Energy consumption simulation method and system suitable for grinding machine

Abstract

The invention relates to the technical field of model simulation, in particular to an energy consumption simulation method and system suitable for a grinding machine. The method comprises the following steps that a common material list of the grinding machine is obtained; extracting material attribute data according to the common material list of the grinding machine to obtain a material attribute data set; acquiring to-be-polished workpiece data and grinding wheel data of a polisher; utilizing a finite element analysis technology and the material attribute data set to carry out contact area grid model construction and material attribute assignment on the to-be-polished workpiece data and the grinding wheel data of the polisher to obtain a grid model endowed with material attributes; performing contact area calculation according to the grid model endowed with the material attributes to obtain contact area data; simulation parameter setting is carried out according to the contact area data, and a simulation parameter setting table is obtained. According to the method, thermal-mechanical dynamic coupling solution is introduced into modeling in the grinding process, so that the problem of unbalanced grinding energy consumption caused by insufficient consideration of the thermal effect is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of model simulation, and in particular to an energy consumption simulation method and system suitable for a grinder. Background Art

[0002] Early simulation methods for energy consumption of grinders were relatively simple, mainly based on empirical formulas or simplified theoretical models. For example, energy consumption was estimated by parameters such as grinding wheel speed, feed rate, and grinding depth. These methods have fast calculation speeds but low accuracy and are difficult to reflect the complexity of the grinding process. With the development of computer technology, finite element analysis (FEA) methods have gradually been applied to the simulation of energy consumption of grinders. FEA can divide the workpiece and grinding wheel into grids and simulate the distribution of stress, strain, and temperature fields during the grinding process, thereby calculating energy consumption more accurately. However, early FEA simulations often ignored or simplified the influence of thermal effects, such as frictional heating, heat conduction, and thermal deformation, resulting in deviations between the simulation results and the actual situation.

[0003] The shortcoming of the existing technology is that the thermal effect is not considered enough, which leads to deviation in the energy consumption prediction during the grinding process, thus causing imbalance in actual energy consumption. Specifically, it is manifested as:

[0004] 1. Existing methods are difficult to accurately describe the temperature field distribution and evolution law of the grinding process, ignoring the heat conduction and convection heat transfer process in the grinding area, resulting in incomplete consideration of energy conversion such as friction heat and deformation heat, and low energy consumption calculation.

[0005] 2. The stress distribution in the grinding contact zone is insufficiently estimated, and the thermal stress coupling effect is not fully considered, resulting in low prediction accuracy of material removal, tool wear and other behaviors, and large errors in the calculation of cutting energy consumption.

[0006] 3. There is a lack of systematic analysis of the impact of factors such as grinding parameters, material properties, and thermal boundary conditions on energy consumption, which makes it difficult to guide process parameter optimization and energy conservation and efficiency improvement. Summary of the invention

[0007] Based on this, it is necessary to provide an energy consumption simulation method and system suitable for a grinder to solve at least one of the above technical problems.

[0008] To achieve the above purpose, a method for simulating energy consumption of a grinding machine includes the following steps:

[0009] Step S1: Obtain a list of commonly used materials for grinders; extract material attribute data according to the list of commonly used materials for grinders to obtain a material attribute data set;

[0010] Step S2: obtaining data of the workpiece to be polished and data of the grinding wheel of the polishing machine; using finite element analysis technology and a material property data set to construct a contact area mesh model for the workpiece to be polished data and the grinding wheel data of the polishing machine and assign material properties to obtain a mesh model assigned with material properties; calculating the contact area according to the mesh model assigned with material properties to obtain contact area data;

[0011] Step S3: setting simulation parameters according to the contact area data to obtain a simulation parameter setting table; performing grinding dynamic energy consumption simulation according to the simulation parameter setting table and the mesh model assigned with material properties to obtain grinding dynamic simulation result data; performing data post-processing on the grinding dynamic simulation result data to obtain thermal distribution data;

[0012] Step S4: Calculate the total energy consumption according to the thermal distribution data to obtain energy consumption data; analyze energy consumption control factors according to the energy consumption data and the thermal distribution data to realize the energy consumption simulation operation of the grinder.

[0013] The present invention ensures the accuracy and integrity of simulation input data by establishing a common material property database and extracting required material data, lays a reliable foundation for subsequent simulation calculations, and avoids simulation deviations caused by missing or incorrect material data. By constructing accurate grinding wheel and workpiece models, and performing grid division and material property assignment, digital simulation of the real grinding contact area is realized, and accurate geometric and physical models are provided for subsequent contact analysis and dynamic simulation. Accurate contact area calculation provides key parameters for accurate energy consumption prediction. By setting reasonable simulation parameters and boundary conditions, and performing grinding dynamic energy consumption simulation, dynamic simulation of the grinding process is realized, and the dynamic changes of key parameters such as temperature field, stress field, friction force, material removal rate, etc. in the grinding process can be obtained, providing comprehensive data support for subsequent energy consumption calculation and analysis. The acquisition of thermal distribution data is the key to in-depth analysis of energy consumption influencing factors. By analyzing and calculating the thermal distribution data, the various energy consumptions in the grinding process can be accurately quantified, including friction energy consumption, cutting energy consumption, and motor drive energy consumption, and the total energy consumption data can be finally obtained. By analyzing the factors affecting energy consumption, key parameters affecting energy consumption can be identified, providing theoretical guidance for optimizing the grinding process and reducing energy consumption, and ultimately achieving the energy consumption simulation operation goal of the grinder, thereby improving the grinding efficiency and economic benefits. Therefore, the present invention provides an energy consumption simulation method suitable for a grinder, which introduces thermal-mechanical dynamic coupling solution into the modeling of the grinding process and comprehensively considers the influence of multi-source data on materials, processes, and energy consumption, thereby effectively solving the problem of unbalanced grinding energy consumption caused by insufficient consideration of thermal effects in the prior art.

[0014] Preferably, step S1 comprises the following steps:

[0015] Step S11: obtaining a list of commonly used materials for grinders; searching for material properties according to the list of commonly used materials for grinders to obtain an initial material property table;

[0016] Step S12: performing data cleaning and standardization on the initial material attribute table to obtain a standardized material attribute table;

[0017] Step S13: constructing a database for the standardized material property table to obtain a material property database;

[0018] Step S14: Exporting a data set according to the material property database to obtain a material property data set.

[0019] The present invention obtains a list of commonly used materials for grinders and searches for the corresponding material properties to generate an initial material property table, thereby realizing the systematic collection and organization of commonly used grinding material information. This lays a foundation for subsequent data processing and simulation analysis, and avoids errors caused by missing or inconsistent material information. Data cleaning and standardization are performed on the initial material property table, eliminating missing values, erroneous data and inconsistencies in the original data, and unifying the dimensions and value ranges of different properties. This improves data quality and reliability, and ensures the accuracy and comparability of subsequent simulation results. The standardized material property table is constructed into a database, and structured storage and management of material property data are realized. This facilitates data query, retrieval and update, improves data utilization efficiency, and also provides a basis for building a more comprehensive material database. The material property data set is exported from the material property database according to simulation requirements, and the function of obtaining material data on demand is realized. This avoids unnecessary redundant data, improves simulation efficiency, and facilitates users to select appropriate material property data according to specific simulation tasks.

[0020] Preferably, step S2 comprises the following steps:

[0021] Step S21: obtaining grinding wheel data of a grinder; constructing a grinding wheel geometric model based on the grinding wheel data to obtain a grinding wheel geometric model;

[0022] Step S22: Acquire data of the workpiece to be polished; construct a workpiece surface model based on the data of the workpiece to be polished to obtain a workpiece surface model;

[0023] Step S23: using finite element analysis technology to perform contact area mesh division on the grinding wheel geometric model and the workpiece surface model to obtain a contact area mesh model;

[0024] Step S24: assigning material properties of the workpiece and the grinding wheel to the contact area mesh model according to the material property data set to obtain a mesh model assigned with material properties;

[0025] Step S25: applying contact pressure to the mesh model assigned with material properties to obtain deformation models under different pressures; calculating contact areas of the deformation models under different pressures to obtain contact area data.

[0026] The present invention achieves accurate digital expression of the geometric shape of the grinding wheel by acquiring the data of the grinding wheel and constructing its geometric model, and provides an accurate geometric basis for subsequent simulation analysis. By acquiring the data of the workpiece to be polished and constructing the workpiece surface model, the surface to be processed of the workpiece is accurately expressed, which provides necessary geometric information for simulating the real polishing process. The contact area grid of the grinding wheel and the workpiece surface model is divided by finite element analysis technology, which provides a discretized geometric model for subsequent contact analysis and simulation calculation, and improves the calculation accuracy by mesh refinement. The material properties of the contact area grid model are assigned according to the material property data set, so that the model can accurately reflect the mechanical and thermodynamic properties of different materials, thereby improving the reliability of the simulation results. By applying contact pressure to the grid model assigned with material properties and calculating the contact area, the actual contact area between the grinding wheel and the workpiece under different pressures is obtained, which provides key parameters for accurately calculating frictional heat generation and material removal rate, and makes the simulation results closer to the actual polishing process.

[0027] Preferably, step S3 comprises the following steps:

[0028] Step S31: Acquire simulation target data; set simulation parameters according to the contact area data and the simulation target data to obtain a simulation parameter setting table;

[0029] Step S32: Establishing a thermal model according to the grid model assigned with material properties to obtain a thermal model;

[0030] Step S33: applying boundary conditions to the thermal model according to the simulation parameter setting table to obtain a model with applied boundary conditions;

[0031] Step S34: performing grinding dynamic energy consumption simulation on the model with applied boundary conditions to obtain grinding dynamic simulation result data;

[0032] Step S35: Post-process the grinding dynamic simulation result data to obtain thermal distribution data, wherein the thermal distribution data includes temperature distribution data, contact surface friction data, stress distribution data and material removal rate data.

[0033] The present invention obtains simulation target data and sets simulation parameters in combination with contact area data, thereby ensuring the pertinence and effectiveness of the simulation process, so that the simulation results can directly serve the predetermined goals, such as predicting energy consumption or optimizing grinding parameters. A thermal model is established based on a grid model with material properties, and mechanical and thermodynamic behaviors are coupled together, which can more comprehensively simulate the complex physical phenomena in the grinding process, thereby improving the simulation accuracy. Boundary conditions are applied to the thermal model according to the simulation parameter setting table, and the real grinding conditions, such as grinding wheel rotation speed, workpiece feed speed and ambient temperature, are simulated, so that the simulation results are closer to the actual situation. The model with boundary conditions is subjected to grinding dynamic energy consumption simulation, and key data such as temperature field, stress field, friction force, material removal amount, etc. at each time step in the grinding process are obtained, which provides a data basis for subsequent energy consumption calculation and analysis. The grinding dynamic simulation result data is post-processed, and thermal distribution data such as temperature distribution, contact surface friction, stress distribution and material removal rate are extracted, which provides necessary data support for in-depth analysis of the thermodynamic characteristics and energy consumption influencing factors of the grinding process.

[0034] Preferably, step S34 includes the following steps:

[0035] Step S341: dividing the model with the boundary conditions into time steps to obtain a time step grid model;

[0036] Step S342: Calculate the contact node temperature field according to the time step grid model to obtain the time step node temperature data; calculate the contact point stress field according to the time step grid model and the time step node temperature data to obtain the time step node stress data;

[0037] Step S343: performing heat transfer effect analysis according to the time step node temperature data and the time step node stress data to obtain heat transfer data;

[0038] Step S344: performing grinding force calculation according to the time step node stress data to obtain grinding force data; performing dynamic stress field evolution analysis according to the grinding force data and the time step node stress data to obtain dynamic stress field data;

[0039] Step S345: performing a dynamic evolution analysis of the grinding temperature field according to the heat transfer data, the time step mesh model and the dynamic stress field data to obtain dynamic temperature field data;

[0040] Step S346: performing thermal stress coupling analysis according to the dynamic temperature field data and the dynamic stress field data to obtain thermal stress coupling data;

[0041] Step S347: Analyze the grinding dynamic simulation results according to the thermal stress coupling data and the grinding force data to obtain the grinding dynamic simulation result data.

[0042] The present invention discretizes the continuous grinding process by dividing the model with applied boundary conditions into time steps, so that the model state of each time step can be gradually analyzed, and a time basis is provided for dynamic simulation. By calculating the contact node temperature field and stress field of each time step grid model, the dynamic changes of temperature and stress in the grinding process can be captured, and data support is provided for subsequent heat transfer and stress field evolution analysis. By analyzing the heat transfer effect of the time step node temperature data and stress data, the heat transfer process between the grinding wheel, the workpiece and the environment, and the mutual influence between temperature and stress can be understood, so as to more accurately simulate the thermodynamic behavior in the grinding process. By calculating the grinding force according to the time step node stress data and performing the dynamic stress field evolution analysis, the variation law of the grinding force over time and the dynamic distribution of the stress field can be understood, and a basis is provided for evaluating the stability and efficiency of the grinding process. By performing the dynamic evolution analysis of the grinding temperature field on the heat transfer data, the time step grid model and the dynamic stress field data, the temperature change in the grinding process can be predicted more accurately, and theoretical guidance is provided for controlling the grinding temperature and avoiding the workpiece burn. By performing thermal stress coupling analysis on dynamic temperature field data and dynamic stress field data, the interaction between temperature and stress can be considered to more accurately simulate the material deformation and failure behavior during the grinding process, thereby improving the reliability of the simulation results. By performing dynamic grinding simulation results analysis on thermal stress coupling data and grinding force data, key indicators such as energy consumption and material removal rate during the grinding process can be obtained, providing a scientific basis for optimizing grinding process parameters and improving grinding efficiency and quality.

[0043] Preferably, step S343 includes the following steps:

[0044] Step S3431: performing heat source generation calculation according to the time step node temperature data and the time step node stress data to obtain the time step heat source data;

[0045] Step S3432: updating the temperature field according to the time step heat source data to obtain the time step temperature field update data;

[0046] Step S3433: Optimize the heat conduction path according to the time step temperature field update data to obtain heat conduction path data; calculate the temperature gradient according to the time step temperature field update data to obtain temperature gradient data;

[0047] Step S3434: updating the heat transfer coefficient according to the temperature gradient data and the material property data set to obtain the heat transfer coefficient; performing heat flow calculation according to the temperature gradient data and the heat transfer coefficient to obtain heat flow data;

[0048] Step S3435: Generate heat transfer data according to the heat conduction path data, the temperature gradient data, the heat transfer coefficient and the heat flow data to obtain heat transfer data.

[0049] The present invention calculates heat source generation according to time step node temperature data and stress data, and can accurately determine the heat source of each time step in the grinding process, providing a basis for subsequent temperature field updates. The temperature field is updated according to the time step heat source data, and the changes in the temperature field during the grinding process can be reflected in real time, thereby improving the simulation accuracy. By optimizing the heat conduction path and calculating the temperature gradient according to the time step temperature field update data, the heat transfer process inside the material can be more accurately simulated, and a basis can be provided for the subsequent heat transfer coefficient update. By updating the heat transfer coefficient according to the temperature gradient data and the material property data set, and calculating the heat flow, the heat transfer characteristics of the material under different temperature gradients can be more accurately simulated, thereby improving the reliability of the simulation results. By integrating the heat conduction path data, the temperature gradient data, the heat transfer coefficient and the heat flow data to generate heat transfer data, the heat transfer process of each time step can be fully described, and comprehensive data support can be provided for the subsequent temperature field evolution analysis.

[0050] Preferably, step S345 includes the following steps:

[0051] Step S3451: performing friction heat generation analysis according to the dynamic stress field data and the material property data set to obtain friction heat generation data;

[0052] Step S3452: Calculate heat source distribution according to dynamic stress field data and heat transfer data to obtain heat source distribution data;

[0053] Step S3453: performing grinding temperature rise effect analysis according to the friction heat generation data and the heat source distribution data to obtain temperature rise effect data;

[0054] Step S3454: performing cumulative temperature field update according to the temperature rise effect data to obtain cumulative temperature field data;

[0055] Step S3455: Calculate the dynamic temperature field evolution of the grinding process based on the accumulated temperature field data to obtain dynamic temperature field data.

[0056] The present invention can accurately calculate the friction heat generated in the grinding process by performing friction heat generation analysis based on dynamic stress field data and material property data sets, which is one of the main heat sources in the grinding process and provides key input for subsequent temperature field analysis. By performing heat source distribution calculation based on dynamic stress field data and heat transfer data, the distribution of heat sources in the grinding area can be more accurately described, thereby improving the accuracy of temperature field simulation. By performing grinding temperature rise effect analysis based on friction heat generation data and heat source distribution data, the temperature rise caused by the grinding process can be quantified, providing a basis for evaluating the impact of grinding temperature on workpieces and grinding wheels. By performing temperature field cumulative update based on temperature rise effect data, the cumulative effect of heat can be considered to more accurately simulate the dynamic changes of the temperature field in the grinding process. By performing dynamic temperature field evolution calculation of the grinding process based on the accumulated temperature field data, the change process of the temperature field in the grinding process can be fully described, providing theoretical guidance for predicting grinding temperature and optimizing grinding parameters.

[0057] Preferably, step S347 includes the following steps:

[0058] Step S3471: Calculate the material removal amount in different areas using a preset material removal rate model according to the thermal stress coupling data, the time step node stress data and the grinding force data to obtain the material removal rate data;

[0059] Step S3472: performing dynamic grinding energy analysis according to the friction heat generation data, grinding force data, and material removal rate data in the dynamic temperature field data to obtain grinding energy data;

[0060] Step S3473: Output simulation results based on the dynamic temperature field data, dynamic stress field data, friction heat generation data, material removal rate data, and grinding energy data to obtain grinding dynamic simulation result data.

[0061] The present invention combines thermal stress coupling data, time step node stress data and grinding force data, and uses a preset material removal rate model to more accurately calculate the material removal amount in different areas, thereby more accurately reflecting the material removal characteristics of the grinding process. By performing dynamic grinding energy analysis based on dynamic temperature field data, grinding force data and material removal rate data, it is possible to more comprehensively understand the energy consumption in the grinding process, including friction energy consumption, plastic deformation energy consumption and material removal energy consumption, and provide a basis for optimizing grinding process parameters. By integrating and outputting dynamic temperature field data, dynamic stress field data, friction heat generation data, material removal rate data and grinding energy data, complete grinding dynamic simulation result data can be provided, which is convenient for users to conduct subsequent analysis and evaluation, and provides comprehensive data support for optimizing grinding processes and improving grinding efficiency and quality.

[0062] Preferably, step S4 comprises the following steps:

[0063] Step S41: Calculate the friction energy consumption according to the temperature distribution data and the contact friction data to obtain the friction energy consumption data;

[0064] Step S42: Calculating cutting energy consumption according to stress distribution data and material removal rate data to obtain cutting energy consumption data;

[0065] Step S43: Calculating the motor drive energy consumption according to the cutting energy consumption data and the simulation parameter setting table to obtain the motor drive energy consumption data;

[0066] Step S44: Calculate the total energy consumption according to the friction energy consumption data, the cutting energy consumption data and the motor drive energy consumption data to obtain energy consumption data;

[0067] Step S45: analyzing the factors affecting energy consumption according to the energy consumption data, the thermal distribution data, the simulation parameter setting table and the material property data set to realize the energy consumption simulation operation of the grinder.

[0068] The present invention can accurately quantify the energy loss caused by friction during the grinding process by calculating the friction energy consumption according to the temperature distribution data and the contact friction data, which is an important part of the total energy consumption of grinding. By calculating the cutting energy consumption according to the stress distribution data and the material removal rate data, the energy consumed in the material removal process can be accurately quantified, which is crucial for evaluating the grinding efficiency. By calculating the motor drive energy consumption according to the cutting energy consumption data and the simulation parameter setting table, the motor efficiency can be taken into account and the actual energy consumption of the grinder can be more accurately estimated. By adding the friction energy consumption, the cutting energy consumption and the motor drive energy consumption, the total energy consumption of the grinding process can be obtained, which provides a basis for evaluating the energy consumption level of the grinding process. By comprehensively analyzing the energy consumption data, the thermal distribution data, the simulation parameter setting table and the material property data set, the key factors affecting the energy consumption can be identified, and guidance can be provided for optimizing the grinding process parameters and reducing the energy consumption, and finally the energy consumption simulation operation goal of the grinder can be achieved.

[0069] Preferably, the present invention further provides an energy consumption simulation system applicable to a grinder, which is used to execute the energy consumption simulation method applicable to a grinder as described above. The energy consumption simulation system applicable to a grinder comprises:

[0070] The material property analysis module is used to obtain a list of commonly used materials for grinders; material property data is extracted based on the list of commonly used materials for grinders to obtain a material property data set;

[0071] The contact model building module is used to obtain the data of the workpiece to be polished and the grinding wheel data of the polishing machine; the contact area mesh model is constructed for the workpiece data to be polished and the grinding wheel data of the polishing machine using the finite element analysis technology and the material property data set, and the material property is assigned to obtain the mesh model with material properties; the contact area is calculated according to the mesh model with material properties to obtain the contact area data;

[0072] The thermal-mechanical coupling simulation module is used to set simulation parameters according to the contact area data to obtain a simulation parameter setting table; perform grinding dynamic energy consumption simulation according to the simulation parameter setting table and a mesh model assigned with material properties to obtain grinding dynamic simulation result data; perform data post-processing on the grinding dynamic simulation result data to obtain thermal distribution data;

[0073] The energy consumption calculation and optimization module is used to calculate the total energy consumption according to the thermal distribution data to obtain the energy consumption data; analyze the energy consumption control factors according to the energy consumption data and the thermal distribution data to realize the energy consumption simulation operation of the grinder.

[0074] The present invention ensures the accuracy and integrity of simulation input data by establishing a common material property database and extracting required material data, lays a reliable foundation for subsequent simulation calculations, and avoids simulation deviations caused by missing or incorrect material data. By constructing accurate grinding wheel and workpiece models, and performing grid division and material property assignment, digital simulation of the real grinding contact area is realized, and accurate geometric and physical models are provided for subsequent contact analysis and dynamic simulation. Accurate contact area calculation provides key parameters for accurate energy consumption prediction. By setting reasonable simulation parameters and boundary conditions, and performing grinding dynamic energy consumption simulation, dynamic simulation of the grinding process is realized, and the dynamic changes of key parameters such as temperature field, stress field, friction force, material removal rate, etc. in the grinding process can be obtained, providing comprehensive data support for subsequent energy consumption calculation and analysis. The acquisition of thermal distribution data is the key to in-depth analysis of energy consumption influencing factors. By analyzing and calculating the thermal distribution data, the various energy consumptions in the grinding process can be accurately quantified, including friction energy consumption, cutting energy consumption, and motor drive energy consumption, and the total energy consumption data can be finally obtained. By analyzing the factors affecting energy consumption, key parameters affecting energy consumption can be identified, providing theoretical guidance for optimizing the grinding process and reducing energy consumption, and ultimately achieving the energy consumption simulation operation goal of the grinder, thereby improving the grinding efficiency and economic benefits. Therefore, the present invention provides an energy consumption simulation method suitable for a grinder, which introduces thermal-mechanical dynamic coupling solution into the modeling of the grinding process and comprehensively considers the influence of multi-source data on materials, processes, and energy consumption, thereby effectively solving the problem of unbalanced grinding energy consumption caused by insufficient consideration of thermal effects in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A schematic diagram of the steps of an energy consumption simulation method applicable to a grinder;

[0076] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0077] Figure 3 for Figure 2 Detailed implementation steps of step S34 are shown in the flowchart.

[0078] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0079] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0080] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0081] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0082] To achieve this, please refer to Figures 1 to 3 , an energy consumption simulation method suitable for a grinding machine, comprising the following steps:

[0083] Step S1: Obtain a list of commonly used materials for grinders; extract material attribute data according to the list of commonly used materials for grinders to obtain a material attribute data set;

[0084] Step S2: obtaining data of the workpiece to be polished and data of the grinding wheel of the polishing machine; using finite element analysis technology and a material property data set to construct a contact area mesh model for the workpiece to be polished data and the grinding wheel data of the polishing machine and assign material properties to obtain a mesh model assigned with material properties; calculating the contact area according to the mesh model assigned with material properties to obtain contact area data;

[0085] Step S3: setting simulation parameters according to the contact area data to obtain a simulation parameter setting table; performing grinding dynamic energy consumption simulation according to the simulation parameter setting table and the mesh model assigned with material properties to obtain grinding dynamic simulation result data; performing data post-processing on the grinding dynamic simulation result data to obtain thermal distribution data;

[0086] Step S4: Calculate the total energy consumption according to the thermal distribution data to obtain energy consumption data; analyze energy consumption control factors according to the energy consumption data and the thermal distribution data to realize the energy consumption simulation operation of the grinder.

[0087] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of the energy consumption simulation method applicable to a grinder according to the present invention. In this example, the energy consumption simulation method applicable to a grinder includes the following steps:

[0088] Step S1: Obtain a list of commonly used materials for grinders; extract material attribute data according to the list of commonly used materials for grinders to obtain a material attribute data set;

[0089] In the embodiment of the present invention, a list of commonly used materials for grinding machines is obtained, such as 45# steel, 304 stainless steel, etc., and its property data, including density, elastic modulus, Poisson's ratio, thermal conductivity, specific heat capacity, thermal expansion coefficient, yield strength and tensile strength, etc., are extracted from a material database (such as MatWeb) or a local database, and finally a data set (material_properties_dataset.csv) containing all required material properties is obtained. This provides the necessary material parameters for subsequent simulation calculations.

[0090] Step S2: obtaining data of the workpiece to be polished and data of the grinding wheel of the polishing machine; using finite element analysis technology and a material property data set to construct a contact area mesh model for the workpiece to be polished data and the grinding wheel data of the polishing machine and assign material properties to obtain a mesh model assigned with material properties; calculating the contact area according to the mesh model assigned with material properties to obtain contact area data;

[0091] In an embodiment of the present invention, the three-dimensional scanning data of the workpiece to be polished is obtained, and the workpiece surface model is reconstructed using reverse engineering software. At the same time, the geometric parameters of the grinding wheel are obtained, and the grinding wheel model is created using CAD software. The two models are imported into finite element analysis software (such as Abaqus), the contact area grid is divided, and the corresponding material properties are given to the grid according to the data in the material_properties_dataset.csv file. Finally, static analysis is performed by applying different contact pressures, and the contact area data (contact_area_data.csv) under different pressures is calculated. This establishes a workpiece and grinding wheel contact model for simulation.

[0092] Step S3: setting simulation parameters according to the contact area data to obtain a simulation parameter setting table; performing grinding dynamic energy consumption simulation according to the simulation parameter setting table and the mesh model assigned with material properties to obtain grinding dynamic simulation result data; performing data post-processing on the grinding dynamic simulation result data to obtain thermal distribution data;

[0093] In an embodiment of the present invention, based on the contact area data (contact_area_data.csv) and the preset simulation target (such as target material removal rate, target surface roughness), the simulation parameters are set, including the grinding wheel rotation speed, feed speed, grinding depth, grinding force, friction coefficient and ambient temperature, and these parameters are recorded in the simulation parameter setting table (simulation_parameters.csv). Then, a thermal coupling model is established using finite element software, boundary conditions (including grinding wheel rotation speed, workpiece feed speed, initial temperature, friction heat generation boundary conditions and workpiece fixed constraints) are applied, and grinding dynamic energy consumption simulation is performed. Finally, the simulation result data (simulation_results.odb) is post-processed to extract data such as temperature distribution, contact surface friction, stress distribution and material removal rate to form thermal distribution data. This simulates the thermodynamic behavior during the grinding process.

[0094] Step S4: Calculate the total energy consumption according to the thermal distribution data to obtain energy consumption data; analyze the energy consumption control factors according to the energy consumption data and the thermal distribution data to realize the energy consumption simulation operation of the grinder;

[0095] In an embodiment of the present invention, based on the thermal distribution data obtained in step S3, the friction energy consumption, cutting energy consumption and motor drive energy consumption are calculated. The friction energy consumption is calculated by the friction coefficient, normal force and relative sliding speed; the cutting energy consumption is calculated by cutting stress, material removal volume and strain increment; the motor drive energy consumption is calculated by cutting energy consumption and motor efficiency. These three parts of energy consumption are added together to obtain the total energy consumption data (total_energy.csv). Finally, the energy consumption data, thermal distribution data, simulation parameter setting table and material property data set are combined to analyze the influence of different parameters on energy consumption, and the analysis results are saved in the energy_analysis.txt file to optimize the grinding process parameters. This analyzes the energy consumption of the grinding process and provides optimization suggestions.

[0096] Preferably, step S1 comprises the following steps:

[0097] Step S11: obtaining a list of commonly used materials for grinders; searching for material properties according to the list of commonly used materials for grinders to obtain an initial material property table;

[0098] Step S12: performing data cleaning and standardization on the initial material attribute table to obtain a standardized material attribute table;

[0099] Step S13: constructing a database for the standardized material property table to obtain a material property database;

[0100] Step S14: Exporting a data set according to the material property database to obtain a material property data set.

[0101] In an embodiment of the present invention, first, a list containing commonly used grinding workpiece materials is established, for example: 45# steel, 304 stainless steel, aluminum alloy 6061, cast iron HT200, etc. The list is stored as a text file (materials.txt), with one material per line. Then, a Python script is written to read the materials.txt file. For each material in the list, the script automatically queries an online material database (such as MatWeb) or a local material database. The query content includes: density, elastic modulus, Poisson's ratio, thermal conductivity, specific heat capacity, thermal expansion coefficient, yield strength, tensile strength, etc. The query results are stored in a CSV file (material_properties_initial.csv) to form an initial table of material properties. The first line of the CSV file contains the property name, and each subsequent line corresponds to a material and its property value.

[0102] Read the material_properties_initial.csv file. Use Python's Pandas library for data cleaning. First, check whether there are missing values. If missing values ​​are found, process them according to the specific situation: if important attribute values ​​are missing, re-query the database to supplement; if it is a non-critical attribute or there are few missing values, use the average value or interpolation method to fill. Secondly, check the data type to ensure that the data type of all numerical attributes is floating point. Then, perform data standardization. Since the dimensions and value ranges of different attributes vary greatly, in order to avoid the excessive influence of certain attributes on the simulation results, the data needs to be standardized. Here, the Min-Max standardization method is used to scale the value of each attribute to the [0, 1] interval. The formula is: X_scaled = (X-X_min) / (X_max-X_min), where X is the original attribute value, X_scaled is the scaled attribute value, and X_min and X_max are the minimum and maximum values ​​of the attribute, respectively. Save the processed data in a new CSV file (material_properties_standardized.csv) to form a standardized material property table.

[0103] Use MySQL database management system. Create a database named "MaterialProperties". Create a table named "Materials" in the database. The fields in the table include: Material Name (MaterialName, VARCHAR (255), PRIMARY KEY), Density (Density, FLOAT), Elastic Modulus (ElasticModulus, FLOAT), Poisson's Ratio (PoissonRatio, FLOAT), Thermal Conductivity (ThermalConductivity, FLOAT), Specific Heat (SpecificHeat, FLOAT), Thermal Expansion Coefficient (ThermalExpansionCoefficient, FLOAT), Yield Strength (YieldStrength, FLOAT), Tensile Strength (TensileStrength, FLOAT), etc. Use Python script to read the material_properties_standardized.csv file and import the data into the Materials table.

[0104] According to the simulation requirements, export the property data of specific materials from the MaterialProperties database. For example, if it is necessary to simulate 45# steel and aluminum alloy 6061, write an SQL query statement: `SELECT * FROM Materials WHERE MaterialName = '45# steel' OR MaterialName = 'aluminum alloy 6061'`. Export the query results as a CSV file (material_properties_dataset.csv) to form a material property dataset. This dataset will be used for subsequent simulation calculations.

[0105] Preferably, step S2 includes the following steps:

[0106] Step S21: Obtain the data of the grinding wheel of the grinding machine; construct a geometric model of the grinding wheel based on the data of the grinding wheel to obtain a geometric model of the grinding wheel;

[0107] Step S22: Obtain the data of the workpiece to be ground; construct a surface model of the workpiece based on the data of the workpiece to be ground to obtain a surface model of the workpiece;

[0108] Step S23: Use finite element analysis technology to perform mesh division on the contact area of the geometric model of the grinding wheel and the surface model of the workpiece to obtain a contact area mesh model;

[0109] Step S24: Assign the material properties of the workpiece and the grinding wheel to the contact area mesh model according to the material property dataset to obtain a mesh model with material properties assigned;

[0110] Step S25: Apply contact pressure to the mesh model with material properties assigned to obtain a deformation model under different pressures; calculate the contact area of the deformation models under different pressures to obtain contact area data.

[0111] In the embodiment of the present invention, first, measure the geometric parameters of the grinding wheel, including diameter, thickness, abrasive type, grit size, binder type, etc. Record these parameters. Then, use CAD software (such as SolidWorks) to create a geometric model of the grinding wheel according to the measured parameters. The model accurately reflects the shape and size of the grinding wheel. To simplify the model, the grinding wheel is simplified to a uniform cylinder. Save the created geometric model of the grinding wheel as an STL format file (grinding_wheel.stl).

[0112] Use a 3D scanner to obtain the surface data of the workpiece to be polished and generate point cloud data. Import the point cloud data into reverse engineering software (such as Geomagic Design X) for point cloud processing, surface reconstruction, and model simplification. Generate a CAD model of the workpiece surface. Extract the workpiece surface portion that needs to be polished and save it as an STL format file (workpiece_surface.stl).

[0113] Import the grinding_wheel.stl and workpiece_surface.stl files into the finite element analysis software (such as Abaqus). Define the contact area between the grinding wheel and the workpiece. Perform meshing in the contact area. Use a hexahedral mesh and refine the mesh in the contact area to improve the calculation accuracy. Set the mesh size to 0.1mm. Generate the contact area mesh model (contact_mesh.inp).

[0114] Read the material property data of the grinding wheel and workpiece from the material_properties_dataset.csv file. In Abaqus, assign the read material property data to the mesh models of the grinding wheel and workpiece respectively. Make sure that each mesh unit is assigned the correct material properties. Save the mesh model with assigned material properties as a new file (material_assigned_mesh.inp).

[0115] In Abaqus, a series of different contact pressures are applied to the material_assigned_mesh.inp model, such as 1MPa, 2MPa, 3MPa, 4MPa, and 5MPa. For each pressure value, a static analysis is performed to calculate the deformation of the model. The deformed model under different pressures is obtained. Then, based on the deformed model, the contact area between the grinding wheel and the workpiece is calculated. The different pressure values ​​and the corresponding contact area data are stored in a CSV file (contact_area_data.csv), with the first column being the pressure value and the second column being the contact area.

[0116] Preferably, step S3 comprises the following steps:

[0117] Step S31: Acquire simulation target data; set simulation parameters according to the contact area data and the simulation target data to obtain a simulation parameter setting table;

[0118] Step S32: Establishing a thermal model according to the grid model assigned with material properties to obtain a thermal model;

[0119] Step S33: applying boundary conditions to the thermal model according to the simulation parameter setting table to obtain a model with applied boundary conditions;

[0120] Step S34: performing grinding dynamic energy consumption simulation on the model with applied boundary conditions to obtain grinding dynamic simulation result data;

[0121] Step S35: Post-process the grinding dynamic simulation result data to obtain thermal distribution data, wherein the thermal distribution data includes temperature distribution data, contact surface friction data, stress distribution data and material removal rate data.

[0122] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0123] Step S31: Acquire simulation target data; set simulation parameters according to the contact area data and the simulation target data to obtain a simulation parameter setting table;

[0124] In an embodiment of the present invention, first, the simulation target is clarified, for example: predicting energy consumption under specific grinding conditions, optimizing grinding parameters to reduce energy consumption, analyzing the grinding performance of different materials, etc. The simulation target is concretized, for example: setting the target material removal rate to 0.1mm / s, and the target surface roughness Ra to 0.8μm. Then, read the contact area data in the contact_area_data.csv file. In combination with the simulation target and the contact area data, the simulation parameters are set, including: grinding wheel speed (1500rpm), feed speed (5mm / s), grinding depth (0.5mm), grinding force (calculated based on contact area and pressure data), friction coefficient (0.3), and ambient temperature (25°C). These parameters are recorded in the simulation parameter setting table (simulation_parameters.csv), where each row represents a set of simulation parameters.

[0125] Step S32: Establishing a thermal model according to the grid model assigned with material properties to obtain a thermal model;

[0126] In the embodiment of the present invention, a thermomechanical coupling model is established in Abaqus based on the material_assigned_mesh.inp file. The model includes the meshes of the grinding wheel and the workpiece, and defines their contact relationship. The thermomechanical coupling analysis module is activated. The thermodynamic properties of the material are set, including thermal conductivity, specific heat capacity, density, etc., which are derived from the material_properties_dataset.csv file.

[0127] Step S33: applying boundary conditions to the thermal model according to the simulation parameter setting table to obtain a model with applied boundary conditions;

[0128] In the embodiment of the present invention, boundary conditions are applied to the thermal model in Abaqus according to the simulation parameter setting table simulation_parameters.csv, including: setting the rotation speed of the grinding wheel, setting the feed speed of the workpiece, setting the initial temperature field to the ambient temperature (25°C), applying friction heat generation boundary conditions on the contact surface, and setting the fixed constraints of the workpiece.

[0129] Step S34: performing grinding dynamic energy consumption simulation on the model with applied boundary conditions to obtain grinding dynamic simulation result data;

[0130] In the embodiment of the present invention, in Abaqus, an explicit dynamics analysis module is used to simulate the dynamic energy consumption of grinding. The simulation time step is set to 0.001 seconds. During the simulation process, the temperature field, stress field, contact surface friction, material removal amount and other data are calculated for each time step. The simulation results are saved as an odb file (simulation_results.odb).

[0131] Step S35: performing data post-processing on the grinding dynamic simulation result data to obtain thermal distribution data, wherein the thermal distribution data includes temperature distribution data, contact surface friction data, stress distribution data and material removal rate data;

[0132] In an embodiment of the present invention, the simulation_results.odb file is extracted and analyzed using the post-processing module of Abaqus. Temperature distribution data, contact surface friction data, stress distribution data, and material removal rate data are extracted. These data are exported as CSV files, for example: temperature_distribution.csv, contact_friction_data.csv, stress_distribution.csv, material_removal_rate.csv. These files constitute the thermal distribution data. The temperature distribution data includes the temperature value of each node; the contact surface friction data includes the friction force, friction heat, etc. on the contact surface; the stress distribution data includes the stress value of each node; and the material removal rate data includes the material removal amount at each time step.

[0133] Preferably, step S34 includes the following steps:

[0134] Step S341: dividing the model with the boundary conditions into time steps to obtain a time step grid model;

[0135] Step S342: Calculate the contact node temperature field according to the time step grid model to obtain the time step node temperature data; calculate the contact point stress field according to the time step grid model and the time step node temperature data to obtain the time step node stress data;

[0136] Step S343: performing heat transfer effect analysis according to the time step node temperature data and the time step node stress data to obtain heat transfer data;

[0137] Step S344: performing grinding force calculation according to the time step node stress data to obtain grinding force data; performing dynamic stress field evolution analysis according to the grinding force data and the time step node stress data to obtain dynamic stress field data;

[0138] Step S345: performing a dynamic evolution analysis of the grinding temperature field according to the heat transfer data, the time step mesh model and the dynamic stress field data to obtain dynamic temperature field data;

[0139] Step S346: performing thermal stress coupling analysis according to the dynamic temperature field data and the dynamic stress field data to obtain thermal stress coupling data;

[0140] Step S347: Analyze the grinding dynamic simulation results according to the thermal stress coupling data and the grinding force data to obtain the grinding dynamic simulation result data.

[0141] As an example of the present invention, refer to Figure 3 As shown, in this example, step S34 includes:

[0142] Step S341: dividing the model with the boundary conditions into time steps to obtain a time step grid model;

[0143] In the embodiment of the present invention, in the explicit dynamics analysis module of Abaqus, the time step is set to 0.001 seconds. The total simulation time is set to 0.1 seconds. This means that the simulation process will be divided into 100 time steps. Each time step will generate a time step mesh model to record the model state at that moment, including node coordinates, unit deformation and other information.

[0144] Step S342: Calculate the contact node temperature field according to the time step grid model to obtain the time step node temperature data; calculate the contact point stress field according to the time step grid model and the time step node temperature data to obtain the time step node stress data;

[0145] In the embodiment of the present invention, Abaqus calculates the temperature and stress of each node in the contact area between the grinding wheel and the workpiece at each time step. The calculation of the temperature field takes into account factors such as frictional heat generation and heat conduction. The calculation of the stress field takes into account factors such as contact pressure and material deformation. The node temperature data of each time step is saved in the temperature_field_*.csv file, and the node stress data is saved in the stress_field_*.csv file, where * represents the time step number.

[0146] Step S343: performing heat transfer effect analysis according to the time step node temperature data and the time step node stress data to obtain heat transfer data;

[0147] In an embodiment of the present invention, the heat transfer effect is analyzed based on the time step node temperature data and stress data. Calculate how the heat in the contact area is transferred between the grinding wheel, the workpiece and the surrounding environment by conduction, convection and radiation in each time step. Use the heat transfer module in Abaqus for calculation, considering parameters such as the thermal conductivity, specific heat capacity, and surface thermal emissivity of the material. Save the heat transfer data of each time step into the heat_transfer_*.csv file, which contains information such as the heat flux density and temperature gradient of each node.

[0148] Step S344: performing grinding force calculation according to the time step node stress data to obtain grinding force data; performing dynamic stress field evolution analysis according to the grinding force data and the time step node stress data to obtain dynamic stress field data;

[0149] In the embodiment of the present invention, the grinding force between the grinding wheel and the workpiece is calculated according to the node stress data of each time step. The grinding force is calculated based on the contact area and the contact pressure. The change of the grinding force over time and the influence of the grinding force on the stress field of the workpiece are analyzed. The stress analysis module in Abaqus is used to perform dynamic stress field evolution analysis, observe the distribution and change of the stress field, and save the results in the dynamic_stress_field_*.csv file.

[0150] Step S345: performing a dynamic evolution analysis of the grinding temperature field according to the heat transfer data, the time step mesh model and the dynamic stress field data to obtain dynamic temperature field data;

[0151] In the embodiment of the present invention, the dynamic evolution of the grinding temperature field is analyzed based on the heat transfer data, the time step mesh model and the dynamic stress field data. The influence of factors such as frictional heat generation, heat conduction, heat convection and heat radiation on the temperature field is considered. The temperature field analysis module in Abaqus is used for calculation, and the temperature field data of each time step is saved in the dynamic_temperature_field_*.csv file.

[0152] Step S346: performing thermal stress coupling analysis according to the dynamic temperature field data and the dynamic stress field data to obtain thermal stress coupling data;

[0153] In the embodiment of the present invention, thermal stress coupling analysis is performed based on dynamic temperature field data and dynamic stress field data. The influence of thermal expansion and contraction of materials caused by temperature changes on the stress field, as well as the influence of stress changes on the temperature field, are considered. The thermal stress coupling analysis module in Abaqus is used for calculation, and the coupling analysis results of each time step are saved in the thermal_stress_coupling_*.csv file.

[0154] Step S347: analyzing the grinding dynamic simulation results according to the thermal stress coupling data and the grinding force data to obtain the grinding dynamic simulation result data;

[0155] In the embodiment of the present invention, the energy consumption in the grinding process is analyzed based on the thermal stress coupling data and the grinding force data. The friction energy consumption, plastic deformation energy consumption, material removal energy consumption, etc. are calculated. The energy consumption data of each time step is saved in the energy_consumption_*.csv file. Finally, the simulation result data of all time steps are integrated to obtain the grinding dynamic simulation result data (simulation_results.odb).

[0156] Preferably, step S343 includes the following steps:

[0157] Step S3431: performing heat source generation calculation according to the time step node temperature data and the time step node stress data to obtain the time step heat source data;

[0158] Step S3432: updating the temperature field according to the time step heat source data to obtain the time step temperature field update data;

[0159] Step S3433: Optimize the heat conduction path according to the time step temperature field update data to obtain heat conduction path data; calculate the temperature gradient according to the time step temperature field update data to obtain temperature gradient data;

[0160] Step S3434: updating the heat transfer coefficient according to the temperature gradient data and the material property data set to obtain the heat transfer coefficient; performing heat flow calculation according to the temperature gradient data and the heat transfer coefficient to obtain heat flow data;

[0161] Step S3435: Generate heat transfer data according to the heat conduction path data, the temperature gradient data, the heat transfer coefficient and the heat flow data to obtain heat transfer data.

[0162] In the embodiment of the present invention, the node temperature data (temperature_field_*.csv) and the node stress data (stress_field_*.csv) of the current time step are read. The friction heat generation model, for example: Q = μ*F*v, where Q is the friction heat generation, μ is the friction coefficient, F is the contact pressure, and v is the relative sliding speed, is used to calculate the friction heat generation of each node in the contact area between the grinding wheel and the workpiece. The calculation results are saved in the heat_source_*.csv file, which contains the heat source intensity of each node.

[0163] Based on the time-step heat source data (heat_source_*.csv), the heat conduction equation is solved using the finite difference method or the finite element method to update the temperature field. Consider the material parameters such as thermal conductivity, specific heat capacity and density, which are read from the material_properties_dataset.csv file. Save the updated temperature field data to the updated_temperature_field_*.csv file, which contains the temperature value of each node.

[0164] According to the updated temperature field data (updated_temperature_field_*.csv), use the Dijkstra algorithm or other path search algorithms to calculate the best conduction path for heat from the high temperature area to the low temperature area. Save the calculation results to the heat_conduction_path_*.csv file, and record the node sequence on each path. At the same time, based on the updated temperature field data, calculate the temperature gradient of each node, that is, the rate of change of temperature in space. Save the temperature gradient data to the temperature_gradient_*.csv file, which contains the temperature gradient vector of each node.

[0165] Update the heat transfer coefficient based on the temperature gradient data (temperature_gradient_*.csv) and the material properties dataset (material_properties_dataset.csv). For example, the thermal conductivity of the material can be corrected based on the temperature gradient. Save the updated heat transfer coefficient to the heat_transfer_coefficient_*.csv file. Then, based on the temperature gradient data and the updated heat transfer coefficient, use Fourier's law to calculate the heat flow at each node, that is, the amount of heat passing through a unit area per unit time. Save the heat flow data to the heat_flow_*.csv file, which contains the heat flux density vector for each node.

[0166] Integrate the heat conduction path data (heat_conduction_path_*.csv), temperature gradient data (temperature_gradient_*.csv), updated heat transfer coefficient (heat_transfer_coefficient_*.csv) and heat flow data (heat_flow_*.csv) to generate the final heat transfer data (heat_transfer_*.csv). This file contains information such as temperature, temperature gradient, heat transfer coefficient and heat flux density of each node, which fully describes the heat transfer process of the current time step.

[0167] Preferably, step S345 includes the following steps:

[0168] Step S3451: performing friction heat generation analysis according to the dynamic stress field data and the material property data set to obtain friction heat generation data;

[0169] Step S3452: Calculate heat source distribution according to dynamic stress field data and heat transfer data to obtain heat source distribution data;

[0170] Step S3453: performing grinding temperature rise effect analysis according to the friction heat generation data and the heat source distribution data to obtain temperature rise effect data;

[0171] Step S3454: performing cumulative temperature field update according to the temperature rise effect data to obtain cumulative temperature field data;

[0172] Step S3455: Calculate the dynamic temperature field evolution of the grinding process based on the accumulated temperature field data to obtain dynamic temperature field data.

[0173] In an embodiment of the present invention, the dynamic stress field data (dynamic_stress_field_*.csv) and the material property data set (material_properties_dataset.csv) are read. Frictional heat generation is calculated based on the friction coefficient of the material, the grinding wheel rotation speed, the feed speed, and the contact pressure. The friction coefficient is obtained from the material property data set. The contact pressure is extracted from the dynamic stress field data. Using the frictional heat generation model, for example: Q = μ*F*v, where Q is the frictional heat generation, μ is the friction coefficient, F is the contact pressure, and v is the relative sliding speed, the frictional heat generation of each node in the contact area between the grinding wheel and the workpiece is calculated. The calculation results are saved in the friction_heat_generation_*.csv file, which contains the frictional heat generation rate of each node.

[0174] Read dynamic stress field data (dynamic_stress_field_*.csv) and heat transfer data (heat_transfer_*.csv). Calculate the distribution of heat source in the contact area between the grinding wheel and the workpiece based on the stress distribution and heat transfer. Consider factors such as frictional heat generation and plastic deformation heat generation. Save the calculation results to the heat_source_distribution_*.csv file, which contains the heat source intensity of each node.

[0175] Analyze the grinding temperature rise effect based on the friction heat generation data (friction_heat_generation_*.csv) and heat source distribution data (heat_source_distribution_*.csv). Calculate the temperature rise due to the heat generated by grinding. Consider heat dissipation factors such as heat conduction, heat convection and heat radiation. Save the calculation results to the temperature_rise_effect_*.csv file, which contains the temperature rise value of each node.

[0176] Read the cumulative temperature field data (cumulative_temperature_field_*.csv) of the previous time step and the temperature rise effect data (temperature_rise_effect_*.csv) of the current time step. Superimpose the temperature rise effect data on the cumulative temperature field data of the previous time step to obtain the cumulative temperature field data of the current time step. Save the calculation results to the cumulative_temperature_field_*.csv file, including the temperature value of each node. If the current time step is the first time step, use the initial temperature field data as the cumulative temperature field data.

[0177] Based on the cumulative temperature field data (cumulative_temperature_field_*.csv), calculate the dynamic temperature field evolution of the grinding process. Consider the influence of factors such as heat conduction, heat convection and heat radiation on the temperature field. Use the finite element method or finite difference method to solve the transient heat conduction equation. Save the calculation results to the dynamic_temperature_field_*.csv file, which contains the temperature value of each node and the temperature change over time.

[0178] Preferably, step S347 includes the following steps:

[0179] Step S3471: Calculate the material removal amount in different areas using a preset material removal rate model according to the thermal stress coupling data, the time step node stress data and the grinding force data to obtain the material removal rate data;

[0180] Step S3472: performing dynamic grinding energy analysis according to the friction heat generation data, grinding force data, and material removal rate data in the dynamic temperature field data to obtain grinding energy data;

[0181] Step S3473: Output simulation results based on the dynamic temperature field data, dynamic stress field data, friction heat generation data, material removal rate data, and grinding energy data to obtain grinding dynamic simulation result data.

[0182] In an embodiment of the present invention, thermal stress coupling data (thermal_stress_coupling_*.csv), time step node stress data (stress_field_*.csv) and grinding force data (grinding_force_*.csv) are read. A preset material removal rate model, such as the Usui model, the Shaw model or the Zhang model, is used, combined with parameters such as hardness and fracture toughness in the material property data (material_properties_dataset.csv), to calculate the material removal amount in different areas. The input parameters of the material removal rate model include equivalent stress, temperature, abrasive diameter, abrasive hardness, etc. The material removal amount data of each node is saved in the material_removal_rate_*.csv file, including the material removal volume or mass of each node.

[0183] Read the friction heat generation data, grinding force data (grinding_force_*.csv) and material removal rate data (material_removal_rate_*.csv) in the dynamic temperature field data (dynamic_temperature_field_*.csv). Calculate the energy consumption in the grinding process, including friction energy consumption, plastic deformation energy consumption and material removal energy consumption. Friction energy consumption is calculated by multiplying the friction force by the relative sliding velocity. Plastic deformation energy consumption is calculated by integrating stress and strain. Material removal energy consumption is calculated by multiplying the material removal volume by the material specific energy. Save the energy consumption data for each time step into the grinding_energy_*.csv file, including friction energy consumption, plastic deformation energy consumption and material removal energy consumption.

[0184] Integrate dynamic temperature field data (dynamic_temperature_field_*.csv), dynamic stress field data (dynamic_stress_field_*.csv), friction heat generation data (friction_heat_generation_*.csv), material removal rate data (material_removal_rate_*.csv), and grinding energy data (grinding_energy_*.csv) into the Abaqus output database (.odb) file. The .odb file contains the simulation results of all time steps and can be used for subsequent visualization analysis and data processing. In addition, key data can be exported as text files or other formats to facilitate user customized analysis. For example, the total energy consumption, average temperature, maximum stress, and other data for each time step can be exported to a summary table to quickly evaluate the efficiency and stability of the grinding process.

[0185] Preferably, step S4 comprises the following steps:

[0186] Step S41: Calculate the friction energy consumption according to the temperature distribution data and the contact friction data to obtain the friction energy consumption data;

[0187] Step S42: Calculating cutting energy consumption according to stress distribution data and material removal rate data to obtain cutting energy consumption data;

[0188] Step S43: Calculating the motor drive energy consumption according to the cutting energy consumption data and the simulation parameter setting table to obtain the motor drive energy consumption data;

[0189] Step S44: Calculate the total energy consumption according to the friction energy consumption data, the cutting energy consumption data and the motor drive energy consumption data to obtain energy consumption data;

[0190] Step S45: analyzing the factors affecting energy consumption according to the energy consumption data, the thermal distribution data, the simulation parameter setting table and the material property data set to realize the energy consumption simulation operation of the grinder.

[0191] In an embodiment of the present invention, the temperature distribution data (temperature_distribution.csv) and the contact friction data (contact_friction_data.csv) are read. The friction energy consumption calculation formula is: Ef = ∫μ*Fn*v dt, where Ef is the friction energy consumption, μ is the friction coefficient (obtained from the material property data set material_properties_dataset.csv), Fn is the normal contact force (obtained from contact_friction_data.csv), v is the relative sliding speed (calculated by the grinding wheel rotation speed and the workpiece feed speed), and dt is the time step. The friction energy consumption of each time step in the entire simulation time period is integrated to obtain the total friction energy consumption. The friction energy consumption data is saved in the friction_energy.csv file.

[0192] Read stress distribution data (stress_distribution.csv) and material removal rate data (material_removal_rate.csv). The cutting energy consumption calculation formula is: Ec = ∫σ*V*dεdt, where Ec is the cutting energy consumption, σ is the cutting stress (obtained from stress_distribution.csv), V is the material removal volume (obtained from material_removal_rate.csv), dε is the strain increment, and dt is the time step. Integrate the cutting energy consumption of each time step during the entire simulation time period to obtain the total cutting energy consumption. Save the cutting energy consumption data in the cutting_energy.csv file.

[0193] Read the cutting energy consumption data (cutting_energy.csv) and the simulation parameter setting table (simulation_parameters.csv). The motor drive energy consumption calculation formula is: Em = Ec / η, where Em is the motor drive energy consumption, Ec is the cutting energy consumption, and η is the motor efficiency (obtained from the simulation parameter setting table, assuming that the motor efficiency is a constant of 0.85). Save the calculated motor drive energy consumption in the motor_energy.csv file.

[0194] Read the friction energy consumption data (friction_energy.csv), cutting energy consumption data (cutting_energy.csv) and motor drive energy consumption data (motor_energy.csv). The total energy consumption calculation formula is: Et = Ef + Em, where Et is the total energy consumption, Ef is the friction energy consumption, and Em is the motor drive energy consumption (including cutting energy consumption). Save the calculated total energy consumption in the total_energy.csv file.

[0195] Read energy consumption data (total_energy.csv), thermal distribution data (temperature_distribution.csv, contact_friction_data.csv, stress_distribution.csv, material_removal_rate.csv), simulation parameter setting table (simulation_parameters.csv) and material property data set (material_properties_dataset.csv). Analyze the impact of different parameters (such as grinding wheel speed, feed rate, grinding depth, material properties, etc.) on energy consumption. Data analysis methods such as regression analysis and variance analysis can be used to quantify the contribution of different parameters to energy consumption. Save the analysis results to the energy_analysis.txt file so that users can understand the main influencing factors of energy consumption and provide guidance for optimizing grinding process parameters.

[0196] Preferably, the present invention further provides an energy consumption simulation system applicable to a grinder, which is used to execute the energy consumption simulation method applicable to a grinder as described above. The energy consumption simulation system applicable to a grinder comprises:

[0197] The material property analysis module is used to obtain a list of commonly used materials for grinders; material property data is extracted based on the list of commonly used materials for grinders to obtain a material property data set;

[0198] The contact model building module is used to obtain the data of the workpiece to be polished and the grinding wheel data of the polishing machine; the contact area mesh model is constructed for the workpiece data to be polished and the grinding wheel data of the polishing machine using the finite element analysis technology and the material property data set, and the material property is assigned to obtain the mesh model with material properties; the contact area is calculated according to the mesh model with material properties to obtain the contact area data;

[0199] The thermal-mechanical coupling simulation module is used to set simulation parameters according to the contact area data to obtain a simulation parameter setting table; perform grinding dynamic energy consumption simulation according to the simulation parameter setting table and a mesh model assigned with material properties to obtain grinding dynamic simulation result data; perform data post-processing on the grinding dynamic simulation result data to obtain thermal distribution data;

[0200] The energy consumption calculation and optimization module is used to calculate the total energy consumption according to the thermal distribution data to obtain the energy consumption data; analyze the energy consumption control factors according to the energy consumption data and the thermal distribution data to realize the energy consumption simulation operation of the grinder.

[0201] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0202] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. 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 invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method and system for simulating energy consumption of a grinding machine, characterized in that: The following steps are involved: Step S1: Obtain a list of commonly used materials for grinders; extract material attribute data according to the list of commonly used materials for grinders to obtain a material attribute data set; Step S2: obtaining data of the workpiece to be polished and data of the grinding wheel of the polishing machine; using finite element analysis technology and a material property data set to construct a contact area mesh model for the workpiece to be polished data and the grinding wheel data of the polishing machine and assign material properties to obtain a mesh model assigned with material properties; calculating the contact area according to the mesh model assigned with material properties to obtain contact area data; Step S3: setting simulation parameters according to the contact area data to obtain a simulation parameter setting table; According to the simulation parameter setting table and the mesh model with material properties, the grinding dynamic energy consumption simulation is performed to obtain the grinding dynamic simulation result data; Post-process the grinding dynamic simulation result data to obtain thermal distribution data; Step S4: Calculate the total energy consumption according to the thermal distribution data to obtain energy consumption data; analyze energy consumption control factors according to the energy consumption data and the thermal distribution data to realize the energy consumption simulation operation of the grinder.

2. The energy consumption simulation method for a grinding machine according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining a list of commonly used materials for grinders; searching for material properties according to the list of commonly used materials for grinders to obtain an initial material property table; Step S12: performing data cleaning and standardization on the initial material attribute table to obtain a standardized material attribute table; Step S13: constructing a database for the standardized material property table to obtain a material property database; Step S14: Exporting a data set according to the material property database to obtain a material property data set.

3. The energy consumption simulation method for a grinding machine according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: obtaining grinding wheel data of a grinder; constructing a grinding wheel geometric model based on the grinding wheel data to obtain a grinding wheel geometric model; Step S22: Acquire data of the workpiece to be polished; construct a workpiece surface model based on the data of the workpiece to be polished to obtain a workpiece surface model; Step S23: using finite element analysis technology to perform contact area mesh division on the grinding wheel geometric model and the workpiece surface model to obtain a contact area mesh model; Step S24: assigning material properties of the workpiece and the grinding wheel to the contact area mesh model according to the material property data set to obtain a mesh model assigned with material properties; Step S25: applying contact pressure to the mesh model assigned with material properties to obtain deformation models under different pressures; calculating contact areas of the deformation models under different pressures to obtain contact area data.

4. The energy consumption simulation method for a grinding machine according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Acquire simulation target data; set simulation parameters according to the contact area data and the simulation target data to obtain a simulation parameter setting table; Step S32: Establishing a thermal model according to the grid model assigned with material properties to obtain a thermal model; Step S33: applying boundary conditions to the thermal model according to the simulation parameter setting table to obtain a model with applied boundary conditions; Step S34: performing grinding dynamic energy consumption simulation on the model with applied boundary conditions to obtain grinding dynamic simulation result data; Step S35: Post-process the grinding dynamic simulation result data to obtain thermal distribution data, wherein the thermal distribution data includes temperature distribution data, contact surface friction data, stress distribution data and material removal rate data.

5. The energy consumption simulation method for a grinding machine according to claim 4, characterized in that: Step S34 includes the following steps: Step S341: dividing the model with the boundary conditions into time steps to obtain a time step grid model; Step S342: Calculate the contact node temperature field according to the time step grid model to obtain the time step node temperature data; calculate the contact point stress field according to the time step grid model and the time step node temperature data to obtain the time step node stress data; Step S343: performing heat transfer effect analysis according to the time step node temperature data and the time step node stress data to obtain heat transfer data; Step S344: performing grinding force calculation according to the time step node stress data to obtain grinding force data; performing dynamic stress field evolution analysis according to the grinding force data and the time step node stress data to obtain dynamic stress field data; Step S345: performing a dynamic evolution analysis of the grinding temperature field according to the heat transfer data, the time step mesh model and the dynamic stress field data to obtain dynamic temperature field data; Step S346: performing thermal stress coupling analysis according to the dynamic temperature field data and the dynamic stress field data to obtain thermal stress coupling data; Step S347: Analyze the grinding dynamic simulation results according to the thermal stress coupling data and the grinding force data to obtain the grinding dynamic simulation result data.

6. The energy consumption simulation method for a grinding machine according to claim 5, characterized in that: Step S343 includes the following steps: Step S3431: performing heat source generation calculation according to the time step node temperature data and the time step node stress data to obtain the time step heat source data; Step S3432: updating the temperature field according to the time step heat source data to obtain the time step temperature field update data; Step S3433: Optimize the heat conduction path according to the time step temperature field update data to obtain heat conduction path data; calculate the temperature gradient according to the time step temperature field update data to obtain temperature gradient data; Step S3434: updating the heat transfer coefficient according to the temperature gradient data and the material property data set to obtain the heat transfer coefficient; performing heat flow calculation according to the temperature gradient data and the heat transfer coefficient to obtain heat flow data; Step S3435: Generate heat transfer data according to the heat conduction path data, the temperature gradient data, the heat transfer coefficient and the heat flow data to obtain heat transfer data.

7. The energy consumption simulation method for a grinding machine according to claim 5, characterized in that: Step S345 includes the following steps: Step S3451: performing friction heat generation analysis according to the dynamic stress field data and the material property data set to obtain friction heat generation data; Step S3452: Calculate heat source distribution according to dynamic stress field data and heat transfer data to obtain heat source distribution data; Step S3453: performing grinding temperature rise effect analysis according to the friction heat generation data and the heat source distribution data to obtain temperature rise effect data; Step S3454: performing cumulative temperature field update according to the temperature rise effect data to obtain cumulative temperature field data; Step S3455: Calculate the dynamic temperature field evolution of the grinding process based on the accumulated temperature field data to obtain dynamic temperature field data.

8. The energy consumption simulation method for a grinding machine according to claim 5, characterized in that: Step S347 includes the following steps: Step S3471: Calculate the material removal amount in different areas using a preset material removal rate model according to the thermal stress coupling data, the time step node stress data and the grinding force data to obtain the material removal rate data; Step S3472: performing dynamic grinding energy analysis according to the friction heat generation data, grinding force data, and material removal rate data in the dynamic temperature field data to obtain grinding energy data; Step S3473: Output simulation results based on the dynamic temperature field data, dynamic stress field data, friction heat generation data, material removal rate data, and grinding energy data to obtain grinding dynamic simulation result data.

9. The energy consumption simulation method for a grinding machine according to claim 4, characterized in that: Step S4 includes the following steps: Step S41: Calculate the friction energy consumption according to the temperature distribution data and the contact friction data to obtain the friction energy consumption data; Step S42: Calculating cutting energy consumption according to stress distribution data and material removal rate data to obtain cutting energy consumption data; Step S43: Calculating the motor drive energy consumption according to the cutting energy consumption data and the simulation parameter setting table to obtain the motor drive energy consumption data; Step S44: Calculate the total energy consumption according to the friction energy consumption data, the cutting energy consumption data and the motor drive energy consumption data to obtain energy consumption data; Step S45: analyzing the factors affecting energy consumption according to the energy consumption data, the thermal distribution data, the simulation parameter setting table and the material property data set to realize the energy consumption simulation operation of the grinder.

10. An energy consumption simulation system suitable for a grinding machine, characterized in that: Used to execute the energy consumption simulation method applicable to a grinder as claimed in claim 1, the energy consumption simulation system applicable to a grinder comprises: The material property analysis module is used to obtain a list of commonly used materials for grinders; material property data is extracted based on the list of commonly used materials for grinders to obtain a material property data set; The contact model building module is used to obtain the data of the workpiece to be polished and the grinding wheel data of the polishing machine; the contact area mesh model is constructed for the workpiece data to be polished and the grinding wheel data of the polishing machine using the finite element analysis technology and the material property data set, and the material property is assigned to obtain the mesh model with material properties; the contact area is calculated according to the mesh model with material properties to obtain the contact area data; The thermal-mechanical coupling simulation module is used to set simulation parameters according to the contact area data to obtain a simulation parameter setting table; perform grinding dynamic energy consumption simulation according to the simulation parameter setting table and a mesh model assigned with material properties to obtain grinding dynamic simulation result data; perform data post-processing on the grinding dynamic simulation result data to obtain thermal distribution data; The energy consumption calculation and optimization module is used to calculate the total energy consumption according to the thermal distribution data to obtain the energy consumption data; analyze the energy consumption control factors according to the energy consumption data and the thermal distribution data to realize the energy consumption simulation operation of the grinder.

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