Automatic grinding control system and method

Abstract

The invention relates to the technical field of automatic grinding, and discloses an automatic grinding control system and method, and the system comprises a monitoring unit which obtains the state information of a to-be-ground material and the operation state information of equipment; the first control module is used for generating state characteristic parameters of the to-be-ground material based on the obtained state information of the to-be-ground material; the second control module is used for constructing a grinding model library based on historical grinding data; the third control module is used for generating a corresponding equipment control instruction according to the state characteristic parameters of the to-be-ground material and the grinding model library; and the fourth control module judges whether an early warning instruction is generated or not based on the running state information of the equipment. The state information of the to-be-ground material and the operation state information of the equipment are obtained based on the monitoring unit, the central control unit generates the control instruction of the equipment based on the two kinds of information, and cooperative control over the material state and equipment operation is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of automated grinding, and in particular to an automated grinding control system and method. Background Art

[0002] During workpiece grinding, a grinding machine is typically used to grind the workpiece surface to meet requirements for workpiece removal, glossiness, and flatness. The grinding machine primarily consists of a grinding table and a grinding disc positioned above the table. During workpiece grinding, the grinding disc descends to the workpiece surface and grinds until the workpiece is completely ground.

[0003] As a critical process in precision machining, grinding has long faced the dilemma of balancing efficiency and precision. However, grinding equipment is susceptible to random factors during the grinding process, such as varying grinding fluid concentrations and uneven mixing, which can cause variations in the amount of material removed during grinding, resulting in workpiece gloss or flatness failing to meet requirements. Summary of the Invention

[0004] The purpose of this invention is to use a monitoring unit to obtain information about the grinding material's status (such as hardness, moisture, and particle size) as well as the equipment's operating status. Based on this information, the central control unit generates control instructions for the equipment, achieving coordinated control of material status and equipment operation. For example, when grinding materials of varying hardness, moisture, and particle size, the equipment can adjust grinding parameters based on the specific material conditions, thereby improving grinding accuracy.

[0005] In order to achieve the above object, the present invention provides an automated grinding control system, comprising: Monitoring unit: obtains status information of the material to be ground and operating status information of the equipment; Central control unit: generates equipment control instructions based on the status information of the material to be ground and the operating status information of the equipment; The central control unit includes: A first control module: generating a state characteristic parameter of the material to be ground based on the acquired state information of the material to be ground; The second control module: builds a grinding model library based on historical grinding data; The third control module generates control instructions for the corresponding equipment based on the state characteristic parameters of the material to be ground and the grinding model library; The fourth control module: determines whether to generate an early warning instruction based on the operating status information of the equipment.

[0006] In some embodiments of the present invention, the first control module is further configured to: Classify the acquired state information of the material to be ground to generate multiple types of material state information, including: hardness information value Sa, humidity information value Sb and particle size information value Sc; Based on the historical status information of the material to be ground, the value range [ai, bi] of each type of material status information is determined, (i=1, 2, 3); Generate state characteristic parameters of the material to be ground by combining the current type of material state information and the corresponding value range [ai, bi] of the material state information; Among them, the hardness characteristic parameter : ; Humidity characteristic parameters : ; Particle size characteristic parameters : ; Among them, a1 is the left endpoint of the value range of the hardness information value Sa, b1 is the right endpoint of the value range of the hardness information value Sa, a2 is the left endpoint of the value range of the humidity information value Sb, b2 is the right endpoint of the value range of the humidity information value Sb, a3 is the left endpoint of the value range of the particle size information value Sc, b3 is the right endpoint of the value range of the particle size information value Sc, is the historical average hardness information value.

[0007] In some embodiments of the present invention, the second control module is further configured to: Obtaining state characteristic parameters of historical grinding materials; Obtaining state characteristic parameters of historical grinding materials to classify the grinding materials and generate multiple types of grinding materials; Obtain equipment operating data for the grinding process of the current type of grinding material; Generate a sample of the current type of grinding material by combining state characteristic parameters of the current type of grinding material and equipment operation data corresponding to the grinding process; Build a grinding model library by combining samples of all types of grinding materials.

[0008] In some embodiments of the present invention, the third control module is further configured to: Determining the corresponding grinding material type based on the acquired state characteristic parameters of the material to be ground; Select samples of corresponding types of abrasive materials based on the abrasive material type and the abrasive model library; and generating corresponding control instructions for the equipment based on equipment operation data in samples of corresponding types of abrasive materials; The generating of the control instruction of the corresponding device also includes: Setting a plurality of grinding sub-stages based on equipment operation data in a sample of a corresponding type of grinding material, and setting a plurality of monitoring cycles for each grinding sub-stage; Setting corresponding grinding control instructions based on each grinding sub-stage; During the operation of the grinding control instruction, the state characteristic parameters of the material to be ground and the operating status information of the equipment are obtained based on the monitoring cycle; Generate a grinding state evaluation value p1 within the current monitoring period based on the acquired state characteristic parameters of the material to be ground and the operating state information of the equipment within the current monitoring period; A state prediction value for the current polishing state sub-stage is generated based on the polishing state evaluation values p1 during a plurality of monitoring cycles in the current polishing sub-stage.

[0009] In some embodiments of the present invention, the step of generating the grinding state evaluation value p1 in the current monitoring period further includes: Acquire a standard set of state characteristics of the grinding material and a standard set of operating state of the equipment based on historical grinding process data; Calculating a standard value average of multiple types of material state information of the grinding material in the current grinding sub-stage based on a standard set of state characteristics of the grinding material; Calculating a mean of standard values of multiple types of equipment status information of the grinding material in the current grinding sub-stage based on a standard set of equipment operation status; Generate a state evaluation value pa of the material to be ground by combining the state characteristic parameters of the material to be ground and the mean values of various types of material state information in the current grinding sub-stage; Generate a state evaluation value pb of the equipment state information by combining the current equipment operation state information and the average of multiple types of equipment state information in the current grinding sub-stage; Combine the state evaluation value pa of the material to be ground and the state evaluation value pb of the equipment state information to generate the grinding state evaluation value p1 within the current monitoring period; p1=w1*c1*pa+w2*c2*pb; Among them, w1 is the weight of the state evaluation value pa of the material to be ground, and w2 is the weight of the state evaluation value pb of the equipment state information; c1 is the fixed coefficient of the state evaluation value pa of the material to be ground, and c2 is the fixed coefficient of the state evaluation value pb of the equipment state information. c1 and c2 are used to make pa and pb in the same value range.

[0010] In some embodiments of the present invention, the step of generating the state evaluation value pa of the material to be ground further includes: ; Among them, w3 is the hardness feature weight w4 is the humidity feature weight, 5 is the granularity feature weight, is the standard mean value of the hardness characteristic parameter, is the standard mean value of humidity characteristic parameter, is the standard mean value of the particle size characteristic parameter.

[0011] In some embodiments of the present invention, the generating of the status evaluation value pb of the device status information further includes: Obtain the current device's operating status information to generate the device's operating status characteristic parameter set D, D = {d1, d2…d j …d n}; ; Among them, d j is the characteristic parameter value of the jth type of equipment operation status information, n is the total number of types of equipment operation status information, is the weight corresponding to the characteristic parameter value of the j-th device operating status information, is the standard mean value of the characteristic parameter value of the j-th equipment operating status information.

[0012] In some embodiments of the present invention, the generating of the state prediction value of the current grinding state sub-stage further includes: Obtain the polishing state evaluation value p1 in n1 monitoring cycles in the current polishing sub-stage; n1≤N; N is the total number of monitoring cycles in the current polishing sub-stage; Obtaining a change curve of n1 grinding state evaluation values p1, and calculating the slope of the change curve to generate a first reference value; Setting a plurality of first reference value scoring intervals based on the historical first reference value, each first reference value scoring interval corresponding to a state prediction value; The state prediction value of the current grinding sub-stage is obtained by comparing the first reference value with the first reference value scoring interval.

[0013] In some embodiments of the present invention, the fourth control module is further configured to: By comparing the state prediction value of the current grinding sub-stage with the preset value, it is determined whether to generate an early warning instruction; Based on the warning instruction type, it is determined whether to modify the control instruction of the current grinding sub-stage.

[0014] An automated grinding control method, applied to any of the automated grinding control systems described above, comprising: Obtain status information of the material to be ground and operating status information of the equipment; generating a control instruction for the equipment based on the status information of the material to be ground and the operating status information of the equipment; The control instructions for generating devices include: generating a state characteristic parameter of the material to be ground based on the acquired state information of the material to be ground; Build a grinding model library based on historical grinding data; Generate control instructions for the corresponding equipment based on the state characteristic parameters of the material to be ground and the grinding model library; Determine whether to generate an early warning instruction based on the operating status information of the equipment.

[0015] Compared with the prior art, the automated grinding control system and method provided by the embodiments of the present invention have the following beneficial effects: The coordinated control of material status and equipment operation can better adapt to the grinding needs of different materials and reduce problems such as poor grinding results or equipment loss caused by changes in material properties.

[0016] The second control module fully considers the multi-factor influence of material state characteristic parameters and equipment operation data by constructing a grinding model library; different types of grinding materials may require different equipment operating parameters during the grinding process. The construction of the grinding model library can provide targeted grinding solutions for different types of materials, thereby improving grinding efficiency and product quality.

[0017] Since the grinding model library covers samples of various types of grinding materials, their state characteristic parameters can be identified by the system, so the appropriate grinding model can be found in the grinding model library, thereby realizing an automated and efficient grinding process.

[0018] When generating control instructions for the equipment, the third control module sets multiple grinding sub-stages based on the equipment operation data in the sample of the corresponding type of grinding material, and sets multiple monitoring cycles for each grinding sub-stage. The fine division enables the grinding process to be controlled more accurately.

[0019] In each grinding sub-stage, the grinding control instructions can be adjusted according to the changes in the characteristics of the material in that stage. In the early stage of grinding, a larger grinding force can be used for materials with larger particle sizes. As the grinding progresses, in the sub-stage where the particle size gradually decreases, the grinding force can be reduced accordingly to avoid over-grinding or damage to the material.

[0020] Real-time status assessment and prediction can promptly detect abnormalities in the grinding process. The status prediction value can provide forward-looking guidance for the subsequent grinding process, and adjust equipment parameters in advance to adapt to changes in material status.

[0021] The fourth control module determines whether to generate an early warning instruction by comparing the state prediction value of the current grinding sub-stage with the preset value, so as to timely discover potential problems and improve the accuracy of the early warning by comparing the preset value, a quantitative standard.

[0022] Judging whether to modify the control instructions of the current grinding sub-stage according to the type of early warning instructions enables the system to adjust the control strategy in time according to the actual situation during the grinding process.

[0023] If the warning is due to a sudden increase in material moisture, which leads to a decrease in grinding efficiency, the system can correct the control instructions, such as adjusting the grinding temperature or ventilation conditions, to restore normal grinding efficiency and ensure the stability of the grinding process and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a structural diagram of an automated grinding control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0026] In the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0027] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0028] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0029] Example 1: An embodiment of the present invention provides an automated grinding control system, such as Figure 1 Shown, including: Monitoring unit: obtains status information of the material to be ground and operating status information of the equipment; Central control unit: generates equipment control instructions based on the status information of the material to be ground and the operating status information of the equipment; The central control unit includes: A first control module: generating a state characteristic parameter of the material to be ground based on the acquired state information of the material to be ground; The second control module: builds a grinding model library based on historical grinding data; The third control module generates control instructions for the corresponding equipment based on the state characteristic parameters of the material to be ground and the grinding model library; The fourth control module: determines whether to generate an early warning instruction based on the operating status information of the equipment.

[0030] Embodiment 2: The first control module is further configured to: Classify the acquired state information of the material to be ground to generate multiple types of material state information, including: hardness information value Sa, humidity information value Sb and particle size information value Sc; Based on the historical status information of the material to be ground, the value range [ai, bi] of each type of material status information is determined, (i=1, 2, 3); Generate state characteristic parameters of the material to be ground by combining the current type of material state information and the corresponding value range [ai, bi] of the material state information; Among them, the hardness characteristic parameter : ; Humidity characteristic parameters : ; Particle size characteristic parameters : ; Among them, a1 is the left endpoint of the value range of the hardness information value Sa, b1 is the right endpoint of the value range of the hardness information value Sa, a2 is the left endpoint of the value range of the humidity information value Sb, b2 is the right endpoint of the value range of the humidity information value Sb, a3 is the left endpoint of the value range of the particle size information value Sc, b3 is the right endpoint of the value range of the particle size information value Sc, is the historical average hardness information value.

[0031] Embodiment 3: The second control module is further configured to: Obtaining state characteristic parameters of historical grinding materials; Obtaining state characteristic parameters of historical grinding materials to classify the grinding materials and generate multiple types of grinding materials; Obtain equipment operating data for the grinding process of the current type of grinding material; Generate a sample of the current type of grinding material by combining state characteristic parameters of the current type of grinding material and equipment operation data corresponding to the grinding process; Build a grinding model library by combining samples of all types of grinding materials.

[0032] In this embodiment, the database is searched for records of historical grinding tasks, which contain detailed information about various grinding materials during past grinding processes, such as material hardness, moisture content, particle size, and other state characteristics.

[0033] Sensor data is also an important source. In the past, during the grinding process, various sensors installed on the grinding equipment (such as hardness sensors, humidity sensors, and particle size analyzers) would collect real-time data on the material's state characteristics. This data was stored and used to obtain historical grinding material state characteristic parameters.

[0034] Clean the acquired raw data to remove outliers, such as obviously unreasonable values caused by sensor failure or operational errors.

[0035] Data standardization is performed to convert state characteristic parameters of different units and magnitudes into a unified standard form for subsequent analysis and calculation. For example, hardness values and humidity values can be converted into values within the range [0, 1] according to a certain proportional relationship.

[0036] Cluster analysis methods, such as the K-Means clustering algorithm, can be used to cluster grinding materials with similar characteristics based on historical grinding material state parameters. For example, materials with higher hardness, lower humidity, and larger particle size can be clustered into one category, while materials with lower hardness, higher humidity, and smaller particle size can be clustered into another category.

[0037] Decision tree classification algorithms can also be applied. By constructing a decision tree, abrasive materials can be classified into different categories based on the range of values of state characteristic parameters. For example, if the hardness of a material is greater than a certain threshold and the moisture content is less than another threshold, it will be classified as a specific type of abrasive material.

[0038] The rationality of the classification results is evaluated using indicators such as the silhouette coefficient. A silhouette coefficient close to 1 indicates a good classification effect, that is, the characteristic parameters of the grinding material state in the same class are relatively similar, while there are large differences between different classes.

[0039] The classification results can be displayed intuitively through visualization methods such as scatter plots or tree diagrams to facilitate further analysis and adjustment of classification strategies.

[0040] Based on the type of material that has been classified, the corresponding equipment operating data is extracted from the database. This data includes the operating parameters of the grinding equipment during the grinding of the current type of material, such as speed, pressure, and temperature.

[0041] Ensure that equipment operating data accurately corresponds to the type of abrasive material. For example, by establishing an index relationship between abrasive material type and equipment operating data in the database, you can quickly and accurately obtain the required data.

[0042] Check the completeness of the acquired equipment operation data. For missing data points, fill in the missing data points based on the time series characteristics of the data or other relevant data. For example, if the temperature data at a certain moment is missing, the average of the temperature data before and after can be used to fill in the missing data points.

[0043] The state characteristic parameters of the current type of grinding material are combined with the equipment operating data of the corresponding grinding process to construct a sample. For example, a sample can be represented as {material hardness value, material moisture value, material particle size value, equipment speed, equipment pressure, equipment temperature}.

[0044] Ensure that the data in the sample is representative. Avoid over-reliance on data from a single specific grinding task, and instead consider data from a variety of grinding processes under different conditions to construct a sample.

[0045] Perform feature selection on the constructed sample. Remove features that have little impact on the grinding results or are highly correlated to simplify the sample structure and improve the efficiency and accuracy of the model. For example, if a high correlation is found between material moisture and particle size, and particle size has a more critical impact on the grinding results, consider removing the moisture feature.

[0046] Select the appropriate model building method based on the sample characteristics of the grinding material. For example, for sample data with strong linear relationships, a linear regression model can be used; for complex nonlinear relationships, a neural network model or a support vector machine model can be selected.

[0047] Use sample data from all types of abrasive materials to train models and build a grinding model library. For example, for different types of abrasive materials, build corresponding linear regression models or neural network models and store these models in the grinding model library.

[0048] Embodiment 4: The third control module is further configured to: Determining the corresponding grinding material type based on the acquired state characteristic parameters of the material to be ground; Select samples of corresponding types of abrasive materials based on the abrasive material type and the abrasive model library; and generating corresponding control instructions for the equipment based on equipment operation data in samples of corresponding types of abrasive materials; The generating of the control instruction of the corresponding device also includes: Setting a plurality of grinding sub-stages based on equipment operation data in a sample of a corresponding type of grinding material, and setting a plurality of monitoring cycles for each grinding sub-stage; Setting corresponding grinding control instructions based on each grinding sub-stage; During the operation of the grinding control instruction, the state characteristic parameters of the material to be ground and the operating status information of the equipment are obtained based on the monitoring cycle; Generate a grinding state evaluation value p1 within the current monitoring period based on the acquired state characteristic parameters of the material to be ground and the operating state information of the equipment within the current monitoring period; A state prediction value for the current polishing state sub-stage is generated based on the polishing state evaluation values p1 during a plurality of monitoring cycles in the current polishing sub-stage.

[0049] Embodiment 5: When generating the grinding state evaluation value p1 in the current monitoring period, the method further includes: Acquire a standard set of state characteristics of the grinding material and a standard set of operating state of the equipment based on historical grinding process data; Calculating a standard value average of multiple types of material state information of the grinding material in the current grinding sub-stage based on a standard set of state characteristics of the grinding material; Calculating a mean of standard values of multiple types of equipment status information of the grinding material in the current grinding sub-stage based on a standard set of equipment operation status; Generate a state evaluation value pa of the material to be ground by combining the state characteristic parameters of the material to be ground and the mean values of various types of material state information in the current grinding sub-stage; Generate a state evaluation value pb of the equipment state information by combining the current equipment operation state information and the average of multiple types of equipment state information in the current grinding sub-stage; Combine the state evaluation value pa of the material to be ground and the state evaluation value pb of the equipment state information to generate the grinding state evaluation value p1 within the current monitoring period; p1=w1*c1*pa+w2*c2*pb; Among them, w1 is the weight of the state evaluation value pa of the material to be ground, and w2 is the weight of the state evaluation value pb of the equipment state information; c1 is the fixed coefficient of the state evaluation value pa of the material to be ground, and c2 is the fixed coefficient of the state evaluation value pb of the equipment state information. c1 and c2 are used to make pa and pb in the same value range.

[0050] In this embodiment, a large amount of historical grinding process data is extracted from a database. This data contains detailed information about the grinding of different materials in the past, such as various material conditions (hardness, moisture, particle size, etc.) and equipment operating status information (speed, temperature, pressure, etc.).

[0051] Filter and organize historical data to ensure its accuracy and completeness. For example, data points due to equipment failure or abnormal operation are eliminated, and only data from normal grinding processes are retained.

[0052] Based on historical grinding process data, a standard set of grinding material condition characteristics is determined. For each type of material condition information (such as hardness, moisture, particle size, etc.), a statistical analysis is performed to determine the normal value range for different grinding tasks.

[0053] For example, for the hardness of a certain metal material, by analyzing historical data, it is found that its hardness values during normal grinding are mostly distributed within a specific range. This range constitutes part of the state characteristic standard set of hardness.

[0054] Based on historical grinding process data, we determine a set of standard operating conditions for the equipment. For various operating conditions (such as speed, temperature, and pressure), we identify the reasonable range of values for these conditions under normal grinding operation.

[0055] For example, for the rotational speed of grinding equipment, the rotational speed range during normal operation is determined based on historical data. This range is the rotational speed part of the equipment operating status standard set.

[0056] Example 6: When generating the state evaluation value pa of the material to be ground, the method further includes: ; Among them, w3 is the hardness feature weight w4 is the humidity feature weight, 5 is the granularity feature weight, is the standard mean value of the hardness characteristic parameter, is the standard mean value of humidity characteristic parameter, is the standard mean value of the particle size characteristic parameter.

[0057] Embodiment 7: When generating the status evaluation value pb of the device status information, the method further includes: Obtain the current device's operating status information to generate the device's operating status characteristic parameter set D, D = {d1, d2…d j …d n}; ; Among them, d jis the characteristic parameter value of the jth type of equipment operation status information, n is the total number of types of equipment operation status information, is the weight corresponding to the characteristic parameter value of the j-th device operating status information, is the standard mean value of the characteristic parameter value of the j-th equipment operating status information.

[0058] In this embodiment, various sensors and monitoring devices are used to obtain information about the current equipment's operating status. This information covers a variety of aspects, such as equipment speed, temperature, pressure, vibration amplitude, etc. Different grinding equipment may require different key operating status information to be collected, depending on the equipment's type, structure, and function.

[0059] For a high-precision grinder, speed stability and temperature control are very critical, so the focus will be on collecting operating status information in these areas; for large-scale grinding equipment, information such as pressure and vibration amplitude may be more important because they are directly related to the safety of the equipment and the grinding effect.

[0060] The collected information about different types of equipment operating status is converted into equipment operating status characteristic parameters, forming a set D = {d1, d2…dj…dn}. Each characteristic parameter dj represents a specific aspect of the equipment operating status.

[0061] For example, d1 can represent the speed characteristic parameter of the equipment, and its value may be the actual speed value collected by the speed sensor and obtained through certain calculations; d2 can represent the temperature characteristic parameter of the equipment, reflecting the real-time temperature conditions of the equipment during the grinding process.

[0062] Embodiment 8: When generating the state prediction value of the current grinding state sub-stage, the method further includes: Obtain the polishing state evaluation value p1 in n1 monitoring cycles in the current polishing sub-stage; n1≤N; N is the total number of monitoring cycles in the current polishing sub-stage; Obtaining a change curve of n1 grinding state evaluation values p1, and calculating the slope of the change curve to generate a first reference value; Setting a plurality of first reference value scoring intervals based on the historical first reference value, each first reference value scoring interval corresponding to a state prediction value; The state prediction value of the current grinding sub-stage is obtained by comparing the first reference value with the first reference value scoring interval.

[0063] In this example, the n1 polishing state evaluation values p1 obtained are arranged in the order of the monitoring cycles, and a change curve is plotted with the monitoring cycle number as the horizontal axis and the polishing state evaluation value p1 as the vertical axis. This curve intuitively shows the change trend of the polishing state over time (in monitoring cycles) within the current polishing sub-stage.

[0064] For example, if the grinding state evaluation value p1 gradually increases with the increase of the monitoring period, it means that the grinding process is developing in the expected direction, such as the particle size of the material is gradually decreasing to the target value, the operating efficiency of the equipment is gradually improving, etc.; if p1 fluctuates or shows a downward trend, it may indicate that there are some problems in the grinding process, such as uneven hardness of the material resulting in unstable grinding effect or equipment failure affecting grinding efficiency.

[0065] Calculate the slope of the plotted change curve. This can be calculated using the slope formula between two points or a more complex curve fitting slope calculation method, depending on the shape of the curve and the characteristics of the data. The slope reflects the rate of change of the grinding state evaluation value p1.

[0066] The generated slope value serves as a first reference value and is of great significance. It can quantify the dynamic characteristics of the grinding process. For example, a large positive slope may indicate rapid progress and good results, while a small positive or negative slope may indicate obstructions or abnormalities in the grinding process, requiring further analysis.

[0067] The system sets multiple scoring intervals based on historical first reference values. These historical first reference values are extracted from data accumulated during past grinding processes. This historical data covers different types of materials, different grinding equipment, and various grinding conditions.

[0068] For example, for the grinding process of a certain type of material on a specific piece of equipment, by analyzing a large amount of historical data, we identified several typical slope ranges corresponding to different grinding conditions. These slope ranges were set as different first reference value scoring intervals. For example, a slope within the interval [a, b] indicates that the grinding process is normal and efficient, a slope within the interval [c, d] indicates that the grinding process may have some minor problems but is still within the controllable range, and a slope outside the interval [e, f] indicates that the grinding process has serious problems.

[0069] The first reference value calculated in the current grinding sub-stage is compared with the set first reference value scoring interval, and the state prediction value of the current grinding sub-stage is determined according to the scoring interval where the first reference value is located.

[0070] If the first reference value falls within the scoring range indicating that the grinding process is normal and efficient, the state prediction value can be set to "good", indicating that the grinding process can continue according to the current control instructions; if the first reference value falls within the scoring range indicating that there may be minor problems, the state prediction value can be set to "needs attention", prompting the system or operator to perform appropriate inspections and adjustments to the grinding process; if the first reference value falls outside the scoring range indicating that there are serious problems, the state prediction value is set to "abnormal", and immediate measures need to be taken to correct the problems in the grinding process, such as adjusting equipment parameters or replacing grinding tools.

[0071] Embodiment 9: The fourth control module is further configured to: By comparing the state prediction value of the current grinding sub-stage with the preset value, it is determined whether to generate an early warning instruction; Based on the warning instruction type, it is determined whether to modify the control instruction of the current grinding sub-stage.

[0072] In this embodiment, when a warning is triggered due to equipment operating parameters (such as excessive temperature or pressure) exceeding preset values, this warning indicates that the equipment may be at risk of damage or is already in an unstable operating state. For example, a warning indicating that the grinding head temperature is too high may be caused by excessive grinding pressure or poor heat dissipation.

[0073] If the warning is triggered because the material condition (e.g., abnormal hardness change, moisture content not meeting requirements) does not match the preset value, it means that the material is not changing as expected during the grinding process. For example, a sudden increase in material hardness could be due to inappropriate grinding process parameters or changes in the material's internal structure.

[0074] Some warnings are caused by a combination of factors, including equipment and materials. For example, when grinding a high-hardness material, the interaction between the grinding force and the material causes the equipment to vibrate beyond normal limits, affecting the uniformity of the material's particle size. This situation generates a combined warning.

[0075] For equipment-related warnings, such as an overtemperature warning, the first consideration when revising the control instructions for the current grinding sub-stage is to reduce the equipment's operating parameters. For example, you can reduce the grinding speed or pressure to reduce the equipment load and thus the equipment temperature. You can also check whether the equipment's cooling system is functioning properly, such as by increasing the coolant flow or adjusting the cooling fan speed.

[0076] When material-related warnings appear, such as when the material moisture content doesn't meet requirements, adjust the grinding process accordingly. If the material moisture content is too high, increase drying measures during the grinding process, such as increasing ventilation or heating power. If the material hardness fluctuates abnormally, the grinding tool type or grinding pressure may need to be adjusted to accommodate the change in material hardness.

[0077] For comprehensive early warning, it is necessary to comprehensively consider the conditions of the equipment and materials and make corrections to the control instructions. For example, if there are problems with both equipment vibration and material particle size uniformity, on the one hand, the balance parameters of the equipment should be adjusted to reduce vibration.

[0078] An automated grinding control method, applied to any of the above-mentioned automated grinding control systems, comprising: Obtain status information of the material to be ground and operating status information of the equipment; generating a control instruction for the equipment based on the status information of the material to be ground and the operating status information of the equipment; The control instructions for generating devices include: generating a state characteristic parameter of the material to be ground based on the acquired state information of the material to be ground; Build a grinding model library based on historical grinding data; Generate control instructions for the corresponding equipment based on the state characteristic parameters of the material to be ground and the grinding model library; Determine whether to generate an early warning instruction based on the operating status information of the equipment.

[0079] Finally, it should be noted that it is apparent that those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, to the extent such modifications and variations fall within the scope of the present invention and its equivalents, the present invention is intended to include such modifications and variations.

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. An automated grinding control system, characterized in that: include: Monitoring unit: obtains status information of the material to be ground and operating status information of the equipment; Central control unit: generates equipment control instructions based on the status information of the material to be ground and the operating status information of the equipment; The central control unit includes: A first control module: generating a state characteristic parameter of the material to be ground based on the acquired state information of the material to be ground; The second control module: builds a grinding model library based on historical grinding data; The third control module generates control instructions for the corresponding equipment based on the state characteristic parameters of the material to be ground and the grinding model library; The fourth control module: determines whether to generate an early warning instruction based on the operating status information of the equipment.

2. An automated grinding control system according to claim 1, characterized in that: The first control module is further configured to: Classify the acquired state information of the material to be ground to generate multiple types of material state information, including: hardness information value Sa, humidity information value Sb and particle size information value Sc; Based on the historical status information of the material to be ground, the value range [ai, bi] of each type of material status information is determined, (i=1, 2, 3); Generate state characteristic parameters of the material to be ground by combining the current type of material state information and the corresponding value range [ai, bi] of the material state information; Among them, the hardness characteristic parameter : ； Humidity characteristic parameters : ； Particle size characteristic parameters : ； Among them, a1 is the left endpoint of the value range of the hardness information value Sa, b1 is the right endpoint of the value range of the hardness information value Sa, a2 is the left endpoint of the value range of the humidity information value Sb, b2 is the right endpoint of the value range of the humidity information value Sb, a3 is the left endpoint of the value range of the particle size information value Sc, b3 is the right endpoint of the value range of the particle size information value Sc, is the historical average hardness information value.

3. An automated grinding control system according to claim 2, characterized in that: The second control module is further configured to: Obtaining state characteristic parameters of historical grinding materials; Obtaining state characteristic parameters of historical grinding materials to classify the grinding materials and generate multiple types of grinding materials; Obtain equipment operating data for the grinding process of the current type of grinding material; Generate a sample of the current type of grinding material by combining state characteristic parameters of the current type of grinding material and equipment operation data corresponding to the grinding process; Build a grinding model library by combining samples of all types of grinding materials.

4. An automated grinding control system according to claim 3, characterized in that: The third control module is further configured to: Determining the corresponding grinding material type based on the acquired state characteristic parameters of the material to be ground; Select samples of corresponding types of abrasive materials based on the abrasive material type and the abrasive model library; and generating corresponding control instructions for the equipment based on equipment operation data in samples of corresponding types of abrasive materials; The generating of the control instruction of the corresponding device also includes: Setting a plurality of grinding sub-stages based on equipment operation data in a sample of a corresponding type of grinding material, and setting a plurality of monitoring cycles for each grinding sub-stage; Setting corresponding grinding control instructions based on each grinding sub-stage; During the operation of the grinding control instruction, the state characteristic parameters of the material to be ground and the operating status information of the equipment are obtained based on the monitoring cycle; Generate a grinding state evaluation value p1 within the current monitoring period based on the acquired state characteristic parameters of the material to be ground and the operating state information of the equipment within the current monitoring period; A state prediction value for the current polishing state sub-stage is generated based on the polishing state evaluation values p1 during a plurality of monitoring cycles in the current polishing sub-stage.

5. An automated grinding control system according to claim 4, characterized in that: When generating the grinding state evaluation value p1 in the current monitoring period, the method further includes: Acquire a standard set of state characteristics of the grinding material and a standard set of operating state of the equipment based on historical grinding process data; Calculating a standard value average of multiple types of material state information of the grinding material in the current grinding sub-stage based on a standard set of state characteristics of the grinding material; Calculating a mean of standard values of multiple types of equipment status information of the grinding material in the current grinding sub-stage based on a standard set of equipment operation status; Generate a state evaluation value pa of the material to be ground by combining the state characteristic parameters of the material to be ground and the mean values of various types of material state information in the current grinding sub-stage; Generate a state evaluation value pb of the equipment state information by combining the current equipment operation state information and the average of multiple types of equipment state information in the current grinding sub-stage; Combine the state evaluation value pa of the material to be ground and the state evaluation value pb of the equipment state information to generate the grinding state evaluation value p1 within the current monitoring period; p1=w1*c1*pa+w2*c2*pb; Among them, w1 is the weight of the state evaluation value pa of the material to be ground, and w2 is the weight of the state evaluation value pb of the equipment state information; c1 is the fixed coefficient of the state evaluation value pa of the material to be ground, and c2 is the fixed coefficient of the state evaluation value pb of the equipment state information. c1 and c2 are used to make pa and pb in the same value range.

6. An automated grinding control system according to claim 5, characterized in that: When generating the state evaluation value pa of the material to be ground, the method further includes: ； Among them, w3 is the hardness feature weight, w4 is the humidity feature weight, w5 is the particle size feature weight, is the standard mean value of the hardness characteristic parameter, is the standard mean value of humidity characteristic parameter, is the standard mean value of the particle size characteristic parameter.

7. An automated grinding control system according to claim 6, characterized in that: When generating the status evaluation value pb of the device status information, the method further includes: Obtain the current device's operating status information to generate the device's operating status characteristic parameter set D, D = {d1, d2…d j …d n }; ； Among them, d j is the characteristic parameter value of the jth type of equipment operation status information, n is the total number of types of equipment operation status information, is the weight corresponding to the characteristic parameter value of the j-th device operating status information, is the standard mean value of the characteristic parameter value of the j-th equipment operating status information.

8. An automated grinding control system according to claim 7, characterized in that: When generating the state prediction value of the current grinding state sub-stage, the method further includes: Obtain the polishing state evaluation value p1 in n1 monitoring cycles in the current polishing sub-stage; n1≤N; N is the total number of monitoring cycles in the current polishing sub-stage; Obtaining a change curve of n1 grinding state evaluation values p1, and calculating the slope of the change curve to generate a first reference value; Setting a plurality of first reference value scoring intervals based on the historical first reference value, each first reference value scoring interval corresponding to a state prediction value; The state prediction value of the current grinding sub-stage is obtained by comparing the first reference value with the first reference value scoring interval.

9. An automated grinding control system according to claim 8, characterized in that: The fourth control module is further configured to: By comparing the state prediction value of the current grinding sub-stage with the preset value, it is determined whether to generate an early warning instruction; Based on the warning instruction type, it is determined whether to modify the control instruction of the current grinding sub-stage.

10. An automated grinding control method, applied to an automated grinding control system according to any one of claims 1 to 9, characterized in that: include: Obtain status information of the material to be ground and operating status information of the equipment; generating a control instruction for the equipment based on the status information of the material to be ground and the operating status information of the equipment; The control instructions for generating devices include: generating a state characteristic parameter of the material to be ground based on the acquired state information of the material to be ground; Build a grinding model library based on historical grinding data; Generate control instructions for the corresponding equipment based on the state characteristic parameters of the material to be ground and the grinding model library; Determine whether to generate an early warning instruction based on the operating status information of the equipment.

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