Energy efficiency evaluation method based on high-precision grinding machine system

Abstract

The invention discloses an energy efficiency evaluation method based on a high-precision grinding machine system, and relates to the technical field of grinding machine energy efficiency evaluation, and the method comprises the following steps: a) collecting force and displacement data generated in the operation process of a high-precision grinding machine through a force position monitoring analysis system based on a GTM module; according to the energy efficiency evaluation method based on the high-precision grinding machine system, the energy consumption efficiency is obtained through integral calculation of effective output energy and total input energy; a correction coefficient is introduced to correct the effective output energy and the total input energy, so that the calculation result is more accurate; by analyzing and determining experimental data of different workpiece materials and grinding process parameter combinations, the real energy efficiency of the grinding machine system under different working conditions is accurately reflected, and an accurate and real energy efficiency evaluation result is obtained; and optimization of the operation performance of the grinding machine system is promoted, and the production efficiency and the energy efficiency ratio are improved.

Description

Technical Field

[0001] The invention relates to the technical field of energy efficiency evaluation of grinders, and in particular to an energy efficiency evaluation method based on a high-precision grinder system. Background Art

[0002] Traditional high-precision grinders may have problems of energy waste and low production efficiency during operation. These systems consume a lot of energy during operation. If this energy cannot be effectively utilized, it will not only lead to increased costs, but also have a negative impact on the environment. Therefore, a method that can accurately monitor the energy consumption of high-precision grinders and effectively evaluate it has become a problem that needs to be solved urgently. In order to improve the energy efficiency and production efficiency of the grinding machine system, this patent proposes an energy efficiency evaluation method based on a high-precision grinder system. Summary of the invention

[0003] In order to solve the above technical problems, the present invention is implemented by the following technical solutions: an energy efficiency evaluation method based on a high-precision grinding machine system, comprising the following steps: a) The force and displacement data generated by the high-precision grinding machine during operation are collected through the force and position monitoring and analysis system based on the GTM module; b) Obtain and monitor energy consumption parameters during the grinding process through multiple sensor modules, the energy consumption parameters include real-time power, torque and position change, wherein the sensor module includes an encoder, a grating scale, a displacement sensor and a pressure sensor, which are used to detect the position, pressure and rotation angle of the grinder in real time; wherein, the real-time power is calculated based on the current and voltage during the grinding process through the combined measurement of the pressure sensor and the displacement sensor; the torque is measured by the encoder installed on the spindle; the position change is measured by the grating scale and the displacement sensor to accurately track the position adjustment during the grinding process c) The collected force and displacement data and energy consumption parameters are stored as energy consumption data in a data file in CSV or TXT format by automatic curve saving, and transmitted to an external programmable logic controller PLC or computer PC through I / OTCP / IP, ModbusTCP and FTP protocols; each set of data can contain parameters from 64 different channels; d) Use statistical analysis function to calculate energy consumption parameters, force and displacement data, generate energy consumption efficiency curve, analyze the energy efficiency of the system, obtain analysis results, give energy efficiency evaluation report based on the analysis results, and display the analysis results and evaluation report through the industrial control integrated machine; the energy efficiency evaluation report includes energy consumption analysis of oil press, manual press, double-station pneumatic press and pneumatic press; e) Combine historical data to calibrate energy efficiency, including force sensor calibration, displacement sensor calibration, angle sensor calibration and torque sensor calibration.

[0004] Preferably, during the acquisition and monitoring of energy consumption parameters in the grinding process, synchronous sensor data acquisition is achieved through four channel signal sensor interfaces, and each channel supports a 64-channel acquisition configuration of 4×8×2 with a sampling frequency of 4KHz; wherein the 64-channel acquisition configuration represents 4 main groups, each group has 8 subgroups, and each subgroup has 2 data streams; multi-channel sampling technology is adopted with a sampling rate of 4KHz to ensure accurate acquisition of energy consumption data in the grinding process and to ensure the real-time and reliability of the data.

[0005] Preferably, according to the grinding principle that effective output energy is related to grinding force and displacement, the energy consumed by material removal during the grinding process is calculated by integration using the collected force and displacement data and energy consumption parameters to obtain energy consumption efficiency. The calculation steps are as follows: S1: Calculate the effective output energy: During the sampling period, the effective output energy is calculated by multiplying the grinding force and the displacement, and then the total effective output energy is obtained by integrating the entire grinding process; S2: Calculate total input energy: Based on the real-time power data, calculate the total input energy by integrating the power data over time; S3: Calculate energy consumption efficiency: Based on the effective output energy and the total input energy, calculate the energy efficiency value at each sampling time point through the ratio of the effective output energy to the total input energy to obtain the energy consumption efficiency.

[0006] Preferably, the energy consumption efficiency curve is generated as follows: Select Matplotlib in Python as the visualization software, create a new drawing area in the selected tool, and set the size and background color of the drawing area so that it can be clearly displayed when drawing curves later; set the coordinate axis, with the horizontal axis as the time axis and the energy consumption efficiency as the vertical axis to establish a coordinate system: According to the time span of data acquisition, determine the time axis scale range, from the grinding start time t0 to the end time t n ; Determine the scale interval of the time axis to clearly display the distribution of data points over time; set it according to the range of energy consumption efficiency values ​​calculated, from the minimum efficiency value To the maximum efficiency , determine the scale interval so that the curve can be displayed completely and clearly within the range of the vertical axis; add the label "time" to the horizontal axis; add the label "energy consumption efficiency" to the vertical axis; The energy consumption efficiency value corresponding to each sampling time point calculated is plotted in a coordinate system in a drawing area to form data points, and each of the plotted data points is sequentially connected using a smooth curve to form an energy consumption efficiency curve; Using the same operation as above, a force / displacement coordinate system is established with force as the horizontal axis and displacement as the vertical axis. The force and displacement data corresponding to each sampling time point are plotted in the force / displacement coordinate system in the drawing area, and a smooth curve is used to connect the plotted points in sequence to form a force / displacement curve. The same operation is performed to obtain the force / time curve, torque / time curve, and torque / angle curve.

[0007] Preferably, the energy efficiency of the system is analyzed by: Trend analysis: Observe the overall trend of the energy consumption efficiency curve, determine the changes in the system's energy efficiency during the entire grinding process, and obtain key features; if the key features of the curve show a downward trend, it means that as the grinding time increases, the system has problems of increased energy loss or reduced efficiency; if the key features of the curve fluctuate, it means that the system is unstable and there are frequent changes in factors affecting energy efficiency; Analyze the performance of energy efficiency in the rough grinding and fine grinding stages, and determine the energy utilization characteristics of each stage; Under standard conditions: the rough grinding stage has low energy efficiency due to large grinding force and fast feed rate; the fine grinding stage has high energy efficiency. If it does not improve, the reasons need to be analyzed; Parameter correlation analysis: Analyze the correlation between force, displacement data and energy consumption parameters and energy efficiency curves; calculate the correlation coefficient between each parameter and energy efficiency, determine the strength of the correlation, and obtain the main conclusions; including that higher grinding forces may increase material removal rates to a certain extent, but if the force is too large, it may lead to reduced energy efficiency. This relationship can be quantified through correlation analysis; Compare the current energy efficiency curve and calculation results with the energy efficiency under the design standard of the grinding machine system to determine the gap between the actual performance and the expected performance. At the same time, combine historical data to analyze whether there is a deterioration trend or abnormal change in the system energy efficiency to obtain the difference. If the current energy efficiency is significantly lower than the historical average, it will prompt the system to perform maintenance on tool wear and poor lubrication, as well as process optimization in adjusting grinding parameters. Summarize the calculation results of energy consumption efficiency, key features of curve analysis, main conclusions of parameter correlation analysis and differences compared with standards or historical data; the calculation results include energy efficiency values ​​at different stages and average energy efficiency; the key features of curve analysis include the time and value of efficiency peak and valley values, and the turning point of trend change; the main conclusions of parameter correlation analysis include the parameters with the strongest correlation with energy efficiency and their correlation coefficients; express the analysis results of system energy efficiency in clear and concise language; including, through good, general and poor evaluation of the current energy efficiency status of the system, pointing out the main factors affecting energy efficiency; make improvement suggestions based on the analysis results, including adjusting process parameters, checking and maintaining sensors, and optimizing and upgrading equipment to improve the energy efficiency of the system; Among them, adjusting process parameters includes optimizing grinding speed and feed rate; maintaining sensors to ensure accurate measurement; optimizing and upgrading equipment; and improving motor performance to reduce energy consumption.

[0008] Preferably, the energy consumption efficiency is calculated: First calculate the effective output energy: Internal, effective output energy Equal to grinding force With displacement The product of Indicates that during the sampling period The effective output energy within ; Integrate the entire grinding process to obtain the total effective output energy; ;in, is the number of sampling points, Indicates Sampling time points; Total input energy calculation: Assume the real-time power is , then the total input energy is: ;in, is the total time of the grinding process; Energy consumption efficiency calculation: Energy consumption efficiency It is defined as the ratio of total effective output energy to total input energy, that is, ; According to the above calculation and , calculate the energy efficiency value at each sampling time point.

[0009] Preferably, based on the material properties of the workpiece and the grinding process parameters, a correction coefficient is introduced, and the corrected total effective output energy calculation formula is: ; Among them, k is the correction factor, and the loss correction factor is obtained by performing an energy balance test on the grinding machine system. , introducing the loss correction factor , the corrected total input energy calculation formula is: ; Preferably, the correction factor : For each combination of workpiece material and grinding process parameters, the measured material removal amount is converted into actual effective output energy; then, the actual effective output energy is compared with the theoretical effective output energy calculated from the force and displacement data, and a preliminary value of the correction factor is calculated. ,in represents the type of workpiece material, j represents the combination of grinding process parameters; the calculation formula for the preliminary value of the correction coefficient is: ; in It is The actual effective output energy of the material under the process parameter combination; is the corresponding theoretical effective output energy; Then, data analysis is performed on the calculated preliminary values ​​of the correction coefficients.

[0010] Preferably, data analysis is performed on the calculated preliminary value of the correction coefficient, and the process is as follows: Firstly, the variation pattern of the correction coefficient under different workpiece materials and grinding process parameters is observed to determine whether there is an obvious correlation and obtain the pattern; including finding that the correction coefficient increases with the increase of the hardness of the workpiece material and decreases with the increase of the grinding speed. Then, the preliminary value of the correction coefficient is linearly regressed according to the pattern, and a mathematical model between the correction coefficient and the workpiece material characteristics and grinding process parameters is established. Finally, the final correction coefficient value is determined according to the established mathematical model.

[0011] Preferably, a linear regression process is performed on the preliminary value of the correction coefficient according to the rule, and a mathematical model between the correction coefficient and the material properties of the workpiece and the grinding process parameters is established. The process is as follows: Linear regression model: Based on the linear relationship between the correction coefficient k and the workpiece material characteristics and grinding process parameters: Assume the workpiece material characteristic parameters: ; Wherein, m represents the number of workpiece material characteristic parameters that affect the correction coefficient k; Grinding process parameters: ; Where n represents the number of grinding process parameters that affect the correction coefficient k; The formula is: ; in, is the regression coefficient to be determined, is the random error term; Features For the experimental data of Experimental data ; The corresponding correction coefficient is initially ; The characteristic parameter value of the workpiece material is ; The grinding process parameter value is ; Objective function construction: The least squares method is used to estimate the regression coefficients. The goal is to minimize the sum of squared errors SSE. The expression of the sum of squared errors is: ; in, is the correction coefficient of the i-th group of data predicted by the regression model, that is: ; Regression coefficient solution formula: In order to minimize SSE, , respectively, find the partial derivatives and set them equal to 0, and obtain the following normal equations: ; Solving this normal system of equations yields the regression coefficients Estimated value of ; Final correction coefficient formula: After determining the regression coefficient, the calculation formula for the final correction coefficient k is: ; in and It is the corresponding workpiece material characteristic parameters and grinding process parameter values ​​in practical applications; Determine the values ​​of m and n based on the specific selected workpiece material characteristic parameters and grinding process parameters, and solve the regression coefficient based on experimental data , thereby obtaining the correction factor for calculating the effective output energy.

[0012] By establishing the energy balance equation, the energy balance test of the grinding machine system is carried out to obtain the loss correction coefficient : The basic form of the energy balance equation: In the grinding machine system, the energy balance equation is expressed as: Total input energy Equal to the effective output energy , various energy losses within the system The sum of ; The industrial control integrated machine has multiple display modes, including real-time curve display, statistical analysis result display and remote diagnosis function, which is used by users to monitor and optimize energy consumption in real time; among which, the real-time curve display includes force / displacement curve, force / time curve, torque / time curve and torque angle curve.

[0013] The present invention provides an energy efficiency evaluation method based on a high-precision grinding machine system, which has the following beneficial effects: The energy efficiency evaluation method based on the high-precision grinding machine system evaluates the energy efficiency of the system by real-time monitoring the operating status of the high-precision grinding machine system, including force and displacement data, and energy consumption parameters, and uses the collected force and displacement data and energy consumption parameters to calculate the effective output energy and the total input energy through integration, and then obtains the energy consumption efficiency; and introduces a correction coefficient to correct the effective output energy and the total input energy to make the calculation result more accurate; and analyzes and determines the experimental data of different workpiece materials and grinding process parameter combinations, and comprehensively considers the influence of different actual factors on the energy efficiency calculation, so as to accurately reflect the real energy efficiency of the grinding machine system under different working conditions; obtain accurate and real energy efficiency evaluation results; and then promote the optimization of the operating performance of the grinding machine system, improve production efficiency and energy efficiency ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a flowchart of an energy efficiency evaluation method based on a high-precision grinding machine system. DETAILED DESCRIPTION

[0015] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0016] like Figure 1 As shown, the present invention provides a technical solution: an energy efficiency evaluation method based on a high-precision grinding machine system, comprising the following steps: a) The force and displacement data generated by the high-precision grinding machine during operation are collected through the force and position monitoring and analysis system based on the GTM module; b) Obtain and monitor energy consumption parameters during the grinding process through multiple sensor modules, the energy consumption parameters include real-time power, torque and position change, wherein the sensor module includes an encoder, a grating scale, a displacement sensor and a pressure sensor, which are used to detect the position, pressure and rotation angle of the grinder in real time; wherein, the real-time power is calculated based on the current and voltage during the grinding process through the combined measurement of the pressure sensor and the displacement sensor; the torque is measured by the encoder installed on the spindle; the position change is measured by the grating scale and the displacement sensor to accurately track the position adjustment during the grinding process c) The collected force and displacement data and energy consumption parameters are stored as energy consumption data in a TXT format data file by automatic curve saving, and transmitted to an external programmable logic controller computer PC through I / OTCP / IP, ModbusTCP and FTP protocols; each set of data can contain parameters from 64 different channels; d) Use statistical analysis function to calculate energy consumption parameters, force and displacement data, generate energy consumption efficiency curve, analyze the energy efficiency of the system, obtain analysis results, give energy efficiency evaluation report based on the analysis results, and display the analysis results and evaluation report through the industrial control integrated machine; the energy efficiency evaluation report includes energy consumption analysis of oil press, manual press, double-station pneumatic press and pneumatic press; e) Combine historical data to calibrate energy efficiency, including force sensor calibration, displacement sensor calibration, angle sensor calibration and torque sensor calibration.

[0017] During the acquisition and monitoring of energy consumption parameters in the grinding process, sensor synchronous data acquisition is realized through four-channel signal sensor interfaces. Each channel supports a 64-channel acquisition configuration of 4×8×2 with a sampling frequency of 4KHz. Among them, the 64-channel acquisition configuration represents 4 main groups, each group has 8 subgroups, and each subgroup has 2 data streams. Multi-channel sampling technology is used with a sampling rate of 4KHz to ensure the accurate acquisition of energy consumption data in the grinding process and to ensure the real-time and reliability of the data.

[0018] According to the grinding principle, the effective output energy is related to the grinding force and displacement. The collected force and displacement data and energy consumption parameters are used to calculate the energy consumed by material removal during the grinding process through integration to obtain the energy consumption efficiency. The calculation steps are as follows: S1: Calculate the effective output energy: During the sampling period, the effective output energy is calculated by multiplying the grinding force and the displacement, and then the total effective output energy is obtained by integrating the entire grinding process; S2: Calculate total input energy: Based on the real-time power data, calculate the total input energy by integrating the power data over time; S3: Calculate energy consumption efficiency: Based on the effective output energy and the total input energy, calculate the energy efficiency value at each sampling time point through the ratio of the total effective output energy to the total input energy to obtain the energy consumption efficiency.

[0019] To generate the energy consumption efficiency curve, the process is as follows: Select Matplotlib in Python as the visualization software, create a new drawing area in the selected tool, and set the size and background color of the drawing area so that it can be clearly displayed when drawing curves later; set the coordinate axis, with the horizontal axis as the time axis and the energy consumption efficiency as the vertical axis to establish a coordinate system: According to the time span of data acquisition, determine the time axis scale range, from the grinding start time t0 to the end time t n ; Determine the scale interval of the time axis to clearly display the distribution of data points over time; set it according to the range of energy consumption efficiency values ​​calculated, from the minimum efficiency value To the maximum efficiency , determine the scale interval so that the curve can be displayed completely and clearly within the range of the vertical axis; add the label "time" to the horizontal axis; add the label "energy consumption efficiency" to the vertical axis; The energy consumption efficiency value corresponding to each sampling time point calculated is plotted in a coordinate system in a drawing area to form data points, and each of the plotted data points is sequentially connected using a smooth curve to form an energy consumption efficiency curve; Using the same operation as above, a force / displacement coordinate system is established with force as the horizontal axis and displacement as the vertical axis. The force and displacement data corresponding to each sampling time point are plotted in the force / displacement coordinate system in the drawing area, and a smooth curve is used to connect the plotted points in sequence to form a force / displacement curve. The same operation is performed to obtain the force / time curve, torque / time curve, and torque / angle curve.

[0020] Analyze the energy efficiency of the system. The process is: Trend analysis: Observe the overall trend of the energy consumption efficiency curve, determine the changes in the system's energy efficiency during the entire grinding process, and obtain key features; if the key features of the curve show a downward trend, it means that as the grinding time increases, the system has problems of increased energy loss or reduced efficiency; if the key features of the curve fluctuate, it means that the system is unstable and there are frequent changes in factors affecting energy efficiency; Analyze the performance of energy efficiency in the rough grinding and fine grinding stages, and determine the energy utilization characteristics of each stage; Under standard conditions: the rough grinding stage has low energy efficiency due to large grinding force and fast feed rate; the fine grinding stage has high energy efficiency. If it does not improve, the reasons need to be analyzed; Parameter correlation analysis: Analyze the correlation between force, displacement data and energy consumption parameters and energy efficiency curves; calculate the correlation coefficient between each parameter and energy efficiency, determine the strength of the correlation, and obtain the main conclusions; including that higher grinding forces may increase material removal rates to a certain extent, but if the force is too large, it may lead to reduced energy efficiency. This relationship can be quantified through correlation analysis; Analyze the impact of torque changes on energy efficiency. Excessive torque may mean that the motor load is too large and energy consumption increases. Observe whether there is a synchronous relationship between torque fluctuations and energy efficiency fluctuations to determine whether torque is a key factor affecting energy efficiency. Analyze the impact of worktable movement speed and grinding wheel feed position on energy efficiency. Unreasonable position adjustment may lead to increased idle stroke or uneven grinding, thereby affecting energy utilization efficiency. Compare the current energy efficiency curve and calculation results with the design standard of the grinding machine system or the energy efficiency under ideal conditions to determine the gap between actual performance and expectations. If the actual energy efficiency is much lower than the design standard, there may be equipment failure, unreasonable process parameters or sensor error problems. Compare the current energy efficiency curve and calculation results with the energy efficiency under the design standard of the grinding machine system to determine the gap between the actual performance and the expected performance. At the same time, combine historical data to analyze whether there is a deterioration trend or abnormal change in the system energy efficiency to obtain the difference. If the current energy efficiency is significantly lower than the historical average, it will prompt the system to perform maintenance on tool wear and poor lubrication, as well as process optimization in adjusting grinding parameters. Summarize the calculation results of energy consumption efficiency, key features of curve analysis, main conclusions of parameter correlation analysis and differences compared with standards or historical data; the calculation results include energy efficiency values ​​at different stages and average energy efficiency; the key features of curve analysis include the time and value of efficiency peak and valley values, and the turning point of trend change; the main conclusions of parameter correlation analysis include the parameters with the strongest correlation with energy efficiency and their correlation coefficients; express the analysis results of system energy efficiency in clear and concise language; including, through good, general and poor evaluation of the current energy efficiency status of the system, pointing out the main factors affecting energy efficiency; make improvement suggestions based on the analysis results, including adjusting process parameters, checking and maintaining sensors, and optimizing and upgrading equipment to improve the energy efficiency of the system; Among them, adjusting process parameters includes optimizing grinding speed and feed rate; maintaining sensors to ensure accurate measurement; optimizing and upgrading equipment: improving motor performance to reduce energy consumption; Through the above process, the statistical analysis function is comprehensively used to process the relevant data of the high-precision grinding machine system, deeply understand the energy efficiency of the system, and provide a basis for subsequent optimization and improvement.

[0021] Calculate energy consumption efficiency: First calculate the effective output energy: Internal, effective output energy Equal to grinding force With displacement The product of ; Integrate the entire grinding process to obtain the total effective output energy; ;in, is the number of sampling points, Indicates Sampling time points; Total input energy calculation: Assume the real-time power is , then the total input energy is: ;in, is the total time of the grinding process; Energy consumption efficiency calculation: Energy consumption efficiency It is defined as the ratio of total effective output energy to total input energy, that is, ; According to the above calculation and , calculate the energy efficiency value at each sampling time point.

[0022] Based on the material characteristics of the workpiece and the grinding process parameters, the correction coefficient is introduced, and the corrected total effective output energy calculation formula is: ; Among them, k is the correction factor, and the loss correction factor is obtained by performing an energy balance test on the grinding machine system. , introducing the loss correction factor , the corrected total input energy calculation formula is: ; Correction factor : For each combination of workpiece material and grinding process parameters, the measured material removal amount is converted into actual effective output energy; then, the actual effective output energy is compared with the theoretical effective output energy calculated from the force and displacement data, and a preliminary value of the correction factor is calculated. ,in represents the type of workpiece material, j represents the combination of grinding process parameters; the calculation formula for the preliminary value of the correction coefficient is: ; in It is The actual effective output energy of the material under the process parameter combination; is the corresponding theoretical effective output energy; Then, data analysis is performed on the calculated preliminary values ​​of the correction coefficients.

[0023] The data analysis of the calculated preliminary correction coefficient is carried out as follows: First, observe the variation law of the correction coefficient under different workpiece materials and grinding process parameters, determine whether there is an obvious correlation, and obtain the law; including finding that the correction coefficient increases with the increase of workpiece material hardness and decreases with the increase of grinding speed, and then perform linear regression processing on the preliminary value of the correction coefficient according to the law, and establish a mathematical model between the correction coefficient and the workpiece material characteristics and grinding process parameters; finally, determine the final correction coefficient value according to the established mathematical model; When determining the final value, the accuracy of the model, the degree of discreteness of the experimental data, and the convenience of practical application are comprehensively considered; if the degree of data discreteness is large, the weighted average method is used to optimize the correction coefficient, giving greater weight to experimental points with higher data reliability; at the same time, ensure that the final correction coefficient value can be effectively applied in actual production and can accurately reflect the degree of correction required for effective output energy calculation under different conditions.

[0024] According to the law, the preliminary value of the correction coefficient is processed by linear regression, and a mathematical model between the correction coefficient, workpiece material characteristics, and grinding process parameters is established. The process is as follows: Linear regression model: Based on the linear relationship between the correction coefficient k and the workpiece material characteristics and grinding process parameters: Assume the workpiece material characteristic parameters: ; Wherein, m represents the number of workpiece material characteristic parameters that affect the correction coefficient k; Grinding process parameters: ; Where n represents the number of grinding process parameters that affect the correction coefficient k; The formula is: ; in, is the regression coefficient to be determined, is the random error term; Features For the experimental data of Experimental data ; The corresponding correction coefficient is initially ; The characteristic parameter value of the workpiece material is ; The grinding process parameter value is ; Objective function construction: The least squares method is used to estimate the regression coefficients. The goal is to minimize the sum of squared errors SSE. The expression of the sum of squared errors is: ; in, is the correction coefficient of the i-th group of data predicted by the regression model, that is: ; Regression coefficient solution formula: In order to minimize SSE, , respectively, find the partial derivatives and set them equal to 0, and obtain the following normal equations: ; Solving this normal system of equations yields the regression coefficients Estimated value of ; Final correction factor formula: After determining the regression coefficient, the calculation formula for the final correction coefficient k is: ; in and It is the corresponding workpiece material characteristic parameters and grinding process parameter values ​​in practical applications; Determine the values ​​of m and n based on the specific selected workpiece material characteristic parameters and grinding process parameters, and solve the regression coefficient based on experimental data , thereby obtaining the correction factor for calculating the effective output energy.

[0025] By establishing the energy balance equation, the energy balance test of the grinding machine system is carried out to obtain the loss correction coefficient : The basic form of the energy balance equation: In the grinding machine system, the energy balance equation is expressed as: Total input energy Equal to the effective output energy , various energy losses within the system The sum of ; Calculation and measurement of various energy terms: Total input energy calculation: The total input energy is calculated by real-time power The integral over time is calculated as follows: ; in is the total time of the grinding process; Effective output energy calculation: Effective output energy According to the calculation model established above, we can get: ; in is the correction factor, is the grinding force, For displacement microelement, the force and displacement data are collected through the force-position monitoring and analysis system and the effective output energy is calculated; Energy loss measurement and calculation: Motor loss: Motor loss mainly includes copper loss and iron loss , copper loss depends on the motor current ,resistance And running time Calculation, where the current and resistance are obtained through motor parameters; The formula is: ; Friction loss of mechanical transmission parts : According to the principle of tribology, the formula is: ; is the friction coefficient of the transmission parts, is positive pressure, is the relative movement distance; Hydraulic system leakage loss The calculation formula is: ; in, is the flow rate of hydraulic oil; is the leakage coefficient; For pressure; Loss correction factor The calculation formula is: The effective output energy obtained by the above calculation and measurement And energy loss: Substituting into the energy balance equation ,get ; Right now ; The loss correction factor Calculated by the following formula: ; The industrial control integrated machine has multiple display modes, including real-time curve display, statistical analysis result display and remote diagnosis function, which is used by users to monitor and optimize energy consumption in real time; among them, the real-time curve display includes force / displacement curve, force / time curve, torque / time curve and torque angle curve.

[0026] It should be further explained that, in the specific implementation process, the energy efficiency of the system is evaluated by real-time monitoring of the operating status of the high-precision grinding machine system, including force and displacement data, and energy consumption parameters. The collected force and displacement data and energy consumption parameters are used to calculate the effective output energy and the total input energy by integration, and then the energy consumption efficiency is obtained. A correction factor is introduced to correct the effective output energy and the total input energy to make the calculation result more accurate. The experimental data of different workpiece materials and grinding process parameter combinations are analyzed and determined, and the influence of different actual factors on the energy efficiency calculation is comprehensively considered, so as to accurately reflect the actual energy efficiency of the grinding machine system under different working conditions. Accurate and true energy efficiency evaluation results are obtained, thereby promoting the optimization of the operating performance of the grinding machine system and improving production efficiency and energy efficiency ratio.

[0027] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. An energy efficiency evaluation method based on a high-precision grinding machine system, characterized in that: The following steps are involved: a) The force and displacement data generated by the high-precision grinding machine during operation are collected through the force and position monitoring and analysis system based on the GTM module; b) obtaining and monitoring energy consumption parameters during the grinding process through multiple sensor modules, the energy consumption parameters including real-time power, torque and position change, wherein the sensor modules include encoders, grating scales, displacement sensors and pressure sensors, which are used to detect the position, pressure and rotation angle of the grinder in real time; c) The collected force and displacement data and energy consumption parameters are stored as energy consumption data in a data file in CSV or TXT format by automatic curve saving, and transmitted to an external programmable logic controller PLC or computer PC through I / OTCP / IP, ModbusTCP and FTP protocols; d) Use statistical analysis functions to calculate energy consumption parameters, force and displacement data, generate energy consumption efficiency curves, analyze the energy efficiency of the system, obtain analysis results, give an energy efficiency evaluation report based on the analysis results, and display the analysis results and evaluation report through the industrial control integrated machine; e) Combine historical data to calibrate energy efficiency, including force sensor calibration, displacement sensor calibration, angle sensor calibration and torque sensor calibration.

2. The energy efficiency evaluation method based on a high-precision grinding machine system according to claim 1 is characterized in that: During the acquisition and monitoring of energy consumption parameters in the grinding process, sensor synchronous data acquisition is realized through four channel signal sensor interfaces. Each channel supports a 64-channel acquisition configuration of 4×8×2 with a sampling frequency of 4KHz; among them, the 64-channel acquisition configuration represents 4 main groups, each group has 8 subgroups, and each subgroup has 2 data streams.

3. The energy efficiency evaluation method based on a high-precision grinding machine system according to claim 2 is characterized in that: According to the grinding principle, the effective output energy is related to the grinding force and displacement. The collected force and displacement data and energy consumption parameters are used to calculate the energy consumed by material removal during the grinding process through integration to obtain the energy consumption efficiency. The calculation steps are as follows: S1: Calculate the effective output energy: During the sampling period, the effective output energy is calculated by multiplying the grinding force and the displacement, and then the total effective output energy is obtained by integrating the entire grinding process; S2: Calculate total input energy: Based on the real-time power data, calculate the total input energy by integrating the power data over time; S3: Calculate energy consumption efficiency: Based on the effective output energy and the total input energy, calculate the energy efficiency value at each sampling time point through the ratio of the total effective output energy to the total input energy to obtain the energy consumption efficiency.

4. The energy efficiency evaluation method based on a high-precision grinding machine system according to claim 3 is characterized in that: To generate the energy consumption efficiency curve, the process is as follows: Select Matplotlib in Python as the visualization software, create a new drawing area in the selected tool, and set the size and background color of the drawing area; Set the coordinate axes, establish a coordinate system with the horizontal axis as the time axis and the energy consumption efficiency as the vertical axis: plot the energy consumption efficiency value corresponding to each sampling time point calculated in the coordinate system in the drawing area to form data points, and use a smooth curve to connect the drawn data points in sequence to form an energy consumption efficiency curve.

5. The energy efficiency evaluation method based on a high-precision grinding machine system according to claim 4 is characterized in that: Analyze the energy efficiency of the system. The process is: Trend analysis: observe the overall trend of the energy consumption efficiency curve, determine the changes in the system's energy efficiency during the entire grinding process, and obtain key features; If the key features of the curve show a downward trend, it means that as the grinding time increases, the system has problems of increased energy loss or reduced efficiency; if the key features of the curve fluctuate, it means that the system is unstable and there are frequent changes in factors affecting energy efficiency; Parameter correlation analysis: Analyze the correlation between force, displacement data and energy consumption parameters and energy efficiency curves; Calculate the correlation coefficient between each parameter and energy efficiency, determine the strength of the correlation, and obtain the main conclusions; Compare the current energy efficiency curve and calculation results with the energy efficiency under the design standard of the grinding machine system to determine the gap between the actual performance and the expected performance. At the same time, combine historical data to analyze whether there is a deterioration trend or abnormal change in the system energy efficiency to obtain the difference. Summarize the calculation results of energy consumption efficiency, key features of curve analysis, main conclusions of parameter correlation analysis and differences compared with standards or historical data; express the analysis results of system energy efficiency in words; evaluate the current energy efficiency status of the system through good, average and poor, and point out the main factors affecting energy efficiency; make improvement suggestions based on the analysis results, including adjusting process parameters, checking and maintaining sensors, and optimizing and upgrading equipment.

6. The energy efficiency evaluation method based on a high-precision grinding machine system according to claim 5 is characterized in that: Calculate energy consumption efficiency: First calculate the effective output energy: Internal, effective output energy Equal to grinding force With displacement The product of Indicates that during the sampling period The effective output energy within ; Integrate the entire grinding process to get the total effective output energy ; Calculation formula: ;in, is the number of sampling points, Indicates Sampling time points; Total input energy calculation: Assume the real-time power is , then the total input energy is: ;in, is the total time of the grinding process; Energy consumption efficiency calculation: Energy consumption efficiency It is defined as the ratio of total effective output energy to total input energy, that is, ; According to the above calculation and , calculate the energy efficiency value at each sampling time point.

7. The energy efficiency evaluation method based on a high-precision grinding machine system according to claim 6 is characterized in that: Based on the material characteristics of the workpiece and the grinding process parameters, the correction coefficient is introduced, and the corrected total effective output energy calculation formula is: ； Among them, k is the correction factor, and the loss correction factor is obtained by performing an energy balance test on the grinding machine system. , introducing the loss correction factor , the corrected total input energy calculation formula is: .

8. The energy efficiency evaluation method based on a high-precision grinding machine system according to claim 7, characterized in that: Correction factor : For each combination of workpiece material and grinding process parameters, the measured material removal amount is converted into actual effective output energy; then, the actual effective output energy is compared with the theoretical effective output energy calculated from the force and displacement data, and a preliminary value of the correction factor is calculated. ,in represents the type of workpiece material, j represents the grinding process parameter combination; Then, data analysis is performed on the calculated preliminary values ​​of the correction coefficients.

9. The energy efficiency evaluation method based on a high-precision grinding machine system according to claim 8, characterized in that: The data analysis of the calculated preliminary correction coefficient is carried out as follows: Firstly, the variation pattern of the correction coefficient under different workpiece materials and grinding process parameters is observed to determine whether there is an obvious correlation and obtain the pattern; then, the preliminary value of the correction coefficient is subjected to linear regression according to the pattern, and a mathematical model between the correction coefficient and the workpiece material characteristics and grinding process parameters is established; finally, the final correction coefficient value is determined according to the established mathematical model.

10. The energy efficiency evaluation method based on a high-precision grinding machine system according to claim 9, characterized in that: According to the law, the preliminary value of the correction coefficient is processed by linear regression, and a mathematical model between the correction coefficient, workpiece material characteristics, and grinding process parameters is established. The process is as follows: Linear regression model: Based on the linear relationship between the correction coefficient k and the workpiece material characteristics and grinding process parameters: Assume the workpiece material characteristic parameters: ; Wherein, m represents the number of workpiece material characteristic parameters that affect the correction coefficient k; Grinding process parameters: ; Where n represents the number of grinding process parameters that affect the correction coefficient k; The formula is: ； in, is the regression coefficient to be determined, is the random error term; Features For the experimental data of Experimental data ; The corresponding correction coefficient is initially ; The characteristic parameter value of the workpiece material is ; The grinding process parameter value is ; Objective function construction: The least squares method is used to estimate the regression coefficients. The goal is to minimize the sum of squared errors SSE. The expression of the sum of squared errors is: ； in, is the correction coefficient of the i-th group of data predicted by the regression model, that is: ； Regression coefficient solution formula: In order to minimize SSE, , respectively, find the partial derivatives and set them equal to 0, and obtain the following normal equations: ； Solving this normal system of equations yields the regression coefficients Estimated value of ; Final correction factor formula: After determining the regression coefficient, the calculation formula for the final correction coefficient k is: ； in and It is the corresponding workpiece material characteristic parameters and grinding process parameter values ​​in practical applications; Determine the values ​​of m and n based on the specific selected workpiece material characteristic parameters and grinding process parameters, and solve the regression coefficient based on experimental data , thereby obtaining the correction factor for calculating the effective output energy.

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