Digital factory intelligent control system

Abstract

The invention discloses a digital factory intelligent control system, which belongs to the technical field of machine tool control systems, and specifically comprises the following steps: acquiring operation parameters of a numerical control machine tool at fixed time intervals; the working time of the numerical control machine tool is divided into a plurality of time periods, fitting is carried out on the change relation of the main shaft temperature along with the motor rotating speed / environment temperature in each time period, and fitting coefficients R1 / R2 are calculated respectively; marking a time period in which R1 is greater than R2 as a rotating speed main factor section; marking a time period in which R1 is smaller than R2 as a temperature main factor section; in the rotating speed main factor section, if the spindle temperature is higher than a preset temperature value, reducing the rotating speed upper limit of the motor; in the temperature main factor section, if the spindle temperature is higher than a preset temperature value, a cooling system of the machine tool is started, and the spindle is cooled; continuously updating the rotating speed main factor section or the temperature main factor section according to the operation parameters; the temperature of the machine tool is dynamically controlled according to actual conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of machine tool control systems, and in particular to a digital factory intelligent control system. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing, the concept of digital factory has been widely accepted and applied. In digital factory, intelligent control system plays a vital role. It integrates advanced information technology, communication technology and control technology to achieve real-time monitoring, data analysis and decision support of production process. This system can not only improve production efficiency and reduce energy consumption, but also ensure the safe operation of equipment.

[0003] In traditional CNC machine tool control systems, the focus is often on the operation and performance optimization of a single machine tool, but lacks comprehensive management and optimization at the factory level. However, with the development of digital factories, people have begun to realize the importance of temperature control in machine tool operation. Temperature is one of the key factors affecting the accuracy and stability of machine tools. Too high or too low a temperature may cause the performance of the machine tool to deteriorate or even damage the machine tool.

[0004] In traditional machine tool temperature control, only a single and fixed means is used to cool down the machine tool with overtemperature, but there is a lack of analysis of the factors that cause temperature rise in different situations. Therefore, developing an intelligent control system that can fully consider factors such as CNC machine tool operating parameters, environmental factors, and working time allocation is of great significance for improving production efficiency, reducing energy consumption, and ensuring equipment safety. Summary of the invention

[0005] The purpose of the present invention is to provide a digital factory intelligent control system to solve the following technical problems: In traditional machine tool temperature control, only a single and fixed means is used to cool down the machine tool with overtemperature, but there is a lack of analysis of the factors that cause temperature increase in different situations.

[0006] The purpose of the present invention can be achieved by the following technical solutions: A digital factory intelligent control system, comprising: A data acquisition module, used to collect the operating parameters of the CNC machine tool at regular intervals, wherein the operating parameters include the machine tool spindle temperature, motor speed and ambient temperature; The data processing module is used to divide the set working time of the CNC machine tools in the factory into several time periods of equal length, fit the relationship between the spindle temperature and the motor speed in each time period, and also fit the relationship between the spindle temperature and the ambient temperature in each time period, and calculate the fitting coefficients R1 / R2 of the spindle temperature and the motor speed / ambient temperature respectively; The data analysis module is used for marking a time period as a speed-main factor period if the fitting coefficient R1 between the spindle temperature and the motor speed is greater than the fitting coefficient R2 between the spindle temperature and the ambient temperature for any time period; marking a time period as a temperature-main factor period if the fitting coefficient R1 between the spindle temperature and the motor speed is less than the fitting coefficient R2 between the spindle temperature and the ambient temperature; The result output module is used to reduce the upper limit of the motor speed if the spindle temperature is higher than the preset temperature value when in the speed main cause stage; and to start the cooling system of the machine tool to cool the spindle if the spindle temperature is higher than the preset temperature value when in the temperature main cause stage; The long-term monitoring module is used to continuously calculate the fitting coefficients R1 / R2 of the spindle temperature and the motor speed / ambient temperature according to the operating parameters, and to update the speed main cause segment or the temperature main cause segment according to the real-time values ​​of the fitting coefficients R1 / R2.

[0007] As a further solution of the present invention, the specific process of fitting the relationship between the spindle temperature and the motor speed / ambient temperature is as follows: With motor speed / ambient temperature as the horizontal axis and spindle temperature as the vertical axis, two rectangular coordinate systems are constructed. The number of times the operating parameters are collected in the past time period T is marked as n. According to the collected operating parameters, the corresponding coordinate points are drawn in the two rectangular coordinate systems. The coordinates in the motor speed and spindle temperature coordinate systems are marked as (x1, y1), and the coordinates in the ambient temperature and spindle temperature coordinate systems are marked as (x2, y2). Based on linear fitting, the best fitting straight line functions of motor speed / ambient temperature and spindle temperature are obtained respectively, and the fitting coefficients of the two best fitting straight line functions are calculated based on the actual operating parameter values ​​and function values: ; ; Among them, y 1i In the coordinate system of motor speed and spindle temperature, the i-th horizontal coordinate x 1i The actual value of , i∈[1,n], Indicates the i-th horizontal coordinate x 1i The function value of the best fitting straight line function, It represents the average value of all spindle temperature coordinates in the motor speed and spindle temperature coordinate system; y 2i In the coordinate system of ambient temperature and spindle temperature, the i-th horizontal coordinate x 2i The actual value of Indicates the i-th horizontal coordinate x 2i The function value of the best fitting straight line function, It represents the average value of all spindle temperature coordinates in the ambient temperature and spindle temperature coordinate system.

[0008] As a further solution of the present invention: the setting process of the past time period T is: Get the average temperature of each day in the past three months. If the difference between the average temperature of any day and the current day is greater than the set value, delete the day from the past time period T.

[0009] As a further solution of the present invention: in the data acquisition module, the motor speed is a weighted average of the speeds of all motors in the CNC machine tool, and the motor closer to the main axis has a higher weight.

[0010] As a further solution of the present invention: in the data acquisition module, the ambient temperature is an average value of temperature values ​​of several temperature sensors near the CNC machine tool.

[0011] As a further solution of the present invention: the value of each of the operating parameters is an average value within the fixed time.

[0012] As a further solution of the present invention, the specific process of evenly dividing the set working time of the CNC machine tools in the factory into several time periods is as follows: The set working time of the CNC machine tool in one day is obtained. If the set working time is continuous and uninterrupted, the set working time is directly divided into several time periods of equal length. If the set working time itself is several divided set time periods, each set time period is separately divided into sub-time periods of equal length.

[0013] Beneficial effects of the present invention: The digital factory intelligent control system of the present invention realizes more accurate and efficient temperature control by analyzing the dominant factors of the temperature increase of the CNC machine tool spindle in different time periods. The system can distinguish whether the temperature increase of the spindle is due to the accumulation of friction heat caused by the increase in motor speed or the influence of heat transfer caused by changes in ambient temperature. This analysis capability enables the system to take targeted measures in different situations: in the "speed main cause section", the internal heat generation is reduced by lowering the upper limit of the motor speed; and in the "temperature main cause section", the heat dissipation system is started to counteract the influence of external heat sources. This data-based intelligent judgment and response strategy not only effectively avoids unnecessary energy consumption and equipment wear, but also ensures the stability of the processing process and product quality, and significantly improves production efficiency and equipment life. The intelligent control system of the present invention can automatically adjust the operating state of the cooling system according to the temperature changes in different time periods to adapt to different working environments and production needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below in conjunction with the accompanying drawings.

[0015] Figure 1 It is a module schematic diagram of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 As shown, the present invention is a digital factory intelligent control system, comprising: The data acquisition module is the basic part of the system, responsible for collecting the key operating parameters of the CNC machine tool at regular intervals. These operating parameters mainly include the machine tool spindle temperature, motor speed and ambient temperature. By acquiring these data in real time, the system can fully understand the operating status of the machine tool and provide data support for subsequent analysis and control.

[0018] In the data processing module, the system divides the set working time of the CNC machine tools in the factory into several time periods of equal length. Then, for each time period, the system fits the relationship between the spindle temperature and the motor speed, and also fits the relationship between the spindle temperature and the ambient temperature. Through this two-way fitting analysis, the system can calculate the fitting coefficients R1 and R2 of the spindle temperature, motor speed, and ambient temperature respectively. These two fitting coefficients represent the trend and degree of change of the spindle temperature under the influence of different factors.

[0019] The data analysis module is the core part of the system. It determines the main reason for the increase in spindle temperature based on the fitting coefficients R1 and R2 provided by the data processing module. Specifically, for any time period, if the fitting coefficient R1 of the spindle temperature and the motor speed is greater than the fitting coefficient R2 of the spindle temperature and the ambient temperature, it indicates that during this time period, the motor speed is the main factor causing the increase in spindle temperature, and the system will mark this time period as the "speed main factor period". On the contrary, if the fitting coefficient R1 is less than R2, it indicates that the ambient temperature is the main influencing factor, and the system will mark this time period as the "temperature main factor period".

[0020] The result output module executes corresponding control measures according to the judgment results of the data analysis module. When the system is in the "speed main cause section", if the spindle temperature is higher than the preset safety temperature value, the system will automatically reduce the upper limit of the motor speed to reduce the spindle temperature increase caused by friction heat. When the system is in the "temperature main cause section", if the spindle temperature is also higher than the preset value, the system will start the machine tool's cooling system to cool the spindle to prevent the machine tool from overheating due to excessively high ambient temperature.

[0021] The long-term monitoring module continuously calculates the fitting coefficients R1 and R2 of the spindle temperature, motor speed, and ambient temperature based on the latest operating parameters. This process is dynamic, which means that the system will continuously update its judgment of the factors affecting the spindle temperature based on real-time data. Based on these real-time updated fitting coefficients, the long-term monitoring module will also adjust and update the "speed main cause segment" or "temperature main cause segment" accordingly to ensure that the system's control strategy is always consistent with the actual situation.

[0022] In another preferred embodiment of the present invention, in the data processing module, the specific process of fitting the relationship between the spindle temperature and the motor speed / ambient temperature is: With motor speed / ambient temperature as the horizontal axis and spindle temperature as the vertical axis, two rectangular coordinate systems are constructed. The number of times the operating parameters are collected in the past time period T is marked as n. According to the collected operating parameters, the corresponding coordinate points are drawn in the two rectangular coordinate systems. The coordinates in the motor speed and spindle temperature coordinate systems are marked as (x1, y1), and the coordinates in the ambient temperature and spindle temperature coordinate systems are marked as (x2, y2). Based on linear fitting, the best fitting straight line functions of motor speed / ambient temperature and spindle temperature are obtained respectively, and the fitting coefficients of the two best fitting straight line functions are calculated based on the actual operating parameter values ​​and function values: ; ; Among them, y 1i In the coordinate system of motor speed and spindle temperature, the i-th horizontal coordinate x 1i The actual value of , i∈[1,n], Indicates the i-th horizontal coordinate x 1i The function value of the best fitting straight line function, It represents the average value of all spindle temperature coordinates in the motor speed and spindle temperature coordinate system; y 2i In the coordinate system of ambient temperature and spindle temperature, the i-th horizontal coordinate x 2i The actual value of Indicates the i-th horizontal coordinate x 2i The function value of the best fitting straight line function, It represents the average value of all spindle temperature coordinates in the ambient temperature and spindle temperature coordinate system.

[0023] The coefficient of determination R, also known as goodness of fit or correlation coefficient, is an important indicator in statistics used to quantify the degree of fit of the regression model to the data. It indicates the degree to which the independent variable explains the dependent variable, or how much variability of the dependent variable can be explained by the independent variable in the model.

[0024] The value of R ranges between 0 and 1, where 1 indicates that the model fits the data perfectly. Specifically, when R equals 1, it means that the model can fully explain all the variations in the dependent variable, that is, all observations fall exactly on the regression line without any deviation. When R is close to 0, it means that the model has almost no explanatory power and there is almost no linear relationship between the independent variable and the dependent variable.

[0025] The closer the R value is to 1, the better the model fits and the higher the proportion of variation in the dependent variable that the independent variable can explain. For example, if R is 0.8, it means that the model can explain 80% of the variability of the dependent variable, while the remaining 20% ​​may be caused by other factors not included in the model or random errors. Through this two-way fitting analysis, the system can calculate the fitting coefficients R1 and R2 of the spindle temperature, motor speed, and ambient temperature respectively. These two fitting coefficients represent the trend and degree of change of the spindle temperature under the influence of different factors.

[0026] In a preferred case of this embodiment, in the data processing module, the setting process of the past time period T is: The process first involves obtaining average daily temperature data for the past three months. This data is the basis for evaluating temperature change patterns and identifying abnormal temperature events.

[0027] Next, the system will compare the average temperature of each day with the actual average temperature of the day. If the difference between the average temperature of any day and the actual average temperature of the day exceeds the preset threshold or set value, it indicates that the temperature data of that day may be abnormal or affected by special factors, so the data of this day will not be included in the past time period T used for analysis. In this way, the system can exclude abnormal data that may be caused by short-term events or measurement errors, thereby improving the quality and credibility of data analysis.

[0028] In another preferred embodiment of the present invention, the design of the data acquisition module is further optimized to ensure that the collected data can more accurately reflect the actual operating status of the CNC machine tool.

[0029] Regarding the data collection of motor speed, a weighted average method is used in this embodiment to calculate the overall motor speed of the CNC machine tool. Specifically, the system takes into account the speed of all motors in the machine tool and assigns weights according to the distance between each motor and the spindle. The closer the motor is to the spindle, the greater the impact of its speed on the spindle temperature, so the speed of these motors will be given a higher weight. The weighted average calculated in this way can more accurately represent the actual impact of the motor speed on the spindle temperature, thereby improving the accuracy of subsequent data analysis and control strategy formulation.

[0030] In another preferred embodiment of the present invention, for the measurement of ambient temperature, the data acquisition module adopts a multi-point sampling strategy. Specifically, the system will arrange multiple temperature sensors around the CNC machine tool, and these sensors are distributed in different positions to capture possible small temperature differences. Then, the system will calculate the average value of these sensor readings as the temperature value of the environment in which the CNC machine tool is located. This multi-sensor averaging method helps to reduce the error that may be introduced by a single sensor and improve the accuracy and reliability of ambient temperature data.

[0031] In addition, it is worth noting that, whether it is the operating parameters such as motor speed, ambient temperature or spindle temperature, the data acquisition module will sample these parameters multiple times at a fixed time interval and calculate the average value within the time period. The purpose of this is to smooth out instantaneous fluctuations and obtain more stable and representative data for better subsequent data analysis and processing.

[0032] In another preferred embodiment of the present invention, in the data processing module, the specific process of evenly dividing the set working time of the CNC machine tools in the factory into several time periods is: The process first involves obtaining the complete set-up working time of the CNC machine tool in a day. This set-up time may vary due to production needs, maintenance schedules or other factors. Once this information is obtained, the next step is to reasonably divide this time into several equal-length time periods for more detailed analysis and control.

[0033] If the set working time of the CNC machine is continuous and uninterrupted, the system will directly divide it according to the predetermined time period length. For example, if the set working time in a day is 8 hours, and we want to divide it into 8 1-hour long time periods, the system will evenly divide the 8 hours from the beginning to the end.

[0034] However, the actual situation may be more complicated. Sometimes the set working time of the CNC machine tool may have been divided into several discontinuous time periods. In this case, the data processing module will first treat each divided set time period as an independent unit. Then, for each independent time period, the system will further subdivide it into several sub-time periods of equal length. The purpose of this is to maintain the consistency of data analysis and the feasibility of comparison, no matter how the original data is distributed.

[0035] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A digital factory intelligent control system, characterized in that: include: A data acquisition module, used to collect the operating parameters of the CNC machine tool at regular intervals, wherein the operating parameters include the machine tool spindle temperature, motor speed and ambient temperature; The data processing module is used to divide the set working time of the CNC machine tools in the factory into several time periods of equal length, fit the relationship between the spindle temperature and the motor speed in each time period, and also fit the relationship between the spindle temperature and the ambient temperature in each time period, and calculate the fitting coefficients R1 / R2 of the spindle temperature and the motor speed / ambient temperature respectively; The data analysis module is used for marking a time period as a speed-main factor period if the fitting coefficient R1 between the spindle temperature and the motor speed is greater than the fitting coefficient R2 between the spindle temperature and the ambient temperature for any time period; marking a time period as a temperature-main factor period if the fitting coefficient R1 between the spindle temperature and the motor speed is less than the fitting coefficient R2 between the spindle temperature and the ambient temperature; The result output module is used to reduce the upper limit of the motor speed if the spindle temperature is higher than the preset temperature value when in the speed main cause stage; and to start the cooling system of the machine tool to cool the spindle if the spindle temperature is higher than the preset temperature value when in the temperature main cause stage; The long-term monitoring module is used to continuously calculate the fitting coefficients R1 / R2 of the spindle temperature and the motor speed / ambient temperature according to the operating parameters, and to update the speed main cause segment or the temperature main cause segment according to the real-time values ​​of the fitting coefficients R1 / R2.

2. A digital factory intelligent control system according to claim 1, characterized in that: In the data processing module, the specific process of fitting the relationship between the spindle temperature and the motor speed / ambient temperature is as follows: With motor speed / ambient temperature as the horizontal axis and spindle temperature as the vertical axis, two rectangular coordinate systems are constructed. The number of times the operating parameters are collected in the past time period T is marked as n. According to the collected operating parameters, the corresponding coordinate points are drawn in the two rectangular coordinate systems. The coordinates in the motor speed and spindle temperature coordinate systems are marked as (x1, y1), and the coordinates in the ambient temperature and spindle temperature coordinate systems are marked as (x2, y2). Based on linear fitting, the best fitting straight line functions of motor speed / ambient temperature and spindle temperature are obtained respectively, and the fitting coefficients of the two best fitting straight line functions are calculated based on the actual operating parameter values ​​and function values: ； ； Among them, y 1i In the coordinate system of motor speed and spindle temperature, the i-th horizontal coordinate x 1i The actual value of , i∈[1,n], Indicates the i-th horizontal coordinate x 1i The function value of the best fitting straight line function, It represents the average value of all spindle temperature coordinates in the motor speed and spindle temperature coordinate system; y 2i In the coordinate system of ambient temperature and spindle temperature, the i-th horizontal coordinate x 2i The actual value of Indicates the i-th horizontal coordinate x 2i The function value of the best fitting straight line function, It represents the average value of all spindle temperature coordinates in the ambient temperature and spindle temperature coordinate system.

3. A digital factory intelligent control system according to claim 2, characterized in that: In the data processing module, the setting process of the past time period T is: Get the average temperature of each day in the past three months. If the difference between the average temperature of any day and the current day is greater than the set value, delete the day from the past time period T.

4. A digital factory intelligent control system according to claim 1, characterized in that: In the data acquisition module, the motor speed is a weighted average of the speeds of all motors in the CNC machine tool, and the motor closer to the main axis has a higher weight.

5. A digital factory intelligent control system according to claim 1, characterized in that: In the data acquisition module, the ambient temperature is the average value of temperature values ​​of several temperature sensors near the CNC machine tool.

6. A digital factory intelligent control system according to claim 4 or 5, characterized in that: In the data acquisition module, the value of each operating parameter is an average value within the fixed time.

7. A digital factory intelligent control system according to claim 1, characterized in that: In the data processing module, the specific process of evenly dividing the set working time of the CNC machine tools in the factory into several time periods is as follows: The set working time of the CNC machine tool in one day is obtained. If the set working time is continuous and uninterrupted, the set working time is directly divided into several time periods of equal length. If the set working time itself is several divided set time periods, each set time period is separately divided into sub-time periods of equal length.

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