Extruder screw wear prediction method and system based on multi-factor coupling

Abstract

The application provides a kind of based on the multi-factor coupling puffing machine screw wear prediction method and system, it is related to screw wear prediction technical field.The application includes: real-time acquisition current puffing operating condition under the operating parameter set, calculate the dynamic contact stress field distribution of screw in the key position of puffing cavity compression section;Material relative sliding speed and screw surface temperature field in the key position of puffing cavity compression section are calculated;Real-time wear rate of screw in the key position of puffing cavity compression section is calculated;Time integration is carried out to real-time wear rate, the cumulative wear amount of key position in puffing cavity compression section is calculated, and the remaining life of screw is predicted based on cumulative wear amount, the technical problem that the influence of multi-factor coupling on screw wear under actual complex working condition is difficult to accurately reflect in the prior art, resulting in low screw wear prediction precision.

Description

Technical Field

[0001] This invention relates to industrial equipment wear prediction technology, and more particularly to a method and system for predicting the wear of extruder screws based on multi-factor coupling, belonging to the field of screw wear prediction technology. Background Technology

[0002] As the core working component of an extruder, the screw is subjected to complex environments such as material extrusion, frictional shearing, temperature rise, and material corrosion over long periods. Especially in the compression section of the extrusion chamber, the material density and chamber pressure increase significantly, making the screw surface more susceptible to combined damage including abrasive wear, adhesive wear, thermal fatigue wear, and corrosive wear. When screw wear reaches a certain level, it alters the screw channel volume, material propulsion capacity, and compression ratio, further affecting extrusion output, maturation degree, energy consumption, and product forming quality. In severe cases, it can even lead to increased equipment vibration, blockage, overload shutdown, or premature screw failure. Therefore, predicting the wear state of the extruder screw is crucial for developing maintenance plans, avoiding sudden shutdowns, and maintaining stability in the extrusion process. Existing methods for judging screw wear mostly rely on operating time, processing output, downtime inspection results, or empirical estimation based on a small number of operating parameters. Some methods infer screw wear trends based on single load, temperature, or current data. However, screw wear during the extrusion process is not determined by a single factor, but is influenced by multiple factors such as material moisture content, feed rate, screw speed, cavity pressure, material friction characteristics, surface temperature, and corrosive components in the material. Due to the coupling relationship between different factors, for example, changes in contact load affect frictional heat accumulation, temperature changes affect material surface strength and corrosion rate, and material slippage state changes local wear distribution. If only empirical thresholds or single-factor models are used for judgment, it is difficult to accurately reflect the impact of the multi-factor coupling effect on screw wear under actual complex working conditions. This results in low accuracy of screw wear prediction at critical locations and delayed assessment of remaining life, failing to effectively meet the needs of screw condition prediction and preventive maintenance for extruders. Summary of the Invention

[0003] The purpose of this invention is to address the technical problem in the prior art that it is difficult to accurately reflect the influence of the coupling effect of multiple factors on screw wear under actual complex working conditions, resulting in low accuracy of screw wear prediction. The invention proposes a method and system for predicting screw wear of extruders based on multi-factor coupling, so as to achieve the technical effect of accurately predicting screw wear.

[0004] To achieve the above objectives, the technical solution adopted by this invention is as follows: Real-time acquisition of the operating parameter set under the current expansion conditions; inputting a pre-constructed screw-material coupled mechanical model; calculating the dynamic contact stress field distribution of the screw at key positions in the compression section of the expansion cavity; based on the operating parameter set, calculating the relative sliding velocity of the material and the screw surface temperature field at key positions in the compression section of the expansion cavity using a pre-constructed material motion and energy model; extracting corrosion-related characteristic parameters from the operating parameter set; inputting these parameters, along with the dynamic contact stress field distribution, the relative sliding velocity of the material, and the screw surface temperature field, into a pre-trained multi-mechanism coupled wear rate model to calculate the real-time wear rate of the screw at key positions in the compression section of the expansion cavity; integrating the real-time wear rate over time to calculate and output the cumulative wear amount at key positions in the compression section of the expansion cavity; and predicting the remaining life of the screw based on the cumulative wear amount.

[0005] Furthermore, the set of operating parameters includes one or more of the following: material moisture content, screw speed, material feed rate, extrusion cavity die pressure, extrusion cavity outlet temperature, material pH, and total screw operating time.

[0006] Furthermore, a virtual simulation environment is established, encompassing the three-dimensional geometry of the screw, the constraints of the expansion cavity wall, and the aggregate of material particles. Within this virtual simulation environment, based on the physical properties of the material particles and the rules governing inter-particle interaction, the microscopic contact, compression, deformation, and macroscopic flow processes of the material particles under the rotational drive of the screw are simulated. During the simulation, the particle forces acting on each location on the screw surface are collected in real time, and based on the force-area mapping relationship, the screw surface pressure distribution data for different operating parameter sets are calculated. Based on the screw surface pressure distribution data, combined with finite element analysis of the screw structure, the stress fields at the overall screw and key local locations are solved. The screw surface pressure distribution data and stress fields under multiple sets of different operating parameter sets are correlated and fitted to establish a mapping relationship from the operating parameter set to the dynamic contact stress field distribution, thus constructing the screw-material coupled mechanical model.

[0007] Furthermore, based on the actual geometry of the expansion chamber and the screw, a set of physical control equations describing the macroscopic flow of the material is established. This set of equations includes at least the mass conservation equation, momentum conservation equation, and energy conservation equation. Boundary conditions and physical property parameters determined by the set of operating parameters are introduced into this set of equations, and the macroscopic distribution of the velocity, pressure, and temperature fields of the material within the expansion chamber is obtained through numerical solution, generating macroscopic flow solution results. An auxiliary model describing the microscopic motion and energy conversion of the material is established to quantify the microscopic shear generated by relative sliding and collision between material particles and the screw surface, and between particles themselves. The shear rate and frictional heat generation rate are used to generate microscopic motion energy conversion results. The macroscopic flow solution results are coupled with the microscopic motion energy conversion results to obtain a stable global temperature distribution. Based on the stable global temperature distribution, the vector difference between the material tangential velocity at a specified position on the screw surface and the screw surface tangential velocity is extracted as the material relative sliding velocity, and the screw surface temperature field at that position is extracted. By changing the set of operating parameters and performing multiple coupled solutions, a quantitative relationship database from the set of operating parameters to the material relative sliding velocity and the screw surface temperature field is established, and the material motion and energy model is constructed.

[0008] Furthermore, the abrasive wear contribution term is a nonlinear function of the dynamic contact stress field distribution and the relative sliding velocity of the material; the contact fatigue wear contribution term is a nonlinear function of the dynamic contact stress field distribution and the number of load applications determined by the relative sliding velocity of the material and the working time; the corrosion wear contribution term is a nonlinear function of the screw surface temperature field and corrosion-related characteristic parameters; the real-time wear rate is the sum of the abrasive wear contribution term, the contact fatigue wear contribution term, and the corrosion wear contribution term.

[0009] Furthermore, the nonlinear function of the corrosion wear contribution term is positively correlated with the temperature field of the screw surface and with the corrosion-related characteristic parameters.

[0010] Furthermore, starting from the initial time point, the real-time wear rate is continuously integrated over time until the current time, to obtain the cumulative wear amount at the current time; a limit wear amount threshold for screw failure is set; the integral trend of the real-time wear rate in the next time period is predicted, and based on the growth rate of the cumulative wear amount, the remaining working time required for the current cumulative wear amount to reach the limit wear amount threshold is calculated as the predicted value of the remaining life of the screw.

[0011] Another objective of this invention discloses a multi-factor coupling-based extruder screw wear prediction system: A first calculation module acquires the current set of operating parameters under the current extrusion conditions in real time, inputs a pre-constructed screw-material coupled mechanical model, and calculates the dynamic contact stress field distribution of the screw at key positions in the compression section of the extrusion chamber; a second calculation module, based on the set of operating parameters and a pre-constructed material motion and energy model, calculates the relative sliding velocity of the material and the screw surface temperature field at key positions in the compression section of the extrusion chamber; a third calculation module extracts corrosion-related characteristic parameters from the set of operating parameters, and inputs them, along with the dynamic contact stress field distribution, the relative sliding velocity of the material, and the screw surface temperature field, into a pre-trained multi-mechanism coupling wear rate model to calculate the real-time wear rate of the screw at key positions in the compression section of the extrusion chamber; a prediction module integrates the real-time wear rate over time, calculates and outputs the cumulative wear amount at key positions in the compression section of the extrusion chamber, and predicts the remaining life of the screw based on the cumulative wear amount.

[0012] This invention dynamically predicts the screw wear state during the operation of an extruder, and has the following advantages compared with the prior art: 1) It can more accurately reflect the comprehensive impact of the coupling effect of multiple factors on the wear state of the screw under actual complex working conditions, and improve the accuracy of screw wear prediction; 2) It can realize continuous real-time monitoring of wear rate and cumulative wear amount at key locations in the compression section of the expansion cavity, improving the real-time performance of wear condition assessment; 3) It can predict the remaining service life of the screw in advance, providing a reliable basis for equipment maintenance, repair and replacement, and reducing the risk of sudden failure; 4) It can reduce the predictive lag caused by traditional methods that rely on experience-based judgment or shutdown testing, and improve the operational stability and maintenance decision-making efficiency of extrusion equipment. Attached Figure Description

[0013] Figure 1 A schematic diagram of the process for predicting screw wear in an extruder based on multi-factor coupling, provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a multi-factor coupling-based extruder screw wear prediction and monitoring structure provided in an embodiment of the present invention. Detailed Implementation

[0014] The present invention will be further described below with reference to the embodiments: Example 1

[0015] like Figure 1 As shown in the figure, this embodiment discloses a method for predicting the wear of an extruder screw based on multi-factor coupling, wherein the method includes: The system acquires the set of operating parameters under the current extrusion conditions in real time, inputs the pre-constructed screw-material coupled mechanical model, and calculates the dynamic contact stress field distribution of the screw at key locations in the compression section of the extrusion chamber.

[0016] During the operation of the extruder, the moisture content of the material, screw speed, material feed rate, extrusion cavity die pressure, extrusion cavity outlet temperature, material pH, and total screw working time are synchronously collected using a moisture content sensor, speed encoder, feeding weighing unit, pressure sensor, temperature sensor, and online pH detection unit. These data are then used to form an operating parameter set according to a unified sampling period. The operating parameter set undergoes outlier removal, moving average filtering, and dimensional normalization. For the i-th operating parameter... , can be adopted Normalization is performed, where, This represents the normalized running parameters. This indicates the real-time collected operating parameters. and These represent the minimum and maximum values ​​of the operating parameter within the preset operating range, respectively. The normalized set of operating parameters is input into a pre-constructed screw-material coupled mechanical model. This model pre-establishes the coupling relationship between the screw's three-dimensional geometry, the expansion chamber wall constraints, the screw groove spatial boundary, and the material particle set. Based on the material's moisture content, feed rate, and temperature parameters, the model determines the material particles' equivalent density, elastic modulus, friction coefficient, and adhesion damping parameters. Based on the screw speed and die pressure, the model determines the screw rotation boundary conditions and the expansion chamber outlet pressure boundary conditions. During the model solution process, the contact points between the material particles and the screw surface are used as mechanical sampling units. The normal contact force and tangential friction force at each contact point are calculated, and multiple contact forces within the same screw surface grid unit are accumulated to obtain the equivalent contact pressure corresponding to that grid unit. In the formula, This represents the equivalent contact pressure of the j-th screw surface mesh element. This represents the resultant force of the forces acting on the material particles within the grid cell. This represents the effective stress area of ​​the mesh element. The equivalent contact pressure of each screw surface mesh element is mapped onto the screw finite element structural model as the load boundary. Finite element analysis is performed using the screw material's elastic modulus, Poisson's ratio, and constraint conditions to obtain the global stress field of the screw at the current moment. Equivalent stress, maximum principal stress, and shear stress are extracted from key locations in the expansion cavity compression section, such as the leading edge of the screw rib, the bottom of the screw groove, the inlet of the compression section, the middle of the compression section, and the outlet of the compression section, forming a dynamic contact stress field distribution. This dynamic contact stress field distribution can be expressed as... In the formula, The equivalent contact stress is represented on the screw surface or at a local structure of the screw. x, y, and z represent the coordinates of the key position of the screw in three-dimensional space, and t represents the current sampling time. As the set of operating parameters is updated in real time, the above load calculation, load mapping, and finite element stress solution process are repeated to obtain the dynamic contact stress field distribution that changes with time. The dynamic contact stress field distribution is then used as the mechanical input parameter for subsequent wear rate calculation.

[0017] Furthermore, the set of operating parameters includes one or more of the following: material moisture content, screw speed, material feed rate, extrusion chamber die pressure, extrusion chamber outlet temperature, material pH, and total screw operating time.

[0018] Specifically, the moisture content of the material is obtained through an online moisture detection unit installed at the feed end of the extruder; the screw speed is obtained through a speed encoder installed on the screw drive shaft or motor output end; the material feed rate is obtained through a feeding weighing mechanism, a screw feeder metering module, or a flow meter; the extrusion cavity die pressure is obtained through a pressure sensor installed at the die or near the extrusion cavity outlet; the extrusion cavity outlet temperature is obtained through a temperature sensor installed in the material flow channel at the extrusion cavity outlet or on the barrel wall; the acidity or alkalinity of the material is obtained through an online acidity / alkalinity detection unit or by periodically sampling and detecting the feed material; and the total acidity / alkalinity is accumulated through the operating timer module of the control system. Record the total working time of the screw. Among them, the material moisture content is used to characterize the degree of plasticization, viscoelasticity and interparticle lubrication of the material; the screw speed is used to characterize the relative motion intensity between the screw and the material and the load cycle frequency; the material feed rate is used to characterize the degree of material filling in the expansion chamber and the compression load; the expansion chamber die pressure is used to characterize the back pressure at the outlet of the compression section and the extrusion strength of the material on the screw surface; the expansion chamber outlet temperature is used to characterize the accumulation state of frictional heat and processing heat; the material pH is used to characterize the tendency of the material to corrode the screw surface; and the total working time of the screw is used to characterize the cumulative service time and the number of cyclic loads experienced by the screw.

[0019] Furthermore, the screw-material coupled mechanical model is constructed as follows: A virtual simulation environment was established, encompassing the three-dimensional geometry of the screw, the constraints of the expansion chamber wall, and the aggregate of material particles. Within this virtual environment, based on the physical properties of the material particles and the rules governing inter-particle interactions, the microscopic contact, compression, deformation, and macroscopic flow processes of the material particles under the rotational drive of the screw were simulated. During the simulation, the particle forces acting on various locations on the screw surface were collected in real time, and based on the force-area mapping relationship, the screw surface pressure distribution data for different operating parameter sets were calculated. Based on the screw surface pressure distribution data, combined with finite element analysis of the screw structure, the stress fields at the overall screw and key local locations were obtained. The screw surface pressure distribution data and stress fields under multiple sets of different operating parameter sets were correlated and fitted to establish a mapping relationship from the operating parameter set to the dynamic contact stress field distribution, thus constructing the screw-material coupled mechanical model.

[0020] First, a three-dimensional geometric model of the screw is established based on its actual structural dimensions. This model includes at least the screw shaft, screw ridges, screw grooves, compression section inlet, compression section middle section, and compression section outlet. A cavity wall constraint model is then established based on the cavity inner wall diameter, cavity length, die position, and outlet constraints. The screw three-dimensional geometric model and the cavity wall constraint model are then assembled to form a virtual simulation environment. Subsequently, the material is discretized into multiple material particles or equivalent particle units, and each particle is assigned equivalent particle size, density, elastic modulus, and Poisson's ratio. The parameters used in the simulation are: particle ratio, interparticle friction coefficient, particle-screw surface friction coefficient, and damping coefficient. Material moisture content is used to correct for interparticle adhesion damping and friction coefficient. Material feed rate is used to determine the number of particles entering the expansion chamber per unit time. Screw rotation speed is used to determine the screw rotation boundary conditions, and expansion chamber die pressure is used to determine the outlet back pressure boundary conditions. In the virtual simulation environment, the discrete element method is used to simulate the conveying, compression, contact, extrusion, collision, and deformation processes of material particles driven by screw rotation. For any particle contact pair, the normal contact force can be used to... and tangential friction Characterizing the interaction between particles or between particles and the screw surface, in the formula, Indicates the normal contact force. This represents tangential friction. and These represent normal stiffness and tangential stiffness, respectively. and These represent the normal overlap and the tangential displacement, respectively. and These represent the normal damping coefficient and the tangential damping coefficient, respectively. and These represent the normal relative velocity and the tangential relative velocity, respectively. This represents the friction coefficient. During the simulation, the screw surface is divided into multiple surface grid cells. The normal and tangential forces exerted by the material particles on the screw surface within each surface grid cell are statistically analyzed in real time. The particle forces within the same grid cell are then vector-synthesized to obtain the resultant force of that grid cell. Then according to The pressure distribution data on the screw surface was calculated, where, This represents the equivalent contact pressure of the j-th surface mesh element. Let represent the effective stress area of ​​the j-th surface mesh element; further, map the screw surface pressure distribution data to the screw finite element model according to the mesh node coordinates, and use the screw material's elastic modulus, Poisson's ratio, yield strength, and fixed constraints as finite element solution conditions to solve for stress at key locations in the overall screw structure and the expansion cavity compression section, obtaining the equivalent stress, principal stress, and shear stress distribution of the screw under different operating parameter sets, and express the stress field as . ,in, The values ​​represent the stress values ​​at local locations on or inside the screw surface, x, y, and z represent the screw structural coordinates, and t represents the simulation time. By changing the material moisture content, screw speed, material feed rate, expansion chamber die pressure, expansion chamber outlet temperature, and material pH, the particle motion simulation, surface pressure calculation, and finite element stress solution are repeatedly executed to obtain multiple sets of operating parameters, screw surface pressure distribution data, and stress field samples. A radial basis function regression method is used to establish the mapping relationship from the operating parameter set to the dynamic contact stress field distribution, that is, firstly, the operating parameters under multiple different expansion conditions are mapped... A set of data is used as the training input sample, and the operating parameters within it are normalized to ensure that operating parameters with different dimensions are within a uniform numerical range. Simultaneously, the stress field at key locations in the compression section of the expansion cavity obtained from finite element analysis is used as the training output sample. Several representative operating condition samples are selected from the training input sample as radial basis centers. These radial basis centers characterize typical expansion conditions, and the width of the radial basis function is determined based on the average distance between the operating condition samples, ensuring that samples with similar operating conditions receive a higher response and samples with significantly different operating conditions receive a lower response. The radial basis function is expressed as follows: In the formula, This represents the response value of the k-th radial basis function to the current running parameter vector. This represents the current running parameter vector. This represents the k-th radial basis center. Indicates the width of the radial basis functions. This represents the squared Euclidean distance between the current operating parameter vector and the radial basis center. Further, a weighted summation is performed based on the response values ​​of each radial basis function to output the predicted stress field corresponding to the current operating parameter set. Using the actual stress field obtained from finite element analysis as the monitoring target, the weights and biases of each radial basis function are trained to gradually reduce the error between the predicted and actual stress fields. When the stress prediction error of the verification sample is lower than a preset error threshold, or the number of training iterations reaches a preset upper limit, the radial basis center, function width, weights, and biases are fixed to obtain the screw-material coupled mechanical model. This model enables the output of the dynamic contact stress field distribution at key locations in the compression section of the expansion cavity after real-time input of the operating parameter set under the current expansion conditions.

[0021] Based on the set of operating parameters, the relative sliding velocity of the material and the temperature field of the screw surface at key positions in the compression section of the expansion chamber are calculated using a pre-constructed material motion and energy model.

[0022] Specifically, the real-time acquired data on material moisture content, screw speed, material feed rate, expansion cavity die pressure, expansion cavity outlet temperature, material pH, and total screw operating time are synchronized, anomaly removed, and smoothed before being input into the material motion and energy model. This model determines the material's equivalent viscosity, density, specific heat capacity, and thermal conductivity based on the moisture content; the material flow rate into the expansion cavity compression section based on the feed rate; the tangential velocity of the screw surface based on the screw speed; the back pressure at the compression section outlet based on the expansion cavity die pressure; and the thermal boundary conditions in the model are corrected based on the expansion cavity outlet temperature. During the model solution process, the screw groove space within the expansion cavity compression section is used as the computational domain. By considering the combined material flow rate, outlet back pressure, and screw rotation boundary, the flow state of the material within the compression section is determined, yielding the flow velocity, local pressure, and shear state at different locations within the screw channel. Simultaneously, based on the friction between the material and the screw surface, the collision between material particles, and the heat generated by shearing, energy calculations are performed on the heat generation, conduction, and accumulation processes within the compression section, resulting in the temperature distribution near the screw surface. For key locations in the expansion chamber compression section, the velocity of the material adjacent to that location along the tangential direction of the screw surface is first extracted. Then, the tangential velocity of the screw surface is calculated based on the screw rotation speed and the distance from that location to the screw rotation axis. The magnitude of the difference between these two values ​​is taken as the relative sliding velocity of the material at that key location, specifically expressed as: ,in, Indicates the relative sliding speed of the material. This indicates the velocity of material near the critical location along the tangential direction of the screw surface. This represents the tangential velocity of the screw surface at the critical location. After obtaining the relative sliding velocity of the material, the corresponding screw surface temperature value is extracted by combining the heat balance result formed by frictional heat, shear heat and conduction heat at that location. The screw surface temperature value is then sorted according to the preset critical locations such as the inlet of the compression section, the middle of the compression section, the outlet of the compression section, the leading edge of the screw rib and the bottom of the screw groove, forming a material relative sliding velocity sequence and screw surface temperature field distribution that correspond one-to-one with the critical locations. This sequence serves as the motion input and heat input for the subsequent multi-mechanism coupled wear rate model.

[0023] Furthermore, constructing the material motion and energy model includes: establishing a set of physical control equations describing the macroscopic flow of the material based on the actual geometry of the expansion chamber and the screw, wherein the set of physical control equations includes at least the mass conservation equation, the momentum conservation equation, and the energy conservation equation; introducing boundary conditions and physical property parameters determined by the set of operating parameters into the set of physical control equations, and obtaining the macroscopic distribution of the velocity field, pressure field, and temperature field of the material in the expansion chamber through numerical solution, generating macroscopic flow solution results; and establishing an auxiliary model describing the microscopic motion and energy conversion of the material, quantifying the relative sliding between material particles and the screw surface, and between particles. The microscopic shear rate and frictional heat generation rate generated by collisions are used to generate microscopic motion energy conversion results. The macroscopic flow solution results are coupled with the microscopic motion energy conversion results to obtain a stable global temperature distribution. Based on the stable global temperature distribution, the vector difference between the material tangential velocity at a specified position on the screw surface and the screw surface tangential velocity is extracted as the material relative sliding velocity, and the screw surface temperature field at that position is extracted. By changing the operating parameter set and performing multiple coupled solutions, a quantitative relationship database from the operating parameter set to the material relative sliding velocity and the screw surface temperature field is established, and the material motion and energy model is constructed.

[0024] Preferably, based on the inner wall dimensions of the expansion cavity, screw outer diameter, screw pitch, screw groove depth, compression section length, die position, and clearance parameters between the screw and the expansion cavity, a three-dimensional flow calculation region for the compression section of the expansion cavity is established, and this three-dimensional flow calculation region is divided into multiple calculation grids. Within the calculation grids, a set of physical control equations is established to describe the macroscopic flow state of the material. This set of physical control equations includes at least the mass conservation equation, momentum conservation equation, and energy conservation equation. The mass conservation equation constrains the continuous conveying process of the material within the expansion cavity; the momentum conservation equation describes the velocity and pressure distribution of the material under the action of screw rotation, cavity wall constraint, and die back pressure; and the energy conservation equation describes the shear heating, friction heating, heat conduction, and heat accumulation processes of the material. Then, boundary conditions and physical property parameters are introduced into the physical control equations according to the set of operating parameters. Specifically, this includes setting the inlet flow rate boundary based on the material feed rate. The screw wall motion boundary is set according to the screw speed, the outlet pressure boundary is set according to the die pressure of the expansion cavity, and the temperature correction boundary is set according to the outlet temperature of the expansion cavity. The equivalent viscosity, density, specific heat capacity, and thermal conductivity of the material are determined based on the material's moisture content. After configuring the boundary conditions and physical property parameters, the physical control equations are numerically discretized using the finite volume method or finite element method. The velocity distribution, pressure distribution, and initial temperature distribution of the material within the compression section of the expansion cavity are obtained through iterative solving, generating macroscopic flow solution results. Simultaneously, an auxiliary model describing the microscopic motion and energy conversion of the material is established. The relative sliding between material particles and the screw surface, the squeezing collisions between material particles, and the internal friction generated by shearing are taken as microscopic energy sources. The microscopic shear rate and frictional heat generation rate within each computational grid are calculated. The frictional heat generation rate can be determined based on local contact pressure, friction coefficient, and relative sliding velocity, for example... In the formula, This represents the rate of frictional heat generation per unit contact area. This represents the coefficient of friction between the material and the screw surface. Indicates local contact pressure. The relative sliding velocity of the material is represented. The microscopic shear rate and frictional heat generation rate are converted into internal heat source terms corresponding to each computational grid, and the internal heat source terms are introduced into the macroscopic energy conservation equation for coupled iterative solution. In each iteration, the microscopic energy conversion results are updated based on the current velocity and pressure distribution, and then the updated internal heat source terms are substituted back into the energy conservation equation to correct the temperature distribution until the temperature change between two adjacent iterations is less than the preset temperature convergence threshold, thus obtaining a stable global temperature distribution. After obtaining a stable global temperature distribution, at the preset locations on the screw surface, such as the inlet of the compression section, the middle of the compression section, the outlet of the compression section, the leading edge of the screw rib, and the bottom of the screw groove, the velocity of the material adjacent to the corresponding location along the tangential direction of the screw surface is extracted, and the velocity is determined based on the screw rotation speed and the distance from the location to the screw rotation axis. The tangential velocity of the screw surface is fixed, and the vector difference between the two is used as the relative sliding velocity of the material. At the same time, the wall temperature value at this location in the stable global temperature distribution is extracted as the screw surface temperature field. Finally, by changing the combination of material moisture content, screw speed, material feed rate, expansion cavity die pressure, expansion cavity outlet temperature, and material pH, macroscopic flow solution, microscopic energy conversion calculation, internal heat source coupling iteration, and key position result extraction are repeatedly performed to obtain multiple sets of operating parameters and corresponding samples of material relative sliding velocity and screw surface temperature field. A quantitative relationship database is established according to the operating parameters, key position number, relative sliding velocity, surface temperature value, and solution timestamp. Thus, a material motion and energy model that can output material relative sliding velocity and screw surface temperature field based on real-time operating parameter set is constructed.

[0025] Corrosion-related characteristic parameters are extracted from the set of operating parameters and input together with the dynamic contact stress field distribution, the relative sliding velocity of the material, and the temperature field of the screw surface into a pre-trained multi-mechanism coupled wear rate model to calculate the real-time wear rate of the screw at the key position of the compression section in the expansion cavity.

[0026] After obtaining the operating parameter set for the current sampling period, the acidity / alkalinity of the material, the outlet temperature of the expansion chamber, the moisture content of the material, and the content of acidic components or salts related to the material formulation are read from the operating parameter set as characteristic parameters related to corrosion. When the operating parameter set does not directly contain data on acidic components or salts, a preset material formulation database can be called according to the current material batch number to read the content of organic acids, chloride salts, or other corrosive components in the corresponding batch of material, and these contents, together with the acidity / alkalinity and moisture content of the material, form a corrosion feature vector. Subsequently, according to the preset key positions of the compression section of the expansion chamber, the dynamic contact stress value, relative sliding speed of the material, screw surface temperature value, and corrosion feature vector corresponding to each key position are timestamped and bound with position numbers to form key position wear input samples. The wear input samples at the key locations are input into a pre-trained multi-mechanism coupled wear rate model. The model calculates the contributions of abrasive wear, contact fatigue wear, and corrosion wear. The abrasive wear contribution is determined based on dynamic contact stress and relative material sliding velocity, characterizing the amount of surface material removed due to relative slippage of material particles on the screw surface. The contact fatigue wear contribution is determined based on dynamic contact stress, screw speed, and total screw operating time, characterizing the cumulative fatigue damage to the screw surface material under cyclic loads. The corrosion wear contribution is determined based on screw surface temperature, material pH, material moisture content, and corrosive component content, characterizing the impact of chemical or electrochemical corrosion on material loss in high-temperature, humid material environments. The real-time wear rate can be expressed as... In the formula, This indicates the real-time wear rate of the screw at a critical location in the compression section of the expansion chamber. Indicates the contribution of abrasive wear. This represents the contribution of contact fatigue wear. This represents the contribution of corrosion and wear, and t represents the current sampling time.

[0027] Furthermore, the multi-mechanism coupled wear rate model is a nonlinear mathematical model characterizing the synergistic effects of abrasive wear, contact fatigue wear, and corrosion wear, including: The abrasive wear contribution term is a nonlinear function of the dynamic contact stress field distribution and the relative sliding velocity of the material; the contact fatigue wear contribution term is a nonlinear function of the dynamic contact stress field distribution and the number of load applications determined by the relative sliding velocity of the material and the working time; the corrosion wear contribution term is a nonlinear function of the screw surface temperature field and corrosion-related characteristic parameters; the real-time wear rate is the sum of the abrasive wear contribution term, the contact fatigue wear contribution term, and the corrosion wear contribution term.

[0028] Preferably, wear rate calculation units can be established according to the key locations of the compression section of the expansion chamber. For any key location, the dynamic contact stress, relative sliding velocity of the material, screw surface temperature, material pH, material moisture content, corrosive component content, screw speed, and screw working time corresponding to that location are read. These data are then normalized and input into the multi-mechanism coupled wear rate model. The abrasive wear contribution term characterizes the amount of material removed due to the continuous sliding and scraping of material particles on the screw surface. It has a non-linear relationship with the dynamic contact stress and relative sliding velocity of the material, and can be expressed as follows: In the formula, Indicates the contribution of abrasive wear. Indicates the abrasive wear coefficient. This represents the dynamic contact stress at critical locations. Indicates the relative sliding speed of the material. An index representing the influence of dynamic contact stress on abrasive wear. The index represents the influence of relative sliding speed on abrasive wear; the contact fatigue wear contribution term characterizes the initiation, propagation, and surface spalling of microcracks on the screw surface under cyclic contact loads. It is determined by both dynamic contact stress and the number of load applications. The number of load applications can be determined based on screw speed, operating time, and the relative sliding state of the material, for example... In the formula, N represents the number of load applications. The load correction factor is determined by the relative sliding state of the material, where n represents the screw speed and t represents the screw operating time; based on this, the contribution of contact fatigue wear can be expressed as... In the formula, This represents the contribution of contact fatigue wear. Indicates the contact fatigue wear coefficient. An index representing the influence of dynamic contact stress on contact fatigue damage. The index represents the influence of the number of load applications on contact fatigue damage; the corrosion and wear contribution term characterizes the material loss caused by chemical or electrochemical corrosion of the screw surface material in high-temperature, humid, and acidic / alkaline material environments. It exhibits a non-linear relationship with the screw surface temperature field and corrosion-related characteristic parameters, and can be expressed as follows: In the formula, Indicates the contribution of corrosion and wear. Indicates the corrosion and wear coefficient. Indicates the screw surface temperature. The intensity of corrosion characteristics is determined by the material's acidity / alkalinity, moisture content, and the content of corrosive components. Indicates the temperature effect coefficient. The corrosion characteristic intensity index represents the influence of corrosion characteristic intensity. This corrosion characteristic intensity can be obtained by weighting the degree to which the material's acidity or alkalinity deviates from neutrality, the material's moisture content, and the content of corrosive components. This ensures that increased acidity or alkalinity, higher moisture content, and increased corrosive components lead to a greater contribution to corrosion wear. Finally, the abrasive wear contribution, contact fatigue wear contribution, and corrosion wear contribution are summed to obtain the real-time wear rate at the current critical location. In the formula, The real-time wear rate is represented; for multiple key locations in the compression section of the expansion cavity, the above calculations are performed to form a real-time wear rate sequence, so that the multi-mechanism coupled wear rate model can simultaneously reflect the coupled effects of contact load, sliding friction, load cycle, surface temperature and corrosion parameters on screw wear.

[0029] Furthermore, the nonlinear function of the corrosion wear contribution term is positively correlated with the temperature field of the screw surface and with the corrosion-related characteristic parameters.

[0030] Specifically, corrosion-related characteristic parameters include one or more of the following: the degree of deviation of the material's pH from neutrality, the material's moisture content, and the content of corrosive components. When calculating the corrosion wear contribution, the pH deviation intensity is first calculated based on the material's pH. This pH deviation intensity can be determined by the absolute value of the difference between the material's pH and its neutral value. Then, the pH deviation intensity, material moisture content, and corrosive component content are normalized and weighted to obtain the corrosion characteristic intensity. Subsequently, the screw surface temperature and the corrosion characteristic intensity are input into a corrosion wear nonlinear function to calculate the corrosion wear contribution at corresponding key locations. When the screw surface temperature increases, the chemical reaction rate or electrochemical reaction activity of the corrosive components in the material on the screw surface increases, and the corrosion wear contribution increases accordingly. An increase in the corrosion characteristic intensity indicates stronger acidity or alkalinity, higher moisture content, or higher corrosive component content in the material, increasing the tendency for corrosion damage to the screw surface, and thus increasing the corrosion wear contribution. The corrosion wear contribution can be expressed as... In the formula, Indicates the contribution of corrosion and wear. Indicates the corrosion and wear coefficient. Indicates the screw surface temperature. Indicates the intensity of corrosion characteristics. Indicates the temperature effect coefficient. The index represents the influence of corrosion characteristic intensity, and , and All model parameters are positive to ensure that the corrosion and wear contribution increases with the increase of screw surface temperature and corrosion characteristic intensity. During model training or calibration, the above model parameters are fitted by screw surface temperature, material acidity and alkalinity, moisture content, corrosive component content and measured corrosion and wear under historical operating conditions, so that the corrosion and wear contribution can reflect the differences in corrosion and wear under different material formulations and different thermal conditions.

[0031] The real-time wear rate is integrated over time to calculate and output the cumulative wear at key locations in the compression section of the expansion chamber, and the remaining life of the screw is predicted based on the cumulative wear.

[0032] Furthermore, the real-time wear rate is integrated over time to calculate and output the cumulative wear at key locations in the compression section of the expansion chamber, and the remaining screw life is predicted based on the cumulative wear, including: Starting from the initial time point, the real-time wear rate is continuously integrated over time until the current time, to obtain the cumulative wear amount at the current time; a limit wear amount threshold for screw failure is set; the integral trend of the real-time wear rate in the next time period is predicted; based on the growth rate of the cumulative wear amount, the remaining working time required for the current cumulative wear amount to reach the limit wear amount threshold is calculated as the predicted value of the remaining life of the screw.

[0033] Preferably, the starting time is the time when the new screw is put into use, the time when the screw is restarted after repair, or the time when the wear monitoring system starts recording. The real-time wear rate of each key location in the compression section of the expansion cavity is continuously recorded according to a preset sampling period. For any key location, starting from the starting time point, the real-time wear rate corresponding to each sampling period is integrated and accumulated in chronological order to obtain the cumulative wear amount at the current moment. The cumulative wear amount can be expressed as... In the formula, This represents the cumulative wear and tear at the current time t. Indicates the start time point. Indicates time The corresponding real-time wear rate, Represents the integral time variable; under discrete sampling conditions, it can be used as follows: Perform recursive calculations, where, This represents the cumulative wear at the k-th sampling time. This represents the cumulative wear at the (k-1)th sampling time. This represents the real-time wear rate at the k-th sampling time. This represents the time interval between two adjacent samples. Subsequently, a threshold for the ultimate wear amount of the screw failure is set based on the allowable wear depth of the screw structure, the allowable change in screw channel volume, the compression ratio reduction limit, or historical failure samples. The current cumulative wear amount is compared with this ultimate wear amount threshold. When the current cumulative wear amount is lower than the ultimate wear amount threshold, the wear growth trend is calculated based on the real-time wear rate sequence over a recent period. This wear growth trend can be obtained using a moving average, linear regression, or exponential smoothing method. When using linear trend prediction, the average growth rate can be fitted based on the cumulative wear amount over the most recent r sampling periods. and in accordance with Calculate the remaining working time, where, This represents the predicted remaining life of the screw. The threshold value representing the maximum wear that can cause screw failure. This indicates the current cumulative wear and tear. This indicates the predicted growth rate of cumulative wear in the next time period. When there are multiple critical positions in the compression section of the expansion chamber, the cumulative wear and remaining life prediction values ​​of each critical position are calculated separately. The critical position with the shortest remaining life is selected as the basis for outputting the overall remaining life of the screw. At the same time, the corresponding critical position number, current cumulative wear, limit wear threshold, and remaining working time are output to facilitate screw maintenance, replacement, or operating condition adjustment decisions.

[0034] Example 2 like Figure 2 As shown, this embodiment discloses a multi-factor coupling-based extruder screw wear prediction system, wherein the system includes: First calculation module 11: Real-time acquisition of the operating parameter set under the current expansion condition, inputting a pre-constructed screw-material coupled mechanical model, and calculating the dynamic contact stress field distribution of the screw at the key position of the compression section of the expansion cavity; Second calculation module 12: Based on the operating parameter set, using a pre-constructed material motion and energy model, calculating the relative sliding velocity of the material and the screw surface temperature field at the key position of the compression section of the expansion cavity; Third calculation module 13: Extracting corrosion-related characteristic parameters from the operating parameter set, and inputting them together with the dynamic contact stress field distribution, the relative sliding velocity of the material, and the screw surface temperature field into a pre-trained multi-mechanism coupled wear rate model to calculate the real-time wear rate of the screw at the key position of the compression section of the expansion cavity; Prediction module 14: Integrating the real-time wear rate over time, calculating and outputting the cumulative wear amount at the key position of the compression section of the expansion cavity, and predicting the remaining life of the screw based on the cumulative wear amount.

[0035] Furthermore, the first calculation module 11 is used to perform the following method: The set of operating parameters includes one or more of the following: material moisture content, screw speed, material feed rate, extrusion cavity die pressure, extrusion cavity outlet temperature, material pH, and total screw operating time.

[0036] Furthermore, the first calculation module 11 is used to perform the following method: A virtual simulation environment was established, encompassing the three-dimensional geometry of the screw, the constraints of the expansion chamber wall, and the aggregate of material particles. Within this virtual environment, based on the physical properties of the material particles and the rules governing inter-particle interactions, the microscopic contact, compression, deformation, and macroscopic flow processes of the material particles under the rotational drive of the screw were simulated. During the simulation, the particle forces acting on various locations on the screw surface were collected in real time, and based on the force-area mapping relationship, the screw surface pressure distribution data for different operating parameter sets were calculated. Based on the screw surface pressure distribution data, combined with finite element analysis of the screw structure, the stress fields at the overall screw and key local locations were obtained. The screw surface pressure distribution data and stress fields under multiple sets of different operating parameter sets were correlated and fitted to establish a mapping relationship from the operating parameter set to the dynamic contact stress field distribution, thus constructing the screw-material coupled mechanical model.

[0037] Furthermore, the second calculation module 12 is used to perform the following method: Based on the actual geometry of the expansion chamber and screw, a set of physical control equations describing the macroscopic flow of materials is established. This set of equations includes at least the mass conservation equation, momentum conservation equation, and energy conservation equation. Boundary conditions and physical property parameters determined by the set of operating parameters are introduced into this set of equations. Through numerical solution, the macroscopic distribution of the velocity, pressure, and temperature fields of the material within the expansion chamber is obtained, generating macroscopic flow solution results. An auxiliary model describing the microscopic motion and energy conversion of the material is established, quantifying the microscopic shear rates generated by relative sliding and collisions between material particles and the screw surface, and between particles themselves. The flow rate and frictional heat generation rate are used to generate microscopic motion energy conversion results. The macroscopic flow solution results are coupled with the microscopic motion energy conversion results to obtain a stable global temperature distribution. Based on the stable global temperature distribution, the vector difference between the material tangential velocity at a specified position on the screw surface and the screw surface tangential velocity is extracted as the material relative sliding velocity, and the screw surface temperature field at that position is extracted. By changing the operating parameter set and performing multiple coupled solutions, a quantitative relationship database from the operating parameter set to the material relative sliding velocity and the screw surface temperature field is established, and the material motion and energy model is constructed.

[0038] Furthermore, the third calculation module 13 is used to perform the following method: The multi-mechanism coupled wear rate model is a nonlinear mathematical model characterizing the synergistic effects of abrasive wear, contact fatigue wear, and corrosion wear. It includes: an abrasive wear contribution term, which is a nonlinear function of the dynamic contact stress field distribution and the relative sliding velocity of the material; a contact fatigue wear contribution term, which is a nonlinear function of the dynamic contact stress field distribution and the number of load applications determined by the relative sliding velocity of the material and the working time; and a corrosion wear contribution term, which is a nonlinear function of the screw surface temperature field and corrosion-related characteristic parameters. The real-time wear rate is the sum of the abrasive wear contribution term, the contact fatigue wear contribution term, and the corrosion wear contribution term.

[0039] Furthermore, the third calculation module 13 is used to perform the following method: The nonlinear function of the corrosion and wear contribution term is positively correlated with the temperature field of the screw surface and with the corrosion-related characteristic parameters.

[0040] Furthermore, the prediction module 14 is used to perform the following method: Starting from the initial time point, the real-time wear rate is continuously integrated over time until the current time, to obtain the cumulative wear amount at the current time; a limit wear amount threshold for screw failure is set; the integral trend of the real-time wear rate in the next time period is predicted; based on the growth rate of the cumulative wear amount, the remaining working time required for the current cumulative wear amount to reach the limit wear amount threshold is calculated as the predicted value of the remaining life of the screw.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting screw wear in an extruder based on multi-factor coupling, characterized in that, The method includes: The system acquires the set of operating parameters under the current extrusion conditions in real time, inputs the pre-constructed screw-material coupled mechanical model, and calculates the dynamic contact stress field distribution of the screw at key positions in the compression section of the extrusion chamber. Based on the set of operating parameters, the relative sliding velocity of the material and the temperature field of the screw surface at key positions in the compression section of the expansion chamber are calculated using a pre-constructed material motion and energy model. Corrosion-related characteristic parameters are extracted from the set of operating parameters and, together with the dynamic contact stress field distribution, the relative sliding velocity of the material, and the temperature field of the screw surface, are input into a pre-trained multi-mechanism coupled wear rate model to calculate the real-time wear rate of the screw at the key position of the compression section of the expansion cavity. The real-time wear rate is integrated over time to calculate and output the cumulative wear at key locations in the compression section of the expansion chamber, and the remaining life of the screw is predicted based on the cumulative wear.

2. The method for predicting extruder screw wear based on multi-factor coupling as described in claim 1, characterized in that, The set of operating parameters includes one or more of the following: material moisture content, screw speed, material feed rate, extrusion cavity die pressure, extrusion cavity outlet temperature, material pH, and total screw operating time.

3. The method for predicting extruder screw wear based on multi-factor coupling as described in claim 1, characterized in that, The screw-material coupled mechanical model is constructed as follows: Establish a virtual simulation environment that includes the three-dimensional geometry of the screw, the constraints of the expansion chamber wall, and the material particle aggregation; In the virtual simulation environment, based on the physical properties of the material particles and the rules of interaction between particles, the microscopic contact, extrusion, deformation and macroscopic flow process of the material particles under the drive of screw rotation are simulated; During the simulation, the particle forces acting on each position on the screw surface are collected in real time, and the pressure distribution data on the screw surface under different operating parameter sets are calculated based on the mapping relationship between force and area. Based on the pressure distribution data on the screw surface, combined with the finite element analysis of the screw structure, the stress field of the screw as a whole and at key local locations is obtained. By correlating and fitting the screw surface pressure distribution data with the stress field under multiple sets of different operating parameters, a mapping relationship from the operating parameter set to the dynamic contact stress field distribution is established, and the screw-material coupled mechanical model is constructed.

4. The method for predicting extruder screw wear based on multi-factor coupling as described in claim 1, characterized in that, Constructing the material motion and energy model includes: Based on the actual geometric structure of the expansion chamber and the screw, a set of physical control equations describing the macroscopic flow of materials is established. The set of physical control equations includes at least the mass conservation equation, the momentum conservation equation, and the energy conservation equation. In the physical control equations, boundary conditions and physical property parameters determined by the set of operating parameters are introduced, and the macroscopic distribution of the velocity field, pressure field and temperature field of the material in the expansion chamber is obtained through numerical solution, generating macroscopic flow solution results; An auxiliary model describing the microscopic motion and energy conversion of materials is established to quantify the microscopic shear rate and frictional heat generation rate generated by the relative sliding and collision between material particles and the screw surface, and between particles, and to generate the results of microscopic motion energy conversion. By coupling the macroscopic flow solution results with the microscopic motion energy conversion results, a stable global temperature distribution can be obtained. Based on the stable global temperature distribution, the vector difference between the material tangential velocity at a specified position on the screw surface and the screw surface tangential velocity is extracted as the material relative sliding velocity, and the screw surface temperature field at that position is extracted. By changing the set of operating parameters and performing multiple coupled solutions, a quantitative relationship database is established from the set of operating parameters to the relative sliding velocity of the material and the temperature field of the screw surface, and the material motion and energy model is constructed.

5. The method for predicting extruder screw wear based on multi-factor coupling as described in claim 1, characterized in that, The multi-mechanism coupled wear rate model is a nonlinear mathematical model characterizing the synergistic effects of abrasive wear, contact fatigue wear, and corrosion wear, including: The abrasive wear contribution term is a nonlinear function of the dynamic contact stress field distribution and the relative sliding velocity of the material. The contribution of contact fatigue wear is a nonlinear function of the dynamic contact stress field distribution and the number of load applications determined by the relative sliding speed of the material and the working time. The corrosion and wear contribution term is a nonlinear function of the temperature field on the screw surface and corrosion-related characteristic parameters. The real-time wear rate is the sum of the contributions from abrasive wear, contact fatigue wear, and corrosion wear.

6. The method for predicting extruder screw wear based on multi-factor coupling as described in claim 5, characterized in that, The nonlinear function of the corrosion and wear contribution term is positively correlated with the temperature field of the screw surface and with the corrosion-related characteristic parameters.

7. The method for predicting extruder screw wear based on multi-factor coupling as described in claim 1, characterized in that, The real-time wear rate is integrated over time to calculate and output the cumulative wear at key locations in the compression section of the expansion chamber. Based on the cumulative wear, the remaining screw life is predicted, including: Starting from the initial time point, the real-time wear rate is continuously integrated over time until the current time, to obtain the cumulative wear amount at the current time; Set the threshold for the maximum wear amount required to prevent screw failure; The integral trend of the real-time wear rate in the next time period is predicted. Based on the growth rate of the cumulative wear, the remaining working time required for the current cumulative wear to reach the limit wear threshold is calculated as the predicted value of the screw's remaining life.

8. A multi-factor coupling-based extruder screw wear prediction system, characterized in that, For implementing the multi-factor coupling-based extruder screw wear prediction method according to any one of claims 1-7, the system comprises: The first calculation module: acquires the set of operating parameters under the current expansion conditions in real time, inputs the pre-constructed screw-material coupled mechanical model, and calculates the dynamic contact stress field distribution of the screw at key positions in the compression section of the expansion cavity; The second calculation module calculates the relative sliding velocity of the material and the temperature field of the screw surface at key positions in the compression section of the expansion chamber, based on the set of operating parameters and a pre-built material motion and energy model. The third calculation module extracts corrosion-related characteristic parameters from the set of operating parameters, and inputs them together with the dynamic contact stress field distribution, the relative sliding speed of the material and the temperature field of the screw surface into a pre-trained multi-mechanism coupled wear rate model to calculate the real-time wear rate of the screw at the key position of the compression section of the expansion cavity. Prediction module: Integrates the real-time wear rate over time, calculates and outputs the cumulative wear at key locations in the compression section of the expansion cavity, and predicts the remaining life of the screw based on the cumulative wear.

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