A service blade repair method based on defect identification diagnosis and adaptive polishing

Abstract

A method for repairing service blades based on defect identification and diagnosis and adaptive polishing is disclosed. The method comprises the following steps: Step 1, positioning and clamping the service blade on a robotic arm; Step 2, establishing a visual defect measurement and detection system; Step 3, identifying and processing the blade defect features; Step 4, diagnosing the type, size, and depth of surface defects; Step 5, determining the condition of the service blade; if it does not meet standards, proceed to Step 6; otherwise, proceed to Step 7; Step 6, scrapping the service blade; Step 7, initiating the adaptive polishing process; Step 8, reconstructing the service model; Step 9, analyzing the amount of material removed by polishing; Step 10, automatically generating polishing paths and parameters; Step 11, polishing the defects and outer surface of the service blade; and Step 12, quality inspection. The advantages of this invention are: automatic identification and diagnosis of surface defects and adaptive polishing repair, improving the efficiency and surface consistency of blade repair.

Description

Technical Field

[0001] This invention relates to the field of service blade repair and processing technology, and in particular to a service blade repair method based on defect identification and diagnosis and adaptive polishing. Background Technology

[0002] Blades are crucial components of aero-engines. From a performance perspective, the quality of blade design and manufacturing directly impacts engine performance, safety, and lifespan. From a reliability perspective, engine blades have a relatively high failure rate, both during development and in service. After service, blades are often repaired using various methods to reduce costs and extend their service life. Low-pressure / low-vortex rotor blades in aero-engines are prone to defects such as cracks, scratches, and dents due to the harsh service environment. Currently, the repair of low-pressure / low-vortex rotor blades for aero-engines generally requires four steps: pre-repair inspection, polishing of dented areas, polishing of the outer surface, and quality inspection.

[0003] In the pre-repair inspection stage: The main method for detecting blade appearance defects is based on the technical requirements for engine compressor repair, and manual visual inspection is used. The inspection results are reliable to a certain extent, but there are problems such as poor consistency in the implementation of inspection standards, low inspection efficiency, and lack of quantitative characteristics in the inspection results.

[0004] In the stage of polishing the recessed area and the outer surface: the main method is manual polishing by workers to achieve a smooth transition between the recess and the surrounding substrate. Then, the outer surface is polished to a light finish. The repair methods in this stage are highly dependent on the workers' skills and are prone to over-polishing, which may result in failure to guarantee the wall thickness.

[0005] In terms of quality inspection: the appearance and wall thickness of the blades are mainly inspected by visual inspection and ultrasonic thickness gauges. Summary of the Invention

[0006] The purpose of this invention is to address the problems of poor detection accuracy, low polishing efficiency, and poor surface quality consistency of low-pressure / low-vortex rotor blades of aero-engines, which rely on manual visual inspection and manual polishing for defect diagnosis after service. This invention proposes a repair method for low-pressure / low-vortex rotor blades of aero-engines, which can realize automated diagnosis of surface defects of service blades and adaptive and efficient repair of surface defects. It can effectively improve the accuracy of surface defect diagnosis of service blades, as well as the efficiency and quality of polishing repair.

[0007] A method for repairing service blades based on defect identification and diagnosis and adaptive polishing, the specific steps of which are as follows:

[0008] Step 1: Position and clamp the blades in service on the robotic arm;

[0009] Step 2: Establish a visual defect measurement and detection system, using any two or three combinations of white light, blue light, and laser light as the light source to complete the image acquisition of the entire blade surface and output the data.

[0010] Step 3: Based on the image of the entire blade profile, perform noise reduction and clustering processing on the acquired image to complete the identification and image processing of blade defect features;

[0011] Step 4: Perform qualitative and quantitative analysis on the surface defects of the blades in service to diagnose the type, size, and depth of the surface defects;

[0012] Step 5: Compare with the engine repair standards to determine whether the service blades are qualified. If they do not meet the standards, proceed to step 6; otherwise, proceed to step 7.

[0013] Step 6: Dispose of the serviced blades as scrapped items;

[0014] Step 7, proceed to the adaptive polishing process;

[0015] Step 8: Based on the optical measurement results of the visual defect measurement and detection system in Step 2, reconstruct the service blade model;

[0016] Step 9: Analyze the amount of material removed by polishing based on the results of the service model reconstruction;

[0017] Step 10: Generate the polishing path and polishing parameters using existing procedures;

[0018] Step 11: Polish the defective parts and outer surfaces of the blades in service;

[0019] Step 12, quality inspection of the blades after polishing;

[0020] Step 13: Determine the compliance of the surface quality and wall thickness dimensional accuracy of the service blades. If they are qualified, the repair process ends; otherwise, proceed to step 11.

[0021] The polishing removal amount mentioned in step 9 is the amount of polishing removal determined after the actual measurement model and the existing theoretical model are registered to achieve a smooth transition between the depression and the surrounding substrate, and under the condition that the maximum value of the wall thickness of each section at the depression is not less than the value specified in the engine repair standard.

[0022] The polishing path and polishing parameters mentioned in step 10 are automatically planned and generated using an adaptive polishing software tool;

[0023] Residual material removal based on defect depth zoning: Based on the statistical data of part defect depth and trial polishing test, the depth of surface defects of the parts are classified into 0.15-0.09mm, 0.09-0.03mm, and 0.03-0mm. The entire part is divided into three material removal processes: first, the defects with a depth of less than 0.03mm are polished; then, the defects with a depth in the range of 0.09-0.06mm are polished; and finally, the defects with a depth in the range of 0.15-0.09mm are polished.

[0024] Step 11: Polishing of defective areas and outer surfaces of service blades. A robotic belt polishing machine is used, with a robotic arm holding the parts. The abrasive material of the belt is SiC or aluminum oxide. For rough polishing, #180, #240, and #320 abrasive belts are selected, and for fine polishing, #1500 and #2000 abrasive belts are selected. The belt tension is controlled at 5~10N.

[0025] Compared with the prior art, the advantages of this invention are:

[0026] It can automatically identify and diagnose surface defects of parts, and adaptively polish and repair surface defects, which can effectively improve the efficiency and surface consistency of the repair of blades in service. Detailed Implementation

[0027] The present invention will be further explained below with reference to specific implementation schemes, but it is not limited to the present invention. All of them should still fall within the scope of the technical content disclosed in the present invention without affecting the effects and objectives that the present invention can produce.

[0028] This invention proposes a method for repairing service blades based on defect identification and diagnosis and adaptive polishing. The specific steps are as follows:

[0029] Step 1: Position and clamp the blades in service on the robotic arm;

[0030] Step 2: Establish a visual defect measurement and detection system, using any two or three combinations of white light, blue light, and laser light as the light source to complete the image acquisition of the entire blade surface and output the data.

[0031] Step 3: Based on the image of the entire blade profile, perform noise reduction and clustering processing on the acquired image to complete the identification and image processing of blade defect features;

[0032] Step 4: Perform qualitative and quantitative analysis on the surface defects of the blades in service to diagnose the type, size, and depth of the surface defects;

[0033] Step 5: Compare with the engine repair standards to determine whether the service blades are qualified. If they do not meet the standards, proceed to step 6; otherwise, proceed to step 7.

[0034] Step 6: Dispose of the serviced blades as scrapped items;

[0035] Step 7, proceed to the adaptive polishing process;

[0036] Step 8: Based on the optical measurement results of the visual defect measurement and detection system in Step 2, reconstruct the service blade model;

[0037] Step 9: Analyze the amount of material removed by polishing based on the results of the service model reconstruction;

[0038] Step 10: Generate the polishing path and polishing parameters using existing procedures;

[0039] Step 11: Polish the defective parts and outer surfaces of the blades in service;

[0040] Step 12, quality inspection of the blades after polishing;

[0041] Step 13: Determine the compliance of the surface quality and wall thickness dimensional accuracy of the service blades. If they are qualified, the repair process ends; otherwise, proceed to step 11.

[0042] The polishing removal amount mentioned in step 9 is the amount of polishing removal determined after the actual measurement model and the existing theoretical model are registered to achieve a smooth transition between the depression and the surrounding substrate, and under the condition that the maximum value of the wall thickness of each section at the depression is not less than the value specified in the engine repair standard.

[0043] The polishing path and polishing parameters mentioned in step 10 are automatically planned and generated using an adaptive polishing software tool;

[0044] Residual material removal based on defect depth zoning: Based on the statistical data of part defect depth and trial polishing test, the depth of surface defects of the parts are classified into 0.15-0.09mm, 0.09-0.03mm, and 0.03-0mm. The entire part is divided into three material removal processes: first, the defects with a depth of less than 0.03mm are polished; then, the defects with a depth in the range of 0.09-0.06mm are polished; and finally, the defects with a depth in the range of 0.15-0.09mm are polished.

[0045] Step 11: Polishing of defective areas and outer surfaces of the service blades. A robotic belt polishing machine is used, with a robotic arm holding the parts. The abrasive material of the belt is SiC or aluminum oxide. For rough polishing, #180, #240, and #320 abrasive belts are selected, while for fine polishing, #1500 and #2000 abrasive belts are selected. The belt tension is controlled between 5 and 10 N. Matters not covered in this invention are known technologies.

[0046] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for repairing service blades based on defect identification and diagnosis and adaptive polishing, characterized in that: The specific steps are as follows: Step 1: Position and clamp the blades in service on the robotic arm; Step 2: Establish a visual defect measurement and detection system, using any two or three combinations of white light, blue light, and laser light as the light source to complete the image acquisition of the entire blade surface and output the data. Step 3: Based on the image of the entire blade profile, perform noise reduction and clustering processing on the acquired image to complete the identification and image processing of blade defect features; Step 4: Perform qualitative and quantitative analysis on the surface defects of the blades in service to diagnose the type, size, and depth of the surface defects; Step 5: Compare with the engine repair standards to determine whether the service blades are qualified. If they do not meet the standards, proceed to step 6; otherwise, proceed to step 7. Step 6: Dispose of the serviced blades as scrapped items; Step 7, proceed to the adaptive polishing process; Step 8: Based on the optical measurement results of the visual defect measurement and detection system in Step 2, reconstruct the service blade model; Step 9: Analyze the amount of material removed by polishing based on the results of the service model reconstruction; Step 10: Generate the polishing path and polishing parameters using existing procedures; Step 11: Polish the defective parts and outer surfaces of the blades in service; Step 12, quality inspection of the blades after polishing; Step 13: Determine the compliance of the surface quality and wall thickness dimensional accuracy of the service blades. If they are qualified, the repair process ends; otherwise, proceed to step 11.

2. The in-service blade repair method based on defect identification and diagnosis and adaptive polishing according to claim 1, characterized in that: The polishing removal amount mentioned in step 9 is the amount of polishing removal determined after the actual measurement model and the existing theoretical model are registered to achieve a smooth transition between the depression and the surrounding substrate, and under the condition that the maximum value of the wall thickness of each section at the depression is not less than the value specified in the engine repair standard.

3. The in-service blade repair method based on defect identification and diagnosis and adaptive polishing according to claim 1, characterized in that: The polishing path and polishing parameters mentioned in step 10 are automatically planned and generated using an adaptive polishing software tool; Residual material removal based on defect depth zoning: Based on the statistical data of part defect depth and trial polishing test, the depth of surface defects of the parts are classified into 0.15-0.09mm, 0.09-0.03mm, and 0.03-0mm. The entire part is divided into three material removal processes: first, the defects with a depth of less than 0.03mm are polished; then, the defects with a depth in the range of 0.09-0.06mm are polished; and finally, the defects with a depth in the range of 0.15-0.09mm are polished.

4. The in-service blade repair method based on defect identification and diagnosis and adaptive polishing according to claim 1, characterized in that: Step 11: Polishing of defective areas and outer surfaces of service blades. A robotic belt polishing machine is used, with a robotic arm holding the parts. The abrasive material of the belt is SiC or aluminum oxide. For rough polishing, #180, #240, and #320 abrasive belts are selected, and for fine polishing, #1500 and #2000 abrasive belts are selected. The belt tension is controlled at 5~10N.