Method and device for testing wear resistance of coating scraper edge

Abstract

The present invention discloses a method and device for detecting the wear resistance of a coating scraper blade edge, and relates to the technical field of scraper performance detection. The method comprises: screening limit state samples according to the scraper unique identification and service life design, and performing loss distribution analysis to obtain a scraper wear extreme value distribution diagram; performing a loss characteristic random perturbation, and performing a loss state back-tracing simulation according to the perturbation result to obtain M time series wear characteristic distributions; fusing the intermediate state loss distribution of the M time series wear characteristic distributions to obtain P stage wear extreme value distribution diagrams; collecting wear characteristics until the service life design, and obtaining P intermittent wear characteristic distributions and limit state wear characteristic distributions; performing wear characteristic verification on the limit state wear characteristic distribution and the P intermittent wear characteristic distributions, and outputting the wear resistance defect of the blade edge. The method solves the technical problem of low accuracy in the detection of wear resistance of the coating scraper blade edge in the prior art, and achieves the technical effect of improving the detection accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of scraper performance detection, and in particular to a method and device for detecting the wear resistance of a coating scraper blade edge. Background Art

[0002] In the context of coating equipment being widely used in precision manufacturing fields such as paper, film, and metal surface treatment, the coating scraper, as a key executive component, has a blade wear resistance that has a direct impact on coating quality and production stability. Existing scrapers are prone to blade wear during long-term and high-frequency use, leading to problems such as uneven coating thickness and increased surface defects, which in turn affect product quality and equipment operating efficiency. In existing technologies, the evaluation of scraper wear resistance mostly relies on periodic manual inspections or experience-based replacement mechanisms, which cannot effectively track and accurately predict the evolution of blade wear. There are technical limitations such as detection lag, low evaluation accuracy, and the inability to adapt to changing working conditions. Summary of the Invention

[0003] The present application provides a method and device for detecting the wear resistance of a coating scraper blade edge, which solves the technical problem of low accuracy in detecting the wear resistance of a coating scraper blade edge in the prior art.

[0004] In a first aspect of the present application, a method for detecting the wear resistance of a coating blade edge is provided, the method comprising:

[0005] According to the unique identification and service life design of the scraper, limit state samples are screened, and the loss distribution analysis is performed based on the screening results to obtain the scraper wear extreme value distribution diagram; after the scraper wear extreme value distribution diagram is subjected to random perturbation of the loss characteristics, the loss state is back-simulated according to the perturbation result to obtain M time series wear characteristic distributions; a preset time series equivalent step length is used to fuse the intermediate state sample loss distributions of the M time series wear characteristic distributions to obtain P stage wear extreme value distribution diagrams; in the process of wear resistance working condition testing of the coating scraper to be tested, the time series equivalent step length is used to locate the detection node for wear characteristic collection until the service life design is performed to obtain P intermittent wear characteristic distributions and limit state wear characteristic distributions; the scraper wear extreme value distribution diagram and the P stage wear extreme value distribution diagrams are used to map the limit state wear characteristic distribution and the P intermittent wear characteristic distributions to perform wear characteristic verification, and output the wear resistance performance defects of the cutting edge.

[0006] The second aspect of the present application provides a device for detecting the wear resistance of a coating blade edge, the device comprising:

[0007] A loss analysis module is used to screen limit state samples according to the unique identification of the scraper and the service life design, and to perform loss distribution analysis based on the screening results to obtain a scraper wear extreme value distribution diagram; a retrospective simulation module is used to perform a loss characteristic random perturbation on the scraper wear extreme value distribution diagram, and then perform a loss state retrospective simulation based on the perturbation result to obtain M time series wear characteristic distributions; a fusion module is used to preset a time series equivalent step length, and perform intermediate state sample loss distribution fusion on the M time series wear characteristic distributions to obtain P stage wear extreme value distribution diagrams; a wear characteristic acquisition module is used to use the time series equivalent step length to locate the detection node during the wear resistance test of the coating scraper to be tested, and to collect wear characteristics until the service life design is achieved, to obtain P intermittent wear characteristic distributions and limit state wear characteristic distributions; a verification module is used to use the scraper wear extreme value distribution diagram and P stage wear extreme value distribution diagrams to map the limit state wear characteristic distribution and P intermittent wear characteristic distributions for wear characteristic verification, and output the wear resistance performance defects of the cutting edge.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, the limit state samples are screened according to the unique identification and service life design of the scraper, and the loss distribution analysis is performed based on the screening results to obtain the extreme value distribution diagram of the scraper wear. Then, after the loss characteristics of the extreme value distribution diagram of the scraper wear are randomly perturbed, the loss state is back-simulated according to the perturbation results to obtain M time series wear characteristic distributions. Then, the time series equivalent step size is preset, and the intermediate state sample loss distribution is fused for the M time series wear characteristic distributions to obtain P stage wear extreme value distribution diagrams. During the wear resistance test of the coating scraper to be tested, the time series equivalent step size is used to locate the detection node for wear characteristic collection until the service life design, and P intermittent wear characteristic distributions and limit state wear characteristic distributions are obtained. Finally, the scraper wear extreme value distribution diagram and P stage wear extreme value distribution diagrams are used to map the wear characteristics of the limit state wear characteristic distribution and the P intermittent wear characteristic distributions to output the wear resistance performance defects of the cutting edge. The technical problem of low accuracy in the detection of wear resistance performance of the coating scraper edge in the prior art is solved, and the technical effect of improving the detection accuracy is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1A schematic flow chart of a method for testing the wear resistance of a coating blade edge provided in an embodiment of the present application;

[0012] Figure 2 Schematic diagram of the structure of the device for detecting the wear resistance of the coating scraper edge provided in an embodiment of the present application.

[0013] Explanation of the reference numerals: loss analysis module 11 , backtracking simulation module 12 , fusion module 13 , wear feature acquisition module 14 , verification module 15 . DETAILED DESCRIPTION

[0014] The present application solves the technical problem of low accuracy in the detection of the wear resistance of the coating scraper edge in the prior art by providing a method and device for detecting the wear resistance of the coating scraper edge.

[0015] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0016] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0017] Example 1, as Figure 1 As shown, the present application provides a method for detecting the wear resistance of a coating scraper edge, wherein the method comprises:

[0018] The limit state samples are screened according to the unique identification and service life design of the scraper, and the loss distribution analysis is performed based on the screening results to obtain the extreme value distribution diagram of the scraper wear.

[0019] In the present embodiment, the scraper blade unique identifier refers to the scraper's production serial number, and the service life design refers to the scraper's intended service life. By using the scraper blade unique identifier and service life design as screening conditions, samples with extreme wear conditions can be screened out from historical scraper usage data.

[0020] After completing the sample screening, the selected limit state samples were subjected to a loss distribution analysis. Specifically, by conducting a detailed analysis of the scraper wear data under different operating conditions, the scraper wear distribution was calculated, especially the degree of wear at each part under the maximum wear state. The scraper wear extreme value distribution diagram is obtained. The scraper wear extreme value distribution diagram is a three-dimensional extreme value distribution diagram of the blade axial position, wear depth, and notch density. It shows the wear distribution of the scraper blade edge in the dimensions of axial position, wear depth, and notch density.

[0021] Furthermore, the limit state samples are screened based on the unique identification and service life design of the scraper, and the loss distribution analysis is performed based on the screening results to obtain the extreme value distribution diagram of the scraper wear. The method includes:

[0022] According to the unique identification and service life design of the scraper, limit state samples are screened to obtain N sample service scrapers; the apparent characteristics of the N sample service scrapers are digitized to obtain N coating loss distribution characteristics and N notch loss distribution characteristics; the loss distribution is fused based on the N coating loss distribution characteristics and the N notch loss distribution characteristics to obtain the scraper wear extreme value distribution diagram.

[0023] Based on the unique identification and service life design of the scraper, limit state sample screening is performed. That is, by analyzing the wear of the scraper under different operating conditions, N sample service scrapers in the extreme wear state are screened. The surface characteristics of the N sample service scrapers selected are digitized. Through testing, the surface coating loss and cutting edge notches of each scraper are measured and recorded, and N coating loss distribution characteristics and N notch loss distribution characteristics are obtained. The coating loss distribution characteristics reflect the degree of loss of the scraper coating during use, while the notch loss distribution characteristics describe the shape and distribution of notches generated on the scraper cutting edge during service. Based on the obtained N coating loss distribution characteristics and N notch loss distribution characteristics, a data fusion algorithm is used to spatially integrate the coating loss and notch loss to generate a scraper wear extreme value distribution map. This distribution map is in three-dimensional form and shows the wear extreme values ​​of the scraper under different service conditions, including the axial position of the scraper cutting edge, wear depth, and notch density.

[0024] Furthermore, the surface characteristics of the N sample service scrapers are digitally processed to obtain N coating loss distribution characteristics and N notch loss distribution characteristics, and the method includes:

[0025] A white light interferometer is started to scan the entire area of ​​the blade edge of the first sample service scraper to obtain blade edge morphology point cloud data; the blade edge morphology point cloud data is aligned to the original CAD model of the coating scraper to be tested through ICP registration to extract morphology deviation data; the morphology deviation data is separated based on the wear type to obtain coating thickness deviation data and blade edge deformation deviation data; the thickness loss rate of the coating thickness deviation data is calculated along the axial direction of the blade edge to obtain the first coating loss distribution feature; the notch feature of the blade edge deformation deviation data is extracted along the axial direction of the blade edge to generate a notch position-size distribution matrix as the first notch loss distribution feature, wherein the notch feature includes notch depth and notch density.

[0026] Preferably, a sample service scraper is selected from N sample service scrapers as the first sample service scraper; a white light interferometer is started to perform high-precision scanning on the entire area of ​​the blade edge of the first sample service scraper to obtain blade edge morphology point cloud data, which reflects the three-dimensional morphology information of the scraper blade edge surface, including the wear condition and surface features of the scraper. Through ICP registration technology, the acquired blade edge morphology point cloud data is aligned with the original CAD model of the coating scraper to be tested, and morphology deviation data is extracted therefrom, that is, the difference data between the original CAD model and the original CAD model. The morphology deviation data is separated based on the wear type (coating wear, blade edge wear) to obtain coating thickness deviation data and blade edge deformation deviation data, wherein the coating thickness deviation data reflects the wear condition of the scraper coating during use, while the blade edge deformation deviation data reveals the deformation and damage of the scraper blade edge during service. Based on the coating thickness deviation data, the thickness loss rate is calculated along the axial direction of the scraper edge. That is, the coating thickness at different positions on the scraper edge is compared, and the coating thickness loss at each position is calculated to obtain the first coating loss distribution feature, where thickness loss rate = (initial coating thickness - current coating thickness) / initial coating thickness. For the edge deformation deviation data, the notch features are extracted along the axial direction of the scraper edge, including notch depth and notch density, to generate a notch position-size distribution matrix. This matrix describes the distribution characteristics of the damaged area of ​​the scraper edge and the severity of the damage, and is provided as the first notch loss distribution feature for subsequent analysis. Similarly, the apparent characteristics of N sample service scrapers are digitized to obtain N coating loss distribution features and N notch loss distribution features.

[0027] Furthermore, the loss distribution is fused according to the N coating loss distribution characteristics and the N notch loss distribution characteristics to obtain a blade wear extreme value distribution map, and the method includes:

[0028] Using preset intervals, multiple discrete coordinate points are positioned on the original CAD model along the axial direction of the cutting edge; after aligning the N coating loss distribution features based on the multiple discrete coordinate points, the maximum thickness loss is extracted to obtain a coating loss distribution curve; after aligning the N notch loss distribution features based on the multiple discrete coordinate points, the maximum notch density and the maximum notch depth are extracted respectively to obtain a notch density distribution curve and a notch depth distribution curve; the coating loss distribution curve, the notch density distribution curve and the notch depth distribution curve are spatially superimposed on the cutting edge axial coordinate system to generate the scraper wear extreme value distribution diagram.

[0029] Preferably, a plurality of discrete coordinate points are positioned on the original CAD model along the axial direction of the scraper edge using a preset interval. These coordinate points are distributed in the axial direction of the scraper edge and represent the wear characteristics of the scraper at different positions. Based on the plurality of discrete coordinate points, N coating loss distribution features are aligned to ensure that the coating loss data of each sample can correspond to the corresponding scraper position; after alignment, the maximum value of the coating thickness loss at each position is extracted to obtain a coating loss distribution curve, which reflects the change in coating loss at different positions of the scraper. Based on the plurality of discrete coordinate points, N notch loss distribution features are aligned to ensure that the notch loss data corresponds one-to-one to the position of the scraper; after alignment, the maximum notch density and the maximum notch depth at each position are extracted respectively to obtain a notch density distribution curve and a notch depth distribution curve, which describe the notch damage at different positions of the scraper. The obtained coating loss distribution curve, notch density distribution curve, and notch depth distribution curve are spatially superimposed on the axial coordinate system of the scraper edge to generate a scraper wear extreme value distribution map. The scraper wear extreme value distribution map represents the historical maximum coating loss rate, notch depth, and notch density at each position of the cutting edge.

[0030] After randomly perturbing the wear characteristics of the scraper wear extreme value distribution diagram, a wear state backtracking simulation is performed based on the perturbation result to obtain M time series wear characteristic distributions.

[0031] In an embodiment of the present application, the scraper wear extreme value distribution diagram is subjected to random perturbation of loss characteristics. Specifically, by introducing certain random errors or changes, the fluctuations of coating loss, notch density and notch depth under different working conditions are simulated. For example, coating loss can increase or decrease within a certain range to reflect the coating wear of the scraper under different friction, pressure and temperature conditions; notch density can be simulated by introducing random fluctuations. The increase in notches caused by friction and impact during long-term use of the scraper; the perturbation of notch depth is simulated by randomly changing the notch depth data to simulate the expansion of notches that may be generated by the scraper under different working conditions. By randomly perturbing the coating loss, notch density and notch depth characteristics in the scraper wear extreme value distribution diagram, the wear process of the scraper during actual use can be more realistically reflected.

[0032] After randomly perturbing the wear characteristics, a retrospective wear simulation is performed based on the perturbation results. This involves using the current wear data to infer and restore the scraper's wear at different points in time, simulating the scraper's wear characteristics at various moments in the past. This retrospective simulation allows prediction of the scraper's wear state at different stages based on the existing wear data, enabling dynamic tracking of scraper wear. After the retrospective simulation, M time-series wear characteristic distribution maps are generated, representing the scraper's wear characteristics at different time points. These time-series wear characteristic distribution maps illustrate the changes in scraper wear from the beginning to the end of its service life, revealing the evolutionary trend of scraper wear.

[0033] Furthermore, based on the disturbance results, a wear state backtracking simulation is performed to obtain M time series wear characteristic distributions. The method includes:

[0034] Taking the N coating loss distribution characteristics and the N notch loss distribution characteristics as the starting point and the scraper wear extreme value distribution diagram as the constraint, the loss characteristics are randomly perturbed to obtain M updated wear distribution models; based on the M updated wear distribution models, limit state sample retrieval is performed to obtain M updated service scrapers; the loss state of the M updated service scrapers is retrospectively simulated to obtain the M time series wear characteristic distributions.

[0035] Preferably, starting with N coating loss distribution characteristics and N notch loss distribution characteristics, and using the blade wear extreme value distribution map as a constraint, the coating loss characteristics and notch loss characteristics are randomly perturbed. By introducing random errors, the wear changes of the blade under different working conditions and environments are simulated to cover wear in extreme scenarios. Through perturbations, M updated wear distribution models are generated. These models reflect the performance of the blade under different wear states, covering changes in characteristics such as coating loss, notch density, and notch depth. Based on the M updated wear distribution models, a limit state sample search is performed. Specifically, data that meets the extreme wear conditions is extracted from these updated wear distribution models to obtain M updated service blade samples. Each updated service blade represents the use of the blade at different time points and different wear states, simulating the wear evolution that may occur in actual operation. The wear state of the M updated service blades is then retrospectively simulated. Specifically, based on the current wear state, the wear state of the blade at various time points in the past is inferred. Through the retrospective simulation, M time-series wear characteristic distributions are obtained, which show the wear evolution of the blade from initial use to the end of its service life.

[0036] Furthermore, a wear state retrospective simulation is performed on the M updated service scrapers to obtain the M time series wear characteristic distributions, and the method includes:

[0037] Extract the first service condition parameters of the first updated service scraper, wherein the first service condition parameters include a coating pressure sequence, a substrate hardness sequence, and a scraper linear speed sequence; locate P equivalent time nodes in the service life design according to the time series equivalent step, wherein the P equivalent time nodes are the ends of P equivalent time stages; construct P dynamic wear driving factors by mapping the first service condition parameters to the P equivalent time nodes; calculate the wear characteristic increments of the P equivalent time stages according to the P dynamic wear driving factors, and accumulate and output the first time series wear characteristic distribution.

[0038] Preferably, a newer service scraper is selected from M newer service scrapers as the first newer service scraper; the first service operating condition parameters of the first newer service scraper are extracted, wherein the first service operating condition parameters include a coating pressure sequence, a substrate hardness sequence, and a scraper linear speed sequence, and these operating condition parameters reflect the working state of the scraper during actual use; based on the time sequence equivalent step length, P equivalent time nodes are located in the service life design, each equivalent time node represents an important moment in the scraper's service cycle, usually corresponding to a certain stage of the scraper wear process, such as the early, middle, or late stages of scraper use. By mapping the first service operating condition parameters to P equivalent time nodes, that is, docking the values ​​of the first service operating condition parameters at different time nodes with the corresponding equivalent time nodes, the specific operating condition information corresponding to each node is obtained. For example, at a certain time node, the coating pressure may be high, the scraper linear speed may be fast, and the substrate hardness may be high. All of this information can be mapped to obtain the working conditions at that time node. Then, P dynamic wear driving factors are constructed. These dynamic wear driving factors are calculated based on the working parameters of the scraper at each equivalent time node, and can reflect the driving factors of wear at each time node, such as the impact of changes in coating pressure, substrate hardness, and scraper linear speed on wear. The calculation formula of dynamic wear driving factors is: ,in, is the dynamic wear driving factor at the i-th equivalent time node, is the coating pressure at the i-th time node, is the substrate hardness at the i-th time node, is the scraper linear velocity at the i-th time node, 、 、 It is the weight coefficient of coating pressure, substrate hardness and scraper linear speed, reflecting the relative influence of each working condition parameter on wear. The weight coefficient can be determined based on actual experience or experimental data.

[0039] Based on P dynamic wear driving factors, we calculate wear characteristic increments for P equivalent time periods. By accumulating these increments, we can obtain the wear characteristic changes of the scraper in each equivalent time period, thereby outputting the first time-series wear characteristic distribution. This time-series wear characteristic distribution shows the wear status of the scraper at different time points. Similarly, we perform a wear state retrospective simulation on M updated scrapers, obtaining M time-series wear characteristic distributions.

[0040] Furthermore, the wear characteristic increments of the P equivalent time stages are calculated based on the P dynamic wear driving factors, and accumulated to output a first time series wear characteristic distribution. The method includes:

[0041] The coating thickness loss rate increments of the P equivalent time stages are calculated based on the P dynamic wear driving factors, and the coating loss rate time series curves are accumulated to generate the coating loss rate time series curves; the notch density increments and notch depth increments of the P equivalent time stages are calculated based on the P dynamic wear driving factors, and the notch density time series curves and notch depth time series curves are accumulated to generate the notch density time series curves and notch depth time series curves; the coating loss rate time series curve, the notch density time series curve and the notch depth time series curve constitute the first time series wear characteristic distribution.

[0042] Preferably, the coating thickness loss rate increment in each equivalent time stage is calculated based on P dynamic wear driving factors. Each dynamic wear driving factor represents the driving force of the scraper's working conditions on wear at different time stages. By calculating the correlation between these driving factors and coating loss, the coating thickness loss rate increment in each equivalent time stage can be obtained, reflecting the wear change of the scraper coating in this stage. These increments are accumulated to generate a time series curve of the coating loss rate, which shows the evolution of the coating loss of the scraper at different time nodes over time. Based on P dynamic wear driving factors, the notch density increment and notch depth increment are calculated for each equivalent time stage. The notch density increment reflects the change in the number of notches on the scraper edge, while the notch depth increment reflects the change in the depth of each notch. Through the influence of the dynamic wear driving factors, the increments of notch density and notch depth in each equivalent time stage are calculated respectively; these increments are accumulated to generate a notch density time series curve and a notch depth time series curve respectively, which show the evolution of the notch characteristics of the scraper throughout its service life.

[0043] A time series equivalent step size is preset, and the intermediate state loss distribution is fused for the M time series wear feature distributions to obtain P stage wear extreme value distribution maps.

[0044] A preset time series equivalent step size is used to divide the scraper's service life into several equivalent time stages. Each equivalent time stage represents the scraper's operating state at that stage. The preset equivalent step size ensures that the time series data of the wear characteristics are evenly distributed within each stage.

[0045] Based on the M time series wear feature distributions, the wear features of each equivalent time stage are fused; by fusing these time series data, the comprehensive wear state of the scraper in each stage can be obtained; based on the fused intermediate state loss distribution, P stage wear extreme value distribution diagrams are generated. The wear extreme value distribution diagram of each stage shows the wear characteristics of the scraper in that stage in terms of coating loss, notch density and notch depth.

[0046] Furthermore, the M time-series wear characteristic distributions are fused with the intermediate state loss distribution to obtain P stage wear extreme value distribution maps, and the method includes:

[0047] 1 / (P+1) of the service life design is used as the time series equivalent step length, where P≥12 and P is a positive integer; the time series equivalent step length is used to perform intermediate sample feature extraction on the M time series wear characteristic distributions to obtain P groups of stage wear characteristic distributions, where each group of stage wear characteristic distributions includes a stage coating loss distribution and a stage notch loss distribution; loss distribution fusion is performed based on the P groups of stage wear characteristic distributions to obtain the P stage wear extreme value distribution maps.

[0048] Preferably, a timing equivalent step size is determined, which is obtained by calculating 1 / (P+1) of the service life design, where P≥12 and P is a positive integer; the timing equivalent step size is used to extract intermediate sample features of the M timing wear characteristic distributions, that is, the entire wear cycle is divided into P equivalent time stages based on the timing equivalent step size, and in each stage, corresponding feature data is extracted from the M timing wear characteristic distributions, including stage coating loss distribution and stage notch loss distribution. The process of fusion of loss distribution based on the extracted P group stage wear characteristic distribution is basically the same as the method of obtaining the extreme value distribution map of scraper wear. Specifically: first, a plurality of discrete coordinate points are evenly positioned on the original CAD model of the coating scraper to be tested along the axial direction of the cutting edge using a preset interval; then, the stage coating loss distribution and the stage notch loss distribution in each group of stage wear characteristic distribution are spatially aligned and corresponded to the plurality of discrete coordinate points; for each coordinate point, the maximum value of the coating thickness loss in that stage is extracted to generate a coating loss distribution curve; further, after aligning the notch loss distribution, the maximum value of the notch density and the maximum value of the notch depth are extracted respectively to form the stage notch density distribution curve and the stage notch loss distribution curve, respectively. The notch depth distribution curve is obtained. Then, for the M coating loss rate time series curves in the same stage, spatial registration is performed based on discrete coordinate points, and maximum value extraction and fitting interpolation are performed at each coordinate point to obtain the coating thickness loss fusion curve corresponding to the stage. The notch density time series curve and the notch depth time series curve are processed in the same way to obtain the notch density fusion curve and the notch depth fusion curve respectively. Then, the three fusion curves obtained in this stage are spatially superimposed in a coordinate alignment manner to construct a stage wear extreme value distribution diagram in the cutting edge axial coordinate system. The above process is performed on P stages in turn, and finally P stage wear extreme value distribution diagrams are output to characterize the wear extreme value state and evolution trend of the scraper blade in different service stages. In the wear resistance test of the coating scraper to be tested, the time series equivalent step length is used to locate the detection node to collect wear characteristics until the service life design, and P intermittent wear characteristic distributions and limit state wear characteristic distributions are obtained.

[0049] During the wear resistance test of the coating scraper to be tested, the time-series equivalent step length is used to gradually locate the wear state of the scraper. The time-series equivalent step length is calculated based on the total usage time and the equivalent time nodes divided in the scraper service life design. It ensures that during the entire wear test process, each test stage of the scraper has a uniform and scientific time distribution. Based on these equivalent time nodes, multiple detection nodes can be located in the axial direction of the scraper's cutting edge, and each node represents the wear characteristics of the scraper under different working conditions. By collecting wear characteristics at these nodes, the wear condition of the scraper can be monitored in real time at each test stage, and key wear data including coating loss, notch density, notch depth, etc. can be collected.

[0050] Throughout the wear test, detection nodes were arranged based on equivalent time steps to ensure coverage of the scraper's entire service life. Wear characteristics were collected at each equivalent time point, and wear data was continuously collected and recorded until the scraper reached the end of its designed service life. This data included, but was not limited to, changes in coating loss, notch density, and notch depth.

[0051] Based on the collected wear characteristic data, P intermittent wear characteristic distributions and limit wear characteristic distributions were obtained. The intermittent wear characteristic distribution represents the wear condition of the scraper at different time points, while the limit wear characteristic distribution shows the most severe wear state of the scraper during the test.

[0052] The scraper wear extreme value distribution diagram and the P stage wear extreme value distribution diagrams are used to map the limit state wear characteristic distribution and the P intermittent wear characteristic distributions to perform wear characteristic verification, and output the edge wear resistance defects.

[0053] Based on the obtained scraper wear extreme value distribution map and P stage wear extreme value distribution maps, the scraper's limit state wear characteristic distribution and P intermittent wear characteristic distributions are mapped. That is, by comparing the scraper's wear characteristics at different time points and operating conditions with the preset wear extreme value distribution map, it is checked whether the wear state meets the design standards. Through this mapping, it is possible to identify whether the scraper's wear state at the limit state and different time points exceeds the expected range, and determine whether the scraper has wear resistance defects.

[0054] Specifically, mapping can be used to compare the actual wear characteristics of the scraper with the limit state wear characteristics, as well as the wear conditions at different stages. If the actual wear characteristics exceed the preset wear resistance standards or excessive wear is observed, it indicates that the scraper has wear resistance defects. These defects may manifest as rapid coating loss, excessive or deep notches, and affect the scraper's service life and operating efficiency.

[0055] Furthermore, the scraper wear extreme value distribution diagram and the P stage wear extreme value distribution diagrams are used to map the limit state wear characteristic distribution and the P intermittent wear characteristic distributions to perform wear characteristic verification, and output the edge wear resistance defect. The method includes:

[0056] The coating loss distribution curve, notch density distribution curve and notch depth distribution curve in the scraper wear extreme value distribution diagram are respectively compared with the limit state coating distribution curve, limit state notch density curve and limit state notch depth curve in the limit state wear characteristic distribution for point-by-point deviation, so as to mark and output the limit state wear resistance performance defect; by analogy, the P stage wear extreme value distribution diagrams are used to map the P intermittent wear characteristic distributions for process state wear feature verification, and output the process state wear resistance performance defect; the process state wear resistance performance defect and the limit state wear resistance performance defect are spatially superimposed to generate the cutting edge wear resistance performance defect.

[0057] Preferably, the coating loss distribution curve, notch density distribution curve and notch depth distribution curve in the extreme value distribution diagram of scraper wear are respectively compared with the limit state coating distribution curve, limit state notch density curve and limit state notch depth curve in the limit state wear characteristic distribution. By comparing the differences between the coating loss, notch density and notch depth at each corresponding point, it is judged whether there is wear beyond expectations. If the measured coating loss rate, notch depth or notch density exceeds the historical extreme value at any axial position, the position is marked as a limit state defect area, and the wear resistance deviation type of the area is identified. 。 In a similar manner, P stage wear extreme value distribution maps are used to map the P intermittent wear characteristic distributions for process wear characteristic verification. Process wear characteristic verification is a stage-by-stage evaluation of the scraper's wear under different operating conditions. By comparing the wear characteristics of each stage, it is detected whether the scraper has experienced abnormal wear during the process. When the wear characteristics of each stage deviate beyond the predetermined standard, the corresponding process wear resistance defects will be output. These defects reflect the abnormal wear of the scraper caused by certain operating conditions during use. Finally, the process wear resistance defects and limit wear resistance defects are spatially superimposed; by combining the two, the wear resistance performance of the scraper throughout its service life can be comprehensively evaluated, and the wear of the scraper under different operating conditions can be comprehensively analyzed. The superimposed results generate cutting edge wear resistance defects, which accurately reflect the wear resistance problems of the scraper in actual use, providing a scientific basis for the optimized design, material improvement, and maintenance of the scraper during use.

[0058] In summary, the embodiments of the present application have at least the following technical effects:

[0059] First, the limit state samples are screened according to the unique identification and service life design of the scraper, and the loss distribution analysis is performed based on the screening results to obtain the extreme value distribution diagram of the scraper wear. Then, after the loss characteristics of the extreme value distribution diagram of the scraper wear are randomly perturbed, the loss state is back-simulated according to the perturbation results to obtain M time series wear characteristic distributions. Then, the time series equivalent step size is preset, and the intermediate state sample loss distribution is fused for the M time series wear characteristic distributions to obtain P stage wear extreme value distribution diagrams. During the wear resistance test of the coating scraper to be tested, the time series equivalent step size is used to locate the detection node for wear characteristic collection until the service life design, and P intermittent wear characteristic distributions and limit state wear characteristic distributions are obtained. Finally, the scraper wear extreme value distribution diagram and P stage wear extreme value distribution diagrams are used to map the wear characteristics of the limit state wear characteristic distribution and the P intermittent wear characteristic distributions to output the wear resistance performance defects of the cutting edge. The technical problem of low accuracy in the detection of wear resistance performance of the coating scraper edge in the prior art is solved, and the technical effect of improving the detection accuracy is achieved.

[0060] Example 2, based on the same inventive concept as the method for testing the wear resistance of the coating blade edge in the previous embodiment, Figure 2 As shown, the present application provides a device for detecting the wear resistance of a coating blade edge, wherein the device comprises:

[0061] The loss analysis module 11 is used to screen the limit state samples according to the unique identification of the scraper and the service life design, and perform loss distribution analysis based on the screening results to obtain the scraper wear extreme value distribution diagram; the retrospective simulation module 12 is used to perform loss characteristic random perturbation on the scraper wear extreme value distribution diagram, and then perform loss state retrospective simulation based on the perturbation result to obtain M time series wear characteristic distributions; the fusion module 13 is used to preset the time series equivalent step size, perform intermediate state sample loss distribution fusion on the M time series wear characteristic distributions, and obtain P stage wear extreme value distribution diagrams; the wear characteristic acquisition module 14 is used to use the time series equivalent step size to locate the detection node during the wear resistance test of the coating scraper to be tested, and perform wear characteristic acquisition until the service life design is achieved, to obtain P intermittent wear characteristic distributions and limit state wear characteristic distributions; the verification module 15 is used to use the scraper wear extreme value distribution diagram and the P stage wear extreme value distribution diagrams to map the limit state wear characteristic distribution and the P intermittent wear characteristic distributions to perform wear characteristic verification, and output the wear resistance performance defects of the cutting edge.

[0062] Furthermore, the loss analysis module 11 is configured to perform the following method:

[0063] According to the unique identification and service life design of the scraper, limit state samples are screened to obtain N sample service scrapers; the apparent characteristics of the N sample service scrapers are digitized to obtain N coating loss distribution characteristics and N notch loss distribution characteristics; the loss distribution is fused based on the N coating loss distribution characteristics and the N notch loss distribution characteristics to obtain the scraper wear extreme value distribution diagram.

[0064] Furthermore, the backtracking simulation module 12 is used to perform the following method:

[0065] Taking the N coating loss distribution characteristics and the N notch loss distribution characteristics as the starting point and the scraper wear extreme value distribution diagram as the constraint, the loss characteristics are randomly perturbed to obtain M updated wear distribution models; based on the M updated wear distribution models, limit state sample retrieval is performed to obtain M updated service scrapers; the loss state of the M updated service scrapers is retrospectively simulated to obtain the M time series wear characteristic distributions.

[0066] Furthermore, the fusion module 13 is configured to perform the following method:

[0067] 1 / (P+1) of the service life design is used as the time series equivalent step length, where P≥12 and P is a positive integer; the time series equivalent step length is used to perform intermediate sample feature extraction on the M time series wear characteristic distributions to obtain P groups of stage wear characteristic distributions, where each group of stage wear characteristic distributions includes a stage coating loss distribution and a stage notch loss distribution; loss distribution fusion is performed based on the P groups of stage wear characteristic distributions to obtain the P stage wear extreme value distribution maps.

[0068] Furthermore, the loss analysis module 11 is configured to perform the following method:

[0069] A white light interferometer is started to scan the entire area of ​​the blade edge of the first sample service scraper to obtain blade edge morphology point cloud data; the blade edge morphology point cloud data is aligned to the original CAD model of the coating scraper to be tested through ICP registration to extract morphology deviation data; the morphology deviation data is separated based on the wear type to obtain coating thickness deviation data and blade edge deformation deviation data; the thickness loss rate of the coating thickness deviation data is calculated along the axial direction of the blade edge to obtain the first coating loss distribution feature; the notch feature of the blade edge deformation deviation data is extracted along the axial direction of the blade edge to generate a notch position-size distribution matrix as the first notch loss distribution feature, wherein the notch feature includes notch depth and notch density.

[0070] Furthermore, the loss analysis module 11 is configured to perform the following method:

[0071] Using preset intervals, multiple discrete coordinate points are positioned on the original CAD model along the axial direction of the cutting edge; after aligning the N coating loss distribution features based on the multiple discrete coordinate points, the maximum thickness loss is extracted to obtain a coating loss distribution curve; after aligning the N notch loss distribution features based on the multiple discrete coordinate points, the maximum notch density and the maximum notch depth are extracted respectively to obtain a notch density distribution curve and a notch depth distribution curve; the coating loss distribution curve, the notch density distribution curve and the notch depth distribution curve are spatially superimposed on the cutting edge axial coordinate system to generate the scraper wear extreme value distribution diagram.

[0072] Furthermore, the backtracking simulation module 12 is used to perform the following method:

[0073] Extract the first service condition parameters of the first updated service scraper, wherein the first service condition parameters include a coating pressure sequence, a substrate hardness sequence, and a scraper linear speed sequence; locate P equivalent time nodes in the service life design according to the time series equivalent step, wherein the P equivalent time nodes are the ends of P equivalent time stages; construct P dynamic wear driving factors by mapping the first service condition parameters to the P equivalent time nodes; calculate the wear characteristic increments of the P equivalent time stages according to the P dynamic wear driving factors, and accumulate and output the first time series wear characteristic distribution.

[0074] Furthermore, the backtracking simulation module 12 is used to perform the following method:

[0075] The coating thickness loss rate increments of the P equivalent time stages are calculated based on the P dynamic wear driving factors, and the coating loss rate time series curves are accumulated to generate the coating loss rate time series curves; the notch density increments and notch depth increments of the P equivalent time stages are calculated based on the P dynamic wear driving factors, and the notch density time series curves and notch depth time series curves are accumulated to generate the notch density time series curves and notch depth time series curves; the coating loss rate time series curve, the notch density time series curve and the notch depth time series curve constitute the first time series wear characteristic distribution.

[0076] Furthermore, the verification module 15 is configured to perform the following method:

[0077] The coating loss distribution curve, notch density distribution curve and notch depth distribution curve in the scraper wear extreme value distribution diagram are respectively compared with the limit state coating distribution curve, limit state notch density curve and limit state notch depth curve in the limit state wear characteristic distribution for point-by-point deviation, so as to mark and output the limit state wear resistance performance defect; by analogy, the P stage wear extreme value distribution diagrams are used to map the P intermittent wear characteristic distributions for process state wear feature verification, and output the process state wear resistance performance defect; the process state wear resistance performance defect and the limit state wear resistance performance defect are spatially superimposed to generate the cutting edge wear resistance performance defect.

[0078] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0080] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for testing the wear resistance of a coating blade edge, characterized in that: The method comprises: Limit state samples are screened based on the unique identification and service life design of the scraper, and loss distribution analysis is performed based on the screening results to obtain a scraper wear extreme value distribution diagram. The method includes: According to the unique identification and service life design of the scraper, limit state samples are screened to obtain N sample service scrapers; Digitally processing the surface characteristics of the N sample service scrapers to obtain N coating loss distribution characteristics and N notch loss distribution characteristics; Performing loss distribution fusion based on the N coating loss distribution characteristics and the N notch loss distribution characteristics to obtain a scraper wear extreme value distribution map; After randomly perturbing the scraper wear extreme value distribution diagram, a wear state backtracking simulation is performed based on the perturbation result to obtain M time series wear characteristic distributions. The method includes: Taking the N coating loss distribution characteristics and the N notch loss distribution characteristics as starting points and the blade wear extreme value distribution map as a constraint, randomly perturbing the loss characteristics to obtain M updated wear distribution models; Perform limit state sample retrieval based on the M updated wear distribution models to obtain M updated service scrapers; Performing a wear state backtracking simulation on the M updated service scrapers to obtain the M time series wear characteristic distributions; Preset the time series equivalent step length, perform intermediate state sample loss distribution fusion on the M time series wear feature distributions, and obtain P stage wear extreme value distribution maps; During the wear resistance test of the coating blade to be tested, the time sequence equivalent step length is used to locate the detection node to collect wear characteristics until the service life design is performed, and P intermittent wear characteristic distributions and limit state wear characteristic distributions are obtained; The scraper wear extreme value distribution diagram and the P stage wear extreme value distribution diagrams are used to map the limit state wear characteristic distribution and the P intermittent wear characteristic distributions to perform wear characteristic verification, and output the edge wear resistance defects.

2. The method for testing the wear resistance of a coating blade edge according to claim 1, wherein: Performing intermediate state sample loss distribution fusion on the M time series wear feature distributions to obtain P stage wear extreme value distribution maps, the method includes: 1 / (P+1) of the service life design is used as the timing equivalent step length, where P≥12 and P is a positive integer; Using the time series equivalent step length, extracting intermediate state sample features from the M time series wear characteristic distributions to obtain P groups of stage wear characteristic distributions, wherein each group of stage wear characteristic distributions includes a stage coating loss distribution and a stage notch loss distribution; The wear distribution fusion is performed based on the P groups of stage wear characteristic distributions to obtain the P stage wear extreme value distribution maps.

3. The method for testing the wear resistance of the coating blade edge according to claim 1, wherein: Digitally processing the surface characteristics of the N sample service scrapers to obtain N coating loss distribution characteristics and N notch loss distribution characteristics, the method comprising: Start the white light interferometer to scan the entire area of ​​the blade edge of the first sample service scraper to obtain the blade edge morphology point cloud data; Aligning the cutting edge topography point cloud data to the original CAD model of the coating blade to be inspected through ICP registration to extract topography deviation data; Separating the topography deviation data based on wear type to obtain coating thickness deviation data and cutting edge deformation deviation data; Calculating the thickness loss rate of the coating thickness deviation data along the axial direction of the cutting edge to obtain a first coating loss distribution characteristic; Notch features are extracted from the edge deformation deviation data along the edge axis to generate a notch position-size distribution matrix as a first notch loss distribution feature, wherein the notch features include notch depth and notch density.

4. The method for testing the wear resistance of a coating blade edge according to claim 3, wherein: According to the N coating loss distribution characteristics and the N notch loss distribution characteristics, loss distribution fusion is performed to obtain a scraper wear extreme value distribution map, and the method includes: Using preset intervals, locating a plurality of discrete coordinate points on the original CAD model along the axial direction of the cutting edge; After aligning the N coating loss distribution features based on the multiple discrete coordinate points, extracting the maximum value of the thickness loss to obtain a coating loss distribution curve; After aligning the N notch loss distribution features based on the multiple discrete coordinate points, extracting the maximum notch density and the maximum notch depth respectively to obtain a notch density distribution curve and a notch depth distribution curve; The coating loss distribution curve, the notch density distribution curve, and the notch depth distribution curve are spatially superimposed on the cutting edge axial coordinate system to generate the scraper wear extreme value distribution diagram.

5. The method for testing the wear resistance of a coating blade edge according to claim 2, wherein: Performing a wear state backtracking simulation on the M updated service scrapers to obtain the M time series wear characteristic distributions, the method comprising: Extracting first service working condition parameters of a first updated service scraper, wherein the first service working condition parameters include a coating pressure sequence, a substrate hardness sequence, and a scraper linear speed sequence; Positioning P equivalent time nodes in the service life design according to the timing equivalent step, wherein the P equivalent time nodes are ends of P equivalent time stages; Constructing P dynamic wear driving factors by mapping the first service condition parameter to the P equivalent time nodes; The wear characteristic increments of the P equivalent time stages are calculated according to the P dynamic wear driving factors, and are accumulated to output a first time series wear characteristic distribution.

6. The method for testing the wear resistance of a coating blade edge according to claim 5, wherein: Calculating the wear characteristic increments of the P equivalent time stages based on the P dynamic wear driving factors, and accumulating and outputting a first time series wear characteristic distribution, the method includes: Calculating coating thickness loss rate increments in the P equivalent time stages based on the P dynamic wear driving factors, and accumulating them to generate a coating loss rate time series curve; Calculating the notch density increments and notch depth increments of the P equivalent time stages according to the P dynamic wear driving factors, and accumulating them to generate a notch density time series curve and a notch depth time series curve; The coating loss rate time series curve, the notch density time series curve, and the notch depth time series curve constitute the first time series wear characteristic distribution.

7. The method for testing the wear resistance of a coating blade edge according to claim 1, wherein: The scraper wear extreme value distribution diagram and the P stage wear extreme value distribution diagrams are used to map the limit state wear characteristic distribution and the P intermittent wear characteristic distributions to perform wear characteristic verification, and output the edge wear resistance defect. The method includes: The coating loss distribution curve, notch density distribution curve and notch depth distribution curve in the blade wear extreme value distribution diagram are respectively compared with the limit state coating distribution curve, the limit state notch density curve and the limit state notch depth curve in the limit state wear characteristic distribution to mark and output the limit state wear resistance defects; Similarly, the P stage wear extreme value distribution maps are used to map the P intermittent wear characteristic distributions to perform process wear characteristic verification, and output process wear resistance performance defects; The process state wear resistance defect and the limit state wear resistance defect are spatially superimposed to generate the cutting edge wear resistance defect.

8. A device for testing the wear resistance of coating blade edges, characterized in that: The device is used to implement the method for detecting the wear resistance of a coating scraper edge according to any one of claims 1 to 7, comprising: The wear analysis module is used to screen limit state samples based on the scraper's unique identification and service life design, and to perform wear distribution analysis based on the screening results to obtain a scraper wear extreme value distribution diagram; A retrospective simulation module is used to perform a random perturbation on the scraper wear extreme value distribution diagram, and then perform a retrospective simulation of the wear state according to the perturbation result to obtain M time series wear characteristic distributions; A fusion module is used to preset a time series equivalent step size, perform intermediate state sample loss distribution fusion on the M time series wear feature distributions, and obtain P stage wear extreme value distribution maps; A wear characteristic acquisition module is used to acquire wear characteristics by positioning the detection nodes using the time sequence equivalent step length during the wear resistance test of the coating blade to be tested until the service life design is performed, thereby obtaining P intermittent wear characteristic distributions and limit state wear characteristic distributions; The verification module is used to use the scraper wear extreme value distribution diagram and the P stage wear extreme value distribution diagrams to map the limit state wear characteristic distribution and the P intermittent wear characteristic distributions to perform wear characteristic verification and output the edge wear resistance defect.

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