A thermal management control method for MPC grinding machine based on finite element

Abstract

The present invention provides a finite element-based MPC grinder thermal management control method, comprising: establishing a physical model related to the thermal management of the grinder system, and using finite element simulation data as a prediction sample space; establishing a target transfer function for predictive model control based on the temperature delay of the grinder system, as the control equation for model prediction; performing rolling optimization and feedback correction on the predictive model through an MPC control algorithm, fusing the predicted temperature change trend with the real data measured by the temperature sensor to obtain the optimal output variation after fusion; determining the input flow rate based on the optimal output variation to achieve the effect of real-time temperature control of the grinder system. While improving the control effect, the present invention can realize predictive control under different working conditions. For the joint control of multiple grinders, a more reasonable thermal management control is achieved through the particle swarm optimization algorithm combined with the coupling adjustment of the Gaussian process.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal management of powder material and liquid slurry micro-nano grinding machines, and in particular to a finite element-based thermal management control method for an MPC grinding machine. Background Art

[0002] The development of new materials technology has not only driven advancements in micro- and nano-grinding technology and control systems for powder materials, but has also placed higher demands on the particle size and production capacity of powder materials. Particle size control, material contamination, and increased production capacity are key factors in the powder grinding process. Particle size control directly impacts material performance and application areas. Currently, through physical and chemical methods, wet grinding of solid particles in liquid media can achieve finenesses below 100 nanometers. Increased production capacity and industrialized production are crucial for production costs and efficiency. Heat generation and temperature control during the grinding process significantly impact the particle size and production capacity of new materials. Traditional grinding mills are water-cooled, and most control methods employ algorithms such as PI control. However, accurate modeling and analysis of the heat generation during the grinding process are lacking, resulting in incomplete heat generation estimates or the direct use of online power as a reference value. Furthermore, when controlling multiple equipment across multiple operating conditions, the high instantaneous cooling water consumption can lead to over-sized equipment, excessive initial investment, and unstable production capacity. Considering that the online power of the equipment itself cannot fully represent the heat generation, the machine is not equivalent to a heating element. The equipment only generates heat during the grinding process when the rotor drives the grinding medium and slurry to shear and rub. A considerable part of the power is used in the grinding and crushing process. Therefore, using the online power as a reference benchmark, the control effect is not perfect, and the stabilization time and overshoot will be too large. Summary of the Invention

[0003] The purpose of the present invention is to provide an improved solution to the shortcomings of the above-mentioned background technology, introduce finite element simulation results as a reference value for the calorific value in a specific time period, and at the same time introduce an MPC control algorithm to effectively track the optimal water-saving path through rolling optimization to achieve optimal control.

[0004] In order to achieve the above object, the present invention provides a finite element-based MPC grinding machine thermal management control method, comprising the following steps:

[0005] S1, establish a physical model related to the thermal management of the grinding machine system and use the finite element simulation data as the prediction sample space;

[0006] S2, based on the delay of the grinding machine system temperature, establish the target transfer function of the prediction model control as the control equation of the model prediction;

[0007] S3 uses the MPC control algorithm to perform rolling optimization and feedback correction on the prediction model, fusing the predicted temperature change trend with the actual data measured by the temperature sensor to obtain the optimal output change. MPC (Model Predictive Control) aims to generate optimized control inputs by predicting future system states and considering constraints.

[0008] S4, determining the input flow rate according to the optimal output change to achieve the effect of real-time temperature control of the grinding machine system.

[0009] Furthermore, the physical model established in S1 is:

[0010] ;

[0011] in, is the specific heat capacity of the polishing slurry, in units of ; is the specific heat capacity of cooling water, in units of ; is the outlet temperature of the grinding slurry, in units of ; is the outlet temperature of cooling water, in units of ; is the density of the polishing slurry, in units of ; is the density of cooling water, in units of ; is the volume inside the grinder, in units of ; is the volume of the cooling jacket in ; is the input flow rate of the grinding slurry, in units of ; is the input flow of cooling water, in units of ; is the inlet temperature of the grinding slurry, in units of ; is the inlet temperature of cooling water, in ; W is the heating power of the grinder, unit is ; U is the heat transfer coefficient, unit is ; S is the heat transfer area, unit is .

[0012] Furthermore, the heating power W of the grinding machine in S1 is used as the input value through the result of finite element simulation, and the input flow rate of the grinding slurry is Set to a fixed value and adjust the cooling water input flow Set as input value to set the outlet temperature of the grinding slurry to and cooling water outlet temperature Set to output value.

[0013] Furthermore, the target transfer function in S2 is for:

[0014] ;

[0015] in, is the sampling time, For the system Output matrix at the sampling moment, including the outlet temperature of the polishing slurry and the cooling water outlet temperature ; is the input value of the system, that is, the input flow of cooling water ; is the input change value of the system; is the output weight matrix of the system, , is the weight factor of cooling water temperature, is the weight factor of the polishing slurry temperature; is the weight factor of cooling water flow.

[0016] Furthermore, according to the needs of the actual project, different weight factors are used to adjust the weights between cooling water and grinding slurry. The value of increases, When the value decreases, the focus is on controlling the temperature. The value of When the value increases, the temperature is controlled while saving flow.

[0017] Furthermore, S3 specifically includes the following sub-steps:

[0018] S31, in each sampling period, optimizing the inlet and outlet temperatures of the grinding machine system at adjacent moments, finding the optimal control sequence of the predicted values, and predicting the outlet temperature of the grinding machine system at the next moment. The controlled variable of the controlled object is obtained based on the predicted outlet temperature at the next moment, and the next prediction is initialized. The iteration continues as the optimization time progresses.

[0019] S32, during each sampling period, the outlet temperature measurement value of the grinding machine is collected in real time to compensate for the temperature trend error generated by the prediction model.

[0020] Furthermore, for the joint control of multiple grinding machines, 2s equipment were set up, divided into two working conditions: coarse grinding s equipment and fine grinding s equipment. During the trial production, the two working conditions were adjusted respectively, and the adjusted T and ω were collected, where is the startup time of the device, For the production cycle, Value and Value Represents the coarse grinding condition, Value and Value Represents fine grinding conditions, represents the cooling water input of the i-th equipment during rough grinding, represents the cooling water input of the i-th equipment during fine grinding, T, ω, 、 All obey Gaussian distribution 、 、 、 、 and ;

[0021] The PSO algorithm is used for optimization. The PSO (Particle Swarm Optimization) algorithm is a particle swarm optimization algorithm. Set the total water consumption to G, the cooling water input of each device to Q, and the rough grinding condition to Value , fine grinding conditions Value , discretize the function G, and the sampling discretization is based on the Gaussian distribution function, so that the sampling density is high in the early stage of the device startup and the sampling density is reduced after the device stabilizes; after determining the sampling point position, linear interpolation is used to select the M sampling points corresponding to and , and convolved with the square wave function to form function G. The square wave function sets the time difference based on the different water demand in different time periods. The PSO algorithm is optimized to obtain the optimized T value. At the same time, the actual production situation is recorded. The actual production cycle and start and stop time of each equipment are recorded, and the earliest updated T value and ω value are returned. After several iterations, the T value of joint control is obtained.

[0022] Furthermore, the sampling formula of the Gaussian distribution function is:

[0023] ;

[0024] Among them, m represents the sampling point, and M sampling points are set. ; is the corresponding time point; These are all fitting parameters, obtained during actual project debugging. w 1 represents the peak height of the Gaussian function, that is, the maximum value of the function at the center position, w2 corresponds to the mean of the Gaussian distribution, that is, the position of the symmetry axis of the distribution, w 3 is related to the variance.

[0025] After determining the sampling point position, use linear interpolation to select M The sampling points correspond to and , combined with the square wave convolution function , which is convolved with the square wave function to form a function G .

[0026] Furthermore, the function G is specifically:

[0027] .

[0028] Furthermore, for the cross-working joint control of multiple grinding machines, the T value and ω value of each device are fused when the Gaussian distribution is statistically analyzed. is the startup time of the device, is the production cycle, which obeys Gaussian distribution 、 , is the cooling water input of the i-th equipment during coarse grinding, is the cooling water input of the i-th equipment during fine grinding, which obeys the Gaussian distribution after fusion ;

[0029] The sampling formula of the Gaussian distribution function is:

[0030] ;

[0031] Among them, m represents the sampling point, and M sampling points are set. ; is the corresponding time point; These are all fitting parameters, obtained during actual project debugging. w 1 represents the peak height of the Gaussian function, that is, the maximum value of the function at the center position, w 2 corresponds to the mean of the Gaussian distribution, that is, the position of the symmetry axis of the distribution, w 3 is related to variance;

[0032] Using the PSO algorithm combined with the Gaussian process, the total water consumption is set to G, and after determining the sampling point location, the linear interpolation method is used to select M The sampling points correspond to and , combined with the square wave convolution function , which is convolved with the square wave function to form a function G :

[0033] ;

[0034] right G The function is optimized by PSO algorithm, and the optimized T Value and ω After several iterations, the joint control T value.

[0035] The above solution of the present invention has the following beneficial effects:

[0036] The finite element-based MPC grinding mill thermal management control method provided by the present invention establishes a physical model related to thermal management based on the grinding mill system, and then uses the results of finite element simulation as a reference value for the calorific value in a specific time period. At the same time, the MPC control algorithm is introduced to effectively track the optimal water-saving path through a rolling optimization method. While improving the control effect, the controller can use different weight factors according to the needs of the actual project to adjust the weights between cooling water and grinding slurry, realizing predictive control under different working conditions, thereby achieving the purpose of saving energy and reducing water consumption.

[0037] The present invention aims at the joint control of multiple grinding machines. By combining the particle swarm optimization algorithm with the coupling adjustment of the Gaussian process, it can achieve reasonable control objectives. It can not only adjust the production cycle of each device and stagger the high heat generation stages of feeding and grinding, but also reduce the impact on the total amount of cooling water, avoiding the problem of uneven cooling water distribution in the early stage of operation. This can not only reduce production interruptions and scrap rates caused by temperature fluctuations, but also improve production efficiency, product quality stability, and reduce production costs.

[0038] Other beneficial effects of the present invention will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of the steps of the present invention;

[0040] Figure 2 is a schematic diagram of a grinding machine system of the present invention;

[0041] Figure 3 The present invention is a flow chart of joint control of multiple grinding machines divided into two types of working conditions;

[0042] Figure 4 This is a flow chart of the joint control of multiple grinding machines across working conditions according to the present invention. DETAILED DESCRIPTION

[0043] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0044] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this disclosure, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

[0045] It should also be noted that the diagrams provided in the following embodiments are merely schematic illustrations of the basic concepts of the present disclosure. The diagrams only show components relevant to the present disclosure and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the configuration, quantity, and proportion of each component may be varied at will, and the component layout may be more complex. Furthermore, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will appreciate that the described aspects may be practiced without these specific details.

[0046] like Figure 1 As shown, the embodiment of the present invention provides a finite element-based MPC grinding machine thermal management control method, the grinding machine system involved is as follows Figure 2 As shown, the method may specifically include the following steps:

[0047] S1, establish a physical model related to the thermal management of the grinding machine system and use the finite element simulation data as the prediction sample space.

[0048] In this step, the physical model established is specifically:

[0049] ;

[0050] in, is the specific heat capacity of the polishing slurry, in units of ; is the specific heat capacity of cooling water, in units of ; is the outlet temperature of the grinding slurry, in units of ; is the outlet temperature of cooling water, in units of ; is the density of the polishing slurry, in units of ; is the density of cooling water, in units of ; is the volume inside the grinder, in units of ; is the volume of the cooling jacket in ; is the input flow rate of the grinding slurry, in units of ; is the input flow of cooling water, in units of ; is the inlet temperature of the grinding slurry, in units of ; is the inlet temperature of cooling water, in ; W is the heating power of the grinder, unit is ; U is the heat transfer coefficient, unit is ; S is the heat transfer area, unit is .

[0051] In this step, the heating power W of the grinding machine itself is used as the input value through the result of finite element simulation, and the input flow rate of the grinding slurry is set based on the actual situation. Set it to a fixed value, and only consider controlling the temperature of the grinding machine system by controlling the flow of cooling water, so the input flow of cooling water can be Set as input value to set the outlet temperature of the grinding slurry to and cooling water outlet temperature Set as the output value. Since it is a single-input multiple-output system, the control stability will deteriorate. Therefore, it is necessary to design a controller based on the prediction model to stabilize the system through model prediction.

[0052] S2, based on the delay of the grinding machine system temperature, the target transfer function of the prediction model control is established as the control equation of the model prediction.

[0053] In this step, the target transfer function Specifically:

[0054] ;

[0055] in, is the sampling time, For the system Output matrix at the sampling time, including the outlet temperature of the polishing slurry and the cooling water outlet temperature ; is the input value of the system, here is the input flow of cooling water , is the input change value of the system; is the output weight matrix of the system, , is the weight factor of cooling water temperature, is the weight factor of the polishing slurry temperature; is the weight factor of cooling water flow.

[0056] Therefore, the controller can use different weight factors to adjust the weights between cooling water and grinding slurry according to the needs of the actual project. The value of is larger, A smaller value indicates that the project needs to focus on controlling temperature and not restricting flow too much. Conversely, temperature control is performed from the perspective of saving flow. By setting different weight factors, predictive control can be achieved under different working conditions of the model. For example, the weight factor of the cooling water outlet temperature can be increased. , to affect the optimal value of the controller, so as to achieve better temperature control, and also by adjusting value to adjust the optimal control of cooling water flow, so as to adapt to the control under different working conditions and achieve the purpose of energy saving and reducing water consumption.

[0057] S3, through MPC control, the prediction model is optimized and feedback corrected, and the predicted temperature change trend is integrated with the real data measured by the temperature sensor to obtain the optimal output change after integration.

[0058] This step may specifically include the following two sub-steps:

[0059] S31, rolling optimization of the prediction model: In each sampling cycle, the inlet and outlet temperatures of the grinding machine system at adjacent moments are optimized to find the optimal control sequence of the predicted values, so as to predict the outlet temperature value of the grinding machine system at the next moment. The controlled variable of the controlled object is obtained based on the outlet temperature predicted value at the next moment, and the next prediction is initialized. The iteration continues as the optimization time progresses;

[0060] In this step, the prediction model is discretized:

[0061] ;

[0062] in, is the sampling time; is the state vector; is the input; Contains disturbance terms, such as the heating power W of the grinding machine and the inlet temperature of the grinding slurry and cooling water inlet temperature ; is the discretized matrix.

[0063] At each sampling moment , MPC solves the optimization problem for the next N steps, and the objective function is:

[0064] ;

[0065] in, Set the outlet temperature of the polishing slurry and the outlet temperature of the cooling water; weight matrix , , which correspond to the weight factors of polishing slurry temperature, cooling water temperature and flow rate respectively; the predicted output Based on the current state and future input sequences calculate.

[0066] When performing rolling optimization, a discrete model is used to predict the output of the next N steps:

[0067] ;

[0068] Solve the optimal input sequence through quadratic programming (QP) ; Only the optimal input at the current moment is applied , discard the subsequent input; at the next moment , repeat the above process.

[0069] S32, feedback correction of the prediction model: During each sampling period, the outlet temperature measurement value of the grinding machine is collected in real time to compensate for the temperature trend error generated by the prediction model.

[0070] Specifically, this step is based on an error compensation mechanism, where at the end of each sampling period, the outlet temperature is actually measured. Modify the forecast model, including:

[0071] Calculate the prediction error:

[0072] ;

[0073] in Where is the predicted value of the current output at the previous moment;

[0074] Update the state estimate:

[0075] ;

[0076] Wherein, L is a correction gain matrix, such as a Kalman filter gain.

[0077] Perturbation estimation:

[0078] If there are unmodeled disturbances (such as W fluctuations), they are modeled as additional states and updated online.

[0079] Through the feedback correction of this step, the system can eliminate model mismatch, such as the cumulative error caused by the deviation of the heat transfer coefficient U, adapt to external disturbances, and improve robustness.

[0080] S4, determining the input flow rate according to the optimal output change to achieve the effect of real-time temperature control of the grinding machine system.

[0081] It should be noted that the above solution is mainly for the thermal management control of a single grinding machine. For the joint control of multiple grinding machines, please also refer to Figure 3 , set up 2s equipment, divided into two working conditions of coarse grinding s equipment and fine grinding s equipment, adjust the two working conditions respectively during the trial production process, collect the adjusted T and ω, where is the startup time of the device, For the production cycle, Value and Value Represents the coarse grinding condition, Value and Value Represents fine grinding conditions, represents the cooling water input of the i-th equipment during rough grinding, represents the cooling water input of the i-th equipment during fine grinding, T, ω, 、 All obey Gaussian distribution 、 、 、 、 and .

[0082] The PSO algorithm is used for optimization. The PSO algorithm is the particle swarm optimization algorithm. The total water consumption is set to G, the cooling water input of each device is Q, and the rough grinding condition is Value , fine grinding conditions Value The PSO algorithm discretizes the function G, namely, discretizing the sampling of the coarse grinding stage and the fine grinding stage, reducing the system's computing power requirements. Because the cooling water input fluctuates greatly during the grinding operation, the sampling discretization is based on the Gaussian distribution function, ensuring that the sampling density is high in the early stage of equipment startup and decreases after it stabilizes in the later stage. The sampling formula of the Gaussian distribution function is as follows:

[0083] ;

[0084] Among them, m is the sampling point representation, for example, set M sampling points, ; is the corresponding time point; These are all fitting parameters, obtained during actual project debugging. w 1 represents the peak height of the Gaussian function, that is, the maximum value of the function at the center position, w 2 corresponds to the mean of the Gaussian distribution, that is, the position of the symmetry axis of the distribution, w 3 is related to the variance, The values ​​can be 51660, 51.08, and 17.1 respectively.

[0085] After determining the sampling point position, use linear interpolation to select the corresponding M sampling points and , combined with the square wave convolution function , and the function G is formed after convolution with the square wave function. The square wave function sets the time difference based on the different water demand in different time periods:

[0086] ;

[0087] The PSO algorithm is used to optimize the function G to obtain the optimized T value. At the same time, the actual production situation is recorded. The actual production cycle and start and stop time of each device are recorded. The earliest updated T value and ω value are returned to avoid system overfitting. After several iterations, a T value that is more in line with the actual joint control of the engineering project is formed.

[0088] If it is a cross-operating situation, it is necessary to consider the T value and ω value of the same equipment at different times and under different operating conditions. Based on the differences in the components and actual working conditions of each equipment, when calculating the Gaussian distribution, the two production lines are not separated for statistics. The T value and ω value of each equipment are integrated to obtain the data. 、 .at the same time, is the cooling water input of the i-th equipment during coarse grinding, is the cooling water input of the i-th equipment during fine grinding. Similarly, the data of the two production lines are fused and obey the Gaussian distribution. .

[0089] Similarly, the PSO algorithm is combined with the Gaussian process, and the total water consumption is set to G:

[0090] ;

[0091] The PSO algorithm is used to optimize the above G function to obtain the optimized T value and ω value. Similarly, after several iterations, a T value that is more in line with the actual joint control of the engineering project is formed.

[0092] Therefore, this method aims at the joint control of multiple grinding machines, especially during the cross-working condition adjustment and optimization period. It adopts data fusion to fuse the production processes of multiple production lines, reducing the computing cost. Through optimal control, it adjusts the water inlet cycle under different working conditions to avoid multiple devices competing for cooling water at the same time, resulting in excessive temperature rise and insufficient cooling water consumption under certain working conditions.

[0093] The following example further illustrates this method, comparing it with PI control. A PHN SuperMaxZeta 400 pin-type nanogrinder was selected for this test. The project was conducted at a local new energy cathode material manufacturer's workshop. Using the same 0.3 mm zirconia beads as the grinding medium, the fill rate was approximately 80%, and the grinding time was 90 minutes. Simulink calculations in MATLAB, with the same rotor structure, current, speed (linear velocity), flow rate, and temperature, showed that both methods were able to control the grinding slurry outlet temperature to around 12°C and the cooling water outlet temperature to below 30°C in a relatively short period of time. However, it can be seen that this method reached stability faster than PID control. Furthermore, this method achieved a faster cooling water balance.

[0094] For 2 s =12, setting 28 sampling points. When manually adjusting the start and stop of multiple devices, there will be competition for cooling water in the early stage of operation. The required cooling water margin needs to be increased. Through the coupled adjustment of this method (PSO-GP, particle swarm optimization algorithm combined with Gaussian process), there is a significant improvement compared with both manual and PSO algorithms, with less impact on the total amount of cooling water, avoiding the problem of uneven cooling water distribution in the early stage of operation.

[0095] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0096] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A finite element-based MPC grinding machine thermal management control method, characterized in that: The steps include: S1, establish a physical model related to the thermal management of the grinding machine system and use the finite element simulation data as the prediction sample space; S2, based on the delay of the grinding machine system temperature, establish the target transfer function of the prediction model control as the control equation of the model prediction; S3, using the MPC control algorithm to perform rolling optimization and feedback correction on the prediction model, fuses the predicted temperature change trend with the real data measured by the temperature sensor to obtain the optimal output change after fusion; S4, determines the input flow rate according to the optimal output change to achieve the effect of real-time temperature control of the grinding machine system; for the joint control of multiple grinding machines, set 2 s equipment, divided into coarse grinding s Table and fine grinding s During the trial production, the two working conditions were adjusted and the adjusted T and ω ,in is the startup time of the device, For the production cycle, Value and Value Represents the coarse grinding condition, Value and Value Represents fine grinding conditions, represents the cooling water input of the i-th equipment during rough grinding, represents the cooling water input of the i-th equipment during fine grinding, T, ω, 、 All obey Gaussian distribution 、 、 、 、 and ; The PSO algorithm is used for optimization. The total water consumption is set to G, the cooling water input of each device is Q, and the rough grinding condition is Value , fine grinding conditions Value , discretize the function G, and the sampling discretization is based on the Gaussian distribution function, so that the sampling density is high in the early stage of the device startup and the sampling density is reduced after the device stabilizes; after determining the sampling point position, linear interpolation is used to select the M sampling points corresponding to and , and convolved with the square wave function to form function G. The square wave function sets the time difference based on the different water demand in different time periods. The PSO algorithm is optimized to obtain the optimized T value. At the same time, the actual production situation is recorded. The actual production cycle and start and stop time of each equipment are recorded, and the earliest updated T value and ω value are returned. After several iterations, the T value of joint control is obtained.

2. The MPC grinding machine thermal management control method based on finite element method according to claim 1, characterized in that: The physical model established in S1 is: ； in, is the specific heat capacity of the polishing slurry, in units of ; is the specific heat capacity of cooling water, in units of ; is the outlet temperature of the grinding slurry, in units of ; is the outlet temperature of cooling water, in units of ; is the density of the polishing slurry, in units of ; is the density of cooling water, in units of ; is the volume inside the grinder, in units of ; is the volume of the cooling jacket in ; is the input flow rate of the grinding slurry, in units of ; is the input flow of cooling water, in units of ; is the inlet temperature of the grinding slurry, in units of ; is the inlet temperature of cooling water, in ; W is the heating power of the grinder, unit is ; U is the heat transfer coefficient, unit is ; S is the heat transfer area, unit is .

3. The MPC grinding machine thermal management control method based on finite element method according to claim 2, characterized in that: The heating power W of the grinding machine in S1 is taken as the input value by the result of finite element simulation, and the input flow rate of the grinding slurry is Set to a fixed value and adjust the cooling water input flow Set as input value to set the outlet temperature of the grinding slurry to and cooling water outlet temperature Set to output value.

4. The MPC grinding machine thermal management control method based on finite element method according to claim 3, characterized in that: Target transfer function in S2 for: ； in, is the sampling time, For the system Output matrix at the sampling moment, including the outlet temperature of the polishing slurry and the cooling water outlet temperature ; is the input value of the system, that is, the input flow of cooling water ; is the output weight matrix of the system, , is the weight factor of cooling water temperature, is the weight factor of the polishing slurry temperature; is the weight factor of cooling water flow.

5. The MPC grinding machine thermal management control method based on finite element method according to claim 4, characterized in that: According to the needs of the actual project, use different weight factors to adjust the weight between cooling water and grinding slurry. The value of increases, When the value decreases, the focus is on controlling the temperature. The value of When the value increases, the temperature is controlled while saving flow.

6. The MPC grinding machine thermal management control method based on finite element method according to claim 5, characterized in that: S3 specifically includes the following sub-steps: S31, in each sampling period, optimizing the inlet and outlet temperatures of the grinding machine system at adjacent moments, finding the optimal control sequence of the predicted values, and predicting the outlet temperature of the grinding machine system at the next moment. The controlled variable of the controlled object is obtained based on the predicted outlet temperature at the next moment, and the next prediction is initialized. The iteration continues as the optimization time progresses. S32, during each sampling period, the outlet temperature measurement value of the grinding machine is collected in real time to compensate for the temperature trend error generated by the prediction model.

7. The MPC grinding machine thermal management control method based on finite element method according to claim 1, characterized in that: The sampling formula of the Gaussian distribution function is: ； in, m Indicates the sampling point, set M sampling points, ; is the corresponding time point; These are all fitting parameters, obtained during actual project debugging. w 1 represents the peak height of the Gaussian function, that is, the maximum value of the function at the center position, w 2 corresponds to the mean of the Gaussian distribution, that is, the position of the symmetry axis of the distribution, w 3 is related to variance; After determining the sampling point position, use linear interpolation to select M The sampling points correspond to and , combined with the square wave convolution function , which is convolved with the square wave function to form a function G .

8. The MPC grinding machine thermal management control method based on finite element method according to claim 7, characterized in that: function Specifically: 。 9. The MPC grinding machine thermal management control method based on finite element analysis according to any one of claim 7, characterized in that: For cross-working joint control of multiple grinding machines, when statistically analyzing Gaussian distribution, the T value and ω value of each device are fused. is the startup time of the device, is the production cycle, which obeys Gaussian distribution 、 , is the cooling water input of the i-th equipment during coarse grinding, is the cooling water input of the i-th equipment during fine grinding, which obeys the Gaussian distribution after fusion ; The sampling formula of the Gaussian distribution function is: ； Among them, m represents the sampling point, and M sampling points are set. ; is the corresponding time point; These are all fitting parameters, obtained during actual project debugging. w 1 represents the peak height of the Gaussian function, that is, the maximum value of the function at the center position, w 2 corresponds to the mean of the Gaussian distribution, that is, the position of the symmetry axis of the distribution, w 3 is related to variance; Using the PSO algorithm combined with the Gaussian process, the total water consumption is set to After determining the sampling point position, linear interpolation is used to select M The sampling points correspond to and , combined with the square wave convolution function , which is convolved with the square wave function to form a function G : ； right G The function is optimized by PSO algorithm, and the optimized T Value and ω After several iterations, the joint control T value.

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