A method, device, and medium for controlling the machining of heat sink spade teeth.

Abstract

This application relates to a method, apparatus, and medium for controlling the tooth-shaving process of a heat sink, belonging to the field of heat sink processing technology. The method includes acquiring tooth-shaving processing data for the heat sink to be processed; receiving raw material thickness data collected by a thickness detection device; generating a tooth-shaving cutter control signal based on the tooth-shaving processing data and the raw material thickness data to control the tooth-shaving cutter to perform tooth-shaving processing on the raw material; acquiring temperature data collected by a temperature detection device at various parts of the tooth-shaving machine during the tooth-shaving process, and performing error compensation at various parts of the tooth-shaving machine based on the temperature data. This application has the effect of improving the accuracy of tooth-shaving processing.

Description

Technical Field

[0001] This application relates to the field of heat sink processing technology, and in particular to a method, device and medium for controlling the processing of heat sink teeth. Background Technology

[0002] In the production process of radiators, the processing of heat sink fins is one of the most important steps. Heat sink fins are usually mass-produced using a tooth-shaving machine. During the processing, the raw material of the heat sink fins is placed on the worktable of the tooth-shaving machine, and the tooth-shaving cutter of the tooth-shaving machine and the worktable work together to cut the raw material of the heat sink fins into multiple neatly arranged fins.

[0003] Since the heat sinks of small radiators are usually small in size and thin in thickness, the precision requirements of the gear-shaping machine are high. However, during the processing, factors such as environmental influences, mechanical errors and thickness differences of different raw materials may cause errors in the gear-shaping machine, resulting in insufficient processing precision, which in turn affects the accuracy of heat sink processing. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, and medium for controlling the machining of heat sink teeth, thereby improving the accuracy of tooth machining.

[0005] Firstly, the heat sink tooth machining control method provided in this application adopts the following technical solution:

[0006] A method for controlling the machining of heat sink spade teeth includes:

[0007] Obtain the machining data of the spade teeth for the heat sink to be processed;

[0008] Receive raw material thickness data collected by thickness detection equipment;

[0009] Based on the shaving tooth processing data and raw material thickness data, a shaving tooth cutter control signal is generated to control the shaving tooth cutter to perform shaving tooth processing on the raw material;

[0010] Temperature data collected by temperature detection equipment at various parts of the tooth-shaving machine during the tooth-shaving process is obtained, and error compensation is performed on various parts of the tooth-shaving machine based on the temperature data.

[0011] By adopting the above technical solution, this application takes into account the influence of raw material thickness on the machining accuracy of the shaving teeth. It collects raw material thickness data using a thickness detection device and controls the shaving teeth machining process based on this data, thereby improving the machining accuracy. This application also considers the influence of ambient temperature on the control accuracy of the shaving teeth machine. Mechanical errors may vary under different temperatures. By collecting the temperature of various parts during the shaving teeth machining process using a temperature detection device and compensating for errors in various parts of the shaving teeth machine based on the temperature, the machining accuracy of the shaving teeth can be improved more comprehensively.

[0012] Furthermore, the acquisition of temperature data collected by temperature detection devices at various parts of the gear-shaving machine during the gear-shaving process, and the subsequent error compensation for various parts of the gear-shaving machine based on the temperature data, specifically includes:

[0013] Acquire temperature data from a temperature detection device located at a specific part of the gear-shaving machine during the processing;

[0014] Obtain the error curve of the corresponding temperature data for this part of the tooth-shaving machine based on the temperature data;

[0015] Temperature error compensation is performed on this part of the tooth-shaving machine based on the error curve and temperature data.

[0016] By adopting the above technical solution, this application takes into account that the error curves of different parts of the tooth shaving machine may be different under different temperature conditions. By obtaining the corresponding error curve based on the collected temperature for error compensation, the error compensation can adapt to the error changes under different temperature conditions, which is more in line with the actual situation. Even if the temperature changes, the error curve can be automatically adjusted to perform temperature error compensation in order to achieve the best compensation effect, and the reliability and accuracy of error compensation can be improved.

[0017] Furthermore, the step of obtaining the error curve of the corresponding temperature data for that part of the tooth-shaving machine based on the temperature data specifically includes:

[0018] Construct an initial model for error curve prediction;

[0019] Obtain error curve samples for this part of the tooth-shaving machine under different temperature samples;

[0020] A sample set is constructed by combining temperature samples and corresponding error curve samples;

[0021] The initial model for error curve prediction is trained based on the sample set to obtain the trained error curve prediction model.

[0022] The collected temperature data is input into the trained error curve prediction model to obtain the error curve of the corresponding temperature data for that part of the tooth shovel.

[0023] By adopting the above technical solution, this application constructs and trains an error curve prediction model, and obtains the error curves corresponding to different temperatures through model prediction. This method is more efficient than manual measurement and calculation, and can improve the intelligence and accuracy of temperature error compensation.

[0024] Furthermore, obtaining the error curve samples corresponding to this part of the tooth-shaving machine under different temperature samples specifically includes:

[0025] Obtain the actual position value during the processing from the position detection device located at this part of the gear hoist at the current temperature;

[0026] Obtain the reference position value of this part of the gear hoist during the machining process;

[0027] The error value of that part of the tooth-shaving machine is obtained based on the position reference value and the actual position value;

[0028] The trend of error value changes during the processing is used to construct an error curve sample for that part of the tooth shaving machine at that temperature sample.

[0029] By changing the current temperature according to the preset temperature sample, error curve samples corresponding to this part of the tooth-shaving machine under different temperature samples are obtained.

[0030] By adopting the above technical solution, this application obtains the error value based on the actual position value collected during the experimental stage and the preset position reference value under the condition of no mechanical error, and obtains the error curve based on the change of the error value with the processing time. This can more intuitively reflect the error situation of the gear shaving machine and improve the accuracy of subsequent error compensation.

[0031] Furthermore, the step of training the initial error curve prediction model based on the sample set to obtain the trained error curve prediction model specifically includes:

[0032] The sample set is divided into a training set and a test set, with a preset number of training rounds;

[0033] The initial model is predicted by inputting the error curve of the training set and trained according to the preset number of training rounds;

[0034] When the initial model for error curve prediction reaches the preset number of training rounds, the trained error curve prediction model is obtained.

[0035] The temperature samples in the test set are input into the trained error curve prediction model to obtain the error curve prediction samples;

[0036] The model parameters of the error curve prediction model are adjusted based on the error curve samples and corresponding error curve prediction samples in the test set, and the trained error curve prediction model is obtained based on the adjusted model parameters.

[0037] By adopting the above technical solution, this application trains the initial model for error curve prediction using the constructed sample set, enabling it to learn the characteristic relationship between temperature samples and error curve samples. After training, the model needs to be tested to determine its accuracy. If the accuracy is not up to standard, the model parameters are adjusted accordingly to ensure that the final error curve prediction model meets the requirements.

[0038] Furthermore, after acquiring the temperature data collected by the temperature detection device located at a certain part of the gear-shaving machine during the processing, the method further includes:

[0039] A first high temperature threshold and a second high temperature threshold are preset for each part of the tooth-shaving machine, wherein the first high temperature threshold is less than the second high temperature threshold;

[0040] The temperature data collected by the temperature detection device at a certain part of the gear hoist during the processing is compared with the first and second high temperature thresholds preset for that part.

[0041] Based on the comparison results, high-temperature protection was applied to this part of the tooth-shaving machine.

[0042] By adopting the above technical solution, this application takes into account the influence of temperature on the processing process. When the temperature is too high, it may cause the tooth-shaving machine to malfunction. Therefore, a temperature threshold is set to monitor the temperature and ensure the reliability of the tooth-shaving processing process.

[0043] Furthermore, the high-temperature protection of this part of the tooth-shaving machine based on the comparison results specifically includes:

[0044] When the temperature data is lower than the first high temperature threshold, control the tooth-shaving machine to perform tooth-shaving processing on the raw material;

[0045] When the temperature data is greater than the first high temperature threshold and less than the second high temperature threshold, the tooth-shaving machine is controlled to perform tooth-shaving processing on the raw material and the processing time of the tooth-shaving machine is recorded.

[0046] When the processing time of the gear shaving machine with temperature data greater than the first high temperature threshold and less than the second high temperature threshold exceeds the preset high temperature processing time threshold, the gear shaving machine is controlled to stop processing and generate a high temperature alarm signal.

[0047] When the temperature data exceeds the second high temperature threshold, the tooth-shaving machine is controlled to stop processing and generate a high temperature alarm signal.

[0048] By adopting the above technical solution, this application considers that continuous high-temperature operation can also lead to increased mechanical errors. It sets a first high-temperature threshold that allows continued operation but not prolonged operation, and a second high-temperature threshold that prohibits continued operation, providing dual protection against high temperatures. When the temperature exceeds the second high-temperature threshold, it indicates that the current temperature is too high, which may easily lead to malfunction of the gear-shaving machine or create safety hazards; therefore, operation must be stopped immediately. When the temperature exceeds the first temperature threshold but not the second temperature threshold, prolonged operation may lead to a decrease in gear-shaving machining accuracy or create safety hazards; therefore, operation must be stopped immediately when the preset machining time threshold is exceeded. Simultaneously with the high-temperature protection stopping operation, a high-temperature alarm signal must be generated to remind operators to take timely cooling measures to ensure the accuracy and reliability of the gear-shaving machining process.

[0049] Furthermore, the step of generating a tooth-shaving cutter control signal based on the tooth-shaving processing data and the raw material thickness data to control the tooth-shaving cutter to perform tooth-shaving processing on the raw material also includes:

[0050] The shovel angle adjustment signal is generated based on the shovel machining data and the thickness of the raw material.

[0051] Generate a tooth cutting tool movement signal based on the tooth cutting data;

[0052] By adopting the above technical solution, this application controls the tooth-shaving cutter through angle adjustment signals and movement signals, and performs error compensation on the control signals to achieve a high-precision tooth-shaving machining process.

[0053] Secondly, the heat sink fin cutting control device provided in this application adopts the following technical solution:

[0054] A heat sink tooth cutting control device includes a processor, the processor being configured to execute a heat sink tooth cutting control method as described in the first aspect.

[0055] It also includes a thickness detection device and a temperature detection device, which are respectively connected to the processor in communication.

[0056] The thickness detection device is used to collect raw material thickness data;

[0057] The temperature detection equipment is installed in various parts of the tooth-shaving machine to collect temperature data from various parts of the tooth-shaving machine.

[0058] By adopting the above technical solution, this application collects raw material thickness data through a thickness detection device and uploads it to the processor, and collects temperature data through a temperature detection device and uploads it to the processor, so that the processor can control the tooth-shaving machine to perform tooth-shaving processing according to the thickness data, and can perform error compensation according to the temperature data to improve the accuracy of tooth-shaving processing.

[0059] Secondly, the computer-readable storage medium provided in this application adopts the following technical solution:

[0060] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the first aspect, a method for controlling the machining of heat sink teeth.

[0061] By adopting the above technical solution, this application realizes tooth shaving machining error compensation through computer program. Even if the temperature changes, it can automatically adjust the error curve to perform temperature error compensation in order to achieve the best compensation effect and improve the reliability and accuracy of error compensation.

[0062] In summary, this application includes at least one of the following beneficial technical effects:

[0063] 1. This application takes into account the influence of raw material thickness on the machining accuracy of the shovel teeth, collects raw material thickness data through a thickness detection device, and controls the shovel tooth machining process based on the thickness data, thereby improving the machining accuracy of the shovel teeth;

[0064] 2. This application also considers the influence of ambient temperature on the control accuracy of the gear hoisting machine. Mechanical errors may vary under different temperatures. By collecting the temperature of various parts during the gear hoisting process through temperature detection equipment and compensating for errors in various parts of the gear hoisting machine based on the temperature, the accuracy of gear hoisting can be improved more comprehensively.

[0065] 3. This application takes into account that the error curves of different parts of the gear hoist may be different under different temperature conditions. Based on the collected temperature, the corresponding error curve is obtained for error compensation. This enables the error compensation to adapt to the error changes under different temperature conditions, which is more in line with the actual situation. Even if the temperature changes, the error curve can be automatically adjusted to perform temperature error compensation to achieve the best compensation effect and improve the reliability and accuracy of error compensation. Attached Figure Description

[0066] Figure 1 This is a flowchart of the heat sink tooth machining control method according to an embodiment of this application;

[0067] Figure 2 This is a flowchart of the error curve prediction model construction method according to an embodiment of this application;

[0068] Figure 3 This is a schematic diagram of the connection of the heat sink fin cutting device according to an embodiment of this application;

[0069] In the diagram, 1 is the processor; 2 is the thickness detection device; 3 is the temperature detection device; and 4 is the tooth cutting tool control device. Detailed Implementation

[0070] The following is in conjunction with the appendix Figure 1 - Appendix Figure 3 This application will be described in further detail below.

[0071] Because the heat sinks of small radiators are typically small in size and thin in thickness, the precision requirements for the tooth-shaving machine are high. However, during the processing, factors such as environmental influences, mechanical errors, and differences in the thickness of different raw materials may cause errors in the tooth-shaving machine, resulting in insufficient processing precision and affecting the accuracy of the heat sink processing. Therefore, this application provides a method for controlling the tooth-shaving process of heat sinks, referring to... Figure 1 ,include:

[0072] S1. Obtain the machining data of the spade teeth of the heat sink to be processed.

[0073] Specifically, since the requirements for tooth cutting of heat sinks of different sizes, models or uses are different, the tooth cutting data of the heat sink to be processed should be obtained before processing.

[0074] S2. Receive the raw material thickness data collected by the thickness detection equipment.

[0075] Specifically, during the tooth-shaving process, the tilt angle of the tooth cutter needs to be adjusted to fit the surface of the raw material. This causes the thickness of the raw material to affect the machining accuracy. Therefore, it is necessary to collect the thickness data of the raw material using a thickness detection device. In practice, the thickness detection device can be a vision inspection device or an infrared detection device, or any other device capable of detecting thickness.

[0076] S3. Generate a shaving cutter control signal based on the shaving tooth processing data and raw material thickness data to control the shaving cutter to perform shaving tooth processing on the raw material.

[0077] Specifically, a tooth cutting angle adjustment signal is generated based on the tooth cutting processing data and the thickness of the raw material to control the angle adjustment device to adjust the tilt angle of the tooth cutting. A tooth cutting movement signal is generated based on the tooth cutting processing data to control the mobile device to move the tooth cutting. Thus, a tooth is cut out of the raw material with each processing. Since the thickness of the raw material changes with each processing, the angle adjustment device needs to be continuously controlled to change the tilt angle of the tooth cutting according to the change in the thickness of the raw material during the processing until the heat sink is processed.

[0078] S4. Obtain temperature data collected by temperature detection equipment at various parts of the tooth-shaving machine during the tooth-shaving process, and perform error compensation for various parts of the tooth-shaving machine based on the temperature data.

[0079] Specifically, since gear hoisting machines typically have multiple components working together to complete the machining process, such as angle adjustment devices and moving equipment, temperature detection devices are installed at key parts of the machine. These devices monitor temperature changes in real time, allowing for error compensation based on the collected temperature data. In practice, the temperature detection devices are temperature sensors. For example, installing a temperature sensor on the spindle of the angle adjustment device allows for real-time monitoring of the spindle bearing temperature. The temperature sensor converts the collected temperature data into an electrical signal and uploads it.

[0080] After obtaining the error curve corresponding to the temperature data, the angle adjustment signal and the movement signal are compensated for the error based on the error curve to obtain the angle adjustment signal and the movement signal with the back difference compensation thickness. The angle adjustment signal and the movement signal after error compensation are sent to the tooth cutting cutter control device to control the tooth cutting cutter to perform tooth cutting on the raw material.

[0081] The implementation principle of this application embodiment is as follows: This application embodiment considers the influence of raw material thickness on the machining accuracy of the shaving teeth. It collects raw material thickness data using a thickness detection device and controls the shaving tooth machining process based on this data, thereby improving the accuracy of the shaving tooth machining. This application embodiment also considers the influence of ambient temperature on the control accuracy of the shaving tooth machine. Mechanical errors may vary under different temperatures. By collecting the temperature of various parts during the shaving tooth machining process using a temperature detection device, and compensating for errors in various parts of the shaving tooth machine based on the temperature, the accuracy of the shaving tooth machining can be improved more comprehensively.

[0082] Since the error curves of different parts of the gear hoist may differ under different temperatures, error compensation can be performed by obtaining the corresponding error curve based on the collected temperature. This allows the error compensation to adapt to error changes under different temperatures, making it more consistent with actual conditions. Even if the temperature changes, the error curve can be automatically adjusted for temperature error compensation to achieve the best compensation effect and improve the reliability and accuracy of error compensation. Therefore, step S4 in this embodiment specifically includes:

[0083] S41. Obtain temperature data collected by a temperature detection device located at a certain part of the gear shaving machine during the processing.

[0084] S42. Obtain the error curve of the corresponding temperature data for this part of the tooth-shaving machine based on the temperature data.

[0085] Specifically, during the experimental phase, the error curves of the tooth-shaving machine under different temperature conditions are tested and the experimental data is retained. During the tooth-shaving process, the corresponding error curves can be obtained from the temperature data collected based on the retained experimental data.

[0086] S43. Perform temperature error compensation on this part of the tooth-shaving machine based on the error curve and temperature data.

[0087] Specifically, through the function blocks of the PLC and CNC control system, the temperature compensation value, reference position, and linear gradient angle parameters are continuously transmitted back to the control system. These parameters, determined experimentally, are optimal for compensation. The setting and adjustment of these parameters can also be modified by calling the corresponding function blocks through the PLC to achieve the best compensation effect.

[0088] This application embodiment also constructs and trains an error curve prediction model to obtain error curves corresponding to different temperatures through model prediction. This method is more efficient than manual measurement and calculation, and can improve the intelligence and accuracy of temperature error compensation. (Refer to...) Figure 2 In this embodiment, step S42 specifically includes:

[0089] S421. Construct an initial model for error curve prediction.

[0090] Specifically, an initial model for error curve prediction is constructed based on LSTM, and the model parameters of the initial model for error curve prediction are configured. The model parameters include, but are not limited to, the number of LSTM units, the activation function, and the length of the input sequence.

[0091] S422. Obtain the error curve samples corresponding to this part of the tooth-shaving machine under different temperature samples.

[0092] Specifically, during the experimental phase, the actual position value of the corresponding part of the gear-shaping machine at the current temperature is acquired by a position detection device during the processing. A reference position value for that part of the gear-shaping machine during processing is obtained. Based on the reference and actual position values, the error value of that part is calculated. The trend of the error value during processing is then used to construct an error curve sample for that part of the gear-shaping machine at that temperature. By changing the current temperature according to a preset temperature sample, error curve samples corresponding to that part of the gear-shaping machine under different temperature samples are obtained. In the actual implementation, the position detection device can be a grating ruler, which can monitor and provide feedback on position changes during processing in real time. The position reference value is a preset standard value obtained under the assumption that there are no mechanical errors.

[0093] S423. Construct a sample set by combining temperature samples and corresponding error curve samples.

[0094] Specifically, the temperature samples and corresponding error curve samples obtained in the experimental phase are constructed into a sample set, where the temperature samples are the input features and the error curve samples are the output features.

[0095] S424. Train the initial model for error curve prediction based on the sample set to obtain the trained error curve prediction model.

[0096] Specifically, the initial model for error curve prediction is trained using a sample set, with temperature samples as input data and error curve samples as output data, enabling the model to learn the feature relationship between temperature samples and error curve samples.

[0097] S425. Input the collected temperature data into the trained error curve prediction model to obtain the error curve of the corresponding temperature data of the tooth shovel machine.

[0098] Specifically, the error curve prediction model predicts the corresponding error curve based on the learned feature relationships and the input temperature data, and then outputs the error curve.

[0099] In another implementation, speed detection devices such as encoders can be used. The speed detection devices are installed on various parts of the tooth-shaving machine. The speed detection devices can monitor and provide feedback on the actual speed value during the processing in real time. The speed error value of that part of the tooth-shaving machine can be obtained by comparing the actual speed value with the preset speed reference value. Based on the changing trend of the speed error value during the processing, a speed error curve sample of that part of the tooth-shaving machine under the temperature sample is constructed. The speed error curve sample can also be used as an error curve sample.

[0100] In the process of model building, in addition to training the model, it is also necessary to test whether the accuracy of the model meets the standard. Therefore, step S424 in this embodiment specifically includes:

[0101] S4241. Divide the sample set into a training set and a test set, and preset the number of training rounds.

[0102] Specifically, 70% of the sample set is usually used as the training set and 30% as the test set.

[0103] S4242. The initial model is predicted by the input error curve of the training set and trained according to the preset number of training rounds.

[0104] Specifically, during training, temperature samples from the training set are used as input data, and error curve samples are used as output data to train the initial model for error curve prediction.

[0105] S4243. When the initial model for error curve prediction reaches the preset number of training rounds, the trained error curve prediction model is obtained.

[0106] S4244. Input the temperature samples from the test set into the trained error curve prediction model to obtain the error curve prediction samples.

[0107] Specifically, after training the model, the model's output is tested using input data from a sample set of known output data.

[0108] S4245. Obtain the mean square error of the error curve based on the error curve samples and the corresponding error curve prediction samples in the test set.

[0109] Specifically, mean-square error (MSE) is a measure that reflects the degree of difference between the estimator and the estimated quantity, i.e., the error curve sample and the corresponding error curve prediction sample. The difference between the error curve sample and the corresponding error curve prediction sample is judged based on the mean-square error, thereby determining whether the trained error curve prediction model meets the requirements.

[0110] S4246. When the mean square error of the error curve is greater than the preset error value, the model parameters of the error curve prediction model are cyclically adjusted until the mean square error of the error curve is less than the preset error value.

[0111] Specifically, if the mean square error of the error curve is greater than the preset error value, the accuracy of the error curve prediction model after training is low and does not meet the requirements, so the model parameters need to be adjusted.

[0112] S4247. When the mean square error of the error curve is less than the preset error value, the trained error curve prediction model will be obtained based on the current model parameters.

[0113] Specifically, the model parameters of the error curve prediction model are adjusted based on the error curve samples and corresponding error curve prediction samples in the test set, and the trained error curve prediction model is obtained based on the adjusted model parameters.

[0114] The implementation principle of this application embodiment is as follows: the initial model for error curve prediction is trained by constructing a sample set so that it can learn the feature relationship between temperature samples and error curve samples. After training, the model needs to be tested to determine its accuracy. When the accuracy is not up to standard, the model parameters are adjusted accordingly to ensure that the final error curve prediction model meets the requirements.

[0115] During the tooth-shaving process, excessively high temperatures can not only affect the accuracy of the process but also potentially cause machine malfunctions or safety hazards such as fire and explosion. Therefore, this application embodiment, in addition to compensating for temperature errors using temperature data from various locations, also requires high-temperature protection during the tooth-shaving process by monitoring real-time temperature. Specifically, this includes:

[0116] A1. Preset the first and second high temperature thresholds for each part of the tooth-shaving machine.

[0117] Specifically, the first high-temperature threshold is lower than the second high-temperature threshold. Considering that continuous high-temperature operation can also lead to increased mechanical errors, the first high-temperature threshold is the temperature at which operation can continue but not for extended periods, while the second high-temperature threshold is the temperature at which operation can no longer continue. In actual implementation, since the high-temperature resistance of different parts may vary, the first and second high-temperature thresholds may vary depending on the specific part.

[0118] A2. Compare the temperature data collected by the temperature detection device at a certain part of the tooth-shaving machine during the processing with the first and second high temperature thresholds preset for that part.

[0119] A31. When the temperature data is less than the first high temperature threshold, control the tooth-shaving machine to perform tooth-shaving processing on the raw material.

[0120] A32. When the temperature data is greater than the first high temperature threshold and less than the second high temperature threshold, control the tooth-shaving machine to perform tooth-shaving processing on the raw material and record the processing time of the tooth-shaving machine. When the processing time of the tooth-shaving machine when the temperature data is greater than the first high temperature threshold and less than the second high temperature threshold exceeds the preset high temperature processing time threshold, control the tooth-shaving machine to stop processing and generate a high temperature alarm signal.

[0121] Specifically, when the temperature exceeds the first temperature threshold but not the second temperature threshold, prolonged operation may lead to a decrease in the machining accuracy of the teeth or create safety hazards. Therefore, when the preset machining time threshold is exceeded, operation must be stopped immediately. In practical implementation, the high-temperature machining time threshold is determined by the duration that this part of the teeth-shaping machine can operate normally continuously in a high-temperature environment, and can be obtained through experiments.

[0122] A33. When the temperature data exceeds the second high temperature threshold, control the tooth shaving machine to stop processing and generate a high temperature alarm signal.

[0123] Specifically, when the temperature exceeds the second high temperature threshold, it indicates that the current temperature is too high, which may easily lead to malfunction of the tooth-shaving machine or create safety hazards. Therefore, it is necessary to stop working immediately. At the same time as the high temperature protection stops working, a high temperature alarm signal should be generated to remind the staff to take cooling measures in time.

[0124] This application also provides a heat sink scraping tooth machining control device, see reference. Figure 3 It includes a processor 1, which is used to execute a heat sink tooth machining control method:

[0125] S1. Obtain the machining data of the spade teeth of the heat sink to be processed.

[0126] S2, Receive the raw material thickness data collected by the thickness detection device 2.

[0127] S3. Generate a shaving cutter control signal based on the shaving tooth processing data and raw material thickness data to control the shaving cutter to perform shaving tooth processing on the raw material.

[0128] S4. Obtain the temperature data collected by the temperature detection device 3 of various parts of the tooth-shaving machine during the tooth-shaving process, and perform error compensation for various parts of the tooth-shaving machine based on the temperature data.

[0129] The heat sink tooth-shaving processing control device also includes a thickness detection device 2 and a temperature detection device 3, which are communicatively connected to the processor 1. The thickness detection device 2 is used to collect raw material thickness data and upload it to the processor 1. In practice, the thickness detection device 2 can be a vision inspection device or an infrared detection device, or other devices capable of detecting thickness. The temperature detection devices 3 are located at various parts of the tooth-shaving machine and are used to collect temperature data from various parts of the machine and upload it to the processor 1. In practice, the temperature detection device 3 is a temperature sensor.

[0130] The heat sink fin shaving control device also includes a shaving cutter control device 4, which is communicatively connected to the processor 1. After the processor 1 obtains the error curve corresponding to the temperature data, it performs error compensation on the angle adjustment signal and the movement signal according to the error curve to obtain the error-compensated angle adjustment signal and movement signal. The error-compensated angle adjustment signal and movement signal are then sent to the shaving cutter control device 4 to control the shaving cutter to perform shaving processing on the raw material. In specific implementation, the shaving cutter control device 4 includes an angle adjustment device and a movement device. The angle adjustment device receives the error-compensated angle adjustment signal and controls the tilt angle of the shaving cutter according to the angle adjustment signal. The movement device receives the error-compensated movement signal and controls the movement of the shaving cutter to process the raw material according to the movement signal.

[0131] The implementation principle of this application embodiment is as follows: the thickness detection device 2 collects the thickness data of the raw material and uploads it to the processor 1, and the temperature detection device 3 collects the temperature data and uploads it to the processor 1, so that the processor 1 can control the tooth-shaving machine to perform tooth-shaving processing according to the thickness data, and can perform error compensation according to the temperature data to improve the accuracy of tooth-shaving processing.

[0132] This application embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by processor 1, performs the following steps:

[0133] S1. Obtain the machining data of the spade teeth of the heat sink to be processed.

[0134] S2, Receive the raw material thickness data collected by the thickness detection device 2.

[0135] S3. Generate a shaving cutter control signal based on the shaving tooth processing data and raw material thickness data to control the shaving cutter to perform shaving tooth processing on the raw material.

[0136] S4. Obtain the temperature data collected by the temperature detection device 3 of various parts of the tooth-shaving machine during the tooth-shaving process, and perform error compensation for various parts of the tooth-shaving machine based on the temperature data.

[0137] In addition, when the processor 1 in the computer device executes the computer program, it performs all the steps of the heat sink tooth machining control method described above.

[0138] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0139] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0140] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, all equivalent changes made to the structure, shape, and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for controlling the machining of heat sink spade teeth, characterized in that, include: Obtain the machining data of the spade teeth for the heat sink to be processed; Receive raw material thickness data collected by thickness detection equipment; Based on the shaving tooth machining data and raw material thickness data, a shaving tooth cutter control signal is generated to control the shaving tooth cutter to perform shaving tooth machining on the raw material, specifically including: The shovel angle adjustment signal is generated based on the shovel machining data and the raw material thickness. Generate a tooth cutting tool movement signal based on the tooth cutting data; Error compensation is performed on the angle adjustment signal and the movement signal to obtain the error-compensated angle adjustment signal and the movement signal; The angle adjustment signal and movement signal after error compensation are sent to the tooth cutting cutter control device to control the tooth cutting cutter to perform tooth cutting on the raw material; The temperature data collected by temperature detection equipment at various parts of the gear hoisting machine during the gear hoisting process is obtained, and error compensation is performed on various parts of the gear hoisting machine based on the temperature data; The acquisition of temperature data collected by temperature detection devices at various parts of the gear-shaving machine during the gear-shaving process, and the subsequent error compensation for various parts of the gear-shaving machine based on the temperature data, specifically includes: Acquire temperature data from a temperature detection device located at a specific part of the gear-shaving machine during the processing; Obtain the error curve of the corresponding temperature data for this part of the tooth-shaving machine based on the temperature data; Temperature error compensation is performed on this part of the tooth-shaving machine based on the error curve and temperature data. The step of obtaining the error curve of the corresponding temperature data for this part of the tooth-shaving machine based on temperature data specifically includes: Construct an initial model for error curve prediction; Obtain error curve samples for this part of the tooth-shaving machine under different temperature samples; A sample set is constructed by combining temperature samples and corresponding error curve samples; The initial model for error curve prediction is trained based on the sample set to obtain the trained error curve prediction model; the collected temperature data is input into the trained error curve prediction model to obtain the error curve of the corresponding temperature data of the tooth shovel machine. The acquisition of error curve samples corresponding to this part of the tooth-shaving machine under different temperature samples specifically includes: Obtain the actual position value during the processing from the position detection device located at this part of the gear hoist at the current temperature; Obtain the reference position value of this part of the gear hoist during the machining process; The error value of that part of the tooth-shaving machine is obtained based on the position reference value and the actual position value; The trend of error value changes during the machining process is constructed as the value of that part of the gear hoisting machine. Error curve sample under temperature sample; By changing the current temperature according to a preset temperature sample, error curve samples corresponding to this part of the tooth-shaving machine are obtained under different temperature samples. The step of training the initial error curve prediction model based on the sample set to obtain the trained error curve prediction model specifically includes: The sample set is divided into a training set and a test set, with a preset number of training rounds; The initial model is predicted by inputting the error curve of the training set and trained according to the preset number of training rounds; When the initial model for error curve prediction reaches the preset number of training rounds, the trained error curve prediction model is obtained. The temperature samples in the test set are input into the trained error curve prediction model to obtain the error curve prediction samples; The model parameters of the error curve prediction model are adjusted based on the error curve samples and corresponding error curve prediction samples in the test set, and the trained error curve prediction model is obtained based on the adjusted model parameters.

2. The method for controlling the machining of heat sink teeth according to claim 1, characterized in that, After acquiring the temperature data collected by the temperature detection device located at a certain part of the gear-shaving machine during the processing, the method further includes: A first high temperature threshold and a second high temperature threshold are preset for each part of the tooth-shaving machine, wherein the first high temperature threshold is less than the second high temperature threshold; The temperature data collected by the temperature detection device at a certain part of the gear hoist during the processing is compared with the first and second high temperature thresholds preset for that part. Based on the comparison results, high-temperature protection was applied to this part of the tooth-shaving machine.

3. The method for controlling the machining of heat sink teeth according to claim 2, characterized in that, The high-temperature protection of this part of the tooth-shaving machine based on the comparison results specifically includes: When the temperature data is lower than the first high temperature threshold, control the tooth-shaving machine to perform tooth-shaving processing on the raw material; When the temperature data is greater than the first high temperature threshold and less than the second high temperature threshold, the tooth-shaving machine is controlled to perform tooth-shaving processing on the raw material and the processing time of the tooth-shaving machine is recorded. When the processing time of the gear shaving machine with temperature data greater than the first high temperature threshold and less than the second high temperature threshold exceeds the preset high temperature processing time threshold, the gear shaving machine is controlled to stop processing and generate a high temperature alarm signal. When the temperature data exceeds the second high temperature threshold, the tooth-shaving machine is controlled to stop processing and generate a high temperature alarm signal.

4. A heat sink fin shaving tooth machining control device, characterized in that, Includes a processor (1), said processor (1) being used to execute a heat sink shovel tooth machining control method according to any one of claims 1-3; It also includes a thickness detection device (2) and a temperature detection device (3), which are respectively connected to the processor (1) in communication. The thickness detection device (2) is used to collect raw material thickness data; the temperature detection device (3) is installed at various parts of the tooth-shaving machine and is used to collect temperature data at various parts of the tooth-shaving machine.

5. A computer-readable storage medium, characterized in that, Storage capable of being processed (1) Load and execute the computer program of the heat sink tooth machining control method as described in any one of claims 1-3.

Images