A method of preparing a surface of an axle for damage

Abstract

A method for pre-fabricating surface damage on axles belongs to the field of railway vehicle manufacturing and maintenance technology. The method includes: S1, determining the pre-fabricated damage location and calibrating the pre-fabricated damage coordinates based on the axle's three-dimensional model and service load spectrum using finite element analysis; S2, using a femtosecond laser to perform laser processing on the pre-fabricated damage location to pre-fabricate cracks or pits; S3, scanning the pre-fabricated damage location using a high-frequency ultrasonic probe, and verifying whether the damage size and morphology meet specifications by comparing the ultrasonic scan image with a preset standard. If the error exceeds the 5μm tolerance range, step S2 is repeated for local compensation processing; S4, electrolytic polishing is performed on the pre-fabricated damage location. This method can pre-fabricate high-precision damage, reduce thermal impact, improve the accuracy and reliability of subsequent axle fatigue test data, and reduce service risks.

Description

Technical Field

[0001] This application relates to the field of railway vehicle manufacturing and maintenance technology, and more specifically, to a method for pre-fabricating surface damage on axles. Background Technology

[0002] During service, railway axles are prone to surface damage such as cracks, pitting, wear scratches, or dents due to alternating loads, damp heat corrosion, and mechanical impacts. Existing technologies, such as damage tolerance design (e.g., CN106596148A), primarily focus on crack propagation patterns and life assessment, but lack systematic research on methods for pre-inducing initial damage and precise dimensional control. Current tests, where cracks or defects are pre-induced through methods like electrical discharge machining or mechanical indentation, often exhibit large dimensional deviations (typically exceeding ±50μm), uncontrollable morphology, poor processing consistency, and wide heat-affected zones, leading to large errors in fatigue life prediction and difficulty in determining maintenance cycles.

[0003] Therefore, there is an urgent need for a prefabrication method that can adapt to complex geometric features and precisely control the size and morphology of surface damage in order to improve the accuracy and reliability of axle fatigue testing. Summary of the Invention

[0004] In view of the above-mentioned shortcomings, this application provides a method for pre-fabricating surface damage on axles to improve the problem of low precision in pre-fabricating surface damage on axles in related technologies.

[0005] This application is implemented as follows: An example of this application provides a method for pre-fabricating surface damage on an axle, including: S1. Based on the three-dimensional model of the axle and the service load spectrum, the damage pre-fabrication location is determined and the damage pre-fabrication coordinates are calibrated through finite element analysis.

[0006] S2. Use a femtosecond laser to perform laser processing on the damage pre-fabrication location to pre-fabricate cracks or pit-type damage.

[0007] S3. Use a high-frequency ultrasonic probe to scan the pre-fabricated damage location. Compare the ultrasonic scan image with the preset standard to verify whether the damage size and morphology meet the specifications. If the error exceeds the tolerance range of 5μm, repeat step S2 to perform local compensation processing.

[0008] S4. Perform electropolishing treatment on the pre-damaged areas.

[0009] In the above implementation process, based on the three-dimensional model of the axle and the service load spectrum, the high-stress area of ​​the axle is determined as the damage pre-fabrication location through finite element analysis. Laser processing is then performed on the axle surface in the high-stress area using a femtosecond laser. The scanning path, scanning speed, laser power, or pulse energy of the laser processing can be adjusted according to the type and size of the target damage. After laser processing, the ultrasonic scan image is compared with a preset standard to verify whether the damage size and morphology meet the specifications. If the error between the preset standard size and the ultrasonic scan image size exceeds the tolerance range of 5μm, step S2 of laser processing can be repeated based on the feedback results, performing local compensation processing or multiple cumulative processing of microcracks until the requirements are met. Then, the laser-processed damage undergoes electrolytic polishing, which not only sharpens the damage tip but also removes slag, simulating the real damage morphology.

[0010] This application utilizes a femtosecond laser for laser processing. Femtosecond lasers can generate ultra-high peak power and extremely short pulses, which can reduce the thermal impact of the processing on the area surrounding the pre-existing damage on the axle. It can perform sub-micron level precision processing and pre-exist high-precision damage. Subsequent fatigue performance testing of the pre-existing damaged axle can improve the accuracy and reliability of axle fatigue test data and reduce service risks.

[0011] In optional embodiments of this application, when the geometric feature of the damage pre-fabrication location is defined as a convex surface, the laser parameters for laser processing are standard parameters. When the geometric feature of the damage pre-fabrication location is a concave surface, the laser parameters are adjusted to 0.8 to 0.9 times the standard parameters. When the geometric feature of the damage pre-fabrication location is a plane, the laser parameters are adjusted to 0.9 to 1.0 times the standard parameters. The laser parameters are laser power, and the standard parameters are standard power. Alternatively, the laser parameters are pulse energy, and the standard parameters are standard energy.

[0012] In the above implementation process, the laser parameters for damage pre-fabrication on convex curved surfaces with good heat dissipation are used as standard parameters. When pre-fabrication on flat surfaces with average heat dissipation, the laser parameters are reduced to 0.9 to 1.0 times compared to the standard parameters. When pre-fabrication on concave curved surfaces with poor heat dissipation, the laser parameters are reduced to 0.8 to 0.9 times compared to the standard parameters. For different parts of the axle (flat, convex, and concave surfaces), the laser power and pulse energy parameters are optimized (when preparing crack-type damage, the laser power is adjusted during scanning to maintain stable continuous output. When preparing pit-type damage, pulse energy control is prioritized to precisely adjust the effect of a single action). This can improve the problems of overheating or under-processing that occur in traditional fixed-parameter processing, and significantly improve the processing consistency of each damage pre-fabrication location.

[0013] In an optional embodiment of this application, the damage is a crack, the standard power is 0.5W~2W, the scanning speed is 50mm / s~200mm / s, the repetition frequency is 50kHz~100kHz, and the etching depth is 0.1mm~2mm.

[0014] In the above implementation process, when laser processing is performed on the surface of the axle, crack-type damage with an etching depth of 0.1mm to 2mm can be obtained by using a standard power of 0.5W to 2W, a scanning speed of 50mm / s to 200mm / s, and a repetition frequency of 50kHz to 100kHz. The dimensional accuracy of the crack-type damage is high. Subsequent fatigue tests on axles with pre-existing crack-type damage can improve the accuracy and reliability of the subsequent axle fatigue test data.

[0015] In an optional embodiment of this application, the scanning speed gradually decreases as the etching depth increases.

[0016] In the above process, when laser processing is performed on the surface of the axle to pre-inflate crack-type damage, the scanning speed is gradually reduced as the etching depth increases. This not only facilitates the control of processing accuracy but also reduces the thermal impact on the pre-inflation area of ​​the axle, thereby improving the accuracy and reliability of subsequent axle fatigue test data.

[0017] In an optional embodiment of this application, the damage is a pit, the standard energy is 0.1mJ~1mJ, the processing time is 1ms~10ms, and a hemispherical pit with a diameter of 0.5mm~3mm and a depth of 0.1mm~1mm is formed.

[0018] In the above-mentioned process, when laser processing is performed on the surface of the axle, a hemispherical pit with a diameter of 0.5 mm to 3 mm and a depth of 0.1 mm to 1 mm can be obtained by using a standard energy of 0.1 mJ to 1 mJ and a processing time of 1 ms to 10 ms. The pit has high dimensional accuracy, which can improve the accuracy and reliability of fatigue test data when the axle with the pre-made pit is subsequently subjected to fatigue test.

[0019] In optional embodiments of this application, the damage pre-fabrication location includes the journal of the axle, the unloading groove, or the gear seat transition area. The geometric feature of the journal is an outwardly convex curved surface, while the geometric features of the unloading groove and the gear seat transition area are both inwardly concave curved surfaces.

[0020] In the aforementioned implementation process, during the service life of the axle, damage typically occurs in high-stress areas such as the journal, unloading groove, or gear seat transition area. Pre-inducing damage in these high-stress areas during fatigue testing improves the accuracy and reliability of subsequent axle fatigue tests. When pre-inducing damage in locations such as the unloading groove and gear seat transition area, the laser power is lowered because these locations typically have inwardly concave curved surfaces with poor heat dissipation efficiency. Similarly, the laser power is increased because the journal has outwardly convex curved surfaces with better heat dissipation efficiency. By dynamically adjusting the laser power or pulse energy to address the differences in geometry and stress state in different parts of the axle, such as the journal, unloading groove, and gear seat transition area, a more realistic and consistent defect morphology can be obtained.

[0021] In optional embodiments of this application, the pulse width of the laser does not exceed 500 fs, and / or the wavelength of the laser is 1020 nm to 1040 nm.

[0022] In the above implementation process, when performing laser processing on the axle surface, a femtosecond laser with a pulse width of no more than 500 fs and a wavelength of 1020 nm to 1040 nm is used. This makes it easier to control the size and morphology of the damage and reduce the thermal impact on the axle itself, thus more realistically simulating the actual fatigue life of the axle.

[0023] In an optional embodiment of this application, the parameters for electropolishing include: a voltage of 4V~6V, and 0.4A / cm. 2 ~0.6A / cm 2 The current density was [value], and the polishing time was 28s~32s.

[0024] In the above implementation process, after laser processing of the axle surface, electrolytic polishing is performed on the damage caused by laser processing. The voltage during electrolytic polishing is controlled at V4~6V and the current density is 0.4A / cm². 2 ~0.6A / cm 2 The polishing time is 28s~32s, which can effectively remove slag and sharpen damaged tips, reduce surface roughness and reduce the heat-affected zone, and further improve the accuracy and reliability of subsequent fatigue test data.

[0025] In an optional embodiment of this application, in step S4, prior to electropolishing, a group of randomly distributed micro-pits is prefabricated in the stress concentration area surrounding the damage. The diameter of the micro-pits is approximately 50~100μm.

[0026] Optionally, when the damage is a crack, the method for forming a cluster of micro-pits includes: applying pulses at fixed points at the tip and edge of the crack to form micro-pits, with a pulse energy of 0.1mJ~0.2mJ and an action time of 1ms~3ms.

[0027] In the above process, after the main defect (such as the main crack or the main pit) is processed by laser, a group of randomly distributed micro-pits is prefabricated in the stress concentration area around the crack tip and other defects. The diameter of the micro-pits is about 50~100μm. Then, electrochemical corrosion is performed, which can simulate the "inducing zone" of actual fatigue damage, so as to reproduce the synergistic damage morphology of multiple factors such as crack / pit-corrosion, and further improve the data accuracy and reliability of subsequent fatigue tests.

[0028] In an optional embodiment of this application, in step S2, while performing laser processing, the pre-fabricated damage location is monitored in real time to assess the real-time size and morphology of the damage, and the laser parameters are adjusted based on the assessment results.

[0029] In the above implementation process, during laser processing, the pre-made damage is monitored in real time. The morphology or size of the damage can be evaluated in real time, and the laser parameters can be adjusted in a timely manner to improve the one-time forming rate and reduce rework. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0031] Figure 1 A schematic diagram showing the distribution of the main crack and micro-pits provided in the embodiments of this application; Figure 2 This is a flowchart of the prefabrication process for axle surface damage provided in an embodiment of this application; Figure 3 A schematic diagram illustrating the operation of the femtosecond laser processor, high-speed camera, acoustic emission, and ultrasonic detection system provided in the embodiments of this application; Figure 4 The damage scan image provided in Embodiment 1 of this application; Figure 5 The damage scan image provided in Embodiment 2 of this application; Figure 6 This is a damage scan image provided in Embodiment 3 of this application.

[0032] Icons: 1-Main crack; 2-Micro-pit; 101-Axle; 102-Femtosecond laser; 103-High-speed camera; 104-Acoustic emission and ultrasonic testing system. Detailed Implementation

[0033] The embodiments of this application will be described in detail below with reference to examples. However, those skilled in the art will understand that the following examples are for illustrative purposes only and should not be considered as limiting the scope of this application. Unless otherwise specified in the examples, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all commercially available conventional products.

[0034] With the development of transportation, higher requirements have been placed on the service life and safety of railway locomotives. As a key component of railway locomotives, the fatigue performance of the axle directly affects the service life of the locomotive.

[0035] During the manufacturing stage of railway locomotives, fatigue tests are typically conducted on axles to simulate the cyclic loads (such as vehicle vibration and load) that the axles experience in actual use, and to evaluate their fatigue strength and lifespan. Fatigue tests can identify design or manufacturing defects, thereby improving material selection or structural optimization and reducing later maintenance costs.

[0036] However, railway axles inevitably experience bumps, scratches, impacts, corrosion, and mechanical collisions during manufacturing, transportation, and service, which can easily cause damage such as cracks and pitting on the axle surface. Current fatigue tests do not take into account these damages during manufacturing, transportation, and service, resulting in a discrepancy between the obtained fatigue test data and the actual service performance of the axles. Railway locomotives designed based on this performance data are prone to safety hazards.

[0037] Therefore, pre-inflicting damage on the surface of the axle before conducting fatigue tests is expected to obtain more realistic fatigue test data and further improve the safety of railway vehicles.

[0038] Currently, commonly used methods for preparing external damage at home and abroad include air gun impact, machining, and electrical spark ablation. However, these methods often have problems such as large dimensional deviations, uncontrollable morphology, and inconsistencies with actual damage, resulting in large life prediction errors and unreasonable maintenance cycles.

[0039] Therefore, this application further improves the pre-damage method for axle surface damage, thereby improving the accuracy of pre-damage to a certain extent.

[0040] The method for pre-fabricating axle surface damage provided in this application includes: S1. Based on the three-dimensional model of the axle and the service load spectrum, the damage pre-fabrication location is determined and the damage pre-fabrication coordinates are calibrated through finite element analysis.

[0041] S2. Use a femtosecond laser to perform laser processing on the pre-designed positions on the axle surface to pre-create cracks or pitted damage.

[0042] S3. Use a high-frequency ultrasonic probe to scan the pre-fabricated damage location. Compare the ultrasonic scan image with a preset standard to verify whether the damage size and morphology meet the specifications. If the error exceeds the tolerance range of 5μm, repeat step S2 for local compensation processing.

[0043] S4. Perform electropolishing treatment on the pre-damaged areas.

[0044] During the manufacturing, transportation, and service of axles, high-stress areas are more prone to defects and damage. These high-stress areas include axle journals, load grooves, and gear seat transition areas. Compared to pre-inducing damage in other low-stress areas of the axle before fatigue testing, this embodiment, based on the three-dimensional model of the axle and its service load spectrum, uses finite element analysis to determine the high-stress areas. Pre-inducing damage in these high-stress areas using a femtosecond laser on the axle surface more realistically simulates actual axle damage, thereby improving the accuracy and reliability of axle fatigue test data.

[0045] When pre-faking damage at the preset location of high-stress areas of the axle, a femtosecond laser is used to laser process the axle surface. The scanning path, scanning speed, laser power or pulse energy of the laser processing can be adjusted according to the type and size of the target damage to achieve high-precision ablation and more accurately control the size and morphology of the damage.

[0046] Existing technologies such as air gun impact (e.g., patent document CN110470446B), machining (e.g., patent document CN202485957U), and electrical discharge machining (EDM) involve a degree of randomness and low degree of freedom in the application position on the axle surface. This can easily introduce unnecessary contact mechanical stress and thermal effects into the axle during processing, impacting fatigue testing. This application's embodiment uses a femtosecond laser for laser processing, offering higher precision compared to the aforementioned traditional methods. Furthermore, it is a non-contact process, mitigating the problem of introducing unnecessary mechanical stress into the axle. Moreover, compared to conventional lasers, femtosecond lasers emit lasers with higher precision, enabling nanometer-level cutting and targeting the heat-affected zone of the axle, further improving the accuracy and reliability of fatigue test data.

[0047] In addition, in this embodiment, after laser processing is completed, an ultrasonic probe is used to scan the pre-damaged area of ​​the axle. The ultrasonic scan image is compared with a preset standard to verify whether the damage size and morphology meet the specifications. If the error exceeds the tolerance range of 5μm, step S2 is repeated for local compensation processing until the requirements are met. In this application, the error exceeding the tolerance range of 5μm means that the error between the preset standard size and the ultrasonic scan image size is greater than +5μm. In this case, the process returns to step S2 for local compensation processing.

[0048] In step S1, this application does not limit the specific type of axle. In some embodiments, the axle material is EA4T steel or carbon steel, and the axle diameter ranges from Φ120mm to Φ2500mm. The prefabrication method provided by the embodiments of this application can be applied to most axles in the field of railway locomotives.

[0049] In this application, EA4T steel is a special alloy steel for high-speed railway axles, which has high strength, high toughness and excellent fatigue resistance, and is mainly used in the manufacture of axles for high-speed trains and high-power locomotives.

[0050] In step S1, the 3D model of the axle can be a CAD model of the axle, and this application does not impose any restrictions. The CAD model of a standard axle can be obtained from a corresponding third-party model resource platform or created independently, and this application does not impose any restrictions.

[0051] The service load spectrum is a statistical representation in mechanical engineering that describes the variation of loads on a load-bearing structure over time or under a specific task. In vehicle engineering, the service load spectrum describes the changing patterns of alternating loads experienced by a vehicle in actual use and is key input data for fatigue analysis. Parameters can be set using the service load spectrum when performing finite element analysis on a three-dimensional axle model. Examples of service load spectra include the EN13103 standard.

[0052] For example, the finite element analysis process includes: First, establishing a full-size wheel and axle finite element model containing wheels, gears, and brake discs. C3D8R elements are used for mesh generation, and the mesh is refined in key areas (circular transition zones and unloading grooves) to ensure accuracy. The wheelset pressing process is simulated by setting an interference fit of 0.3 mm, defining contact pairs (with the inner surfaces of components such as wheels as the master surfaces and the axle mating surfaces as the slave surfaces), and applying a friction coefficient of 0.6. Regarding loads and boundary conditions, the X and Y displacements of the wheel-rail contact area are constrained, and a vertical load spectrum based on measured data is applied at the axle-bearing mating point. Finally, static analysis is used to obtain the stress field distribution of the actual axle, focusing on the axial stress S11 to identify stress concentration areas such as the circular transition zone of the journal, the unloading groove, and the inner circular transition zone of the wheel seat. The coordinates (X / Y / Z) and angle (θ) of the pre-damaged location are calibrated in the 3D model, and geometric information such as the normal vector of this area is derived.

[0053] In some embodiments, the damage pre-fabrication location includes the journal of the axle, the load groove, or the gear seat transition area.

[0054] In step S2, after determining the damage pre-fabrication location, the three-dimensional coordinates and angle (θ) of the damage pre-fabrication location are sent to the numerical control system of the femtosecond laser. According to the design requirements, the scanning path, scanning speed, scanning mode, laser power, scanning speed or pulse energy of the laser emitted by the femtosecond laser are set, and then the axle surface is laser-processed.

[0055] In some embodiments, when the geometric feature of the damage pre-fabrication location is defined as a convex surface, the laser parameters used for laser processing are standard parameters. When the geometric feature of the damage pre-fabrication location is a concave surface, the laser parameters are adjusted to 0.8 to 0.9 times the standard parameters. When the geometric feature of the damage pre-fabrication location is a plane, the laser parameters are adjusted to 0.9 to 1.0 times the standard parameters. The laser parameters refer to laser power, and the standard parameters refer to standard power. Alternatively, the laser parameters refer to pulse energy, and the standard parameters refer to standard energy.

[0056] By dynamically adjusting parameters such as laser power and scanning speed according to the surface geometry features (planar, convex, and concave) of different parts of the axle, the problems of overheating or under-processing that occur in traditional fixed-parameter processing are avoided, and the processing consistency is greatly improved.

[0057] In some embodiments, when it is necessary to pre-inflate crack-type damage on the surface of the axle, the laser parameters can be set as follows: standard power of 0.5W~2W, scanning speed of 50mm / s~200mm / s, repetition frequency of 50kHz~100kHz, and etching depth of 0.1mm~2mm.

[0058] In this application, repetition frequency refers to the number of laser pulses emitted per second. Scanning speed refers to the rate at which the laser spot moves horizontally in the laser scanning system. In this application, crack-type damage refers to a slender defect with a certain length and depth formed on the axle surface, as opposed to pit-type damage. Pit-type damage refers to a hemispherical-like damage with a certain depth and diameter. When laser processing the axle surface, crack-type damage with an etching depth of 0.1mm to 2mm and a designed length can be obtained by using a standard power of 0.5W to 2W, a scanning speed of 50mm / s to 200mm / s, and a repetition frequency of 50kHz to 100kHz. Through the coordination of the above laser parameters, the dimensional accuracy of crack-type damage is high. Subsequent fatigue tests on axles with pre-existing crack-type damage can improve the accuracy and reliability of subsequent axle fatigue test data.

[0059] As an example, the standard power can be one of 0.5W, 0.6W, 0.7W, 0.8W, 0.9W, 1.0W, 1.1W, 1.2W, 1.3W, 1.4W, 1.5W, 1.6W, 1.7W, 1.8W, 1.9W, or 2W, or any combination thereof.

[0060] As an example, the scanning speed can be one of, or any combination thereof, 50 mm / s, 60 mm / s, 70 mm / s, 80 mm / s, 90 mm / s, 100 mm / s, 110 mm / s, 120 mm / s, 130 mm / s, 140 mm / s, 150 mm / s, 160 mm / s, 170 mm / s, 180 mm / s, 190 mm / s, or 200 mm / s.

[0061] As an example, the repetition frequency can be one of 50kHz, 60kHz, 70kHz, 80kHz, 90kHz, or 100kHz, or any range between two of them.

[0062] As an example, when pre-crack damage is incurred at the journal, a standard power of 1.0W is used. When pre-crack damage is incurred in the unloading groove or gear seat transition zone, a laser power of 0.8~0.9W is used. As an example, when pre-crack damage is incurred at the journal, a standard power of 2.0W is used. When pre-crack damage is incurred in the unloading groove or gear seat transition zone, a laser power of 1.6~1.8W is used.

[0063] In some embodiments, when creating crack-type damage, the scanning speed gradually decreases as the etching depth increases.

[0064] When laser processing is used to pre-inflate crack-type damage on the surface of axles, the scanning speed is gradually reduced as the etching depth increases. This not only facilitates the control of processing accuracy but also reduces the thermal impact on the pre-inflation area of ​​the axle, thereby improving the accuracy and reliability of subsequent axle fatigue test data.

[0065] As an example, when creating crack-type damage with an etching depth of 1 mm, laser scanning can be performed at a scanning speed of 100 mm / s to 200 mm / s during the etching process when the etching depth is 0 to 0.5 mm. When the etching depth reaches 0.5 mm or more, scanning can be performed at a scanning speed of 50 mm / s to 100 mm / s.

[0066] This application does not limit the specific scanning method. In some embodiments, the scanning method is spiral scanning, and the path spacing is 5μm~10μm.

[0067] In this application, spiral scanning refers to the light spot moving axially along the axle surface in a spiral manner, and path spacing refers to the distance between two spiral coils.

[0068] As an example, the path spacing can be one of 5μm, 6μm, 7μm, 8μm, 9μm or 10μm or any range between two of them.

[0069] When pre-inducing crack-type damage on the axle surface, scanning with a spiral scanning method at a path spacing of 5μm~10μm can facilitate the control of crack size and morphological accuracy, reduce thermal impact, and improve the accuracy and reliability of subsequent axle fatigue test data.

[0070] Compared to flat scanning, which involves multiple pauses and causes significant changes in temperature and mechanical stress, this application employs a spiral scanning method, which further reduces the effects of temperature and stress, thereby improving the accuracy and reliability of subsequent axle fatigue test data.

[0071] In some embodiments, complex defect structures, such as bifurcated cracks, network cracks, and crack-micropit composite damage, can be fabricated at any position on the axle surface through trajectory planning and multiple processing by the femtosecond laser processing CNC system.

[0072] In some embodiments, the damage is a pit, the standard energy is 0.1mJ~1mJ, the processing time is 1ms~10ms, forming a hemispherical pit with a diameter of 0.5mm~3mm and a depth of 0.1mm~1mm.

[0073] In this application, pit-type damage refers to the formation of hemispherical pits on the surface of the axle. Pulse energy refers to the total energy released in a single laser pulse. Processing time refers to the total time the laser irradiates the axle.

[0074] When laser processing is performed on the surface of axles, a hemispherical pit with a diameter of 0.5 mm to 3 mm and a depth of 0.1 mm to 1 mm can be obtained by using a standard energy of 0.1 mJ to 1 mJ and a processing time of 1 ms to 10 ms. The pit has high dimensional accuracy, which can improve the accuracy and reliability of the test data when fatigue tests are performed on axles with pre-made pits.

[0075] As an example, when performing laser processing on the surface of an axle, the standard energy can be one of 0.1mJ, 0.2mJ, 0.3mJ, 0.4mJ, 0.5mJ, 0.6mJ, 0.7mJ, 0.8mJ, 0.9mJ or 1mJ or any range between two of them.

[0076] As an example, the processing time can be any one of 1ms, 2ms, 3ms, 4ms, 5ms, 6ms, 7ms, 8ms, 9ms, or 10ms, or any combination thereof. Understandably, the processing time needs to be adjusted accordingly based on the etching depth and the laser pulse energy. For example, when the etching depth increases while the laser pulse energy remains constant, the processing time can be appropriately extended within the above range.

[0077] As an example, according to the prefabrication method provided in the embodiments of this application, pits with diameters of 0.5mm, 1mm, 1.5mm, 2mm, 2.5mm or 3mm and depths of 0.1mm, 0.2mm, 0.3mm, 0.4mm, 0.5mm, 0.6mm, 0.7mm, 0.8mm, 0.9mm or 1mm can be obtained.

[0078] Understandably, the size of the laser beam irradiating the axle surface can be on the nanometer scale. To obtain a hemispherical pit with a diameter of 0.5mm to 3mm and a depth of 0.1mm to 1mm, the laser needs to scan or strike the axle surface multiple times in a specific manner and at a certain speed. As an example, the scanning method can be concentric circle scanning.

[0079] As an example, when pre-forming pit-type damage at the journal, a standard energy of 1 mJ is used. When pre-forming pit-type damage in the unloading groove or gear seat transition zone, a pulse energy of 0.8~0.9 mJ is used.

[0080] In some embodiments, the pulse width of the laser does not exceed 500 fs.

[0081] In some embodiments, the wavelength of the laser is 1020nm~1040nm.

[0082] Pulse width refers to the duration of a laser pulse. When laser processing the surface of an axle, using a femtosecond laser with a pulse width of no more than 500 fs and a wavelength of 1020 nm to 1040 nm allows for better control of damage size and morphology, reduces the thermal impact on the axle itself, and more realistically simulates the actual fatigue life of the axle. Using lasers with nanosecond or millisecond pulse widths for laser processing not only affects processing accuracy but also creates a larger thermal impact on the axle surface, thus affecting the accuracy and reliability of subsequent fatigue test data.

[0083] As an example, the pulse width of the laser can be one of 200fs, 250fs, 300fs, 350fs, 400fs, 450fs, or 500fs, or any range between two of them.

[0084] As an example, the wavelength of the laser can be one of 1020nm, 1030nm, or 1040nm, or any range in between.

[0085] In some embodiments, during step S2, while performing laser processing, the pre-fabricated damage location is monitored in real time to assess the real-time size and morphology of the damage, and the laser parameters are adjusted based on the assessment results.

[0086] As an example, a white light interferometer can be integrated to measure the depth and morphology of damage, combined with a laser power detector and emission monitoring to achieve feedback. For large cracks, a high-speed camera can be used to monitor the crack propagation process, and image processing algorithms can be used to evaluate the crack length and tip radius. If the measurement results deviate from the expected value by more than the set value, the laser parameters can be adjusted immediately or additional processing can be performed.

[0087] In some embodiments, if the damage size is detected to be close to the design size during laser processing, the laser processing is stopped immediately.

[0088] In step S3, the high frequency of the high-frequency ultrasonic probe refers to a frequency of 10MHz to 50MHz. Combining A / B / C scanning modes with phased array full-field imaging technology, precise depth and internal defect localization are achieved. Ultrasonic A-scan is used for depth localization, while B-scan or C-scan is used for morphology inspection.

[0089] In some embodiments, in step S3, acoustic emission monitoring may be introduced, or Lamb wave detection may be used to identify the propagation of microcracks, laser confocal microscopy may be used to measure the size, and ultrasonic scanning images may be compared with preset standards to verify whether the damage size and morphology meet the specifications.

[0090] In step S4, after the laser processing is completed, the pre-damaged location is subjected to electrolytic polishing, which can remove the slag layer and sharpen the damaged tip.

[0091] In some embodiments, the parameters for electropolishing include: a voltage of 4V~6V, and an A / cm² temperature range. 2 ~0.6A / cm 2 The current density was [value], and the polishing time was 28s~32s.

[0092] As an example, during electropolishing, the voltage can be controlled to be one of 4V, 4.5V, 5V, 5.5V, or 5V, or any combination thereof, while the current density can be controlled to be 0.4A / cm². 2 0.5A / cm 2 Or 0.46 / cm 2 The range between one or any two of these values ​​controls the polishing time to be one or any two of the following: 28s, 29s, 30s, 31s, or 32s.

[0093] Furthermore, in some embodiments, in step S4, prior to electropolishing, a group of randomly distributed micro-pits can be prefabricated in the stress concentration area surrounding the damage. The diameter of the micro-pits is approximately 50 μm to 100 μm.

[0094] As an example, when the damage is a crack, the method for forming a cluster of micro-pits includes: applying pulses at fixed points at the tip and edge of the crack to form micro-pits, with pulse energy of 0.1mJ~0.2mJ and an action time of 1ms~3ms.

[0095] In some embodiments, randomly distributed microcrack clusters can also be pre-formed in the stress concentration zone surrounding the damage.

[0096] As an example, a cluster of randomly distributed microcracks or micropits (approximately 50 μm to 100 μm in diameter) is pre-fabricated using a low-energy laser in the stress concentration zone around the crack tip, simulating the "inducing zone" of actual fatigue damage. Short-term electrochemical corrosion, such as salt spraying or mild electrolytic corrosion, is then performed to reproduce the synergistic damage morphology caused by crack-corrosion and other factors. Please refer to the schematic diagram of a main crack 1 and several randomly distributed micropits 2 around it. Figure 1 Understandably, Figure 1 The images shown are for illustrative purposes only and do not represent the actual morphology.

[0097] In some embodiments, please combine Figure 2 and Figure 3 This application provides a method for pre-fabricating surface damage on an axle, comprising: S1. Intelligent planning of pre-fabricated damage locations: A CAD finite element model of axle 101 was established, and finite element static analysis was performed to identify high stress areas, determine the location of damage prefabrication, calibrate damage coordinates or angles, and establish a laser parameter mapping database.

[0098] S2, Pre-existing damage from laser processing: The processing system of the femtosecond laser 102 reads the corresponding coefficients from the laser mapping database, selects the damage type, and then selects the scanning mode for laser processing based on the selected damage type. For crack-type damage, a continuous multi-layer scanning mode is selected; for pit-type damage, a fixed-point pulse or ring scanning mode is selected. During laser processing, a white light interferometer or a high-speed camera 103 is used for real-time monitoring.

[0099] S3. Ultrasonic monitoring and closed-loop verification: The location of the pre-existing damage is scanned using a high-frequency ultrasonic scanning system 104 to verify the damage dimensions and determine if they meet the tolerances. If not, the data is fed back to the CNC system for laser processing, and laser processing is repeated. If the data meets the tolerances, the verification is successful.

[0100] S4. Post-processing and confirmation of damage morphology: Low-energy lasers were used to prefabricate micro-damage clusters, and electropolishing was performed on the prefabricated damage sites of the axle to remove the slag layer and sharpen the damage tips, thus reproducing the synergistic damage morphology. Finally, a high-magnification optical microscope was used to observe and determine the final morphology.

[0101] Based on the above method, laser processing is performed using a femtosecond laser. Femtosecond lasers can generate ultra-high peak power and extremely short pulses, reducing the thermal impact on the area surrounding the pre-existing damage on the axle during processing. This allows for sub-micron level precision machining, creating high-precision pre-existing damage (more than 10 times more accurate than the ±50μm error of traditional EDM). Subsequent fatigue performance testing of the pre-existing damaged axle improves the accuracy and reliability of axle fatigue test data, reducing service risks. Highly realistic pre-existing damage samples can be used to calibrate non-destructive testing equipment and algorithms, improving detection sensitivity.

[0102] Employing parameter mapping and dynamic adjustment technology, this system automatically optimizes power and speed parameters for different parts of the axle (planar, convex, and concave surfaces), avoiding overheating or under-machining issues that occur in traditional fixed-parameter machining, and significantly improving machining consistency. Through trajectory planning and multiple machining operations, this system can create complex defect structures at any location, such as bifurcated cracks, network cracks, and crack-micro-pit composite damage, providing test samples for high-fidelity fatigue life assessment and research into novel repair techniques.

[0103] Online measurement and closed-loop feedback ensure high first-pass yield, reduce rework, and shorten the overall experimental cycle. Furthermore, based on the prefabrication method provided in this application's real-time example, the processing and inspection data can be automatically recorded, generating a digital defect database. Combining digital twins and big data analytics, defect evolution can be predicted, improving the reliability of subsequent non-destructive testing criteria and maintenance decisions.

[0104] The following describes in further detail the method for pre-fabricating axle surface damage according to the present application, with reference to the embodiments.

[0105] Example 1 This embodiment provides a method for pre-fabricating surface cracks in EA4T steel axles, including: (1) Intelligent planning of damage prefabrication location: Axle specifications: Φ180mm×2200mm, material is EA4T alloy steel hollow axle. A CAD finite element model of the axle was established, and finite element static analysis was performed to identify high stress areas. It was determined that crack-type damage pre-fabrication should be carried out in the unloading groove root area (concave curved surface) 500nm from the axle end. The coordinates of the pre-fabrication damage were calibrated, and the target crack depth was 0.80mm and the length was 1.23mm.

[0106] S2, Pre-existing damage from laser processing: The femtosecond laser processing system reads the corresponding coefficients from the laser mapping database, selects crack as the damage type, and performs continuous multi-layer scanning. The standard power is 1.2W. The laser power is reduced to 0.85 times the standard power, which is 1.2W × 0.85 = 1.02W. The scanning speed is 100mm / s, the repetition frequency is 100kHz, and multi-layer spiral scanning is used for layer-by-layer etching.

[0107] S3. Ultrasonic monitoring and closed-loop verification: After processing, ultrasonic A / B scanning and laser confocal microscopy were used to measure the crack length, which was 1.23 mm and the depth was 0.798 mm (error -2 μm). Both the crack length and width met the design values.

[0108] S4. Post-processing and confirmation of damage morphology: Electropolishing was performed on the pre-existing damaged areas of the axle to remove the slag layer and sharpen the damaged tips. Electropolishing parameters included: approximately 5V voltage and approximately 0.5A / cm² current density. 2 The time is approximately 30 seconds.

[0109] The crack morphology is regular, and the sidewalls are smooth and natural. High-magnification microscopy reveals no obvious molten beads or heat-affected zones, meeting design requirements. See scanning and cross-sectional images for details. Figure 4 .

[0110] Example 2 This embodiment provides a method for prefabricating surface pits on carbon steel axles, including: (1) Intelligent planning of damage prefabrication location: Axle specifications: Φ160mm×2000mm, material is hollow carbon steel axle. A CAD finite element model of the axle was established, and finite element static analysis was performed to identify high stress areas. It was determined that pit-type damage prefabrication should be performed in the journal (convex curved surface) area. The coordinates of the prefabricated damage were calibrated. The target pit diameter is 1.5mm and the depth is 0.3mm.

[0111] S2, Pre-existing damage from laser processing: The femtosecond laser processing system reads the corresponding coefficients from the laser mapping database, selects the damage type as pit, uses the standard energy of 0.5mJ for the pulse energy, and the action time is 5ms (multiple superpositions at a single point) to form a hemispherical pit at a fixed point perpendicular to the surface.

[0112] S3. Ultrasonic monitoring and closed-loop verification: After processing, ultrasonic A / B scanning and laser confocal microscopy were used to measure the pit diameter, which was found to be 1.50 mm and the depth to be 0.302 mm (error +2 μm).

[0113] S4. Post-processing and confirmation of damage morphology: Electropolishing was performed on the pre-existing damaged areas of the axle to remove the slag layer. Electropolishing parameters included: approximately 5V voltage and approximately 0.5A / cm² current density. 2 The time is approximately 30 seconds.

[0114] The surface roughness Ra≈0.18μm, the pit edges are regular and smooth, and there are no cracks or thermal damage. See the scan image. Figure 5 .

[0115] Example 3 This embodiment provides a method for prefabricating surface pits on AE4T steel axles, including: (1) Intelligent planning of damage prefabrication location: Axle specifications: Φ200mm×2500mm, hollow axle made of EA4T alloy steel. A CAD finite element model of the axle was established, and finite element static analysis was performed to identify high-stress areas. The main crack was determined to be located in the fillet transition area (convex curved surface) of the gear seat. Pre-fabricated damage coordinates were calibrated, with a target crack depth of 0.50mm and a length of 2mm. Three micro-pits (approximately 100μm in diameter) were randomly pre-fabricated in the stress concentration area at the crack tip.

[0116] S2, Pre-existing damage from laser processing: The femtosecond laser processing system reads the corresponding coefficients from the laser mapping database, selects crack as the damage type, and performs continuous multi-layer scanning. It uses a standard power of 1.5W, a scanning speed of 80mm / s, a repetition frequency of 100kHz, and employs multi-layer spiral scanning for layer-by-layer etching.

[0117] S3. Ultrasonic monitoring and closed-loop verification: After processing, ultrasonic A / B scanning and laser confocal microscopy were used to measure the main crack length, which was 2.001 mm (error +1 μm) and the depth, which was 0.503 mm (error +3 μm).

[0118] S4. Post-processing and confirmation of damage morphology: Micro-pits were created by applying pulses at specific points on the crack tip and edge (pulse energy 0.2 mJ, duration 3 ms), followed by overall electropolishing. Electropolishing parameters included: voltage approximately 5 V, current density approximately 0.5 A / cm². 2 The time is approximately 30 seconds.

[0119] After polishing, the crack tip had a radius of approximately 7 μm. Three micro-pits were naturally distributed, each with a diameter of 0.10 mm and a depth of approximately 0.05 mm. Comparative analysis showed that the crack sidewall morphology matched that of fatigue fracture cracks. This composite sample could be directly used for subsequent fatigue testing, and no tool residue or new damage was detected. See the magnified images of the distribution and morphology of the three pits. Figure 6 .

[0120] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for pre-fabricating surface damage on an axle, characterized in that, include: S1. Based on the three-dimensional model of the axle and the service load spectrum, the damage pre-fabrication location is determined and the damage pre-fabrication coordinates are calibrated through finite element analysis; S2. A femtosecond laser is used to laser process the pre-existing damage location to pre-exist crack or pit-type damage; when the geometric feature of the pre-existing damage location is defined as an outwardly convex surface, the laser parameters for laser processing are standard parameters; when the geometric feature of the pre-existing damage location is an inwardly concave surface, the laser parameters are adjusted to 0.8 to 0.9 times the standard parameters; when the geometric feature of the pre-existing damage location is a plane, the laser parameters are adjusted to 0.9 to 1.0 times the standard parameters; the laser parameters are laser power, and the standard parameters are standard power; or, the laser parameters are pulse energy, and the standard parameters are standard energy. The damage pre-fabrication location includes the journal of the axle, the unloading groove, or the gear seat transition area. The geometric feature of the journal is the convex curved surface, and the geometric features of the unloading groove and the gear seat transition area are both the concave curved surfaces. S3. Use a high-frequency ultrasonic probe to scan the pre-fabricated damage location. Compare the ultrasonic scan image with a preset standard to verify whether the damage size and morphology meet the specifications. If the error exceeds the tolerance range of 5μm, repeat step S2 to perform local compensation processing. S4. Prefabricate a group of randomly distributed micro-pits in the stress concentration area surrounding the damage, wherein the diameter of the micro-pits is 50μm~100μm. When the damage is a crack, the method for forming the micro-pit group includes: applying pulses at fixed points at the tip and edge of the crack to form the micro-pits, with a pulse energy of 0.1mJ~0.2mJ and an action time of 1ms~3ms; and performing electrolytic polishing on the pre-formed damage location.

2. The method according to claim 1, characterized in that, The damage is a crack, the standard power is 0.5W~2W, the scanning speed is 50mm / s~200mm / s, the repetition frequency is 50kHz~100kHz, and the etching depth is 0.1mm~2mm.

3. The method according to claim 2, characterized in that, As the etching depth increases, the scanning speed gradually decreases.

4. The method according to claim 1, characterized in that, The damage is a pit, the standard energy is 0.1mJ~1mJ, the processing time is 1ms~10ms, forming a hemispherical pit with a diameter of 0.5mm~3mm and a depth of 0.1mm~1mm.

5. The method according to any one of claims 1 to 4, characterized in that, The pulse width of the laser does not exceed 500 fs; and / or the wavelength of the laser is 1020 nm to 1040 nm.

6. The method according to any one of claims 1 to 4, characterized in that, In step S4, the parameters for electropolishing include: 4V~6V voltage, 0.4A / cm 2 ~0.6A / cm 2 The current density was [value], and the polishing time was 28s~32s.

7. The method according to claim 1, characterized in that, In step S2, while performing the laser processing, the pre-fabricated damage location is monitored in real time to assess the real-time size and morphology of the damage, and the laser parameters are adjusted based on the assessment results.

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