Method and device for detecting wear resistance of cutting edge of coating scraper

Abstract

The invention discloses a method and a device for detecting wear resistance of a blade of a coating scraper, and relates to the technical field of scraper performance detection. The method comprises the following steps: carrying out limit state sample screening according to a scraper unique identifier and a service life design, and carrying out loss distribution analysis to obtain a scraper wear extreme value distribution diagram; after loss characteristic random disturbance is carried out, loss state backtracking simulation is carried out according to a disturbance result, and M time sequence wear characteristic distributions are obtained; performing intermediate pattern loss distribution fusion on the M time sequence wear feature distributions to obtain P stage wear extreme value distribution diagrams; wear feature collection is carried out until the service life is designed, and P intermittent wear feature distributions and limit state wear feature distributions are obtained; and carrying out wear characteristic verification on the limit state wear characteristic distribution and the P intermittent wear characteristic distribution, and outputting the wear resistance defect of the cutting edge. The technical problem that in the prior art, the wear resistance detection precision of a coating scraper blade is low is solved, and the technical effect of improving the detection precision is achieved.

Description

Technical Field

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

[0002] In the context where coating equipment is widely used in precision manufacturing fields such as paper, film, and metal surface treatment, as a key execution component, the wear resistance of the cutting edge of the coating doctor blade directly affects the coating quality and production stability. During the long-term and high-frequency use of existing doctor blades, the cutting edge is prone to wear, resulting in problems such as uneven coating thickness and increased surface defects, which in turn affect product quality and equipment operation efficiency. In the prior art, the evaluation of the wear resistance of doctor blades mostly relies on periodic manual inspections or experience-based replacement mechanisms, and it is impossible to effectively track and accurately predict the evolution process of cutting edge wear, suffering from technical limitations such as detection lag, low evaluation accuracy, and inability to adapt to changing working conditions. Summary of the Invention

[0003] This application provides a method and device for detecting the wear resistance of the cutting edge of a coating doctor blade, solving the technical problem of low detection accuracy of the wear resistance of the cutting edge of a coating doctor blade in the prior art.

[0004] In the first aspect of this application, a method for detecting the wear resistance of the cutting edge of a coating doctor blade is provided. The method includes: Screening extreme state samples according to the unique identifier of the doctor blade and the designed service life, and performing loss distribution analysis based on the screening results to obtain a doctor blade wear extreme value distribution map; after randomly disturbing the loss characteristics of the doctor blade wear extreme value distribution map, performing loss state backtracking simulation based on the disturbance results to obtain M time-series wear characteristic distributions; presetting a time-series equivalent step length, performing intermediate state sample loss distribution fusion on the M time-series wear characteristic distributions to obtain P stage wear extreme value distribution maps; during the wear resistance working condition test of the coating doctor blade to be detected, using the time-series equivalent step length to locate detection nodes to collect wear characteristics until the designed service life, obtaining P intermittent wear characteristic distributions and an extreme state wear characteristic distribution; using the doctor blade wear extreme value distribution map and the P stage wear extreme value distribution maps, mapping to perform wear characteristic verification on the extreme state wear characteristic distribution and the P intermittent wear characteristic distributions, and outputting the wear resistance performance defects of the cutting edge.

[0005] In the second aspect of this application, a device for detecting the wear resistance of the cutting edge of a coating doctor blade is provided. The device includes: A loss analysis module, which is used to screen extreme state samples according to the unique identifier of the doctor blade and the service life design, and perform loss distribution analysis according to the screening results to obtain a doctor blade wear extreme value distribution map; a backtracking simulation module, which is used to randomly perturb the loss characteristics of the doctor blade wear extreme value distribution map, and perform loss state backtracking simulation according to the perturbation results to obtain M time-series wear characteristic distributions; a fusion module, which is used to preset a time-series equivalent step length, and fuse the loss distributions of intermediate state samples of the M time-series wear characteristic distributions to obtain P stage wear extreme value distribution maps; a wear characteristic acquisition module, which is used to collect wear characteristics by positioning detection nodes using the time-series equivalent step length during the wear-resistant working condition test of the to-be-detected coating doctor blade until the service life design, to obtain P intermittent wear characteristic distributions and an extreme state wear characteristic distribution; a verification module, which is used to map and verify the wear characteristics of the extreme state wear characteristic distribution and the P intermittent wear characteristic distributions by using the doctor blade wear extreme value distribution map and the P stage wear extreme value distribution maps, and output the wear-resistant performance defects of the blade edge.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, screen extreme state samples according to the unique identifier of the doctor blade and the service life design, and perform loss distribution analysis according to the screening results to obtain a doctor blade wear extreme value distribution map. Next, randomly perturb the loss characteristics of the doctor blade wear extreme value distribution map, and perform loss state backtracking simulation according to the perturbation results to obtain M time-series wear characteristic distributions. Then, preset a time-series equivalent step length, and fuse the loss distributions of intermediate state samples of the M time-series wear characteristic distributions to obtain P stage wear extreme value distribution maps. During the wear-resistant working condition test of the to-be-detected coating doctor blade, collect wear characteristics by positioning detection nodes using the time-series equivalent step length until the service life design, to obtain P intermittent wear characteristic distributions and an extreme state wear characteristic distribution. Finally, map and verify the wear characteristics of the extreme state wear characteristic distribution and the P intermittent wear characteristic distributions by using the doctor blade wear extreme value distribution map and the P stage wear extreme value distribution maps, and output the wear-resistant performance defects of the blade edge. This solves the technical problem of low detection accuracy of the wear-resistant performance of the coating doctor blade edge in the prior art, and achieves the technical effect of improving the detection accuracy. Description of the Drawings

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0008] Figure 1Schematic flow chart of the method for detecting the wear resistance of the coating blade edge provided by the embodiment of the present application; Figure 2 Schematic structural diagram of the device for detecting the wear resistance of the coating blade edge provided by the embodiment of the present application.

[0009] Explanation of reference numerals: loss analysis module 11, backtracking simulation module 12, fusion module 13, wear feature acquisition module 14, verification module 15. Detailed implementation manners

[0010] By providing a method and a device for detecting the wear resistance of the coating blade edge, the present application solves the technical problem of low detection accuracy of the wear resistance of the coating blade edge in the prior art.

[0011] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0012] It should be noted that the terms "include" and "have" are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily limit to those clearly listed steps or units, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0013] Embodiment 1, as Figure 1 shown, the present application provides a method for detecting the wear resistance of the coating blade edge, wherein the method includes: Screen extreme-state samples according to the unique identifier of the blade and the service life design, and perform loss distribution analysis according to the screening results to obtain the extreme value distribution diagram of blade wear.

[0014] In the embodiment of the present application, the unique identifier of the blade refers to the production serial number of the blade, and the service life design refers to the predetermined usage cycle of the blade. By using the unique identifier of the blade and the service life design as the screening conditions, samples in the extreme wear state can be screened out from the historical blade usage data.

[0015] After completing the sample screening, perform a wear distribution analysis on the selected extreme state samples. Specifically, by analyzing in detail the wear data of the scraper under different working conditions, calculate the wear distribution of the scraper, especially the wear degree of each part in the maximum wear state, and obtain the extreme value distribution map of scraper wear. The extreme value distribution map of scraper wear refers to a three-dimensional extreme value distribution map of the axial position of the blade edge - wear depth - notch density, which shows the wear distribution of the scraper blade edge in dimensions such as the axial position, wear depth, and notch density.

[0016] Furthermore, based on the unique identifier of the scraper and the service life design, perform extreme state sample screening, and perform a wear distribution analysis according to the screening results to obtain the extreme value distribution map of scraper wear. The method includes: Based on the unique identifier of the scraper and the service life design, perform extreme state sample screening to obtain N sample service scrapers; perform digital processing on the apparent characteristics of the N sample service scrapers to obtain N coating loss distribution characteristics and N notch loss distribution characteristics; based on the N coating loss distribution characteristics and N notch loss distribution characteristics, perform wear distribution fusion to obtain the extreme value distribution map of scraper wear.

[0017] Based on the unique identifier of the scraper and the service life design, perform extreme state sample screening, that is, by analyzing the wear conditions of the scraper under different usage conditions, select N sample service scrapers in the extreme wear state. Perform digital processing on the apparent characteristics of the N selected sample service scrapers. Through detection, measure and record the surface coating loss and blade edge notch of each scraper to obtain N coating loss distribution characteristics and N notch loss distribution characteristics. Among them, the coating loss distribution characteristics reflect the loss degree of the scraper coating during use, while the notch loss distribution characteristics describe the notch shape and distribution generated on the scraper blade edge during service. According to the obtained N coating loss distribution characteristics and N notch loss distribution characteristics, use a data fusion algorithm to integrate the coating loss and notch loss in space to generate an extreme value distribution map of scraper wear. 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, wear depth, and notch density of the scraper blade edge.

[0018] Furthermore, perform digital processing on the apparent characteristics of the N sample service scrapers to obtain N coating loss distribution characteristics and N notch loss distribution characteristics. The method includes: Start the white light interferometer to scan the entire area of the cutting edge of the first sample service scraper to obtain the point cloud data of the cutting edge topography; align the point cloud data of the cutting edge topography to the original CAD model of the to-be-detected coating scraper through ICP registration, and extract the topography deviation data; separate the topography deviation data based on the wear type to obtain the coating thickness deviation data and the cutting edge deformation deviation data; calculate the thickness loss rate of the coating thickness deviation data along the axial direction of the cutting edge to obtain the first coating loss distribution characteristic; extract the notch characteristics of the cutting edge deformation deviation data along the axial direction of the cutting edge to obtain the generated notch position-size distribution matrix as the first notch loss distribution characteristic, where the notch characteristics include notch depth and notch density.

[0019] Preferably, select one sample service scraper from N sample service scrapers as the first sample service scraper; start the white light interferometer to perform high-precision scanning on the entire area of the cutting edge of the first sample service scraper to obtain the point cloud data of the cutting edge topography, which reflects the three-dimensional topography information of the cutting edge surface of the scraper, including the wear condition and surface characteristics of the scraper. Through the ICP registration technology, align the obtained point cloud data of the cutting edge topography with the original CAD model of the to-be-detected coating scraper, and extract the topography deviation data from it, that is, the difference data from the original CAD model. Separate the topography deviation data based on the wear type (coating wear, cutting edge wear) to obtain the coating thickness deviation data and the cutting edge deformation deviation data. Among them, the coating thickness deviation data reflects the wear condition of the scraper coating during use, while the cutting edge deformation deviation data reveals the deformation and damage conditions of the cutting edge of the scraper during service. Based on the coating thickness deviation data, calculate the thickness loss rate along the axial direction of the scraper cutting edge, that is, compare the coating thicknesses at different positions of the scraper cutting edge, calculate the coating thickness loss at each position, and obtain the first coating loss distribution characteristic, where the thickness loss rate = (initial coating thickness - current coating thickness) / initial coating thickness. For the cutting edge deformation deviation data, extract the notch characteristics along the axial direction of the cutting edge, specifically including notch depth and notch density, so as to generate a notch position-size distribution matrix, which describes the distribution characteristics and severity of the damage area of the scraper cutting edge, and provide it as the first notch loss distribution characteristic for subsequent analysis. By analogy, perform apparent feature digitization processing on N sample service scrapers to obtain N coating loss distribution characteristics and N notch loss distribution characteristics.

[0020] Furthermore, perform loss distribution fusion based on the N coating loss distribution characteristics and the N notch loss distribution characteristics to obtain the extreme value distribution map of scraper wear. The method includes: Locate a plurality of discrete coordinate points along the axial direction of the cutting edge in the original CAD model at a preset interval; after aligning the N coating loss distribution characteristics based on the plurality of discrete coordinate points, extract the maximum thickness loss value to obtain a coating loss distribution curve; after aligning the N notch loss distribution characteristics based on the plurality of discrete coordinate points, extract the maximum notch density value and the maximum notch depth value respectively to obtain a notch density distribution curve and a notch depth distribution curve; spatially superimpose the coating loss distribution curve, the notch density distribution curve and the notch depth distribution curve onto the axial coordinate system of the cutting edge to generate the extreme value distribution map of the wear of the scraper.

[0021] Preferably, a plurality of discrete coordinate points are located on the original CAD model along the axial direction of the cutting edge of the scraper at a preset interval. These coordinate points are distributed in the axial direction of the cutting edge of the scraper and represent the wear characteristics of the scraper at different positions. Based on the plurality of discrete coordinate points, align the N coating loss distribution characteristics to ensure that the coating loss data of each sample can correspond to the corresponding position of the scraper; after alignment, extract the maximum value of the coating thickness loss at each position, and then obtain a coating loss distribution curve, which reflects the change of the coating loss at different positions of the scraper. Based on the plurality of discrete coordinate points, align the N notch loss distribution characteristics to ensure that the notch loss data corresponds one by one to the position of the scraper; after alignment, extract the maximum notch density value and the maximum notch depth value at each position respectively, and then obtain a notch density distribution curve and a notch depth distribution curve. The notch density distribution curve and the notch depth distribution curve describe the notch damage conditions at different positions of the scraper. Spatially superimpose the obtained coating loss distribution curve, notch density distribution curve and notch depth distribution curve into the axial coordinate system of the cutting edge of the scraper to generate an extreme value distribution map of the wear of the scraper. The extreme value distribution map of the wear of the scraper characterizes the historical maximum coating loss rate, notch depth and notch density at each position of the cutting edge.

[0022] After randomly perturbing the loss characteristics of the extreme value distribution map of the wear of the scraper, perform a backtracking simulation of the loss state according to the perturbation result to obtain M time-series wear characteristic distributions.

[0023] In the embodiments of the present application, random perturbations are performed on the extreme value distribution map of the scraper wear, specifically, by introducing a certain amount of random error or variation to simulate the fluctuations of coating loss, notch density, and notch depth under different working conditions. For example, the coating loss can be increased or decreased within a certain range to reflect the coating wear of the scraper under different friction, pressure, and temperature conditions; the notch density can be simulated by introducing random fluctuations to represent the increase in notches on the scraper during long-term use due to friction and impact; the perturbation of the notch depth is achieved by randomly changing the notch depth data to simulate the possible notch expansion of the scraper under different working conditions. By randomly perturbing the characteristics such as coating loss, notch density, and notch depth in the extreme value distribution map of the scraper wear, the wear process of the scraper during actual use can be more realistically reflected.

[0024] After performing random perturbations on the loss characteristics, a loss state backtracking simulation is carried out based on the perturbation results, that is, through the current loss data, the wear conditions of the scraper at different time points are inversely deduced and restored, and the wear characteristics of the scraper at each past moment are simulated. Through the backtracking simulation, starting from the existing loss data, the wear states of the scraper at different stages can be predicted, thereby realizing the dynamic tracking of the scraper wear. After the backtracking simulation, M time-series wear characteristic distribution maps can be obtained, which represent the wear characteristics of the scraper at different time nodes. These time-series wear characteristic distribution maps show the wear changes of the scraper from the initial stage of service to the end of service, revealing the trend of the scraper wear evolution.

[0025] Furthermore, according to the perturbation results, a loss state backtracking simulation is carried out to obtain M time-series wear characteristics distributions. The method includes: Starting from the N coating loss distribution characteristics and N notch loss distribution characteristics, with the extreme value distribution map of the scraper wear as a constraint, random perturbations are performed on the loss characteristics to obtain M updated wear distribution models; based on the M updated wear distribution models, extreme state sample retrieval is carried out to obtain M updated service scrapers; and a loss state backtracking simulation is carried out on the M updated service scrapers to obtain the M time-series wear characteristic distributions.

[0026] Preferably, starting from N coating loss distribution characteristics and N notch loss distribution characteristics, combined with the extreme value distribution diagram of doctor blade wear as a constraint condition, random perturbations are performed on the coating loss characteristics and notch loss characteristics; by introducing random errors, the wear changes of the doctor blade under different working conditions and environments are simulated to cover the wear in extreme scenarios; through perturbations, M updated wear distribution models are generated, which reflect the performance of the doctor blade in different wear states and cover the changes in characteristics such as coating loss, notch density, and notch depth. Based on the obtained M updated wear distribution models, an extreme state sample retrieval is carried out, that is, data meeting the extreme wear conditions are extracted from these updated wear distribution models to obtain M updated in-service doctor blade samples. Each updated in-service doctor blade represents the usage of the doctor blade at different time points and different wear states, simulating the wear evolution that may occur during the actual operation of the doctor blade. A loss state backtracking simulation is performed on the M updated in-service doctor blades, that is, based on the current wear state, the wear conditions of the doctor blade at each past time node are deduced; through the backtracking simulation, M time-series wear characteristic distributions are obtained, which show the change process of the wear of the doctor blade from the initial use to the end of the final service life.

[0027] Furthermore, performing a loss state backtracking simulation on the M updated in-service doctor blades to obtain the M time-series wear characteristic distributions, the method includes: Extracting the first service condition parameters of the first updated in-service doctor blade, where the first service condition parameters include a coating pressure sequence, a substrate hardness sequence, and a doctor blade linear velocity sequence; determining P equivalent time nodes at the design position of the service life according to the time-series equivalent step length, where the P equivalent time nodes are the ends of P equivalent time stages; by mapping the first service condition parameters to the P equivalent time nodes, P dynamic wear driving factors are constructed; calculating the wear characteristic increments of the P equivalent time stages according to the P dynamic wear driving factors, and accumulating and outputting the first time-series wear characteristic distribution.

[0028] Preferably, select one updated service scraper from the M updated service scrapers as the first updated service scraper; extract the first service condition parameters of the first updated service scraper, where the first service condition parameters include a coating pressure sequence, a substrate hardness sequence, and a scraper linear velocity sequence, and these condition parameters reflect the working state of the scraper during actual use; according to the time series equivalent step length, locate P equivalent time nodes in the service life design, and each equivalent time node represents an important moment in the service cycle of the scraper, usually corresponding to a certain stage in the scraper wear process, such as the early, middle, or late stage of scraper use. By mapping the first service condition parameters to the P equivalent time nodes, that is, docking the numerical values of the first service condition parameters at different time nodes with the corresponding equivalent time nodes, the specific condition information corresponding to each node can be obtained. For example, at a certain time node, the coating pressure may be high, the scraper linear velocity may be fast, and the substrate hardness may be large, and all these information can be obtained through mapping to get the working conditions at this time node. Furthermore, construct P dynamic wear driving factors, which are calculated based on the condition parameters of the scraper at each equivalent time node and can reflect the driving factors of wear at each time node, such as the influence of changes in coating pressure, substrate hardness, and scraper linear velocity on wear. Dynamic wear driving factor calculation formula: , where, is the dynamic wear driving factor of 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, , , are the weight coefficients of the coating pressure, substrate hardness, and scraper linear velocity, reflecting the relative influence degree of each condition parameter on wear, and the weight coefficients can be determined according to actual experience or experimental data.

[0029] According to the P dynamic wear driving factors, calculate the wear characteristic increments of the P equivalent time stages. By accumulating these increments, the wear characteristic changes of the scraper in each equivalent time stage can be obtained, and thus output the first time series wear characteristic distribution, which shows the wear conditions of the scraper at different time nodes. By analogy, perform a loss state backtracking simulation on the M updated service scrapers to obtain M time series wear characteristic distributions.

[0030] Furthermore, calculating the wear characteristic increments of the P equivalent time stages according to the P dynamic wear driving factors and accumulating and outputting the first time series wear characteristic distribution, the method includes: Calculate the increment of the coating thickness loss rate for the P equivalent time stages based on the P dynamic wear driving factors, and accumulate them to generate a time series curve of the coating loss rate; calculate the increment of the notch density and the increment of the notch depth for the P equivalent time stages based on the P dynamic wear driving factors, and accumulate them to generate a time series curve of the notch density and a time series curve of the notch depth; the time series curve of the coating loss rate, the time series curve of the notch density, and the time series curve of the notch depth constitute the first time series wear characteristic distribution.

[0031] Preferably, calculate the increment of the coating thickness loss rate for each equivalent time stage based on the P dynamic wear driving factors. Each dynamic wear driving factor represents the driving force of the working conditions of the scraper at different time stages on wear. By calculating the correlation between these driving factors and the coating loss, the increment of the coating thickness loss rate for each equivalent time stage can be obtained, which reflects the wear change of the scraper coating at this stage. Accumulate these increments to generate a time series curve of the coating loss rate, which shows the evolution process of the coating loss of the scraper over time at different time nodes. Calculate the increment of the notch density and the increment of the notch depth for each equivalent time stage based on the P dynamic wear driving factors. The increment of the notch density reflects the change in the number of notches on the edge of the scraper, while the increment of the notch depth reflects the change in the depth of each notch. Through the influence of the dynamic wear driving factors, calculate the increments of the notch density and the notch depth for each equivalent time stage respectively; accumulate these increments to generate a time series curve of the notch density and a time series curve of the notch depth respectively, which shows the evolution process of the notch characteristics of the scraper during the entire service life cycle.

[0032] Preset a time series equivalent step size, 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.

[0033] Preset a time series equivalent step size, which is used to divide the service life cycle of the scraper into several equivalent time stages. Each equivalent time stage represents the working state of the scraper at this stage. By presetting the equivalent step size, it can ensure that the time series data of the wear characteristics are evenly distributed within each stage.

[0034] Based on the M time series wear characteristic distributions, fuse the wear characteristics of each equivalent time stage; by fusing these time series data, the comprehensive wear state of the scraper at each stage can be obtained; based on the fused intermediate state sample loss distribution, generate P stage wear extreme value distribution diagrams, and each stage wear extreme value distribution diagram shows the wear characteristics of the scraper in terms of coating loss, notch density, and notch depth at this stage.

[0035] Furthermore, perform intermediate state sample loss distribution fusion on the M time series wear characteristic distributions to obtain P stage wear extreme value distribution diagrams. The method includes: Take 1 / (P + 1) of the designed service life as the time-sequence equivalent step length, where P ≥ 12 and P is a positive integer; use the time-sequence equivalent step length to extract intermediate-state sample features from the M time-sequence wear feature distributions, obtaining P sets of stage wear feature distributions, where each set of stage wear feature distributions includes a stage coating loss distribution and a stage notch loss distribution; based on the P sets of stage wear feature distributions, perform loss distribution fusion to obtain the P stage wear extreme value distribution diagrams.

[0036] Preferably, determine the time-sequence equivalent step length, which is calculated by taking 1 / (P + 1) of the designed service life, where P ≥ 12 and P is a positive integer; use the time-sequence equivalent step length to extract intermediate-state sample features from the M time-sequence wear feature distributions, that is, divide the entire wear cycle into P equivalent time stages based on the time-sequence equivalent step length. Within each stage, extract the corresponding feature data from the M time-sequence wear feature distributions, including the stage coating loss distribution and the stage notch loss distribution. The process of performing loss distribution fusion based on the extracted P sets of stage wear feature distributions is basically the same as the method for obtaining the extreme value distribution diagram of the blade wear. Specifically: First, use a preset interval to uniformly locate multiple discrete coordinate points along the axial direction of the blade edge on the original CAD model of the to-be-detected coating blade; then, perform spatial alignment processing on the stage coating loss distribution and the stage notch loss distribution in each set of stage wear feature distributions respectively, and map them to the multiple discrete coordinate points; for each coordinate point, extract the maximum value of the stage coating thickness loss to generate a coating loss distribution curve; further, after aligning the notch loss distribution, extract the maximum value of the notch density and the maximum value of the notch depth respectively to form a stage notch density distribution curve and a stage notch depth distribution curve; subsequently, for the M coating loss rate time-sequence curves in the same stage, perform spatial registration based on the discrete coordinate points, and perform maximum value extraction and fitting interpolation at each coordinate point to obtain the coating thickness loss fusion curve corresponding to this stage; process the notch density time-sequence curve and the notch depth time-sequence curve in the same way to obtain the notch density fusion curve and the notch depth fusion curve respectively; then, stack the three fusion curves obtained in this stage in a spatially aligned manner to construct a stage wear extreme value distribution diagram in the blade-edge axial coordinate system; perform the above process on each of the P stages in turn, and finally output P stage wear extreme value distribution diagrams to characterize the extreme value state of the blade edge wear at different service stages and its evolution trend. During the wear-resistant working condition test of the to-be-detected coating blade, use the time-sequence equivalent step length to locate the detection nodes to collect wear features until the designed service life, obtaining P intermittent wear feature distributions and an extreme-state wear feature distribution.

[0037] During the wear-resistant working condition test of the coating doctor blade to be detected, the wear state of the doctor blade is gradually located by using the time-sequence equivalent step length. The time-sequence equivalent step length is calculated based on the total usage time and the divided equivalent time nodes in the service life design of the doctor blade, which ensures that in the whole wear test process, each test stage of the doctor blade has a uniform and scientific time distribution. According to these equivalent time nodes, multiple detection nodes can be located along the edge axis of the doctor blade, and each node represents the wear characteristics of the doctor blade under different working conditions. By collecting the wear characteristics at these nodes, the wear condition of the doctor blade can be monitored in real time during each test stage, and key wear data including coating loss, notch density, notch depth, etc. can be collected.

[0038] During the whole wear-resistant working condition test, the selection of the detection nodes is arranged according to the time-sequence equivalent step length to ensure that the test covers different stages of the entire service cycle of the doctor blade. Wear characteristics are collected at each equivalent time node, and the wear data of the doctor blade are continuously collected and recorded until the end of the service life design is reached. These data include but are not limited to the changes in coating loss, notch density, and notch depth.

[0039] Based on the collected wear characteristic data, P intermittent wear characteristic distributions and the extreme-state wear characteristic distribution are obtained. The intermittent wear characteristic distribution represents the wear condition of the doctor blade at different time nodes, while the extreme-state wear characteristic distribution shows the most severe wear state of the doctor blade during the test process.

[0040] Using the extreme-value distribution map of the doctor blade wear and the extreme-value distribution maps of P stages of wear, a mapping is performed on the extreme-state wear characteristic distribution and the P intermittent wear characteristic distributions to check the wear characteristics, and the wear-resistant performance defects of the edge are output.

[0041] Based on the obtained extreme-value distribution map of the doctor blade wear and the extreme-value distribution maps of P stages of wear, a mapping is performed on the extreme-state wear characteristic distribution and the P intermittent wear characteristic distributions of the doctor blade, that is, by comparing the wear characteristics of the doctor blade at different time nodes and working conditions with the preset extreme-value distribution map of wear, to check whether the wear state meets the design standard. Through this mapping, it can be identified whether the wear states of the doctor blade in the extreme state and at different time nodes exceed the expected range, and it can be determined whether the doctor blade has wear-resistant performance defects.

[0042] Specifically, through the mapping, the deviation between the actual wear characteristics of the doctor blade and the extreme-state wear characteristics, and its wear conditions at different stages can be compared. If the actual wear characteristics exceed the preset wear resistance standard or there is an overwear phenomenon, it means that the doctor blade has wear-resistant performance defects, and these defects may be manifested as too fast coating loss, too many notches or too deep notches, which will affect the service life and working efficiency of the doctor blade.

[0043] Furthermore, by using the extreme value distribution map of the scraper wear and the extreme value distribution maps of P stages of wear, a wear feature verification is performed on the limit state wear feature distribution and the wear feature distributions of P intermittent wear stages, and defects in the edge wear resistance performance are output. The method includes: The coating loss distribution curve, notch density distribution curve, and notch depth distribution curve in the extreme value distribution map of the scraper wear are respectively compared point by point with the limit state coating distribution curve, limit state notch density curve, and limit state notch depth curve in the limit state wear feature distribution to mark and output defects in the limit state wear resistance performance; and so on. By using the extreme value distribution maps of the P stages of wear, a process state wear feature verification is performed on the wear feature distributions of the P intermittent wear stages, and defects in the process state wear resistance performance are output. The defects in the process state wear resistance performance and the defects in the limit state wear resistance performance are spatially superimposed to generate the defects in the edge wear resistance performance.

[0044] Preferably, the coating loss distribution curve, notch density distribution curve, and notch depth distribution curve in the extreme value distribution map of the scraper wear are respectively compared point by point with the limit state coating distribution curve, limit state notch density curve, and limit state notch depth curve in the limit state wear feature distribution; by comparing the differences in coating loss, notch density, and notch depth at each corresponding point, it is determined whether there is excessive wear beyond expectation; if the measured coating loss rate, notch depth, or notch density exceeds the historical extreme value at any axial position, mark this position as the limit state defect area and identify the deviation type of the wear resistance performance of this area 。 In a similar manner, by using the extreme value distribution maps of the P stages of wear, a process state wear feature verification is performed on the wear feature distributions of the P intermittent wear stages. The process state wear feature verification is to evaluate the wear of the scraper stage by stage under different working conditions. By comparing the wear features of each stage, it is detected whether the scraper has abnormal wear during the process. When the wear feature deviation of each stage exceeds the predetermined standard, corresponding defects in the process state wear resistance performance are output, and these defects reflect the abnormal wear of the scraper caused by certain working conditions during use. Finally, the defects in the process state wear resistance performance and the defects in the limit state wear resistance performance are spatially superimposed; by combining the two, the wear resistance performance of the scraper during the entire service life can be comprehensively evaluated, and the wear conditions of the scraper under different working conditions can be comprehensively analyzed. The result after superposition generates the defects in the edge wear resistance performance, accurately reflecting the wear resistance performance problems of the scraper in actual use, and providing a scientific basis for the optimization design, material improvement, and maintenance during the use of the scraper.

[0045] In summary, the embodiments of the present application have at least the following technical effects: First, perform extreme state sample screening according to the unique identifier of the doctor blade and the service life design, and conduct wear distribution analysis based on the screening results to obtain the extreme value distribution map of doctor blade wear. Then, after randomly perturbing the wear characteristics of the extreme value distribution map of doctor blade wear, perform backtracking simulation of the wear state according to the perturbation results to obtain M time-series wear characteristic distributions. Next, preset the time-series equivalent step size, and fuse the intermediate state sample wear distributions of the M time-series wear characteristic distributions to obtain P stage extreme value distribution maps of wear. During the wear-resistant working condition test of the to-be-detected coating doctor blade, use the time-series equivalent step size to locate the detection nodes to collect wear characteristics until the service life design, obtaining P intermittent wear characteristic distributions and an extreme state wear characteristic distribution. Finally, use the extreme value distribution map of doctor blade wear and the P stage extreme value distribution maps of wear to map and verify the wear characteristics of the extreme state wear characteristic distribution and the P intermittent wear characteristic distributions, and output the wear-resistant performance defects of the blade edge. This solves the technical problem of low detection accuracy of the wear-resistant performance of the coating doctor blade edge in the prior art and achieves the technical effect of improving the detection accuracy.

[0046] Embodiment 2, based on the same inventive concept as the method for detecting the wear-resistant performance of the coating doctor blade edge in the foregoing embodiment, as Figure 2 shown, the present application provides a device for detecting the wear-resistant performance of the coating doctor blade edge, wherein the device includes: A loss analysis module 11, configured to perform extreme state sample screening according to the unique identifier of the doctor blade and the service life design, and conduct wear distribution analysis based on the screening results to obtain the extreme value distribution map of doctor blade wear; a backtracking simulation module 12, configured to randomly perturb the wear characteristics of the extreme value distribution map of doctor blade wear, and perform backtracking simulation of the wear state according to the perturbation results to obtain M time-series wear characteristic distributions; a fusion module 13, configured to preset the time-series equivalent step size, and fuse the intermediate state sample wear distributions of the M time-series wear characteristic distributions to obtain P stage extreme value distribution maps of wear; a wear characteristic acquisition module 14, configured to use the time-series equivalent step size to locate the detection nodes to collect wear characteristics until the service life design during the wear-resistant working condition test of the to-be-detected coating doctor blade, obtaining P intermittent wear characteristic distributions and an extreme state wear characteristic distribution; a verification module 15, configured to use the extreme value distribution map of doctor blade wear and the P stage extreme value distribution maps of wear to map and verify the wear characteristics of the extreme state wear characteristic distribution and the P intermittent wear characteristic distributions, and output the wear-resistant performance defects of the blade edge.

[0047] Further, the loss analysis module 11 is configured to execute the following method: Screen the extreme state samples according to the unique identifier and service life design of the doctor blade to obtain N service doctor blades of the samples; perform digital processing on the apparent characteristics of the N service doctor blades of the samples to obtain N coating loss distribution characteristics and N notch loss distribution characteristics; perform loss distribution fusion based on the N coating loss distribution characteristics and N notch loss distribution characteristics to obtain the extreme value distribution map of doctor blade wear.

[0048] Further, the backtracking simulation module 12 is used to execute the following method: Starting from the N coating loss distribution characteristics and N notch loss distribution characteristics, and taking the extreme value distribution map of doctor blade wear as a constraint, perform random perturbation of loss characteristics to obtain M updated wear distribution models; perform extreme state sample retrieval based on the M updated wear distribution models to obtain M updated service doctor blades; perform backtracking simulation of the loss state on the M updated service doctor blades to obtain the M time-series wear characteristic distributions.

[0049] Further, the fusion module 13 is used to execute the following method: Take 1 / (P + 1) of the service life design as the time-series equivalent step length, where P≥12 and P is a positive integer; use the time-series equivalent step length to extract the intermediate state sample characteristics of the M time-series wear characteristic distributions to obtain P sets of stage wear characteristic distributions, where each set of stage wear characteristic distributions includes stage coating loss distribution and stage notch loss distribution; perform loss distribution fusion based on the P sets of stage wear characteristic distributions to obtain the P stage extreme value distribution maps of wear.

[0050] Further, the loss analysis module 11 is used to execute the following method: Start a white light interferometer to scan the entire region of the edge of the first sample service doctor blade to obtain edge morphology point cloud data; align the edge morphology point cloud data to the original CAD model of the to-be-detected coating doctor blade through ICP registration, and extract morphology deviation data; separate the morphology deviation data based on the wear type to obtain coating thickness deviation data and edge deformation deviation data; calculate the thickness loss rate of the coating thickness deviation data along the axial direction of the edge to obtain the first coating loss distribution characteristic; extract notch characteristics from the edge deformation deviation data along the axial direction of the edge to obtain a generated notch position-size distribution matrix as the first notch loss distribution characteristic, where the notch characteristics include notch depth and notch density.

[0051] Further, the loss analysis module 11 is used to execute the following method: Locate a plurality of discrete coordinate points along the axial direction of the blade edge at a preset interval in the original CAD model; after aligning the N coating loss distribution characteristics based on the plurality of discrete coordinate points, extract the maximum thickness loss value to obtain a coating loss distribution curve; after aligning the N notch loss distribution characteristics based on the plurality of discrete coordinate points, extract the maximum notch density value and the maximum notch depth value respectively to obtain a notch density distribution curve and a notch depth distribution curve; spatially superimpose the coating loss distribution curve, the notch density distribution curve and the notch depth distribution curve onto the axial coordinate system of the blade edge to generate the extreme value distribution map of the scraper wear.

[0052] Further, the backtracking simulation module 12 is used to execute the following method: Extract the first service condition parameters of the first updated service scraper, where the first service condition parameters include a coating pressure sequence, a substrate hardness sequence and a scraper linear velocity sequence; according to the time series equivalent step size, locate P equivalent time nodes in the service life design, where the P equivalent time nodes are the ends of P equivalent time stages; by mapping the first service condition parameters to the P equivalent time nodes, construct P dynamic wear driving factors; 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.

[0053] Further, the backtracking simulation module 12 is used to execute the following method: Calculate the increment of the coating thickness loss rate of the P equivalent time stages according to the P dynamic wear driving factors, and accumulate and generate a time series curve of the coating loss rate; calculate the increment of the notch density and the increment of the notch depth of the P equivalent time stages according to the P dynamic wear driving factors, and accumulate and generate a time series curve of the notch density and a time series curve of the notch depth; the time series curve of the coating loss rate, the time series curve of the notch density and the time series curve of the notch depth constitute the first time series wear characteristic distribution.

[0054] Further, the verification module 15 is used to execute the following method: Perform a point-by-point deviation comparison between the coating loss distribution curve, the notch density distribution curve and the notch depth distribution curve in the extreme value distribution map of the scraper wear and the extreme state coating distribution curve, the extreme state notch density curve and the extreme state notch depth curve in the extreme state wear characteristic distribution respectively, so as to mark and output the extreme state wear resistance defects; by analogy, use the extreme value distribution maps of the P stages to map and perform a process state wear characteristic verification on the P intermittent wear characteristic distributions, and output the process state wear resistance defects; spatially superimpose the process state wear resistance defects and the extreme state wear resistance defects to generate the edge wear resistance defects.

[0055] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. The processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0057] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for detecting the wear resistance of the edge of a coating blade, characterized in that, The method includes: Performing extreme state sample screening according to the unique identifier of the doctor blade and the service life design, and performing loss distribution analysis based on the screening results to obtain a doctor blade wear extreme value distribution map; After performing random perturbation of the loss characteristics on the doctor blade wear extreme value distribution map, performing loss state backtracking simulation according to the perturbation results to obtain M time-series wear characteristic distributions; Presetting a time-series equivalent step length, and performing intermediate state sample loss distribution fusion on the M time-series wear characteristic distributions to obtain P stage wear extreme value distribution maps; During the wear-resistant working condition test of the to-be-detected coating doctor blade, using the time-series equivalent step length to locate detection nodes to collect wear characteristics until the service life design, obtaining P intermittent wear characteristic distributions and an extreme state wear characteristic distribution; Using the doctor blade wear extreme value distribution map and the P stage wear extreme value distribution maps, mapping to perform wear characteristic verification on the extreme state wear characteristic distribution and the P intermittent wear characteristic distributions, and outputting the edge wear-resistant performance defect.

2. The wear resistance detection method for the coating blade edge according to claim 1, wherein, Performing extreme state sample screening according to the unique identifier of the doctor blade and the service life design, and performing loss distribution analysis based on the screening results to obtain a doctor blade wear extreme value distribution map, the method includes: Performing extreme state sample screening according to the unique identifier of the doctor blade and the service life design to obtain N sample service doctor blades; Performing digital processing on the apparent characteristics of the N sample service doctor blades 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 doctor blade wear extreme value distribution map.

3. The method for detecting the wear resistance of the coating blade edge according to claim 2, wherein Performing loss state backtracking simulation according to the perturbation results to obtain M time-series wear characteristic distributions, the method includes: Starting from the N coating loss distribution characteristics and the N notch loss distribution characteristics, and using the doctor blade wear extreme value distribution map as a constraint, performing random perturbation of the loss characteristics to obtain M updated wear distribution models; Performing extreme state sample retrieval based on the M updated wear distribution models to obtain M updated service doctor blades; Performing loss state backtracking simulation on the M updated service doctor blades to obtain the M time-series wear characteristic distributions.

4. The method for detecting the wear resistance of the coating blade edge according to claim 3, characterized in that, Performing intermediate state sample loss distribution fusion on the M time-series wear characteristic distributions to obtain P stage wear extreme value distribution maps, the method includes: Taking 1 / (P + 1) of the service life design as the time-series equivalent step length, where P ≥ 12 and P is a positive integer; Using the time-series equivalent step length to perform intermediate state 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; Performing loss distribution fusion based on the P groups of stage wear characteristic distributions to obtain the P stage wear extreme value distribution maps.

5. The method for detecting the wear resistance of the coating blade edge according to claim 2, characterized in that, Performing digital processing on the apparent characteristics of the N sample service doctor blades to obtain N coating loss distribution characteristics and N notch loss distribution characteristics, the method includes: Starting a white light interferometer to perform full-area scanning of the edge of the first sample service doctor blade to obtain edge topography point cloud data; Align the edge profile point cloud data to the original CAD model of the to-be-detected coating blade through ICP registration, and extract the profile deviation data; Separate the profile deviation data based on the wear type to obtain the coating thickness deviation data and the edge deformation deviation data; Calculate the thickness loss rate of the coating thickness deviation data along the edge axis to obtain the first coating loss distribution feature; Extract the notch feature of the edge deformation deviation data along the edge axis to obtain the generated notch position-size distribution matrix as the first notch loss distribution feature, where the notch feature includes notch depth and notch density.

6. The method for detecting the wear resistance of the coating blade edge according to claim 5, characterized in that, Perform loss distribution fusion based on the N coating loss distribution features and the N notch loss distribution features to obtain the extreme value distribution map of the blade wear. The method includes: Use a preset interval to locate multiple discrete coordinate points along the edge axis in the original CAD model; After aligning the N coating loss distribution features based on the multiple discrete coordinate points, extract the maximum thickness loss to obtain the coating loss distribution curve; After aligning the N notch loss distribution features based on the multiple discrete coordinate points, extract the maximum notch density and the maximum notch depth respectively to obtain the notch density distribution curve and the notch depth distribution curve; Spatially superimpose the coating loss distribution curve, the notch density distribution curve and the notch depth distribution curve onto the edge axis coordinate system to generate the extreme value distribution map of the blade wear.

7. The method for detecting the wear resistance of the blade edge of a coating doctor blade according to claim 4, wherein Perform loss state backtracking simulation on the M updated service blades to obtain the M time-series wear feature distributions. The method includes: Extract the first service condition parameters of the first updated service blade, where the first service condition parameters include the coating pressure sequence, the substrate hardness sequence and the blade linear velocity sequence; Locate P equivalent time nodes in the service life design according to the time-series equivalent step length, where 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 feature increments of the P equivalent time stages according to the P dynamic wear driving factors, and accumulate and output the first time-series wear feature distribution.

8. The method for detecting the wear resistance of the coating blade edge according to claim 7, characterized in that, Calculate the wear feature increments of the P equivalent time stages according to the P dynamic wear driving factors, and accumulate and output the first time-series wear feature distribution. The method includes: Calculate the increment of the coating thickness loss rate of the P equivalent time stages according to the P dynamic wear driving factors, and accumulate and generate the time-series curve of the coating loss rate; Calculate the increment of the notch density and the increment of the notch depth of the P equivalent time stages according to the P dynamic wear driving factors, and accumulate and generate the time-series curve of the notch density and the time-series curve of the notch depth; The time-series curve of the coating loss rate, the time-series curve of the notch density and the time-series curve of the notch depth constitute the first time-series wear feature distribution.

9. The abrasion resistance detection method for the coating blade edge according to claim 1, wherein Use the extreme value distribution map of the blade wear and the extreme value distribution maps of the P-stage wear to map and perform wear feature verification on the extreme state wear feature distribution and the P intermittent wear feature distributions, and output the edge wear resistance performance defect. The method includes: Perform a point-by-point deviation comparison between the coating loss distribution curve, notch density distribution curve, and notch depth distribution curve in the extreme wear value distribution diagram of the scraper, and the extreme state coating distribution curve, extreme state notch density curve, and extreme state notch depth curve in the extreme state wear characteristic distribution respectively, so as to mark and output the extreme state wear resistance performance defects; By analogy, use the extreme wear value distribution diagrams of the P stages to map and verify the process state wear characteristics of the P intermittent wear characteristic distributions, and output the process state wear resistance performance defects; Spatially superimpose the process state wear resistance performance defects and the extreme state wear resistance performance defects to generate the edge wear resistance performance defects.

10. A device for detecting the wear resistance of the edge of a coating blade, characterized in that, For implementing the method for detecting the edge wear resistance performance of a coating scraper according to any one of claims 1-9, the device includes: A loss analysis module, configured to screen extreme state samples according to the unique identifier of the scraper and the service life design, and perform loss distribution analysis according to the screening results to obtain the extreme wear value distribution diagram of the scraper; A backtracking simulation module, configured to perform random perturbation on the loss characteristics of the extreme wear value distribution diagram of the scraper, and perform loss state backtracking simulation according to the perturbation results to obtain M time-sequence wear characteristic distributions; A fusion module, configured to preset a time-sequence equivalent step length, and perform intermediate state sample loss distribution fusion on the M time-sequence wear characteristic distributions to obtain the extreme wear value distribution diagrams of P stages; A wear characteristic acquisition module, configured to collect wear characteristics by using the time-sequence equivalent step length to locate detection nodes during the wear resistance working condition test of the coating scraper to be detected until the service life design, so as to obtain P intermittent wear characteristic distributions and an extreme state wear characteristic distribution; A verification module, configured to use the extreme wear value distribution diagram of the scraper and the extreme wear value distribution diagrams of P stages to map and verify the wear characteristics of the extreme state wear characteristic distribution and the P intermittent wear characteristic distributions, and output the edge wear resistance performance defects.

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