A wear resistance test system for high-strength guide plates

Abstract

The present application provides a wear test system for a high-strength guide plate, which relates to the field of wear test technology, including: a simulated load module for performing load control on a test guide plate sample with a load range and a wood environment as simulated conditions; a wear analysis module for performing wear analysis on the test guide plate sample; a model building module for building a guide plate wear prediction model based on simulated conditions, an accelerated aging environment, and guide plate wear data; a wear prediction module for predicting wear of a target guide plate through a guide plate wear prediction model to obtain target wear data; a wear coefficient calculation module for calculating a wear coefficient based on target wear data to obtain a target wear coefficient. The present application can solve the technical problem in the prior art that the accuracy of wear tests is poor due to the lack of comprehensive consideration of the impact of complex environmental conditions on wear, and improve the accuracy of guide plate wear resistance performance tests by simulating actual use environments and accelerated aging tests.

Description

Technical Field

[0001] The present application relates to the technical field of wear resistance testing, and in particular to a wear resistance testing system for a high-strength guide plate. Background Art

[0002] The guide bar is one of the key components of tools such as chain saws and cutting machines. Its main function is to guide the chain to maintain a stable motion trajectory during the cutting process and to withstand the forces generated during the cutting process. At present, most wear resistance test systems are often only carried out under fixed and idealized conditions, such as standardized load or temperature conditions, to simulate the friction behavior of the guide bar in contact with wood. They cannot fully simulate the wear of the guide bar in a complex working environment and cannot reflect the actual performance of the guide bar in a real working environment. In actual work, the guide bar will experience uneven and variable load conditions, especially the high-strength guide bar, which has a load far exceeding that of an ordinary guide bar. The wear characteristics and wear resistance evaluation under this load may be very different from those under fixed test conditions, resulting in poor accuracy and representativeness of the wear resistance test results.

[0003] In summary, the prior art has a technical problem in which the accuracy of the guide plate wear resistance test is poor due to the lack of comprehensive consideration of the impact of complex environmental conditions on wear. Summary of the invention

[0004] The purpose of this application is to provide a high-strength guide plate wear test system to solve the technical problem in the prior art that the guide plate wear test has poor accuracy due to the lack of comprehensive consideration of the impact of complex environmental conditions on wear.

[0005] In order to achieve the above-mentioned purpose, the present application provides a wear resistance testing system for a high-strength guide plate, wherein the wear resistance testing system for a high-strength guide plate includes: a simulation load module, the simulation load module is used to use the load range and the wood environment as simulation conditions, and perform a continuous control cycle of load control on the test guide plate sample according to the simulation conditions and the accelerated aging environment to obtain the guide plate simulation load; a wear analysis module, the wear analysis module is used to perform wear analysis on the test guide plate sample according to the guide plate simulation load to obtain the guide plate wear data; a model building module, the model building module is used to build a guide plate wear prediction model according to the simulation conditions, the accelerated aging environment and the guide plate wear data; a wear prediction module, the wear prediction module is used to predict the wear of the target guide plate through the guide plate wear prediction model to obtain the target wear data; a wear resistance coefficient calculation module, the wear resistance coefficient calculation module is used to calculate the wear resistance coefficient according to the target wear data to obtain the target wear resistance coefficient.

[0006] Optionally, a load frequency is preset, and a control period is determined according to the load range and the load frequency; a continuous working condition simulation based on the simulation conditions and the accelerated aging environment is performed according to the control period to obtain a simulated working condition; fatigue loading is performed on the test guide plate sample according to the simulated working condition to obtain the guide plate simulated load.

[0007] Optionally, a sample limit load is extracted based on the sample chainsaw load; the sample limit load is used as the load starting point, and random load jumps within a preset jump multiple are performed from the load starting point to obtain multiple load stop points; a load domain is selected based on the multiple load stop points to obtain the load range.

[0008] Optionally, sample resin, sample gravel and sample sawdust are extracted according to a sample wood environment; a sample mixing ratio is extracted based on the sample wood environment, and a ratio tolerance is adjusted on the sample mixing ratio to obtain a wood mixing ratio; resin, gravel and sawdust are mixed with the wood mixing ratio to obtain the wood environment.

[0009] Optionally, a target working environment of the target guide plate is acquired; and environmental accelerated aging adjustment is performed according to the target working environment to obtain the accelerated aging environment.

[0010] Optionally, the wear depth and wear breadth of the test guide plate sample are measured to obtain the wear amount; fatigue points and crack propagation trends of the test guide plate sample are analyzed based on the wear amount to obtain wear data; fatigue failure of the test guide plate sample is judged based on the wear data, and if a valid sample result is obtained, the wear data is used as the guide plate wear data.

[0011] Optionally, the target guide plate is measured in depth according to the target wear data to obtain cutting depth data, wherein the cutting depth data includes a time node; and the cutting depth degradation coefficient is calculated for the cutting depth data based on the time node to obtain the target wear resistance coefficient.

[0012] Optionally, a wear resistance degradation analysis is performed based on the target wear resistance coefficient to obtain a guide plate failure trend; failure prediction is performed on the target guide plate based on the guide plate failure trend to obtain target failure prediction data, and the target failure prediction data is added to the target wear resistance coefficient.

[0013] The technical solution provided in this application has at least the following technical effects or advantages:

[0014] Through the simulation load module, the simulation load module is used to take the load range and the wood environment as the simulation conditions, and perform the load control of the test guide plate sample in a continuous control cycle according to the simulation conditions and the accelerated aging environment to obtain the guide plate simulation load; the wear analysis module is used to perform the wear analysis on the test guide plate sample according to the guide plate simulation load to obtain the guide plate wear data; the model construction module is used to construct the guide plate wear prediction model according to the simulation conditions, the accelerated aging environment and the guide plate wear data; the wear prediction module is used to predict the wear of the target guide plate through the guide plate wear prediction model to obtain the target wear data; the wear resistance coefficient calculation module is used to calculate the wear resistance coefficient according to the target wear data to obtain the target wear resistance coefficient. That is to say, by simulating the real working environment and accelerated aging conditions, combining the wear data and the prediction model, the durability of the guide plate under different working environments is accurately evaluated, and the accuracy of the guide plate wear resistance performance test is improved.

[0015] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0017] Figure 1 This is a schematic structural diagram of a wear resistance testing system for a high-strength guide plate of the present application;

[0018] Figure 2 This is a schematic diagram of the process of obtaining guide plate wear data in a wear resistance testing system for a high-strength guide plate in the present application.

[0019] Explanation of the reference numerals: load simulation module 11 , wear analysis module 12 , model building module 13 , wear prediction module 14 , wear resistance coefficient calculation module 15 . DETAILED DESCRIPTION

[0020] This application provides a high-strength guide plate wear test system to solve the technical problem in the prior art that the accuracy of the guide plate wear test is poor due to the lack of comprehensive consideration of the impact of complex environmental conditions on wear. By simulating the real working environment and accelerated aging conditions, combining wear data with prediction models, the durability of the guide plate in different working environments is accurately evaluated, thereby improving the accuracy of the guide plate wear performance test.

[0021] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0022] For examples, please see the attached Figure 1 The present application provides a wear resistance test system for a high-strength guide plate, wherein the wear resistance test system for a high-strength guide plate specifically includes the following steps:

[0023] The simulated load module 11 is used to use the load range and the wood environment as simulation conditions, perform load control of a continuous control cycle on the test guide plate sample according to the simulation conditions and the accelerated aging environment, and obtain the guide plate simulation load.

[0024] Specifically, the load range is set according to the target guide's use environment, and is determined based on the guide's working scenario and expected load. The load range refers to the upper and lower limits of the load intensity applied during the test. The wood environment refers to the simulated wood properties, including components such as resin, gravel, and sawdust. The different components of the wood environment will affect the friction, wear rate and other properties of the guide when cutting wood. Simulating the wood environment through different wood mixing ratios helps understand the performance of the guide under different wood properties. The accelerated aging environment refers to accelerating the aging process of the guide material through artificially set environmental conditions (such as high temperature, high humidity, etc.), simulating the aging and wear of the guide in long-term use.

[0025] Depending on the load range and wood environment, load-controlled testing is performed in combination with an accelerated aging environment, such as high temperature and humidity. During the test, the test guide sample is subjected to cyclical loads within the set load range. Accelerated aging environments are usually set at higher temperatures and humidity to simulate the wear and tear of long-term use. During this process, loads are periodically applied to the guide to simulate the repeated loads that the guide will experience in actual use.

[0026] In the continuous control cycle, the load is applied according to the set load range and frequency. By applying repeated loads (i.e. fatigue cycles), the wear and tear experienced by the test guide sample during long-term work is simulated. The fatigue cycle test evaluates the performance of the test guide sample under repeated force. During the test, the load is periodically applied to the test guide sample to simulate the repeated force in actual use. Through fatigue testing, the fatigue life, fatigue resistance and durability of the test guide sample under long-term use are evaluated. During the test, the changes of the test guide sample, such as surface wear and temperature changes, are monitored in real time, and the corresponding data are recorded. By setting the load range, load frequency, wood environment and accelerated aging environment, the load conditions and environmental impacts that the test guide sample may be subjected to in actual work are simulated, and the fatigue performance of the test guide sample under long-term repeated load is evaluated. The overall performance of the test guide sample, including its wear resistance, fatigue resistance and performance in long-term use, is comprehensively evaluated.

[0027] The wear analysis module 12 is used to perform wear analysis on the test guide plate sample according to the guide plate simulated load to obtain guide plate wear data.

[0028] Specifically, the simulated guide plate load refers to the load applied to the test guide plate sample according to the actual working conditions and simulated environment during the test. It is set by factors such as load range, load frequency, and accelerated aging environment. The purpose is to simulate the load that the guide plate will bear for a long time in the real working environment. A simulated load is applied to the test guide plate sample, and during the application of the simulated load, the test guide plate sample is subjected to wear analysis. During the test, the test guide plate sample will experience different degrees of wear. The goal of the analysis is to measure the changes in the surface of the guide plate and its relationship with the load application conditions. Wear analysis is to analyze the wear resistance of the guide plate by evaluating the changes in the surface morphology, structure, and performance of the guide plate during the test. It usually includes measuring indicators such as the degree of damage on the surface of the guide plate, the depth of wear, and the amount of wear. During the analysis process, microscopes, surface profilers, 3D scanners, and other tools are usually used for measurement.

[0029] Weigh the test guide sample and make initial measurements of the surface morphology. Set the parameters of the wear tester according to the simulated load conditions, such as sliding speed, contact pressure, wear stroke, etc. Perform wear tests on the guide sample in a simulated wood environment. Measure the surface of the test guide sample with high-precision instruments (such as profilometers, microscopes, etc.) to obtain specific data on wear, and accurately measure the depth of wear, surface roughness, and distribution of wear. Observe the fatigue cracks and plastic deformation of the guide to further evaluate the fatigue life and wear resistance of the guide under different working conditions. For example, use a 3D surface scanner to obtain 3D morphological data of wear on the guide surface, including the area, depth, and damage morphology of wear. After the wear test, weigh the guide sample again and calculate the amount of wear.

[0030] Through wear analysis, we can obtain the guide plate wear data, that is, various data about the guide plate wear, including wear amount, wear speed, surface roughness change, crack generation, etc. By analyzing the wear data of the test guide plate samples, we can accurately evaluate the wear resistance of the guide plate under different working conditions. According to the analysis of wear data, we can improve the wear resistance of the guide plate, optimize its material selection and design, increase the service life of the guide plate, and reduce maintenance and replacement costs.

[0031] The model building module 13 is used to build a guide bar wear prediction model according to the simulation conditions, the accelerated aging environment and the guide bar wear data.

[0032] Specifically, according to the simulation conditions, i.e. the load range and the wood environment, the various situations that the guide plate may encounter in the real working environment are reproduced. Combining the simulation conditions, the accelerated aging environment and the guide plate wear data, all the experimental data during the test are collected and sorted, including simulation condition data (such as load range, load frequency, wood environment composition, etc.), accelerated aging environment data (such as temperature, humidity and other environmental variables related to accelerated aging during the test), wear data (such as wear depth, wear amount, surface changes, crack conditions, wear rate, etc.).

[0033] The collected raw data usually contains noise, missing values ​​and features of different dimensions, so preprocessing is required. All experimental data are preprocessed, including data cleaning and standardization / normalization, to ensure that the collected data is complete, clear, and has no missing values ​​or outliers. For some variables (such as load, resin ratio, humidity, etc.), logarithmic transformation or standardization (Z-Score standardization) is performed to make them more suitable for modeling. The processed simulation conditions, accelerated aging environment, and guide wear data are divided into training and test sets, usually 80% for training and 20% for testing. The simulation conditions, accelerated aging environment, and guide wear data are used as input to train a random forest regression model and use it to predict the wear of the guide under different working conditions. Random forest is an ensemble learning method that generates multiple decision trees, trains each tree, and finally obtains the prediction result through the average value (regression problem). In the guide wear prediction problem, random forest regression is used to predict the wear data.

[0034] Initialize the random forest regression model, including the number of trees in the forest (usually set to 100 or more), the maximum depth of the tree (control the complexity of the tree to prevent overfitting), the minimum number of samples per node, the minimum number of samples per leaf node, etc. Use the training data set to train the random forest model and build multiple decision trees. Each tree is trained based on a different subset of the data, and the best feature is selected from the randomly selected features at each split. Use the test set to evaluate the performance of the model, and calculate the difference between the model's predicted value and the true value through the mean square error. In order to evaluate the generalization ability of the model, use the cross-validation method to ensure that the model does not overfit to a certain part of the data. For example, through K-fold cross-validation, the data is divided into K subsets, and different subsets are used as validation sets in a loop, and the rest are used as training sets to calculate the average evaluation index of the model.

[0035] Grid search is used to adjust the model's hyperparameters, such as the number of trees, maximum depth, etc., to further improve the model's predictive capabilities. After training is completed and verified, the final model can be used in actual applications. According to different working conditions (load, wood environment, accelerated aging environment, etc.), the trained model is used to predict the wear of the target guide. By combining simulation conditions, accelerated aging environment and wear data, the constructed wear prediction model can accurately predict the wear of the guide under different environmental conditions, optimize the design of the guide, reduce the number of experiments, and thus save R&D costs. At the same time, it predicts the service life of the guide, avoids overuse or premature replacement, and reduces maintenance and replacement costs. Multivariate regression analysis is used to quantify the combined effect of various factors on guide wear.

[0036] The wear prediction module 14 is used to predict the wear of the target guide plate by using the guide plate wear prediction model to obtain target wear data.

[0037] Specifically, the working conditions of the target guide, that is, the target working environment of the target guide, are determined. According to the actual application scenario of the target guide, suitable working conditions and environmental conditions are extracted as input features to drive the model to predict wear data. The target working conditions include load conditions (the load size of the target guide during operation), wood environment (the proportion of impurities such as resin, gravel, sawdust, etc. in the wood), and environmental conditions (such as temperature, humidity, etc.).

[0038] The target working conditions of the target guide plate (such as load, wood environment, accelerated aging environment, etc.) are input as features into the trained random forest wear prediction model. The corresponding wear prediction results are generated through the learned rules combined with the various environmental conditions and working conditions input. After all relevant data are input into the model, the prediction process is carried out through the decision tree of the model. Each tree judges and splits the input data, and finally integrates the prediction results of all trees to obtain an overall prediction value. Each decision tree is trained with different training data and feature subsets, and the data is split according to different feature values ​​at each layer. The model will eventually calculate a weighted average as the wear prediction result. Through the wear prediction model, combined with simulation conditions and accelerated aging environment, the wear data of the target guide plate is obtained.

[0039] The wear resistance coefficient calculation module 15 is used to calculate the wear resistance coefficient according to the target wear data to obtain a target wear resistance coefficient.

[0040] Specifically, the target wear data refers to the wear information of the guide plate obtained during use under certain working conditions, including wear depth, wear width, wear rate, fatigue crack extension, etc., which is an important indicator for evaluating the wear resistance of the guide plate. The wear coefficient is calculated based on the collected target wear data. The calculation method of the wear coefficient may vary depending on the application, but generally involves comparing the amount of wear with the applied load or number of cycles. For example, the wear coefficient may be calculated by the following formula: wear coefficient = wear depth / (number of load cycles × load size). Through calculation, the target wear coefficient can be obtained to evaluate the wear resistance of the target guide plate under actual use conditions. If the target wear coefficient is high, it indicates that the target guide plate has good wear resistance. By calculating the target wear coefficient, the wear resistance of the target guide plate under actual use conditions is quantitatively evaluated.

[0041] Furthermore, the simulated load module 11 in the wear resistance test system of the high-strength guide plate is also used for:

[0042] A load frequency is preset, and a control period is determined according to the load range and the load frequency; a continuous working condition simulation based on the simulation conditions and the accelerated aging environment is performed according to the control period to obtain a simulated working condition; fatigue loading is performed on the test guide plate sample according to the simulated working condition to obtain the guide plate simulated load.

[0043] Specifically, a preset load frequency, that is, the number of times the load is applied per unit time, is pre-set according to the actual use condition of the target guide. For example, in some working environments, the guide may be frequently subjected to high loads, so a higher load frequency needs to be set for simulation. The control cycle is calculated based on the load range (i.e., the load intensity range that the guide can withstand) and the load frequency. The control cycle is a time-based unit that determines how the load is applied within a certain period of time to achieve the simulation effect of the guide bearing the load for a long time. According to the control cycle, continuous working condition simulation is performed according to the simulation conditions (such as wood composition, temperature, humidity, etc.) and accelerated aging environment (such as high temperature, high humidity, etc.) to simulate the actual working environment. By simulating the continuous use of the guide in a real working environment, the guide can experience a load and aging process similar to long-term use in a short period of time.

[0044] During the continuous working condition simulation, fatigue loading is applied according to the set control cycle and simulation conditions, that is, loads of different intensities are applied periodically to simulate the repeated loads and continuous wear experienced by the guide in actual use. Fatigue loading refers to the periodic load applied to the test guide, which simulates the situation where the guide is subjected to repeated loads during long-term work, and evaluates the durability and reliability of the guide in long-term use. During the process of applying fatigue loading, the deformation, wear and other performance changes of the guide sample are recorded to evaluate its performance in the simulated environment. By combining the load frequency, load range and accelerated aging environment, the long-term use of the guide under actual working conditions is simulated, and this process is accelerated to quickly evaluate the durability of the guide under high load, high frequency and extreme environment. Through fatigue loading, the performance of the guide under repeated loads, especially the fatigue resistance of the guide, is evaluated, the accuracy and authenticity of the test are improved, and the test results are ensured to be closer to the working conditions that the guide may encounter in the actual environment.

[0045] Furthermore, the simulated load module 11 in the wear resistance test system of the high-strength guide plate is also used for:

[0046] Extract the sample limit load according to the sample chainsaw load; take the sample limit load as the load starting point, perform random load jumps within a preset jump multiple from the load starting point, and obtain multiple load stop points; select the load domain according to the multiple load stop points to obtain the load range.

[0047] Specifically, the sample load data is collected using load sensors (such as pressure sensors, current sensors, etc.) through the operation of a standard chain saw device in a specific working environment. The sample chain saw load refers to the load applied to the high-strength guide bar sample using a chain saw during the test, which is usually monitored and recorded by a sensor or measuring instrument. The purpose is to test the guide bar's ability to withstand a specific load. The chain saw load simulates the force that the guide bar may withstand in actual use. The sample limit load is extracted from the sample chain saw load, which is the maximum load value that an ordinary guide bar can withstand. If this load is exceeded, the guide bar may be damaged, deformed or fail.

[0048] The sample limit load of the ordinary guide plate is used as the load starting point. Based on the characteristic that the high-strength guide plate can withstand higher loads, random load jumps are performed through preset jump multiples to obtain multiple load stop points. The working load of the high-strength guide plate may exceed the load of the ordinary guide plate by several times, usually 2 times, 3 times, or even more. By performing load jumps (such as 2 times, 3 times, 4 times) on the limit load of the ordinary guide plate (such as 300N), the load is gradually increased, and the performance of the guide plate after each load jump is recorded. After each test, the load stop point is recorded, that is, the maximum load that the guide plate can no longer withstand, and finally the load range of the high-strength guide plate is determined.

[0049] Load jump refers to the process of randomly increasing the load by a preset multiple, such as 2x, 3x, 4x. The load stop point refers to the maximum load point at which the chain saw applies load to the guide bar after each load jump. Beyond this point, the chain saw or guide bar may be damaged, overloaded, stuck, etc., and therefore cannot continue to withstand higher loads. The load stop point will be recorded in the results of each test.

[0050] Determine the load range of the guide bar based on multiple load stop points. Load range selection means that during the test, by analyzing the results of multiple load stop points, the load range that the guide bar can withstand is determined, indicating that the chain saw can work stably within this load range without damage or performance degradation. Through multiple load jump tests and stop point records, it is possible to more accurately evaluate the performance of the guide bar under different loads, especially the load-bearing capacity of high-strength guide bars under loads exceeding those of ordinary guide bars.

[0051] Furthermore, the simulated load module 11 in the wear resistance test system of the high-strength guide plate is also used for:

[0052] Extract sample resin, sample gravel and sample sawdust according to the sample wood environment; extract a sample mixing ratio based on the sample wood environment, adjust the sample mixing ratio by a ratio tolerance to obtain a wood mixing ratio; mix resin, gravel and sawdust with the wood mixing ratio to obtain the wood environment.

[0053] Specifically, sample resin, sample grit and sample sawdust are extracted according to the sample wood environment. Resin, grit and sawdust are important components of the simulated wood environment, representing the sticky substances, abrasive particles and debris components in the wood, respectively. The sample wood environment refers to the working conditions of the wood under test, including components such as resin, grit and sawdust, representing the physical properties and environmental factors in the wood cutting process. Resin is usually an organic matter naturally present in wood, which has a certain viscosity and can affect the wear of the guide plate. The resin contacts the surface of the guide plate during the wood cutting process, which may increase the friction of the guide plate and lead to different wear modes. Grit represents small particles that may exist in the wood, which has strong abrasiveness and will aggravate the wear of the guide plate during the working process. The presence of simulated grit helps to test the durability of the guide plate in a high wear environment. Sawdust refers to the debris generated during the wood cutting process, which is usually fine particles peeled off the surface of the wood during the cutting process, affecting the wear mode and load bearing capacity of the guide plate, because it may increase the friction between the guide plate and the wood, thereby accelerating the wear.

[0054] The resin, grit and sawdust in the sample wood environment were quantitatively analyzed to determine the mixing ratio of the three in the original wood environment, which was used as the preliminary mixing ratio. In multiple experiments and analyses, the mixing ratio was adjusted according to the wear of the guide plate to ensure that the test can cover the complex conditions of different wood environments, such as wet environment, dry environment, and wood with grit. Ratio adjustment refers to the appropriate adjustment of the ratio of each component (such as resin, grit and sawdust) in the wood environment to ensure that these components can simulate a variety of complex working environments under different experimental conditions to obtain the wear of the guide plate in various environments. The final wood mixing ratio was obtained through multiple adjustments and experiments to ensure that the wear of the guide plate can be simulated under a variety of complex working conditions. According to the final ratio, the resin, grit and sawdust are mixed according to certain standards to obtain the final wood environment, which is used to test the wear resistance and service life of the guide plate under these complex conditions. By adjusting the ratio of resin, grit and sawdust, the cutting environment of various types of wood can be simulated, including wet wood, dry wood, hardwood with more grit, etc., to ensure that the wear of the guide plate under different conditions can be accurately reflected.

[0055] Furthermore, the simulated load module 11 in the wear resistance test system of the high-strength guide plate is also used for:

[0056] A target working environment of the target guide plate is obtained; and environmental accelerated aging adjustment is performed according to the target working environment to obtain the accelerated aging environment.

[0057] Specifically, the target guide refers to the guide sample that needs to be evaluated during the entire test process, and the target working environment of the target guide in actual application is determined, including temperature, humidity, chemical exposure, mechanical stress and other factors. According to the target working environment of the target guide, the accelerated aging environment adjustment is designed and implemented. In order to simulate the long-term aging process that the guide may experience in actual use, tools such as temperature control boxes and humidity control equipment can be used in the experiment to set the high temperature, high humidity and other conditions of the environment. Environmental accelerated aging adjustment accelerates the aging process of the target guide by artificially changing some environmental factors (such as temperature, humidity, etc.). The purpose is to simulate the effect of long-term use of the target guide in the actual working environment in a shorter period of time. Accelerated aging adjustment is usually used to test the long-term durability and wear performance of the guide in different working environments.

[0058] According to the main influencing factors of the target working environment, determine the parameters of accelerated aging, such as temperature, humidity, ultraviolet radiation, chemical corrosion, etc. Develop an accelerated aging test plan, including aging conditions, cycles, test items, etc., and select appropriate accelerated aging test equipment, such as ovens, wet heat boxes, UV aging boxes, etc. Adjust the temperature, humidity, ultraviolet radiation, chemical corrosion, etc. to obtain one or more accelerated aging environmental conditions to ensure that the accelerated aging environment can simulate the main aging factors in the target working environment and accelerate their effects. By simulating the effect of long-term use of the target guide in the actual working environment in a short period of time, accelerated aging adjustment can help quickly obtain the durability evaluation of the guide. Testing the wear of the guide under a controlled environment (such as high temperature and high humidity) can more accurately predict its performance in an actual complex working environment. It can timely discover potential problems of the guide in extreme environments such as high temperature and high humidity, such as material aging, excessive surface wear, etc., so as to improve the material selection and manufacturing process of the guide in a targeted manner.

[0059] Further, as attached Figure 2 As shown, the wear analysis module 12 in the wear resistance test system of the high-strength guide plate is also used for:

[0060] The wear depth and the wear width of the test guide plate sample are measured to obtain the wear amount; the fatigue point and the crack extension trend of the test guide plate sample are analyzed according to the wear amount to obtain the wear data; the fatigue failure of the test guide plate sample is judged according to the wear data, and if a valid sample result is obtained, the wear data is used as the guide plate wear data.

[0061] Specifically, the wear depth and wear breadth of the test guide plate samples are measured. The wear depth refers to the change in the surface of the guide plate caused by friction and contact wear, which is usually measured by laser scanning and mechanical measuring instruments. The laser scanner can accurately scan the surface of the guide plate, obtain three-dimensional data of the surface deformation, and measure the height change of each point on the surface to obtain the wear depth. The wear breadth refers to the area range of the wear on the surface of the guide plate. The wear area of ​​the entire guide plate is usually scanned or measured by a three-dimensional scanner or a high-resolution camera to calculate the distribution area of ​​the wear. Based on the wear depth and wear breadth measurements, the wear amount is obtained, which reflects the overall wear degree of the guide plate under specific working conditions.

[0062] The fatigue point and crack extension trend analysis of the test guide plate sample is carried out through the wear volume to determine whether the test guide plate sample has the risk of fatigue failure under continuous load, including the initiation and extension of microcracks. Fatigue point refers to the area on the surface of the guide plate where microcracks occur due to repeated loading, stress concentration, material fatigue and other factors, including microcracks or visible pits on the surface, and obvious stress textures or fatigue spots on the material surface. For example, the surface of the guide plate can be observed through a high-power microscope to identify possible crack sources and fatigue spots.

[0063] Crack growth trend analysis refers to monitoring the growth behavior of existing cracks through long-term friction and load. For example, the trend of crack growth can be monitored through acoustic emission technology or digital image correlation technology (DIC). Acoustic emission detection can capture the high-frequency wave signal emitted when the crack grows, and the speed and direction of crack growth can be understood through signal analysis. Wear data can be obtained through fatigue point and crack growth trend analysis, including the location of fatigue point, crack growth rate and final crack morphology.

[0064] According to the wear data, the fatigue failure of the test guide plate samples is judged to determine whether the guide plate has fractures, pores, crack extension or other damage caused by fatigue during use, resulting in the guide plate being unable to continue to bear the load. For example, by macroscopic observation or using non-destructive testing technology (such as ultrasound, X-ray, etc.), check whether the guide plate has cracks extending to the bearing surface of the guide plate, causing the guide plate to break; observe the material surface of the guide plate through a microscope to check whether there are pores or holes, which will cause the structure of the guide plate to be unstable and thus affect the bearing capacity of the guide plate. When it is detected that there are no serious fatigue cracks or pores in the guide plate, and the wear data of the guide plate is within an acceptable range, the test result is considered valid. By measuring the wear depth and wear width of the test guide plate samples, analyzing the fatigue points and crack extension trends, and making fatigue failure judgments on this basis, comprehensive wear data can be obtained, which can not only help identify the performance degradation of the guide plate in the working environment, but also foresee potential failure modes.

[0065] Furthermore, the wear resistance coefficient calculation module 15 in the wear resistance test system for a high-strength guide plate is also used for:

[0066] The target guide plate is measured in depth according to the target wear data to obtain cutting depth data, wherein the cutting depth data includes a time node; the cutting depth degradation coefficient is calculated for the cutting depth data based on the time node to obtain the target wear resistance coefficient.

[0067] Specifically, the depth of the target guide plate is measured according to the predicted target wear data, and the change in the cutting depth of the guide plate over time during the actual cutting process is measured, reflecting the degree of surface wear and performance degradation of the guide plate when it contacts and cuts the wood. The cutting depth data refers to the cutting depth data at each time node during the guide plate cutting wood under specific working conditions, reflecting the degree of wear of the guide plate over time during actual work. The time node refers to the predetermined time or work cycle point in the entire test process, which is used to record the cutting depth data of the guide plate at different time points.

[0068] During the experiment, select multiple time nodes (such as every 5 minutes, 10 minutes or 1 hour) to record the cutting depth of the guide plate at these nodes. Use high-precision measuring equipment, such as a laser rangefinder or depth sensor, to accurately measure the cutting depth of the guide plate and record the depth data at each time node. The data format may include time (unit: hours or minutes) and cutting depth (unit: mm).

[0069] The cutting depth data at each time node is fitted with time, usually using an exponential function or a linear regression formula. The degradation coefficient is obtained by fitting the curve or regression analysis, which indicates the rate at which the cutting depth degrades over time. The target wear resistance coefficient is calculated by the degradation coefficient. The wear resistance coefficient is usually inversely proportional to the degradation coefficient. The lower the degradation coefficient, the higher the wear resistance. For example, assuming that the depth shows an exponential decay trend over time, the degradation coefficient is obtained by using the exponential decay formula through the cutting depth data at two time nodes. The cutting depth degradation coefficient is used to measure the rate of change of the cutting depth over time during the cutting process of the guide plate, which indicates the wear resistance of the guide plate, that is, the relationship between the degree of wear of the guide plate and time under specific conditions. The larger the degradation coefficient, the faster the wear of the guide plate and the worse the durability. The target wear resistance coefficient is an indicator of the wear resistance of the guide plate under actual working conditions. The wear resistance coefficient can be obtained by calculating the cutting depth degradation coefficient. The higher the wear resistance coefficient, the better the wear resistance of the guide plate. The wear resistance of the guide bar under specific working conditions is evaluated by calculating the cutting depth degradation coefficient and obtaining the target wear resistance coefficient.

[0070] Furthermore, the wear resistance coefficient calculation module 15 in the wear resistance test system for a high-strength guide plate is also used for:

[0071] Wear resistance degradation analysis is performed according to the target wear resistance coefficient to obtain the guide plate failure trend; failure prediction is performed on the target guide plate according to the guide plate failure trend to obtain target failure prediction data, and the target failure prediction data is added to the target wear resistance coefficient.

[0072] Specifically, the degradation of wear resistance is analyzed according to the target wear resistance coefficient, and the changes in the wear resistance of the target guide during operation with time, load, environment and other factors are analyzed. By monitoring the wear rate, cutting depth, working environment and other factors of the guide, the degradation process of the wear resistance of the target guide is predicted and the long-term performance of the guide is evaluated. The failure trend of the guide refers to the process from the change of wear resistance to the final failure of the guide during continuous operation. The failure trend is usually manifested as increased wear, material fatigue, crack propagation, etc., until the guide can no longer work normally.

[0073] Wear resistance degradation analysis can provide quantitative predictions for guide plate failure. For example, under given working environment and load conditions, the wear rate of the guide plate may show linear decay. The failure trend of the guide plate is obtained by subtracting the product of the decay coefficient and time from the target wear coefficient. The failure trend is manifested as a gradual decrease in the guide plate wear coefficient until it reaches a critical value, indicating that the guide plate is about to fail. The decay coefficient determines the rate of wear resistance degradation. The decay coefficient is obtained by fitting the wear data of the guide plate under specific working conditions, and usually represents the reduction in the wear resistance coefficient per unit time. The size of the decay coefficient determines the durability of the material or component. The larger the decay coefficient, the faster the wear resistance of the material degrades.

[0074] Failure prediction refers to the prediction of the possible failure of the guide plate at a certain time in the future or after a period of time based on the failure trend and wear data of the guide plate. It is usually manifested as the performance of the guide plate deteriorating to the extent that it cannot meet the working requirements, such as crack extension, rupture, etc. Target failure prediction data refers to the prediction of the failure time, failure type and failure cause of the guide plate in the future based on the failure trend analysis results, usually including failure time, failure type (such as fracture, excessive wear, crack extension, etc.) and related parameters.

[0075] According to the failure trend of the guide plate, determine the failure mode, such as wear failure, fatigue failure, heat loss failure, etc. According to the collected data, select a suitable failure prediction model. For example, select a regression model, and by analyzing historical data, establish the relationship between the wear or crack extension of the guide plate and factors such as time, load, and temperature, so as to predict future failures. Through experimental data, it is found that the wear resistance coefficient of the guide plate has a certain linear relationship with the use time, load, and wood environmental components (such as resin and sawdust content), and a regression model is established. The working conditions of the target guide plate (such as load, environmental conditions, use time, etc.) are input into the established failure prediction model to obtain the failure prediction data of the guide plate, including failure time prediction, failure probability, failure mode, etc.

[0076] Based on the failure prediction data, the target wear coefficient is corrected. For example, if it is predicted that the guide plate will fail at a certain point in the future (for example, after 200 hours), the degradation rate of the wear coefficient is appropriately adjusted according to the prediction results to obtain an updated wear coefficient. After comprehensively considering the failure prediction data, the calculation of the wear coefficient will be more accurate, and the actual use and failure risk of the guide plate at different stages can be considered. By analyzing the failure trend of the guide plate, preventive measures can be taken before the guide plate actually fails, such as early replacement, repair or adjustment of the working environment, thereby reducing unexpected downtime and production interruptions.

[0077] In summary, the wear resistance testing system for a high-strength guide plate provided in this application has the following technical effects:

[0078] Through the simulation load module, the simulation load module is used to take the load range and the wood environment as the simulation conditions, and perform the load control of the test guide plate sample in a continuous control cycle according to the simulation conditions and the accelerated aging environment to obtain the guide plate simulation load; the wear analysis module is used to perform the wear analysis on the test guide plate sample according to the guide plate simulation load to obtain the guide plate wear data; the model construction module is used to construct the guide plate wear prediction model according to the simulation conditions, the accelerated aging environment and the guide plate wear data; the wear prediction module is used to predict the wear of the target guide plate through the guide plate wear prediction model to obtain the target wear data; the wear resistance coefficient calculation module is used to calculate the wear resistance coefficient according to the target wear data to obtain the target wear resistance coefficient. That is to say, by simulating the real working environment and accelerated aging conditions, combining the wear data and the prediction model, the durability of the guide plate under different working environments is accurately evaluated, and the accuracy of the guide plate wear resistance performance test is improved.

[0079] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0080] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A wear resistance testing system for a high-strength guide plate, characterized in that: include: A simulated load module, wherein the simulated load module is used to perform load control of a continuous control cycle on the test guide plate sample according to the simulated conditions and the accelerated aging environment with a load range and a wood environment as simulated conditions, so as to obtain a simulated load of the guide plate; A wear analysis module, the wear analysis module is used to perform wear analysis on the test guide plate sample according to the guide plate simulated load to obtain guide plate wear data; A model building module, the model building module is used to build a guide plate wear prediction model according to the simulation conditions, the accelerated aging environment and the guide plate wear data; A wear prediction module, the wear prediction module is used to predict the wear of the target guide plate through the guide plate wear prediction model to obtain target wear data; A wear resistance coefficient calculation module, wherein the wear resistance coefficient calculation module is used to calculate the wear resistance coefficient according to the target wear data to obtain a target wear resistance coefficient; Among them, the load range is obtained, including: Extracting sample limit loads based on sample chain saw loads; Taking the sample limit load as the load starting point, performing random load jumps within a preset jump multiple from the load starting point to obtain multiple load stop points; A load domain is selected according to the multiple load stop points to obtain the load range.

2. A wear resistance testing system for a high-strength guide plate as claimed in claim 1, characterized in that: The step of performing load control of a continuous control cycle on the test guide plate sample according to the simulation conditions and the accelerated aging environment to obtain the guide plate simulation load comprises: Preset the load frequency, and determine the control period according to the load range and the load frequency; Performing a continuous operating condition simulation based on the simulation conditions and the accelerated aging environment according to the control period to obtain a simulated operating condition; The test guide plate sample is fatigue loaded according to the simulated working condition to obtain the guide plate simulated load.

3. A wear resistance testing system for a high-strength guide plate as claimed in claim 2, characterized in that: Get a wood environment, including: Extract sample resin, sample grit and sample sawdust according to the sample wood environment; Extracting a sample mixing ratio based on the sample wood environment, performing a ratio tolerance adjustment on the sample mixing ratio, and obtaining a wood mixing ratio; The wood environment is obtained by mixing resin, grit and sawdust in the wood mixing ratio.

4. A wear resistance testing system for a high-strength guide plate as claimed in claim 2, characterized in that: Obtain an accelerated aging environment, including: Acquire a target working environment of the target guide plate; Environmental accelerated aging adjustment is performed according to the target working environment to obtain the accelerated aging environment.

5. A wear resistance testing system for a high-strength guide plate as claimed in claim 1, characterized in that: The performing wear analysis on the test guide plate sample to obtain guide plate wear data comprises: Measuring the wear depth and wear width of the test guide plate sample to obtain the wear amount; Perform fatigue point and crack extension trend analysis on the test guide plate sample according to the wear amount to obtain wear data; The test guide plate sample is subjected to sample fatigue failure judgment based on the wear data. If a valid sample result is obtained, the wear data is used as the guide plate wear data.

6. A wear resistance testing system for a high-strength guide plate as claimed in claim 1, characterized in that: The step of calculating the wear resistance coefficient according to the target wear data to obtain the target wear resistance coefficient includes: Measuring the depth of the target guide plate according to the target wear data to obtain cutting depth data, wherein the cutting depth data includes a time node; The cutting depth degradation coefficient is calculated for the cutting depth data based on the time node to obtain the target wear resistance coefficient.

7. A wear resistance testing system for a high-strength guide plate as claimed in claim 1, characterized in that: The obtaining of the target wear resistance coefficient also includes: Performing wear resistance degradation analysis according to the target wear resistance coefficient to obtain the guide plate failure trend; Failure prediction is performed on the target guide plate according to the guide plate failure trend to obtain target failure prediction data, and the target failure prediction data is added to the target wear resistance coefficient.

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