Blade three-coordinate detection data analysis method

Abstract

The blade three-coordinate detection data analysis method comprises the following steps: step 1, collecting a blade body profile detection report of a blade, and generating a CSV file; 2, reading the CSV file of each blade, and obtaining the number of sections, the number of evaluation items, the names of the sections, the names of the evaluation items and the tolerance of the evaluation items; 3, extracting an evaluation item tolerance or artificially set tolerance in the CSV file, and judging whether the blade is qualified or not according to a measured value and a tolerance value of an evaluation item; 4, qualified leaves and out-of-tolerance leaves are automatically segmented into different folders in batches; 5, counting batch numbers, detection time, detection personnel and out-of-tolerance value information of all the leaves to form a statistical table; 6, counting the number of qualified blades and out-of-tolerance blades in different tolerance value ranges; and 7, according to the measurement time and the batch number, automatically screening a blade body profile detection report by a detector, and drawing a broken line analysis chart of evaluation items.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blade three-coordinate detection data analysis, and particularly relates to a blade three-coordinate detection data analysis method. BACKGROUND

[0002] With the digital transformation of aero-engine blades and the construction of digital production lines, the efficient demand for three-coordinate detection data processing, monitoring and analysis is becoming more and more urgent. Especially for batch production parts such as blades, manual data viewing, recording and analysis is low in efficiency and high in error rate, which cannot meet the current industry development needs. Taking aero-engine compressor blades as an example, the number of detection sections of different levels and different structures of blades is between 5 and 20, the number of detection points of each section is between 200 and 800, and each engine has several hundred or thousands of blades. A blade needs to be detected for tens of thousands of sections, and it is difficult for a person to input, analyze, convert and record data, and there is a large error in manual calculation. Especially for digital production lines, the computer should automatically determine the detection results, and the production line automatically distinguishes qualified and unqualified blades according to the determination results. Although a professional blade data analysis software can improve the detection speed and reduce the detection error, the development cost of the blade data analysis software is high, and there is a problem of high investment cost. SUMMARY

[0003] To solve the above technical problems, the purpose of the present application is to provide a blade three-coordinate detection data analysis method.

[0004] The present application provides a blade three-coordinate detection data analysis method, comprising:

[0005] Step 1: Collect the blade body surface detection report of the blade, and generate a CSV file;

[0006] Step 2: Read the CSV file of each blade to obtain the number of sections, the number of evaluation items, the section name, the evaluation item name and the evaluation item tolerance;

[0007] Step 3: Extract the evaluation item tolerance or the manually set tolerance in the CSV file, and determine whether the blade is qualified according to the measured value and the tolerance value of the evaluation item;

[0008] Step 4: Batch automatically segment the qualified blades and the out-of-tolerance blades into different folders;

[0009] Step 5: Statistically analyze the batch number, detection time, detection personnel and out-of-tolerance value information of all blades to form a statistical table;

[0010] Step 6: Statistically analyze the number of qualified blades and out-of-tolerance blades in different tolerance value ranges;

[0011] Step 7: Automatically filter the blade profile inspection reports based on measurement time, batch number, and inspector, and draw a line graph of the evaluation items.

[0012] This invention provides a method for analyzing blade coordinate measuring machine (CMM) inspection data, solving the problems of large processing volumes and expensive dedicated data processing software for blade CMM inspection data. This invention enables automatic input, analysis, and statistical processing of batch blade CMM inspection data, and similarly can process CMM inspection data for various other products. Attached Figure Description

[0013] Figure 1 This is a flowchart of a method for analyzing blade coordinate measuring machine data according to the present invention. Detailed Implementation

[0014] like Figure 1 As shown, the present invention provides a method for analyzing blade coordinate measuring machine (CMM) detection data, comprising:

[0015] Step 1: Collect the leaf blade profile inspection report and generate a CSV file, specifically:

[0016] Step 1.1: Obtain the storage path of the blade profile inspection report, and obtain the blade profile inspection report in both PDF and RPT formats.

[0017] Step 1.2: Convert the blade profile inspection report into a CSV file.

[0018] Step 1.3: Obtain the blade coordinate measuring machine (CMM) data from the CSV file. The blade CMM data is a set of point coordinates, where the coordinates of each point are x, y, z, and the vector directions of each point are i, j, k.

[0019] Step 1.4: If the coordinate system of the blade three-coordinate detection data is inconsistent with the expected direction, traverse all detection points and perform mirror transformation relative to the X, Y, and Z axes and flip transformation around the Z axis.

[0020] In practice, the mirror transformation and the flip transformation around the Z-axis are performed according to the following formulas:

[0021] x = x0 × dx

[0022] y = y0 × dy

[0023] z = z0 × dz

[0024] i = i0 × di

[0025] j = j0 × dj

[0026] k = k0 × dk

[0027] Where x0, y0, z0, i0, j0, and k0 are the data before transformation, and x, y, z, i, j, and k are the data after transformation;

[0028] When mirroring relative to the X-axis is required, dx = 1, dy = -1, dz = 1, di = 1, dj = -1, dk = 1;

[0029] When mirroring relative to the Y-axis is required, dx = -1, dy = 1, dz = 1, di = -1, dj = 1, dk = 1;

[0030] When a mirror image relative to the Z-axis is required, dx = -1, dy = -1, dz = 1, di = -1, dj = -1, dk = 1;

[0031] When it is necessary to rotate around the Z-axis, dx = 1, dy = 1, dz = -1, di = 1, dj = 1, dk = -1.

[0032] Step 2: Read the CSV file for each blade and obtain the number of sections, the number of evaluation items, the section name, the evaluation item name, and the evaluation item tolerance, specifically:

[0033] Step 2.1: Obtain the storage path of the blade's CSV file.

[0034] Step 2.2: Traverse all CSV files in the storage path.

[0035] Step 2.3: Read the blade coordinate measuring machine data from the CSV file and obtain the number of sections, the number of evaluation items, the section name, the evaluation item name, and the evaluation item tolerance.

[0036] Step 3: Extract the evaluation item tolerances from the CSV file or manually set tolerances. Determine whether the blade is qualified based on the measured values ​​and tolerance values ​​of the evaluation items. Specifically:

[0037] Step 3.1: Select the evaluation item name that needs to be controlled for tolerance.

[0038] Step 3.2: Extract the tolerance values ​​of the corresponding evaluation items from the CSV file or manually set the tolerances.

[0039] Step 3.3: Compare the difference between the measured value and the nominal value of the evaluation item with the upper tolerance and lower tolerance to determine whether the evaluation item is qualified. If all evaluation items of the blade are qualified, the blade is determined to be a qualified part; otherwise, the blade is an out-of-tolerance part.

[0040] Step 3.4: Output the judgment results, part batch number, and evaluation items to an Excel file for easy viewing.

[0041] Step 4: Automatically split qualified and substandard blades into different folders in batches, specifically as follows:

[0042] Step 4.1: Distinguish between qualified and unqualified blades based on the judgment results of qualified and out-of-tolerance parts.

[0043] Step 4.2: Move the blade profile inspection reports and CSV files of all qualified blades to the same folder, and move the blade profile inspection reports and CSV files of all unqualified blades to the same folder to achieve automatic separation.

[0044] Step 5: Compile all blade batch numbers, inspection times, inspection personnel, and out-of-tolerance values ​​into a statistical table, as follows:

[0045] Step 5.1: Output the basic information and judgment results corresponding to all blade profile inspection reports to a unified Excel file for easy viewing.

[0046] Step 5.2: In the Excel file, distinguish whether the blades are qualified or not by color, determine the direction of the deviation based on the deviation value and mark it with an arrow.

[0047] Step 6: Count the number of qualified and out-of-tolerance blades within different tolerance ranges, specifically:

[0048] Step 6.1: Set multiple tolerance value ranges for the evaluation items simultaneously.

[0049] Step 6.2: Compare the difference between the measured value and the nominal value of all evaluation items for each blade with the upper tolerance and lower tolerance to determine whether the evaluation item is qualified based on different tolerance value ranges.

[0050] Step 6.3: Output the number of out-of-tolerance blades and the pass rate under different tolerance ranges.

[0051] Step 7: Automatically filter blade profile inspection reports based on measurement time, batch number, and inspector, and draw a line graph of the evaluation items, as follows:

[0052] Step 7.1: Set measurement time, batch number, part number, and inspector as filter options.

[0053] Step 7.2: Select the leaf CSV files that meet the filtering criteria based on the filter options.

[0054] Step 7.3: Extract the measured values ​​of the corresponding evaluation items from each of the selected CSV files, and draw a line graph of the measured values ​​of each evaluation item.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of analyzing blade three-coordinate inspection data, characterized by, The application relates to a method for automatically screening and analyzing blade profile detection reports. The method comprises the following steps: Step 1: collecting a blade profile detection report and generating a CSV file; Step 2: reading the CSV file of each blade to obtain the number of sections, the number of evaluation items, the section name, the evaluation item name and the evaluation item tolerance; Step 3: extracting the evaluation item tolerance or the manually set tolerance in the CSV file, and determining whether the blade is qualified according to the measured value and the tolerance value of the evaluation item; Step 4: automatically segmenting qualified blades and out-of-tolerance blades into different folders in batches; Step 5: counting all batch numbers, detection times, detection personnel and out-of-tolerance values to form a statistical table; Step 6: counting the number of qualified blades and out-of-tolerance blades in different tolerance value ranges; 2. The blade three-coordinate inspection data analysis method according to claim 1, characterized in that, Step 7: automatically screening the blade profile detection report according to the measurement time, batch number and detection personnel, and drawing a broken line analysis diagram of the evaluation item. The step 1 specifically comprises the following steps: Step 1.1: obtaining the storage path of the blade profile detection report, and obtaining the blade profile detection report in PDF and RPT formats; Step 1.2: converting the blade profile detection report into a CSV file; Step 1.3: obtaining the blade three-coordinate detection data in the CSV file, wherein the blade three-coordinate detection data is a point coordinate set, the coordinate position of each point is x, y and z, and the vector direction of each point is i, j and k; 3. The blade tridimensional inspection data analysis method according to claim 2, characterized in that, Step 1.4: if the coordinate system of the blade three-coordinate detection data is inconsistent with the expected direction, the mirror image transformation of the relative X, Y and Z axes and the flip transformation around the Z axis are performed on all detection points. The mirror image transformation and the flip transformation around the Z axis in the step 1.4 are performed according to the following formulae: x = x0 * dx y = y0 * dy z = z0 * dz i = i0 * di j = j0 * dj k = k0 * dk Wherein x0, y0, z0, i0, j0 and k0 are the data before transformation, and x, y, z, i, j and k are the data after transformation; When the mirror image relative to the X axis is needed, dx = 1, dy = -1, dz = 1, di = 1, dj = -1 and dk = 1; When the mirror image relative to the Y axis is needed, dx = -1, dy = 1, dz = 1, di = -1, dj = 1 and dk = 1; When the mirror image relative to the Z axis is needed, dx = -1, dy = -1, dz = 1, di = -1, dj = -1 and dk = 1; 4. The blade tridimensional inspection data analysis method according to claim 1, characterized in that, When the flip around the Z axis is needed, dx = 1, dy = 1, dz = -1, di = 1, dj = 1 and dk = -1. The step 2 specifically comprises the following steps: Step 2.1: obtaining the storage path of the CSV file of the blade; Step 2.2: traversing all CSV files in the storage path; 5. The blade tridimensional inspection data analysis method according to claim 1, characterized in that, Step 2.3: reading the blade three-coordinate detection data in the CSV file to obtain the number of sections, the number of evaluation items, the section name, the evaluation item name and the evaluation item tolerance. The step 3 specifically comprises the following steps: Step 3.1: selecting the evaluation item name that needs to be controlled in tolerance; Step 3.2: extracting the tolerance value or the manually set tolerance of the corresponding evaluation item in the CSV file; Step 3.3: compare the difference between the measured value and the nominal value of the evaluation item with the upper and lower tolerances, determine whether the evaluation item is qualified, when all evaluation items of the blade are qualified, the blade is determined to be a qualified part, otherwise the blade is an out-of-tolerance part; Step 3.4: output the determination result, part batch number and evaluation item to the excel file for easy viewing.

6. The blade tridimensional inspection data analysis method according to claim 1, wherein, Said step 4 is specifically: Step 4.1: according to the determination result of qualified parts and out-of-tolerance parts, distinguish qualified and unqualified blades; Step 4.2: move the blade body surface detection report and CSV file of all qualified blades to the same folder, and move the blade body surface detection report and CSV file of all unqualified blades to the same folder, to realize automatic segmentation.

7. The blade tridimensional inspection data analysis method according to claim 1, wherein, Said step 5 is specifically: Step 5.1: output the basic information and determination result corresponding to all blade body surface detection reports to a unified excel file for easy viewing; Step 5.2: distinguish qualified and unqualified blades by color in the excel file, and determine the out-of-tolerance direction according to the out-of-tolerance value and identify it by arrow.

8. The blade tridimensional inspection data analysis method according to claim 1, wherein, Said step 6 is specifically: Step 6.1: set multiple tolerance value ranges for each evaluation item; Step 6.2: compare the difference between the measured value and the nominal value of all evaluation items of each blade with the upper and lower tolerances, and determine whether the evaluation item is qualified based on different tolerance value ranges; Step 6.3: output the number of out-of-tolerance blades and the qualified rate under different tolerance ranges.

9. The blade tridimensional inspection data analysis method according to claim 1, wherein, Said step 7 is specifically: Step 7.1: set the measurement time, batch number, part number and detection personnel as the screening items; Step 7.2: select the blade CSV file that meets the screening conditions according to the screening items; Step 7.3: extract the measured value of the corresponding evaluation item in each screened CSV file, and draw a line analysis chart of the measured value of each evaluation item.

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