Method, device, terminal and medium for use and management of intelligent grinding equipment

Abstract

This application discloses a method, device, terminal, and medium for managing the use of intelligent grinding equipment. The method includes: obtaining first balance data based on coolant information, grinding wheel information, and workpiece information; obtaining second balance data based on the grinding wheel information, grinding wheel control system information, and workpiece information; obtaining third balance data based on the grinding wheel control system information, machine tool information, and workpiece information; and obtaining target usage rules based on the first balance data, the second balance data, the third balance data, and the workpiece accuracy standard. This application aims to accurately propose usage rules for intelligent grinding equipment, thereby improving the processing quality of intelligent grinding equipment.

Description

Technical Field

[0001] The present application relates to the technical field of grinding equipment management, and in particular to a method, device, terminal and medium for use management of intelligent grinding equipment. Background Art

[0002] Intelligent grinding equipment is a modern processing device that integrates advanced sensor technology, automated control technology, artificial intelligence technology, and communication technology based on traditional grinding equipment. Equipped with a variety of high-precision sensors, such as force sensors, displacement sensors, and temperature sensors, it can sense various physical quantities during the grinding process in real time, such as grinding force, workpiece dimensional changes, and grinding zone temperature. Through an automated control system, it automatically adjusts grinding parameters such as grinding wheel speed, feed rate, and grinding depth based on preset parameters and real-time monitoring data, achieving efficient and precise grinding.

[0003] However, the vast amount of data generated by intelligent grinding equipment comes from different types of sensors and systems, with inconsistent data formats and standards, making data integration difficult. Under complex working conditions, intelligent grinding equipment's adaptability is insufficient. For example, when the workpiece material, shape, and processing requirements change, the equipment may be unable to accurately adjust the grinding parameters, requiring intervention and adjustment by highly skilled technical personnel. This, in turn, makes it difficult to guarantee the processing quality of the grinding equipment. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, terminal and medium for the use and management of intelligent grinding equipment, aiming to accurately propose the use rules of intelligent grinding equipment and thereby improve the processing quality of intelligent grinding equipment.

[0005] To achieve the above objectives, the present application provides a method for managing the use of an intelligent grinding device, the method comprising:

[0006] Acquiring coolant information of the intelligent grinding device, grinding wheel information of the intelligent grinding device, machine tool information of the intelligent grinding device, grinding wheel control system information of the intelligent grinding device, workpiece information corresponding to the intelligent grinding device, and workpiece precision standards corresponding to the intelligent grinding device, wherein the workpiece information is used to characterize the precision of the workpiece processed by the intelligent grinding device;

[0007] Based on the coolant information, the grinding wheel information, and the workpiece information, first balance data is obtained, wherein the first balance data is used to characterize a dynamic balance relationship between the coolant and the grinding wheel;

[0008] obtaining second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, wherein the second balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the grinding wheel;

[0009] Obtaining third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information, wherein the third balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the machine tool;

[0010] Based on the first balance data, the second balance data, the third balance data and the workpiece precision standard, a target usage rule is obtained to ensure that the precision condition of the workpiece processed by the intelligent grinding equipment meets the workpiece precision standard.

[0011] Specifically, obtaining first balance data based on the coolant information, the grinding wheel information, and the workpiece information includes:

[0012] The first balancing data is obtained by presetting a first model according to the coolant information, the grinding wheel information and the workpiece information.

[0013] Specifically, the preset first model includes a first input layer, a first hidden layer, and a first output layer, the first hidden layer includes a first sub-hidden layer and a second sub-hidden layer, the number of neurons in the first sub-hidden layer is twice the number of neurons in the second sub-hidden layer, and the first output layer includes one neuron;

[0014] The first balancing data is obtained by presetting the first model according to the grinding wheel information and the workpiece information, including:

[0015] Obtaining a first input vector through the first input layer according to the coolant information, the grinding wheel information, and the workpiece information;

[0016] Obtaining a first intermediate vector according to the first input vector through the first sub-hidden layer;

[0017] Obtaining a second intermediate vector according to the first intermediate vector through the second sub-hidden layer;

[0018] The first balanced data is obtained through the first output layer according to the first intermediate vector.

[0019] Specifically, obtaining the second balance data based on the grinding wheel information, the grinding wheel control system information, and the processed workpiece information includes:

[0020] By presetting a second model, the second balance data is obtained according to the grinding wheel information, the grinding wheel control system information and the processed workpiece information, wherein the preset second model includes a second input layer, a second hidden layer and a second output layer, the second hidden layer includes a third sub-hidden layer and a fourth sub-hidden layer, the number of neurons in the third sub-hidden layer is twice the number of the fourth sub-hidden layer, and the second output layer contains one neuron.

[0021] Specifically, obtaining the third balance data based on the grinding wheel control system information, the machine tool information, and the processed workpiece information includes:

[0022] The third balancing data is obtained by presetting a third model according to the grinding wheel control system information, the machine tool information and the processed workpiece information.

[0023] Specifically, the preset third model includes a long short-term memory network layer, a first dense layer, and a second dense layer, wherein the second dense layer includes one neuron;

[0024] The third balancing data is obtained by presetting the third model according to the grinding wheel control system information, the machine tool information, and the workpiece information, including:

[0025] Obtaining a sequence processing vector according to the grinding wheel control system information, the machine tool information, and the workpiece information through the long short-term memory network layer;

[0026] Processing the vector according to the sequence through the first dense layer to obtain a third intermediate vector;

[0027] The third balanced data is obtained according to the third intermediate vector through the second dense layer.

[0028] Specifically, obtaining the target usage rule based on the first balance data, the second balance data, the third balance data, and the machining workpiece accuracy standard includes:

[0029] Normalizing the first balance data, the second balance data, the third balance data, and the workpiece precision standard to obtain a normalized data matrix;

[0030] Acquiring initial usage rules according to the normalized data matrix, and encoding and generating chromosomes based on the initial usage rules, wherein the initial usage rules correspond to the chromosomes one to one;

[0031] The initial usage rules corresponding to each chromosome are evaluated by a support vector machine, and a fitness value corresponding to the initial usage rule is calculated, wherein the fitness value is used to represent the degree to which the workpiece accuracy meets the processing workpiece accuracy standard when the initial usage rule is applied;

[0032] Based on the chromosomes, randomly generating an initial population;

[0033] Selecting chromosomes whose fitness values ​​meet a preset fitness value standard from the initial population, and performing a crossover operation and a mutation operation on the chromosomes whose fitness values ​​meet the preset fitness value standard to obtain operated chromosomes;

[0034] Repeating the steps of selecting chromosomes whose fitness values ​​meet the preset fitness value standard from the initial population, and performing a crossover operation and a mutation operation on the chromosomes whose fitness values ​​meet the preset fitness value standard to obtain the steps corresponding to the operated chromosomes, until the fitness value of the operated chromosomes no longer increases, thereby obtaining a target chromosome;

[0035] The target usage rule is determined based on the target chromosome.

[0036] To achieve the above objectives, the present application also provides a device for managing the use of an intelligent grinding device, the device comprising:

[0037] The first unit is used to obtain coolant information of the intelligent grinding device, grinding wheel information of the intelligent grinding device, machine tool information of the intelligent grinding device, grinding wheel control system information of the intelligent grinding device, workpiece information corresponding to the intelligent grinding device, and workpiece accuracy standard corresponding to the intelligent grinding device, wherein the workpiece information is used to represent the accuracy of the workpiece processed by the intelligent grinding device;

[0038] a second unit, configured to obtain first balance data based on the coolant information, the grinding wheel information, and the workpiece information, wherein the first balance data is used to characterize a dynamic balance relationship between the coolant and the grinding wheel;

[0039] a third unit, configured to obtain second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, wherein the second balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the grinding wheel;

[0040] a fourth unit, configured to obtain third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information, wherein the third balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the machine tool;

[0041] The fifth unit is used to obtain target usage rules based on the first balance data, the second balance data, the third balance data and the processing workpiece precision standard to ensure that the precision condition of the workpiece processed by the intelligent grinding equipment meets the processing workpiece precision standard.

[0042] To achieve the above objectives, the present application also provides a terminal, comprising a memory storing a plurality of instructions; the processor loads instructions from the memory to execute the steps in any one of the methods provided in the present application.

[0043] To achieve the above objectives, the present application also provides a medium, which stores multiple instructions, and the instructions are suitable for a processor to load to execute the steps of any method provided in the present application.

[0044] The present application provides a method, device, terminal and medium for use and management of intelligent grinding equipment, which can first obtain the coolant information of the intelligent grinding equipment, the grinding wheel information of the intelligent grinding equipment, the machine tool information of the intelligent grinding equipment, the grinding wheel control system information of the intelligent grinding equipment, the processing workpiece information corresponding to the intelligent grinding equipment and the processing workpiece accuracy standard corresponding to the intelligent grinding equipment, wherein the processing workpiece information is used to characterize the accuracy status of the workpiece processed by the intelligent grinding equipment; then, based on the coolant information, the grinding wheel information and the processing workpiece information, first balance data is obtained, wherein the first balance data is used to characterize the dynamic balance relationship between the coolant and the grinding wheel; then, based on the grinding wheel information, the grinding wheel information and the processing workpiece information, the first balance data is obtained, wherein the first balance data is used to characterize the dynamic balance relationship between the coolant and the grinding wheel; The method comprises the following steps: first, a grinding wheel control system information and the processed workpiece information, and obtains second balance data, wherein the second balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the grinding wheel; second, a grinding wheel control system information and the processed workpiece information are used to obtain second balance data, wherein the second balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the machine tool; and finally, a target usage rule is obtained based on the first balance data, the second balance data, the third balance data and the processing workpiece precision standard to ensure that the precision condition of the workpiece processed by the intelligent grinding equipment meets the processing workpiece precision standard, thereby improving the processing quality of the intelligent grinding equipment by implementing the target usage rule. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of a process for the method provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of the structure of the device provided in the embodiment of the present application;

[0047] Figure 3 A schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0048] Description of the drawings: 200 - usage management device for intelligent grinding equipment, 201 - first unit, 202 - second unit, 203 - third unit, 204 - fourth unit, 205 - fifth unit, 300 - terminal, 301 - processor, 302 - memory, 303 - power supply, 304 - input module, 305 - communication module. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0050] Because intelligent grinding equipment generates a large amount of data from various sensors and systems, with inconsistent data formats and standards, data integration is difficult. Under complex working conditions, intelligent grinding equipment's adaptive capabilities are insufficient. For example, when the workpiece material, shape, and processing requirements change, the equipment may not be able to accurately adjust the grinding parameters, requiring intervention and adjustment by highly skilled technical personnel. This, in turn, makes it difficult to guarantee the processing quality of the grinding equipment.

[0051] Therefore, the embodiments of the present application provide a method, device, terminal and medium for use and management of intelligent grinding equipment to solve practical technical problems.

[0052] In some embodiments, the device may be integrated into an electronic device, which may be a terminal, a server, or other device.

[0053] In some embodiments, the server may also be implemented in the form of a terminal.

[0054] Among them, the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0055] The terminal may be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited thereto. The terminal and the server may be directly or indirectly connected via wired or wireless communication, and this application does not impose any restrictions thereon.

[0056] It should be noted that the serial numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0057] The present application embodiment provides a method for managing the use of an intelligent grinding device, such as Figure 1 , the specific process of the method can be as follows:

[0058] S110. Obtain coolant information of the intelligent grinding equipment, grinding wheel information of the intelligent grinding equipment, machine tool information of the intelligent grinding equipment, grinding wheel control system information of the intelligent grinding equipment, workpiece information corresponding to the intelligent grinding equipment, and workpiece precision standard corresponding to the intelligent grinding equipment, wherein the workpiece information is used to characterize the precision condition of the workpiece processed by the intelligent grinding equipment.

[0059] In some embodiments, the coolant information may include the following:

[0060] Temperature: The temperature of the coolant affects its cooling effect and chemical stability. Excessively high temperatures may cause the coolant to lose performance and fail to effectively remove the heat generated by grinding, which in turn affects the surface quality of the workpiece and the life of the grinding wheel. For example, when the coolant temperature exceeds a certain threshold, it may cause thermal deformation of the workpiece and reduce machining accuracy.

[0061] Concentration: Coolant is usually a mixture of base fluid and additives. Its concentration has a significant impact on lubrication and rust prevention performance. Too high a concentration may increase costs and lead to reduced cleaning performance. Too low a concentration may not provide adequate lubrication and rust prevention protection, accelerating grinding wheel wear and workpiece rust.

[0062] Liquid level: The coolant level must be maintained within the appropriate range. A low level may result in insufficient coolant supply, affecting cooling and lubrication effects; a high level may cause overflow, wasting coolant and polluting the working environment.

[0063] Acidity (pH): The pH value of the coolant affects its corrosiveness to metals and the growth of microorganisms. Generally speaking, the appropriate pH value range can prevent rust on workpieces and equipment, inhibit the growth of microorganisms, and ensure the service life of the coolant;

[0064] Additive content: The content of additives in the coolant, such as preservatives and defoaming agents, will affect its various performances. For example, insufficient preservative content may cause the coolant to deteriorate easily, and inappropriate defoaming agent content may produce excessive foam, affecting the cooling and lubrication effects.

[0065] In some embodiments, the grinding wheel information may include the following:

[0066] Grain size: refers to the size of the abrasive particles. The finer the particle size, the smoother the grinding surface, but the grinding efficiency is relatively low; the coarser the particle size, the higher the grinding efficiency, but the surface roughness is greater. Different processing requirements require the selection of grinding wheels with appropriate particle sizes.

[0067] Hardness: reflects the difficulty of the grinding wheel abrasive grains falling off under the action of grinding force. If the hardness is too high, the abrasive grains will not fall off easily after being worn, which will lead to increased grinding force and burns on the workpiece surface; if the hardness is too low, the abrasive grains will fall off easily, the grinding wheel will wear quickly, and the processing accuracy will be affected.

[0068] Bond type: Common bonds include ceramics, resins, rubber, etc. Grinding wheels with different bonds have different performance characteristics. For example, ceramic bond grinding wheels have high heat resistance and chemical stability and are suitable for high-speed grinding; resin bond grinding wheels have good elasticity and are suitable for grinding thin-walled parts and form grinding;

[0069] Wear: includes radial wear and axial wear of the grinding wheel. Excessive wear may cause the outer diameter of the grinding wheel to become smaller and the thickness to become thinner, affecting the processing dimensional accuracy and surface quality;

[0070] Wear forms: such as abrasive wear, binder wear, grinding wheel clogging, etc. Different wear forms will have different effects on the grinding process. For example, grinding wheel clogging will lead to increased grinding force, increased grinding temperature, and reduced processing quality.

[0071] In some embodiments, the machine tool information may include the following:

[0072] Guide rail accuracy: The straightness, parallelism and other accuracy indicators of the guide rail will affect the movement accuracy of the worktable, and thus affect the processing accuracy of the workpiece. Low guide rail accuracy may cause straightness errors and flatness errors on the workpiece surface;

[0073] Spindle accuracy: Parameters such as the spindle's rotation accuracy, radial runout and axial play have an important influence on the grinding accuracy. Insufficient spindle accuracy may cause roundness error, cylindricity error, etc. on the workpiece surface.

[0074] In some embodiments, the grinding wheel control system information may include the following:

[0075] Grinding wheel speed control: Accurately controlling the grinding wheel speed is crucial to ensuring grinding quality and efficiency. Different workpiece materials and processing requirements require different grinding wheel speeds. Too high a speed may cause increased grinding wheel wear, while too low a speed may affect grinding efficiency.

[0076] Feed speed control: including the radial feed speed and axial feed speed of the grinding wheel. Too fast feed speed may lead to increased grinding force and burns on the workpiece surface; too slow feed speed will reduce processing efficiency.

[0077] In some embodiments, the processing workpiece information may include the following:

[0078] Shape accuracy: such as roundness, cylindricity, flatness, etc. Shape accuracy affects the performance and assembly accuracy of the workpiece. For example, excessive cylindricity error may cause the workpiece to vibrate during rotation;

[0079] Surface roughness: refers to the error in the microscopic geometric shape of the workpiece surface. The smaller the surface roughness value, the smoother the surface. Appropriate surface roughness can improve the wear resistance, corrosion resistance and fitting accuracy of the workpiece.

[0080] Surface defects: such as cracks, scratches, burns, etc. Surface defects will reduce the strength and service life of the workpiece and affect product quality.

[0081] In some embodiments, the machining workpiece accuracy standards may include the following:

[0082] Dimensional tolerance standard: specifies the allowable deviation range of each dimension of the workpiece. For example, for a shaft with a diameter of φ50mm, its dimensional tolerance may be specified as φ50±0.05mm. As long as the diameter of the processed shaft is within this range, the dimensional accuracy is considered to meet the standard;

[0083] Form and position tolerance standards: These include the allowable deviation ranges for form tolerances (such as roundness and cylindricity) and position tolerances (such as coaxiality and perpendicularity). These standards ensure that the shape and position accuracy of the workpiece meet the design requirements and guarantee the performance of the workpiece during assembly and use.

[0084] Surface quality standards: requirements for workpiece surface roughness, surface defects, etc. For example, some precision parts may require a surface roughness of Ra0.8μm or less, and no cracks or scratches are allowed.

[0085] S120. Obtain first balance data based on the coolant information, the grinding wheel information, and the workpiece information, wherein the first balance data is used to characterize a dynamic balance relationship between the coolant and the grinding wheel.

[0086] In some embodiments, obtaining first balance data based on the coolant information, the grinding wheel information, and the workpiece information includes:

[0087] The first balancing data is obtained by presetting a first model according to the coolant information, the grinding wheel information and the workpiece information.

[0088] Specifically, the preset first model includes a first input layer, a first hidden layer and a first output layer, the first hidden layer includes a first sub-hidden layer and a second sub-hidden layer, the number of neurons in the first sub-hidden layer is twice the number of neurons in the second sub-hidden layer, and the first output layer contains one neuron.

[0089] The first balancing data is obtained by presetting the first model according to the grinding wheel information and the workpiece information, including steps S121 to S124 as shown below:

[0090] S121. Obtain a first input vector through the first input layer according to the coolant information, the grinding wheel information, and the workpiece information.

[0091] S122. Obtain a first intermediate vector according to the first input vector through the first sub-hidden layer.

[0092] S123. Obtain a second intermediate vector according to the first intermediate vector through the second sub-hidden layer.

[0093] S124. Obtain the first balanced data according to the first intermediate vector through the first output layer.

[0094] Specifically, for the first input layer, assuming that the coolant information has n1 features, the grinding wheel information has n2 features, and the workpiece information has n3 features, then the dimension of the first input vector of the first input layer is n1+ n2+ n3.

[0095] Continuing with the above embodiment, the first sub-hidden layer can be a fully connected layer containing 64 neurons and using ReLU (Rectified Linear Unit) as the activation function. A fully connected layer means that each neuron in this layer is connected to all neurons in the first input layer. Each input neuron of the first input vector has a corresponding weight associated with each of the 64 neurons in the hidden layer. Each neuron in the first sub-hidden layer performs a weighted summation on the input, then performs a nonlinear transformation using the ReLU activation function, ultimately outputting 64 values, resulting in an output dimension of 64. This results in the first intermediate vector.

[0096] Continuing with the above example, the second sub-hidden layer is also a fully connected layer, containing 32 neurons and also using ReLU as the activation function. The second sub-hidden layer further extracts and transforms the features output by the first sub-hidden layer to develop higher-level feature representations. The input to the second sub-hidden layer comes from the output of the first hidden layer, with an input dimension of 64. Each input value is connected to the 32 neurons in this hidden layer with a weight. The 32 neurons in the second sub-hidden layer perform a weighted summation of the inputs and process them with the ReLU activation function, resulting in an output dimension of 32, thus generating the second intermediate vector.

[0097] Continuing with the above example, the first output layer is a fully connected layer containing one neuron and using no activation function (i.e., a linear activation function). Since the goal is to predict the first balanced data, which is a continuous value, a linear activation function can be used to directly output the prediction result. The input to the first output layer comes from the output of the second hidden layer, with an input dimension of 32. Each input value is connected to a neuron in the first output layer by a weight. The neurons in the first output layer perform a weighted summation of the inputs and directly output a single value, which is the predicted first balanced data. The output dimension is 1.

[0098] S130. Obtain second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, wherein the second balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the grinding wheel.

[0099] Continuing with the above embodiment, the second balance data is obtained based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, including:

[0100] By presetting a second model, the second balance data is obtained according to the grinding wheel information, the grinding wheel control system information and the processed workpiece information, wherein the preset second model includes a second input layer, a second hidden layer and a second output layer, the second hidden layer includes a third sub-hidden layer and a fourth sub-hidden layer, the number of neurons in the third sub-hidden layer is twice the number of the fourth sub-hidden layer, and the second output layer contains one neuron.

[0101] Specifically, the network structure of the preset second model is similar to that of the preset first model, and will not be described in detail here.

[0102] S140. Obtain third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information, wherein the third balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the machine tool.

[0103] In some embodiments, obtaining the third balance data based on the grinding wheel control system information, the machine tool information, and the processed workpiece information includes:

[0104] The third balancing data is obtained by presetting a third model according to the grinding wheel control system information, the machine tool information and the processed workpiece information.

[0105] Specifically, the preset third model includes a long short-term memory network layer, a first dense layer, and a second dense layer, wherein the second dense layer includes one neuron;

[0106] The third balancing data is obtained by presetting the third model according to the grinding wheel control system information, the machine tool information, and the workpiece information, including the steps S141 to S143 as shown below:

[0107] S141. Obtain a sequence processing vector through the long short-term memory network layer according to the grinding wheel control system information, the machine tool information, and the processed workpiece information.

[0108] S142. Process the vector according to the sequence through the first dense layer to obtain a third intermediate vector.

[0109] S143. Obtain the third balanced data according to the third intermediate vector through the second dense layer.

[0110] Continuing with the above example, the LSTM layer can be an LSTM layer, which can include 64 LSTM units. LSTM units can learn long-term dependencies in sequence data and control the flow and memory of information through gating mechanisms (input gate, forget gate, and output gate). The input dimension of the LSTM layer is (number of samples, time step, number of features). The output of the LSTM layer is the hidden state of each sample at the last time step, with an output dimension of (number of samples, 64), i.e., the sequence processing vector.

[0111] Continuing with the above example, the first dense layer can be a fully connected layer containing 32 neurons, using ReLU as the activation function to further extract and transform features from the LSTM layer's output. The first dense layer's input comes from the LSTM layer's output, with an input dimension of (number of samples, 64), i.e., the sequence processing vector. The 32 neurons in the first dense layer perform a weighted sum of the inputs and process them with the ReLU activation function, resulting in an output dimension of (number of samples, 32), i.e., the third intermediate vector.

[0112] Continuing with the above embodiment, the second dense layer can be a fully connected layer containing one neuron without an activation function (i.e., a linear activation function). It is used to output the predicted third balanced data. The input of the second dense layer comes from the output of the first fully connected layer, with an input dimension of (number of samples, 32), i.e., the third intermediate vector. The neurons in the second dense layer perform a weighted sum of the inputs and directly output a single value with an output dimension of (number of samples, 1), i.e., the third balanced data.

[0113] S150: Based on the first balance data, the second balance data, the third balance data, and the workpiece precision standard, a target usage rule is obtained to ensure that the precision of the workpiece processed by the intelligent grinding equipment meets the workpiece precision standard.

[0114] In some embodiments, obtaining the target usage rule based on the first balance data, the second balance data, the third balance data, and the workpiece precision standard includes steps S151 to S157 as follows:

[0115] S151 , normalizing the first balance data, the second balance data, the third balance data, and the workpiece precision standard to obtain a normalized data matrix.

[0116] In some embodiments, the first, second, and third balance data, as well as the workpiece accuracy standard, are normalized using a Min-Max normalization method, scaling the data to the [0, 1] range. For example, for the coolant temperature in the first balance data, assuming its original value range is [20°C, 60°C], after normalization, the temperature value will be mapped to the [0, 1] range. Similar normalization can be performed on other data.

[0117] S152. Acquire initial usage rules according to the normalized data matrix, and encode and generate chromosomes based on the initial usage rules, wherein the initial usage rules correspond to the chromosomes one-to-one.

[0118] Continuing with the above example, some simple initial usage rules can be developed based on the normalized data matrix. For example, Rule 1: If the coolant temperature (normalized) is less than 0.3 and the grinding wheel speed (normalized) is greater than 0.7, then the machining accuracy standard is likely met. These rules are encoded into chromosomes, with each rule corresponding to a chromosome. Assume that 50 initial usage rules are developed, each represented by a binary vector of length 12. Each element in the vector corresponds to a feature in the normalized data matrix. A value of 1 indicates that the feature participates in the rule evaluation, and a value of 0 indicates that it does not participate.

[0119] S153. Evaluate the initial usage rules corresponding to each chromosome respectively through a support vector machine, and calculate the fitness value corresponding to the initial usage rule, wherein the fitness value is used to characterize the degree to which the workpiece accuracy meets the processing workpiece accuracy standard when the initial usage rule is applied.

[0120] Continuing with the previous example, a support vector machine (SVM) is used to evaluate the initial usage rules corresponding to each chromosome. For each chromosome, the features involved in rule evaluation are determined based on its encoding. The corresponding data is extracted, and the SVM model is trained. Its accuracy on the training data is calculated, and this accuracy is used as the fitness value of the rule.

[0121] S154. Randomly generate an initial population based on the chromosome.

[0122] Continuing with the above embodiment, for example, the initial population may include 50 chromosomes.

[0123] S155. Select chromosomes whose fitness values ​​meet a preset fitness value standard from the initial population, and perform a crossover operation and a mutation operation on the chromosomes whose fitness values ​​meet the preset fitness value standard to obtain operated chromosomes.

[0124] Continuing with the above example, chromosomes whose fitness values ​​meet a preset fitness value standard (e.g., a fitness value greater than 0.8) can be selected from the initial population. A crossover operation can be performed on these chromosomes, such as randomly selecting a crossover point and swapping some genes between the two chromosomes. A mutation operation can also be performed, randomly changing a gene in the chromosome with a certain probability (e.g., 0.01).

[0125] S156. Repeat the steps of selecting a chromosome whose fitness value meets the preset fitness value standard from the initial population, and performing a crossover operation and a mutation operation on the chromosome whose fitness value meets the preset fitness value standard to obtain the step content corresponding to the operated chromosome, until the fitness value of the operated chromosome no longer increases, and obtaining the target chromosome.

[0126] Continuing with the above embodiment, step S155 is repeated until the fitness value of the chromosome after the operation no longer increases. In each iteration, the fitness value of the newly generated chromosome is re-evaluated, and the chromosome with the higher fitness value is selected for the next round of crossover and mutation operations until the fitness value of the chromosome after the operation no longer increases.

[0127] S157. Determine the target usage rule based on the target chromosome.

[0128] Continuing with the above example, based on the target chromosome, the features involved in rule determination are determined and combined with the SVM model to generate a target usage rule. For example, if bits 2, 5, and 8 of the target chromosome are 1, this indicates that the coolant temperature, the grinding wheel control system's speed control accuracy, and the machine tool's guide rail accuracy are involved in the rule determination. By training the SVM models corresponding to these three features, a target usage rule based on these three features is generated, which is used to guide the use of the intelligent grinding equipment to ensure that workpiece accuracy meets the machining accuracy standard.

[0129] In summary, this application provides a method for managing the use of intelligent grinding equipment, accurately proposes usage rules for intelligent grinding equipment, and thereby improves the processing quality of the intelligent grinding equipment.

[0130] To better implement the above method, the present application also provides an intelligent grinding equipment usage management device. The device can be integrated into an electronic device, such as a terminal or server. The terminal can be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, personal computer, etc. The server can be a single server or a server cluster consisting of multiple servers.

[0131] For example, in this embodiment, the method of the embodiment of the present application will be described in detail by taking the specific integration of the use management device of the intelligent grinding equipment in the terminal as an example.

[0132] For example, Figure 2 As shown, the use management device 200 of the intelligent grinding equipment may include a first unit 201, a second unit 202, a third unit 203, a fourth unit 204 and a fifth unit 205, and the device includes:

[0133] The first unit is used to obtain coolant information of the intelligent grinding device, grinding wheel information of the intelligent grinding device, machine tool information of the intelligent grinding device, grinding wheel control system information of the intelligent grinding device, workpiece information corresponding to the intelligent grinding device, and workpiece accuracy standard corresponding to the intelligent grinding device, wherein the workpiece information is used to represent the accuracy of the workpiece processed by the intelligent grinding device;

[0134] a second unit, configured to obtain first balance data based on the coolant information, the grinding wheel information, and the workpiece information, wherein the first balance data is used to characterize a dynamic balance relationship between the coolant and the grinding wheel;

[0135] a third unit, configured to obtain second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, wherein the second balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the grinding wheel;

[0136] a fourth unit, configured to obtain third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information, wherein the third balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the machine tool;

[0137] The fifth unit is used to obtain target usage rules based on the first balance data, the second balance data, the third balance data and the processing workpiece precision standard to ensure that the precision condition of the workpiece processed by the intelligent grinding equipment meets the processing workpiece precision standard.

[0138] In specific implementation, the above units can be implemented as independent entities, or can be arbitrarily combined to be implemented as the same or several entities. The specific implementation of the above units can be found in the previous method embodiments and will not be repeated here.

[0139] From the above, it can be seen that the embodiments of the present application can accurately propose usage rules for intelligent grinding equipment, thereby improving the processing quality of the intelligent grinding equipment.

[0140] The present application also provides an electronic device, which may be a terminal, a server, or the like. The terminal may be a mobile phone, a tablet computer, a smart Bluetooth device, a laptop computer, a personal computer, or the like; the server may be a single server or a server cluster consisting of multiple servers, or the like.

[0141] In some embodiments, the product processing device may also be integrated into multiple electronic devices. For example, the product processing device may be integrated into multiple servers, and the use management method of the intelligent grinding equipment of the present application may be implemented by the multiple servers.

[0142] In this embodiment, the electronic device of this embodiment is a terminal as an example for detailed description, for example, Figure 3 As shown, it shows a schematic diagram of the structure of the terminal 300 involved in the embodiment of the present application, specifically:

[0143] The terminal 300 may include one or more processors 301, one or more storage media 302, a power supply 303, an input module 304, and a communication module 305. Those skilled in the art will appreciate that Figure 3 The structure of the terminal 300 shown in the figure does not constitute a limitation on the terminal 300, and the terminal 300 may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0144] Processor 301 is the processor of terminal 300. It connects various components of terminal 300 using various interfaces and circuits. By running or executing software programs and / or modules stored in memory 302 and accessing data stored in memory 302, it performs various functions of terminal 300 and processes data, thereby providing overall monitoring of terminal 300. In some embodiments, processor 301 may include one or more processing cores. In some embodiments, processor 301 may integrate an application processor and a modem processor. The application processor primarily processes the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 301.

[0145] Memory 302 can be used to store software programs and modules. Processor 301 executes various functional applications and data processing by running the software programs and modules stored in memory 302. Memory 302 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback). The data storage area may store data generated based on the use of terminal 300. Memory 302 may also include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory 302 may also include a memory controller to provide processor 301 with access to memory 302.

[0146] Terminal 300 also includes a power supply 303 for supplying power to various components. In some embodiments, power supply 303 can be logically connected to processor 301 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. Power supply 303 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0147] The terminal 300 may further include an input module 304 , which may be configured to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0148] The terminal 300 may also include a communication module 305. In some embodiments, the communication module 305 may include a wireless module. The terminal 300 may use the wireless module of the communication module 305 to perform short-range wireless transmission, thereby providing the user with wireless broadband Internet access. For example, the communication module 305 may be used to help the user send and receive emails, browse web pages, and access streaming media.

[0149] Although not shown, the terminal 300 may further include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the terminal 300 will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302 to implement various functions as follows:

[0150] Acquiring coolant information of the intelligent grinding device, grinding wheel information of the intelligent grinding device, machine tool information of the intelligent grinding device, grinding wheel control system information of the intelligent grinding device, workpiece information corresponding to the intelligent grinding device, and workpiece precision standards corresponding to the intelligent grinding device, wherein the workpiece information is used to characterize the precision of the workpiece processed by the intelligent grinding device;

[0151] Based on the coolant information, the grinding wheel information, and the workpiece information, first balance data is obtained, wherein the first balance data is used to characterize a dynamic balance relationship between the coolant and the grinding wheel;

[0152] obtaining second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, wherein the second balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the grinding wheel;

[0153] Obtaining third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information, wherein the third balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the machine tool;

[0154] Based on the first balance data, the second balance data, the third balance data and the workpiece precision standard, a target usage rule is obtained to ensure that the precision condition of the workpiece processed by the intelligent grinding equipment meets the workpiece precision standard.

[0155] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0156] From the above, it can be seen that the embodiments of the present application can accurately propose usage rules for intelligent grinding equipment, thereby improving the processing quality of the intelligent grinding equipment.

[0157] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a medium and loaded and executed by a processor.

[0158] To this end, an embodiment of the present application provides a medium storing a plurality of instructions that can be loaded by a processor to execute the steps of any of the methods for managing the use of an intelligent grinding device provided in the embodiments of the present application. For example, the instructions can execute the following steps:

[0159] Acquiring coolant information of the intelligent grinding device, grinding wheel information of the intelligent grinding device, machine tool information of the intelligent grinding device, grinding wheel control system information of the intelligent grinding device, workpiece information corresponding to the intelligent grinding device, and workpiece precision standards corresponding to the intelligent grinding device, wherein the workpiece information is used to characterize the precision of the workpiece processed by the intelligent grinding device;

[0160] Based on the coolant information, the grinding wheel information, and the workpiece information, first balance data is obtained, wherein the first balance data is used to characterize a dynamic balance relationship between the coolant and the grinding wheel;

[0161] obtaining second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, wherein the second balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the grinding wheel;

[0162] Obtaining third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information, wherein the third balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the machine tool;

[0163] Based on the first balance data, the second balance data, the third balance data and the workpiece precision standard, a target usage rule is obtained to ensure that the precision condition of the workpiece processed by the intelligent grinding equipment meets the workpiece precision standard.

[0164] The medium may include: Read Only Memory (ROM), Random Access Memory (RAM), a magnetic disk or an optical disk, etc.

[0165] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a medium. A processor of a computer device reads the computer instructions from the medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations provided in the above embodiments.

[0166] Since the instructions stored in the medium can execute the steps in any method for managing the use of intelligent grinding equipment provided in the embodiments of the present application, the beneficial effects that can be achieved by any method for managing the use of intelligent grinding equipment provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0167] The above is a detailed introduction to the use and management method, device, terminal and medium of an intelligent grinding equipment provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application; at the same time, for technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for managing the use of intelligent grinding equipment, characterized in that: The method comprises: Acquiring coolant information of the intelligent grinding device, grinding wheel information of the intelligent grinding device, machine tool information of the intelligent grinding device, grinding wheel control system information of the intelligent grinding device, workpiece information corresponding to the intelligent grinding device, and workpiece precision standards corresponding to the intelligent grinding device, wherein the workpiece information is used to characterize the precision of the workpiece processed by the intelligent grinding device; Based on the coolant information, the grinding wheel information, and the workpiece information, first balance data is obtained, wherein the first balance data is used to characterize a dynamic balance relationship between the coolant and the grinding wheel; obtaining second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, wherein the second balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the grinding wheel; Obtaining third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information, wherein the third balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the machine tool; Based on the first balance data, the second balance data, the third balance data and the workpiece precision standard, a target usage rule is obtained to ensure that the precision condition of the workpiece processed by the intelligent grinding equipment meets the workpiece precision standard.

2. The method according to claim 1, wherein The obtaining of first balance data based on the coolant information, the grinding wheel information, and the workpiece information includes: The first balancing data is obtained by presetting a first model according to the coolant information, the grinding wheel information and the workpiece information.

3. The method according to claim 2, wherein The preset first model includes a first input layer, a first hidden layer, and a first output layer, the first hidden layer includes a first sub-hidden layer and a second sub-hidden layer, the number of neurons in the first sub-hidden layer is twice the number of neurons in the second sub-hidden layer, and the first output layer includes one neuron; The first balancing data is obtained by presetting the first model according to the grinding wheel information and the workpiece information, including: Obtaining a first input vector through the first input layer according to the coolant information, the grinding wheel information, and the workpiece information; Obtaining a first intermediate vector according to the first input vector through the first sub-hidden layer; Obtaining a second intermediate vector according to the first intermediate vector through the second sub-hidden layer; The first balanced data is obtained through the first output layer according to the first intermediate vector.

4. The method according to claim 1, wherein The obtaining of second balance data based on the grinding wheel information, the grinding wheel control system information, and the processed workpiece information includes: By presetting a second model, the second balance data is obtained according to the grinding wheel information, the grinding wheel control system information and the processed workpiece information, wherein the preset second model includes a second input layer, a second hidden layer and a second output layer, the second hidden layer includes a third sub-hidden layer and a fourth sub-hidden layer, the number of neurons in the third sub-hidden layer is twice the number of the fourth sub-hidden layer, and the second output layer contains one neuron.

5. The method according to claim 1, wherein The obtaining of third balance data based on the grinding wheel control system information, the machine tool information, and the processed workpiece information includes: The third balancing data is obtained by presetting a third model according to the grinding wheel control system information, the machine tool information and the processed workpiece information.

6. The method according to claim 5, wherein The preset third model includes a long short-term memory network layer, a first dense layer, and a second dense layer, wherein the second dense layer includes one neuron; The third balancing data is obtained by presetting the third model according to the grinding wheel control system information, the machine tool information, and the workpiece information, including: Obtaining a sequence processing vector according to the grinding wheel control system information, the machine tool information, and the workpiece information through the long short-term memory network layer; Processing the vector according to the sequence through the first dense layer to obtain a third intermediate vector; The third balanced data is obtained according to the third intermediate vector through the second dense layer.

7. The method according to claim 1, wherein The obtaining of target usage rules based on the first balance data, the second balance data, the third balance data, and the machining workpiece accuracy standard includes: Normalizing the first balance data, the second balance data, the third balance data, and the workpiece precision standard to obtain a normalized data matrix; Acquiring initial usage rules according to the normalized data matrix, and encoding and generating chromosomes based on the initial usage rules, wherein the initial usage rules correspond to the chromosomes one to one; The initial usage rules corresponding to each chromosome are evaluated by a support vector machine, and a fitness value corresponding to the initial usage rule is calculated, wherein the fitness value is used to represent the degree to which the workpiece accuracy meets the processing workpiece accuracy standard when the initial usage rule is applied; Based on the chromosomes, randomly generating an initial population; Selecting chromosomes whose fitness values ​​meet a preset fitness value standard from the initial population, and performing a crossover operation and a mutation operation on the chromosomes whose fitness values ​​meet the preset fitness value standard to obtain operated chromosomes; Repeating the steps of selecting chromosomes whose fitness values ​​meet the preset fitness value standard from the initial population, and performing a crossover operation and a mutation operation on the chromosomes whose fitness values ​​meet the preset fitness value standard to obtain the steps corresponding to the operated chromosomes, until the fitness value of the operated chromosomes no longer increases, thereby obtaining a target chromosome; The target usage rule is determined based on the target chromosome.

8. A usage management device for intelligent grinding equipment, characterized in that: The device comprises: The first unit is used to obtain coolant information of the intelligent grinding device, grinding wheel information of the intelligent grinding device, machine tool information of the intelligent grinding device, grinding wheel control system information of the intelligent grinding device, workpiece information corresponding to the intelligent grinding device, and workpiece accuracy standard corresponding to the intelligent grinding device, wherein the workpiece information is used to represent the accuracy of the workpiece processed by the intelligent grinding device; a second unit, configured to obtain first balance data based on the coolant information, the grinding wheel information, and the workpiece information, wherein the first balance data is used to characterize a dynamic balance relationship between the coolant and the grinding wheel; a third unit, configured to obtain second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, wherein the second balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the grinding wheel; a fourth unit, configured to obtain third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information, wherein the third balance data is used to characterize a dynamic balance relationship between the grinding wheel control system and the machine tool; The fifth unit is used to obtain target usage rules based on the first balance data, the second balance data, the third balance data and the processing workpiece precision standard to ensure that the precision condition of the workpiece processed by the intelligent grinding equipment meets the processing workpiece precision standard.

9. A terminal, characterized in that: The method comprises a processor and a memory, wherein the memory stores a plurality of instructions; the processor loads instructions from the memory to execute the steps in the method according to any one of claims 1 to 7.

10. A medium, characterized in that The medium stores a plurality of instructions, which are suitable for loading by a processor to execute the steps in the method according to any one of claims 1 to 7.

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