The weight of the measurement points, is the total number of measurement points;

[0073] S413: Based on the average processing quality, verify whether the quality of the titanium alloy material surface processing meets the design requirements, using the formula:

[0074]

[0075] Get the mass ratio ,like The material processing quality is qualified, among which, is the average processing quality, is the desired quality standard threshold.

[0076] Compared with the prior art, the advantages and positive effects of the present invention are:

[0077] In the present invention, by performing time series and frequency domain analysis on the temperature and vibration data in the processing of titanium alloy materials, the degree of tool wear can be evaluated more accurately, data support is provided for the adjustment of tool parameters, and the use efficiency and durability of the tool are optimized. Especially in the processing of high-precision complex surfaces, the processing quality can be effectively controlled. Real-time image monitoring and dynamic cutting parameter adjustment further ensure the adaptability and feedback speed of the processing process, allow instant adjustment, ensure the processing accuracy and quality of titanium alloy materials, and reduce material waste and production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A flow chart of a method for high-precision complex curved surface processing of titanium alloy material is proposed for the present invention;

[0079] Figure 2 This is a flow chart for identifying key frequency domain features in the present invention;

[0080] Figure 3 A flowchart for obtaining wear feature identification data in the present invention;

[0081] Figure 4 A flow chart of tool parameter adjustment in the present invention;

[0082] Figure 5 A flowchart for obtaining the structural optimization index in the present invention;

[0083] Figure 6 It is a monitoring flow chart of real-time images in the present invention;

[0084] Figure 7 A flowchart for obtaining adjustment feedback judgment records in the present invention;

[0085] Figure 8 It is a flow chart for obtaining the qualified quality result of titanium alloy in the present invention. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0087] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0088] Example

[0089] See also Figure 1 The present invention provides a technical solution: a method for high-precision complex curved surface processing of titanium alloy materials, comprising the following steps:

[0090] S1: Collect temperature data and vibration data during titanium alloy material processing, perform time series analysis and frequency domain analysis on the data, identify key frequency domain features, compare historical wear data, evaluate the wear degree of the tool, and generate wear feature identification data;

[0091] S2: Based on the wear feature identification data, adjust the tool parameters, including cutting speed and feed speed, record and analyze the tool performance data, optimize the tool microstructure and processing efficiency by adjusting the heat treatment process, and generate structural optimization indicators; the tool microstructure is specifically the organizational structure of the tool material at the microscopic level, including grain size, morphology, phase distribution and its interface characteristics.

[0092] S3: Based on the structural optimization index, the machine vision technology is used to monitor the real-time image of the titanium alloy material surface processing, identify the changes in the tool trajectory and the material contact surface, determine whether the cutting parameters need to be adjusted, and generate adjustment feedback judgment records;

[0093] S4: According to the adjustment feedback judgment record, the tool and processing parameters are adjusted again, the tool performance and material processing information are monitored again, and it is verified whether the surface processing quality of the titanium alloy material meets the design requirements, and the titanium alloy quality qualified result is generated.

[0094] Wear feature identification data include hot spot areas, vibration frequency, and wear rate. Structural optimization indicators include hardness enhancement information, corrosion resistance, and elastic modulus. Adjustment feedback judgment records include parameter compatibility and safety margin. Titanium alloy quality qualification results include dimensional accuracy, geometric integrity, and material mechanical properties.

[0095] See also Figure 2 , the identification steps of key frequency domain features are as follows:

[0096] S111: Collect temperature and vibration data during titanium alloy material processing, adjust the data scale, and use the formula:

[0097]

[0098] Get time Normalized data ,in, Indicates time The original data points, is the data mean, is the standard deviation, is the coefficient that adjusts the normalized intensity, is a constant used to ensure numerical stability;

[0099] S112: Perform fast Fourier transform on the normalized data, identify the frequency components in the data, and perform frequency domain transformation using the formula:

[0100]

[0101] Get frequency domain features ,in, It's time The normalized data, Represents the total number of data points, is the frequency adjustment factor, is a frequency variable, is an imaginary unit, is the time index;

[0102] S113: Based on the frequency domain features, the key features are identified through the threshold, and the judgment formula is adopted:

[0103]

[0104] Get the key frequency domain features ,in, represents the amplitude of the frequency domain feature, is the amplitude threshold, is the coefficient for adjusting the recognition sensitivity.

[0105] Assume data set , the calculation process is as follows:

[0106]

[0107]

[0108] Assumptions , and ;

[0109] calculate When (corresponding to data point 10):

[0110]

[0111] After normalization It is approximately -0.117, which means that after normalization, the original data point 10 is adjusted to a position closer to the center of the dataset relative to the range and distribution of the dataset.

[0112] Use the previous step Data, assumptions and ,choose (Base frequency):

[0113]

[0114] The value of will provide the specific frequency in the frequency domain The amplitude and phase information reflects the performance of the time series at this frequency.

[0115] Assumptions is 4, is 0.5, is 0.2;

[0116]

[0117] Since 0.5 is less than 0.8, is 0, the frequency The amplitude of is not enough to exceed the threshold and is therefore not considered a key frequency domain feature, indicating that the signal at this frequency is not significant.

[0118] See also Figure 3 , the specific steps for acquiring wear feature recognition data are:

[0119] S121: Based on historical wear data, including vibration and load data, calculate the statistical average of historical data using the formula:

[0120]

[0121] Get the historical average ,in, Representative Historical data points, is the number of data points;

[0122] S122: Based on the historical average value, in the current machining cycle, the same tool performance data is monitored and recorded in real time, and the real-time data is processed using the formula:

[0123]

[0124] Calculate real-time average , It is Real-time data points, is the number of data points;

[0125] S123: Based on the historical average and the real-time average, the absolute difference is used to evaluate the degree of wear, using the formula:

[0126]

[0127] Get wear difference data ,in, is the historical average, It is the real-time average value.

[0128] Assume the following data:

[0129] for ;

[0130] ;

[0131] Calculate the historical average:

[0132]

[0133] The historical average is 12.2, which represents the average vibration value of the tool in past vibration tests.

[0134] Assumptions:

[0135] for ;

[0136] ;

[0137] Calculate real-time average:

[0138]

[0139] The real-time average value is 14.0, indicating the average vibration value of the current tool in the vibration test.

[0140] Based on the above results and .

[0141] Calculate the wear difference:

[0142]

[0143] The wear difference is 1.8, which means that the vibration level of the current tool has increased by 1.8 compared to the historical data, indicating that there is increased wear.

[0144] See also Figure 4 , the specific steps for adjusting tool parameters are:

[0145] S211: Based on the wear feature identification data, the cutting force and surface roughness are analyzed, combined with the temperature and tool usage time, using the formula:

[0146]

[0147] Get the performance degradation factor ,in, and is the weight coefficient used to adjust the influence of differential wear parameters, and Indicates the new and old cutting forces, and Indicates the roughness of the new and old surfaces. and It is the coefficient to adjust the temperature change and usage time. and are the old and new temperature values, is the new usage duration;

[0148] S212: Based on the performance degradation factor, the cutting speed and feed speed are adjusted using the formula:

[0149]

[0150] and

[0151]

[0152] Get the adjusted cutting speed and feed speed ,in, and is the adjustment coefficient used to adjust the impact intensity of the performance degradation factor. and are the original cutting speed and feed rate, is the performance degradation factor.

[0153] Assume the following data:

[0154] Newton;

[0155] Newton;

[0156] micrometer;

[0157] micrometer;

[0158] Spend;

[0159] Spend;

[0160] Hour;

[0161] .

[0162] Calculation process:

[0163]

[0164]

[0165]

[0166] Performance degradation factor The calculated value of is 0.822, which indicates the overall performance degradation after considering cutting force, surface roughness, temperature and usage time.

[0167] Assume data:

[0168] m / min;

[0169] mm / rev;

[0170] .

[0171] Calculation process:

[0172]

[0173]

[0174] The adjusted cutting speed is 96.7m / min and the feed rate is 0.45665mm / rev, reflecting the reduced cutting speed and feed rate according to the wear factor to optimize tool performance.

[0175] See also Figure 5 , the specific steps for obtaining the structural optimization index are:

[0176] S221: Get the microstructure data of the tool, combine it with the grain size, and calculate the average grain size using the formula:

[0177]

[0178] Get the average grain size ,in, It is The observed grain size, is the sample size;

[0179] S222: Based on the average grain size, adjust the heat treatment temperature and optimize the microstructure using the formula:

[0180]

[0181] Get the adjusted heat treatment temperature ,in, is the temperature regulation coefficient, is the base temperature, is the average grain size;

[0182] S223: Test the adjusted heat treatment temperature and collect performance data using the formula:

[0183]

[0184] Calculate the improvement index ,in, and These are the performance data before and after adjustment.

[0185] Assume there are 5 samples with grain sizes: 10μm, 12μm, 11μm, 13μm, 14μm.

[0186] Calculation process:

[0187] Calculate the average grain size:

[0188]

[0189] The average grain size is 12 μm, which will be used to adjust the heat treatment parameters.

[0190] Assumptions:

[0191] 1000℃;

[0192] It is 0.5℃ / μm.

[0193] Calculation process:

[0194] Adjust the temperature according to the average grain size:

[0195]

[0196] The adjusted heat treatment temperature is 994°C, reflecting the effect of grain size on heat treatment temperature.

[0197] Assumptions is 200;

[0198] is 220.

[0199] Calculation process:

[0200] Calculate performance improvement index:

[0201]

[0202] A performance improvement index of 0.1 means a 10% improvement in performance.

[0203] See also Figure 6 , the real-time image monitoring steps are as follows:

[0204] S311: Based on the structural optimization index, a camera is set to continuously capture real-time images of the titanium alloy material surface processing process, and each frame of the image is analyzed in real time using the formula:

[0205]

[0206] Get the total size of the image , used to evaluate the lighting conditions and image quality of the processing area, where It is The brightness value of the pixel, is the total number of pixels in the image;

[0207] S312: Based on the total measurement of the image, edge detection and contour tracking technology are used to analyze the real-time image and automatically identify the key visual features of the tool path and the material contact surface, using the formula:

[0208]

[0209] Calculate trajectory contrast , which is used to quantify the visual contrast between the tool path and the material interface, where It is The brightness value of the pixel, and Indicates the maximum and minimum brightness values ​​in the image.

[0210] Assume that a frame image contains a total of 1000 pixels;

[0211] The brightness value of each pixel is randomly distributed between 0 and 255;

[0212] Assumptions is 128.

[0213] Calculate the total brightness:

[0214]

[0215] The total brightness is 128000, which indicates the overall brightness level of the image and helps to evaluate the lighting conditions and image quality of the processing area.

[0216] In image analysis, it is assumed that the maximum brightness value is 240 and the minimum brightness value is 60.

[0217] Calculate the contrast:

[0218]

[0219] The contrast value is 0.6. A higher contrast value indicates good visual contrast between the tool path and the material contact surface, which helps the machine vision system to accurately identify the machining features.

[0220] See also Figure 7 , the specific steps for obtaining the feedback judgment record are as follows:

[0221] S321: Based on the real-time image, identify the changes in the contact surface between the tool and the material, using the formula:

[0222]

[0223] Get the change index ,in, Indicates The visual parameters of the frame, is the average visual parameter, is the number of frames analyzed;

[0224] S322: Based on the change index, determine whether the cutting parameters need to be adjusted. If the change index exceeds the threshold, record the feedback information that needs to be adjusted, using the judgment formula:

[0225]

[0226] Get adjustment feedback judgment record , where 1 means adjustment is required and 0 means no adjustment is required. is the threshold value, is the change index.

[0227] Assume there are 5 frames of images. (For example, brightness) are: 100, 105, 95, 110, 90

[0228] Calculated as:

[0229] (100+105+95+110+90) / 5=100

[0230] Change Index Calculated as:

[0231]

[0232]

[0233] Change Index , reflects the average deviation of brightness in five frames, and the value represents the degree of deviation from the average brightness of each frame.

[0234] Assumptions is 5, combined , we get 6>5, so When it is 1, it means the cutting parameters need to be adjusted.

[0235] See also Figure 8 The specific steps for obtaining the qualified quality results of titanium alloy are as follows:

[0236] S411: Recalculate and set tool parameters based on adjustment feedback judgment record, using the formula:

[0237]

[0238] and

[0239]

[0240] Get the adjusted cutting speed and feed speed ,in, and Indicates the current cutting speed and feed speed. is the speed proportional parameter adjusted based on feedback, It is the feed ratio parameter adjusted based on feedback;

[0241] S412: Collect tool performance data and material processing quality after readjustment, evaluate the adjustment effect, and use the formula:

[0242]

[0243] Get average processing quality ,in, Representative Quality indicators of each measuring point, It is The weight of the measurement points, is the total number of measurement points;

[0244] S413: Based on the average processing quality, verify whether the quality of titanium alloy material surface processing meets the design requirements, using the formula:

[0245]

[0246] Get the mass ratio ,like The material processing quality is qualified, among which, is the average processing quality, is the desired quality standard threshold.

[0247] Assume the following data:

[0248] Assume 100mm / min;

[0249] Assume the adjustment is to increase by 10%, which is 0.1;

[0250] Assume 0.5mm / rev;

[0251] Assume the adjustment is a 5% decrease, or -0.05.

[0252] For cutting speed:

[0253]

[0254] For feed rate:

[0255]

[0256] Assumptions There are 5 measurement points;

[0257] Assume that the quality index of each measurement point is:

[0258]

[0259]

[0260] calculate

[0261]

[0262] Assume it is 0.85.

[0263] According to the previous step and :

[0264]

[0265] The results show that the processing quality meets or exceeds the required standards.

[0266] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for high-precision complex curved surface processing of titanium alloy material, characterized in that: The following steps are involved: Collect temperature and vibration data during titanium alloy material processing, perform time series analysis and frequency domain analysis on the data, identify key frequency domain features, compare historical wear data, evaluate tool wear, and generate wear feature identification data; Based on the wear feature identification data, tool parameters are adjusted, including cutting speed and feed speed, tool performance data is recorded and analyzed, and the microstructure and processing efficiency of the tool are optimized by adjusting the heat treatment process to generate structural optimization indicators; Based on the structural optimization index, the real-time image of the titanium alloy material surface processing is monitored by using machine vision technology, the change of the tool trajectory and the material contact surface is identified, whether the cutting parameters need to be adjusted, and the adjustment feedback judgment record is generated; According to the adjustment feedback judgment record, the tool and processing parameters are readjusted, the tool performance and material processing information are monitored again, and it is verified whether the surface processing quality of the titanium alloy material meets the design requirements to generate a qualified titanium alloy quality result.

2. The high-precision complex curved surface processing method of titanium alloy material according to claim 1 is characterized in that: The identification steps of the key frequency domain features are specifically as follows: Collect temperature and vibration data during titanium alloy material processing, adjust the data scale, and use the formula: Get time Normalized data ,in, Indicates time The original data points, is the data mean, is the standard deviation, is the coefficient that adjusts the normalized intensity, is a constant; The normalized data is subjected to a fast Fourier transform to identify the frequency components in the data, and the frequency domain transform is performed using the formula: Get frequency domain features ,in, It's time The normalized data, Represents the total number of data points, is the frequency adjustment factor, is a frequency variable, is an imaginary unit, is the time index; Based on the frequency domain characteristics, the key features are identified through thresholds, and the judgment formula is adopted: Get the key frequency domain features ,in, represents the amplitude of the frequency domain feature, is the amplitude threshold, is the coefficient for adjusting the recognition sensitivity.

3. The high-precision complex curved surface processing method of titanium alloy material according to claim 1 is characterized in that: The steps for acquiring the wear feature identification data are specifically as follows: Based on the historical wear data, including vibration and load data, the statistical average of the historical data is calculated using the formula: Get the historical average ,in, Representative Historical data points, is the number of data points; Based on the historical average, in the current machining cycle, the same tool performance data is monitored and recorded in real time, and the real-time data is processed using the formula: Calculate real-time average , It is Real-time data points, is the number of data points; Based on the historical average and the real-time average, the absolute difference is used to evaluate the degree of wear, using the formula: Get wear difference data ,in, is the historical average, It is the real-time average value.

4. The high-precision complex curved surface processing method of titanium alloy material according to claim 1 is characterized in that: The steps of adjusting the tool parameters are specifically as follows: Based on the wear feature identification data, the cutting force and surface roughness are analyzed, combined with the temperature and tool usage time, and the formula is used: Get the performance degradation factor ,in, and is the weight coefficient, and Indicates the new and old cutting forces, and Indicates the roughness of the new and old surfaces. and It is the coefficient to adjust the temperature change and usage time. and are the old and new temperature values, is the new usage duration; Based on the performance degradation factor, the cutting speed and feed rate are adjusted using the formula: and Get the adjusted cutting speed and feed speed ,in, and is the adjustment factor, and are the original cutting speed and feed rate, is the performance degradation factor.

5. The high-precision complex curved surface processing method of titanium alloy material according to claim 1 is characterized in that: The steps for obtaining the structural optimization index are specifically as follows: The microstructure data of the tool is obtained, combined with the grain size, and the average grain size is calculated using the formula: Get the average grain size ,in, It is The observed grain size, is the sample size; Based on the average grain size, the heat treatment temperature was adjusted to optimize the microstructure using the formula: Get the adjusted heat treatment temperature ,in, is the temperature regulation coefficient, is the base temperature, is the average grain size; The adjusted heat treatment temperature was tested and performance data was collected using the formula: Calculate the improvement index ,in, and These are the performance data before and after adjustment.

6. The high-precision complex curved surface processing method of titanium alloy material according to claim 1 is characterized in that: The real-time image monitoring steps are specifically as follows: Based on the structural optimization index, a camera is set to continuously capture real-time images of the titanium alloy material surface processing process, and each frame of the image is analyzed in real time using the formula: Get the total size of the image , used to evaluate the lighting conditions and image quality of the processing area, where It is The brightness value of the pixel, is the total number of pixels in the image; Based on the total measurement of the image, edge detection and contour tracking technology are applied to analyze the real-time image to automatically identify the key visual features of the tool path and the material contact surface, using the formula: Calculate trajectory contrast , which is used to quantify the visual contrast between the tool path and the material interface, where It is The brightness value of the pixel, and Indicates the maximum and minimum brightness values ​​in the image.

7. The high-precision complex curved surface processing method of titanium alloy material according to claim 1 is characterized in that: The steps for obtaining the adjustment feedback judgment record are specifically as follows: According to the real-time image, the change of the contact surface between the tool and the material is identified, using the formula: Get the change index ,in, Indicates The visual parameters of the frame, is the average visual parameter, is the number of frames analyzed; Based on the change index, determine whether the cutting parameters need to be adjusted. If the change index exceeds the threshold, record the feedback information that needs to be adjusted, using the judgment formula: Get adjustment feedback judgment record , where 1 means adjustment is required and 0 means no adjustment is required. is the threshold value, is the change index.

8. The high-precision complex curved surface processing method of titanium alloy material according to claim 1 is characterized in that: The specific steps for obtaining the qualified quality result of the titanium alloy are: According to the adjustment feedback judgment record, recalculate and set the tool parameters, using the formula: and Get the adjusted cutting speed and feed speed ,in, and Indicates the current cutting speed and feed speed. is the speed proportional parameter adjusted based on feedback, It is the feed ratio parameter adjusted based on feedback; Collect tool performance data and material processing quality after readjustment, evaluate the adjustment effect, and use the formula: Get average processing quality ,in, Representative Quality indicators of each measuring point, It is The weight of the measurement points, is the total number of measurement points; Based on the average processing quality, verify whether the quality of titanium alloy material surface processing meets the design requirements, using the formula: Get the mass ratio ,like The material processing quality is qualified, among which, is the average processing quality, is the desired quality standard threshold.

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