Titanium alloy processing machine tool control method and device, computer equipment and medium

Abstract

The invention relates to a titanium alloy machining machine tool control method and device, computer equipment and a medium, and the method comprises the steps that material hardness information, tool load data and temperature change conditions in the titanium alloy machining process are collected in real time, and the material hardness information, the tool load data and the temperature change conditions are uploaded to a cloud computing platform; a predictive torque vector control algorithm is adopted, the tool load data is compared with a preset torque range, a required torque value is calculated in real time, and the output torque of the machining tool is adjusted according to the required torque value; the temperature change condition is monitored in real time, under the condition that the temperature change condition exceeds a preset temperature fluctuation range, a temperature adjusting instruction is triggered, and a cooling device or a heating device in the machining area is adjusted according to the temperature adjusting instruction; and the real-time machining state is obtained through the AR equipment, an operation instruction of an operator is obtained in real time, and secondary adjustment is conducted according to the operation instruction. The method has the effect of improving the titanium alloy machining efficiency and quality.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent manufacturing, and in particular to a titanium alloy processing machine tool control method, device, computer equipment and medium. Background Art

[0002] At present, titanium alloys are widely used in aerospace, medical equipment and high-end manufacturing due to their excellent strength-to-weight ratio and corrosion resistance. However, due to the high hardness and poor thermal conductivity of titanium alloy materials, problems such as overheating and rapid tool wear are prone to occur during processing, resulting in low processing efficiency and low yield.

[0003] Most existing titanium alloy processing equipment relies on fixed torque output and manual adjustment, and is unable to dynamically adjust torque and environmental parameters according to real-time processing status. This can easily lead to processing errors and equipment losses when faced with complex processing tasks.

[0004] The above-mentioned prior art solutions have the following defects: the traditional titanium alloy processing method cannot adjust the torque and temperature control during the processing in real time, so there is room for improvement. Summary of the invention

[0005] In order to improve the efficiency and quality of titanium alloy processing, the present application provides a titanium alloy processing machine tool control method, device, computer equipment and medium.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A titanium alloy processing machine tool control method based on the Internet of Things, the titanium alloy processing machine tool control method comprising: Collect material hardness information, tool load data and temperature changes during titanium alloy processing in real time, and upload the material hardness information, tool load data and temperature changes to the cloud computing platform through the Internet of Things hub; According to the material hardness and tool load data, a predictive torque vector control algorithm is used to compare the tool load data with a preset torque range, calculate a required torque value in real time, and adjust the output torque of the processing machine tool according to the required torque value; Monitor the temperature change in real time, and when the temperature change exceeds a preset temperature fluctuation range, trigger a temperature adjustment instruction, and adjust a cooling device or a heating device in the processing area according to the temperature adjustment instruction; The real-time processing status is obtained through the AR device, and the operator's operating instructions are obtained in real time, and secondary adjustments are made according to the operating instructions.

[0007] By adopting the above technical solution, by real-time collection of material hardness information, tool load data and temperature changes in the titanium alloy processing process, and uploading the material hardness information, tool load data and temperature changes to the cloud computing platform through the Internet of Things hub, it is possible to achieve accurate monitoring and data transmission of key parameters in the processing process, thereby improving the real-time and accuracy of data collection and ensuring precise control of the processing process; by using a predictive torque vector control algorithm based on the material hardness and tool load data, the tool load data is compared with the preset torque range, the required torque value is calculated in real time, and the output torque of the processing machine tool is adjusted according to the required torque value, which can dynamically adjust the machine tool according to real-time load changes. Torque output, thereby ensuring that the torque output matches the processing requirements and improving the processing quality and efficiency; by real-time monitoring of the temperature change, when the temperature change exceeds the preset temperature fluctuation range, triggering the temperature adjustment instruction, and adjusting the cooling device or heating device in the processing area according to the temperature adjustment instruction, the temperature of the processing environment can be effectively controlled during the processing, avoiding the impact of temperature fluctuations on the processing accuracy, thereby ensuring the yield and consistency of the product; by obtaining the real-time processing status through the AR device, and obtaining the operator's operation instructions in real time, and performing secondary adjustments according to the operation instructions, the operator can intuitively grasp the processing status and make timely adjustments, thereby optimizing the operation process and improving the efficiency and accuracy of human-machine collaboration.

[0008] In one example, the present application may be further configured as follows: according to the material hardness and tool load data, a predictive torque vector control algorithm is used to compare the tool load data with a preset torque range, and the required torque value is calculated in real time, specifically including: By using a predictive torque vector control algorithm, the material hardness information and the tool load data are analyzed one by one to obtain corresponding parameter fluctuation trends; Based on the parameter fluctuation trend, a correlation model based on material hardness and tool load is established to determine the deviation value between the current processing state and the preset ideal state, and the required torque value is obtained according to the deviation value.

[0009] By adopting the above technical solution, the material hardness information and the tool load data are analyzed one by one through the predictive torque vector control algorithm to obtain the corresponding parameter fluctuation trend, which can deeply analyze the changing trend of material hardness and tool load during the processing process, so as to predict in advance the problems that may occur in the processing process and improve the processing stability and predictability; by establishing a correlation model based on material hardness and tool load based on the parameter fluctuation trend, judging the deviation value between the current processing state and the preset ideal state, and obtaining the required torque value according to the deviation value, the torque demand during processing can be accurately calculated, thereby ensuring the accuracy of torque regulation and further improving the accuracy and efficiency of the processing process.

[0010] In one example, the present application may be further configured as follows: analyzing the material hardness information and the tool load data one by one to obtain corresponding parameter fluctuation trends, specifically including: Collecting the continuous data stream of the material hardness information and the tool load data in real time, and performing denoising processing on the continuous data stream; The denoised material hardness information and the tool load data are analyzed respectively, and the corresponding material hardness fluctuation trend and tool load change trend are determined by weighted average calculation.

[0011] By adopting the above technical solution, by collecting the continuous data stream of the material hardness information and the tool load data in real time and denoising the continuous data stream, it is possible to eliminate noise interference in the data, ensure the accuracy and stability of the data, and thus improve the reliability of subsequent analysis; by separately analyzing the denoised material hardness information and the tool load data, and determining the corresponding material hardness fluctuation trend and tool load change trend through weighted average calculation, it is possible to more accurately identify the changing laws of material hardness and load during the processing process, thereby providing more reliable data support for subsequent torque adjustment and improving processing accuracy.

[0012] In one example, the present application may be further configured as follows: the temperature change is monitored in real time, and when the temperature change exceeds a preset temperature fluctuation range, a temperature adjustment instruction is triggered, and a cooling or heating device of the processing area is adjusted according to the temperature adjustment instruction, specifically including: Monitor the temperature of the processing area through an optical fiber sensor, and calculate the temperature change within a preset time period in real time based on the temperature; When it is detected that the temperature change exceeds a preset temperature fluctuation range, the cooling device or the heating device is activated through the temperature adjustment instruction to keep the temperature of the processing area stable; When the temperature change reaches the temperature fluctuation range, the adjustment operation is stopped and the temperature change is continuously monitored.

[0013] By adopting the above technical solution, the temperature of the processing area is monitored by an optical fiber sensor, and the temperature change within a preset time period is calculated in real time based on the temperature, so that continuous monitoring and real-time calculation of the temperature of the processing area can be achieved, so that the temperature fluctuation can be grasped in time to avoid affecting the processing quality due to unstable temperature; when it is detected that the temperature change exceeds the preset temperature fluctuation range, the cooling device or the heating device is activated by the temperature adjustment instruction to keep the temperature of the processing area stable, and the temperature abnormality can be quickly responded to and the corresponding adjustment device can be started in time, so as to ensure the stability of the ambient temperature during the processing and improve the yield; when the temperature change reaches the temperature fluctuation range, the adjustment operation is stopped and the temperature change is continuously monitored, so that the temperature adjustment can be stopped when the target temperature is reached to avoid excessive adjustment, thereby saving energy and ensuring the long-term stability of the temperature of the processing area.

[0014] In one example, the present application may be further configured as follows: obtaining the real-time processing status through the AR device, and obtaining the operator's operation instruction in real time, and performing secondary adjustment according to the operation instruction, specifically including: The real-time processing status is displayed to the operator through the AR device, and a corresponding processing strategy is generated according to the real-time processing status; The operation instruction of the operator is obtained, and the processing strategy is adjusted in real time or the system parameters are fine-tuned according to the operation instruction.

[0015] By adopting the above technical solution, the real-time processing status is displayed to the operator through the AR device, and the corresponding processing strategy is generated according to the real-time processing status, so that the operator can intuitively view the status of the processing process through the AR device and adjust the processing strategy according to the current situation, thereby improving the convenience and flexibility of operation; by obtaining the operator's operating instructions, and adjusting the processing strategy in real time or fine-tuning the system parameters according to the operating instructions, it is possible to respond quickly and make real-time adjustments according to the operator's instructions, thereby enhancing the control accuracy of the processing process and improving the response speed and operating efficiency of the system.

[0016] In one example, the present application may be further configured as follows: the titanium alloy processing machine tool control method further includes: Based on the historical data collected during the processing, a data analysis is performed on the historical material hardness, historical tool load and historical temperature changes in the historical data through a self-learning algorithm to identify trends and anomalies in the historical data; The parameter analysis function in the predictive torque vector control algorithm is optimized according to the analysis results to optimize future torque adjustment parameters.

[0017] By adopting the above-mentioned technical solution, based on the historical data collected during the processing, the historical material hardness, historical tool load and historical temperature changes in the historical data are analyzed through a self-learning algorithm to identify trends and anomalies in the historical data. By analyzing the historical data, potential processing problems and trends can be discovered, thereby providing optimization references for future processing and reducing processing errors; by optimizing the parameter analysis function in the predictive torque vector control algorithm according to the analysis results to optimize the future torque adjustment parameters, the torque control algorithm can be optimized according to the analysis results of the historical data, thereby improving the torque adjustment accuracy and efficiency in future processing tasks, and further enhancing the system's adaptability and stability.

[0018] The second object of the invention is achieved by the following technical solutions: A titanium alloy processing machine tool control device, characterized in that the titanium alloy processing machine tool control device comprises: A data acquisition module is used to collect material hardness information, tool load data and temperature changes in the titanium alloy processing process in real time, and upload the material hardness information, tool load data and temperature changes to the cloud computing platform through the Internet of Things hub; A torque adjustment module, for comparing the tool load data with a preset torque range using a predictive torque vector control algorithm based on the material hardness and tool load data, calculating a required torque value in real time, and adjusting the output torque of the processing machine tool according to the required torque value; A temperature monitoring module is used to monitor the temperature change in real time, and when the temperature change exceeds a preset temperature fluctuation range, trigger a temperature adjustment instruction, and adjust a cooling device or a heating device in a processing area according to the temperature adjustment instruction; The AR interaction module is used to obtain the real-time processing status through the AR device, and obtain the operator's operation instructions in real time, and perform secondary adjustments according to the operation instructions.

[0019] By adopting the above technical solution, by real-time collection of material hardness information, tool load data and temperature changes in the titanium alloy processing process, and uploading the material hardness information, tool load data and temperature changes to the cloud computing platform through the Internet of Things hub, it is possible to achieve accurate monitoring and data transmission of key parameters in the processing process, thereby improving the real-time and accuracy of data collection and ensuring precise control of the processing process; by using a predictive torque vector control algorithm based on the material hardness and tool load data, the tool load data is compared with the preset torque range, the required torque value is calculated in real time, and the output torque of the processing machine tool is adjusted according to the required torque value, which can dynamically adjust the machine tool according to real-time load changes. Torque output, thereby ensuring that the torque output matches the processing requirements and improving the processing quality and efficiency; by real-time monitoring of the temperature change, when the temperature change exceeds the preset temperature fluctuation range, triggering the temperature adjustment instruction, and adjusting the cooling device or heating device in the processing area according to the temperature adjustment instruction, the temperature of the processing environment can be effectively controlled during the processing, avoiding the impact of temperature fluctuations on the processing accuracy, thereby ensuring the yield and consistency of the product; by obtaining the real-time processing status through the AR device, and obtaining the operator's operation instructions in real time, and performing secondary adjustments according to the operation instructions, the operator can intuitively grasp the processing status and make timely adjustments, thereby optimizing the operation process and improving the efficiency and accuracy of human-machine collaboration.

[0020] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned titanium alloy processing machine tool control method when executing the computer program.

[0021] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the titanium alloy processing machine tool control method are implemented.

[0022] In summary, this application includes the following beneficial technical effects: 1. By collecting material hardness information, tool load data and temperature changes in the titanium alloy processing process in real time, and uploading the material hardness information, tool load data and temperature changes to the cloud computing platform through the Internet of Things hub, it is possible to achieve accurate monitoring and data transmission of key parameters in the processing process, thereby improving the real-time and accuracy of data collection and ensuring precise control of the processing process; by using a predictive torque vector control algorithm based on the material hardness and tool load data, the tool load data is compared with the preset torque range, the required torque value is calculated in real time, and the output torque of the processing machine tool is adjusted according to the required torque value, and the torque output of the machine tool can be dynamically adjusted according to real-time load changes. , thereby ensuring that the torque output matches the processing requirements and improving the processing quality and efficiency; by real-time monitoring of the temperature change, when the temperature change exceeds the preset temperature fluctuation range, triggering the temperature adjustment instruction, and adjusting the cooling device or heating device of the processing area according to the temperature adjustment instruction, the temperature of the processing environment can be effectively controlled during the processing, avoiding the influence of temperature fluctuation on the processing accuracy, thereby ensuring the yield and consistency of the product; by obtaining the real-time processing status through the AR device and the operator's operation instructions in real time, and performing secondary adjustments according to the operation instructions, the operator can intuitively grasp the processing status and make adjustments in time, thereby optimizing the operation process and improving the efficiency and accuracy of human-machine collaboration; 2. Through the predictive torque vector control algorithm, the material hardness information and the tool load data are analyzed one by one to obtain the corresponding parameter fluctuation trend, which can deeply analyze the change trend of material hardness and tool load during the processing, so as to predict the problems that may occur in the processing in advance and improve the processing stability and predictability; by establishing a correlation model based on material hardness and tool load based on the parameter fluctuation trend, judging the deviation value between the current processing state and the preset ideal state, and obtaining the required torque value according to the deviation value, the torque demand during processing can be accurately calculated, thereby ensuring the accuracy of torque regulation and further improving the accuracy and efficiency of the processing process; 3. By collecting the continuous data stream of the material hardness information and the tool load data in real time and denoising the continuous data stream, the noise interference in the data can be eliminated, ensuring the accuracy and stability of the data, thereby improving the reliability of subsequent analysis; by analyzing the denoised material hardness information and the tool load data respectively, and determining the corresponding material hardness fluctuation trend and tool load change trend through weighted average calculation, the change law of material hardness and load during the processing process can be more accurately identified, thereby providing more reliable data support for subsequent torque adjustment and improving processing accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1is a flow chart of a titanium alloy processing machine tool control method in one embodiment of the present application; Figure 2 It is a flowchart for implementing step S20 in the titanium alloy processing machine tool control method in one embodiment of the present application; Figure 3 It is a flowchart for implementing step S21 in the titanium alloy processing machine tool control method in one embodiment of the present application; Figure 4 It is a flowchart for implementing step S30 in the titanium alloy processing machine tool control method in one embodiment of the present application; Figure 5 It is a flowchart for implementing step S40 in the titanium alloy processing machine tool control method in one embodiment of the present application; Figure 6 This is another implementation flow chart of the titanium alloy processing machine tool control method in one embodiment of the present application; Figure 7 This is a principle block diagram of a titanium alloy processing machine tool control device in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application is further described in detail below in conjunction with the accompanying drawings.

[0025] In one embodiment, if Figure 1 As shown, the present application discloses a titanium alloy processing machine tool control method, which specifically includes the following steps: S10: Collect material hardness information, tool load data and temperature changes during titanium alloy processing in real time, and upload the material hardness information, tool load data and temperature changes to the cloud computing platform through the Internet of Things hub.

[0026] Specifically, data is collected in real time through multiple sensors installed in the processing machine tools and processing areas. These sensors include hardness sensors for measuring material hardness, load sensors for monitoring tool loads, and temperature sensors. The sensors transmit the collected data to the IoT hub via a wireless network. The IoT hub will perform preliminary processing and storage on the data and upload it to the cloud computing platform for further analysis and processing. The data collection frequency of the sensor can be dynamically adjusted according to changes in the processing environment to ensure the real-time and accuracy of the data. The data will also be compressed during the upload process to reduce network bandwidth usage.

[0027] S20: Based on the material hardness and tool load data, a predictive torque vector control algorithm is used to compare the tool load data with the preset torque range, calculate the required torque value in real time, and adjust the output torque of the processing machine tool according to the required torque value.

[0028] Specifically, when using the predictive torque vector control algorithm, the system combines the collected material hardness and tool load data and inputs these real-time data into the control algorithm for processing. The algorithm predicts future torque requirements based on historical processing experience and current load information. The tool load data is compared with the preset optimal torque range. When the load changes beyond the normal range, the system automatically adjusts the torque output of the machine tool. For example, when a sudden increase in tool load is detected, the algorithm predicts the maximum torque that the tool may withstand and makes corresponding adjustments in advance to avoid excessive tool wear or material damage.

[0029] S30: Monitor the temperature change in real time, and when the temperature change exceeds a preset temperature fluctuation range, trigger a temperature adjustment instruction, and adjust the cooling device or heating device of the processing area according to the temperature adjustment instruction.

[0030] Specifically, the temperature changes in the processing area can be continuously monitored through optical fiber sensors. This sensor has high sensitivity and can accurately capture small temperature fluctuations. The system will calculate the temperature changes in each preset time period to ensure that the temperature always remains within the ideal processing range. When the temperature exceeds the set fluctuation range, the control system will immediately send a temperature adjustment instruction to the cooling device or heating device to adjust the working state of the equipment. For example, when the temperature is too high, the cooling device will increase the flow rate of the coolant or the speed of the fan. Conversely, when the temperature is too low, the system will start the heating device to increase the temperature of the processing area to ensure the stability of the processing environment.

[0031] S40: Obtain the real-time processing status through the AR device, obtain the operator's operation instructions in real time, and make secondary adjustments according to the operation instructions.

[0032] Specifically, by wearing AR smart glasses or using devices such as tablets, operators can view key data in the processing process in real time, including current material hardness, tool load, and processing temperature. The system will generate a visual display of the processing status based on this information, and the operator can intuitively see the execution of each step. If the system detects an abnormal situation, such as excessive temperature fluctuations or loads exceeding the preset range, the operator can issue adjustment instructions through the AR device, and the system will make secondary adjustments to the processing parameters according to the operator's instructions, such as adjusting the operation of the cooling device or recalculating the torque output, to ensure the smooth progress of the entire processing process.

[0033] In one embodiment, if Figure 2 As shown, in step S20, the tool load data is compared with the preset torque range by using a predictive torque vector control algorithm according to the material hardness and tool load data, and the required torque value is calculated in real time, specifically including: S21: Through the predictive torque vector control algorithm, the material hardness information and tool load data are analyzed one by one to obtain the corresponding parameter fluctuation trend.

[0034] Specifically, the predictive torque vector control algorithm first analyzes the material hardness and tool load data in segments to ensure that the fluctuation trend of each machining cycle can be handled independently. For each set of data, the system uses a machine learning algorithm to analyze the historical change trend of the hardness and load data. Especially in the case of high material hardness, the system uses a weighted analysis method to evaluate the possible fluctuations in future hardness and predict the range of tool load changes. For example, when the machining process is nearing completion, the hardness may decrease. The system can predict this change in advance and adjust the torque in real time to cope with the load changes caused by hardness fluctuations.

[0035] S22: Based on the parameter fluctuation trend, a correlation model based on material hardness and tool load is established to determine the deviation value between the current processing state and the preset ideal state, and obtain the required torque value based on the deviation value.

[0036] Specifically, based on the real-time changes in material hardness and tool load, a multi-dimensional correlation model is established. The model will comprehensively consider factors such as hardness changes, tool wear and load trends to determine whether the current processing state is consistent with the preset ideal state. When the deviation value exceeds the set safety range, the system will automatically adjust the torque output. For example, if the model detects that the current load is too high and the hardness drops too quickly, it may mean that the processing is abnormal. The system will immediately adjust the torque output according to the deviation value to ensure the stability and accuracy of the processing process.

[0037] In one embodiment, if Figure 3 As shown, in step S21, the material hardness information and tool load data are analyzed one by one to obtain the corresponding parameter fluctuation trend, which specifically includes: S211: collecting continuous data streams of material hardness information and tool load data in real time, and performing denoising processing on the continuous data streams.

[0038] Specifically, the material hardness and tool load data may be affected by environmental noise, equipment vibration or other external interference during the sensor acquisition process. To solve this problem, the system will first perform filtering when collecting data, and use digital filters to remove high-frequency noise in the signal to ensure the purity of the data. After denoising, the system will smooth the continuous data stream to ensure the continuity of the data. The denoised and smoothed data can more accurately reflect the hardness and load changes in actual processing, thereby improving the accuracy of subsequent analysis.

[0039] S212: Analyze the denoised material hardness information and tool load data respectively, and determine the corresponding material hardness fluctuation trend and tool load change trend by weighted average calculation.

[0040] Specifically, the denoised hardness information and load data are used as input, and a weighted average calculation method is used to evaluate the impact of each data point on the overall trend. For example, recent hardness fluctuations and load changes may be more representative than earlier data, so the system will give these data a higher weight. Through this weighted method, the system can capture the changing trends of the current processing environment more quickly and accurately, thereby ensuring the accuracy of subsequent torque adjustments. In addition, the system will also record these changing trends for future data analysis and self-learning optimization.

[0041] In one embodiment, if Figure 4 As shown, in step S30, the temperature change is monitored in real time. When the temperature change exceeds the preset temperature fluctuation range, the temperature adjustment instruction is triggered, and the cooling or heating device of the processing area is adjusted according to the temperature adjustment instruction, which specifically includes: S31: The temperature of the processing area is monitored by an optical fiber sensor, and the temperature change within a preset time period is calculated in real time based on the temperature.

[0042] Specifically, fiber optic sensors can collect temperature data in the processing area at a high frequency, collecting multiple data points per second to ensure that every slight fluctuation in temperature is monitored. The system will average the collected temperature data within each preset time period (for example, every 5 seconds) and evaluate the amplitude of temperature fluctuations. If the temperature fluctuation amplitude exceeds the safe range, the system will record the fluctuation and transmit it to the control center for subsequent adjustments.

[0043] S32: When it is detected that the temperature change exceeds the preset temperature fluctuation range, the cooling device or the heating device is activated through a temperature adjustment instruction to keep the temperature of the processing area stable.

[0044] Specifically, when the system detects that the temperature change exceeds the set range, it will immediately send an adjustment instruction to the cooling device or heating device. The cooling device can be a coolant spraying system or an air circulation system, while the heating device can be an electric heater. The intensity of cooling or heating will be dynamically adjusted according to the degree of temperature deviation. For example, when the temperature is high, the cooling device will increase the coolant spraying rate, and when the temperature is low, the heating device will increase the power to quickly restore the normal temperature. The entire process is fully automated and does not require human intervention, ensuring that the temperature of the processing area is always within the ideal range.

[0045] S33: When the temperature change reaches the temperature fluctuation range, the adjustment operation is stopped and the temperature change is continuously monitored.

[0046] Specifically, the system will automatically stop the cooling or heating device when the temperature returns to the set fluctuation range to avoid energy waste caused by over-adjustment. After stopping the adjustment, the system will continue to monitor the temperature changes in real time to ensure that the temperature stability of the processing area is not affected by external factors. If the temperature exceeds the fluctuation range again, the system will restart the adjustment program. Through this automated adjustment mechanism, the system can achieve long-term control of the temperature of the processing area and ensure the consistency of product quality.

[0047] In one embodiment, if Figure 5 As shown, in step S40, the real-time processing status is obtained through the AR device, and the operation instruction of the operator is obtained in real time, and secondary adjustment is performed according to the operation instruction, which specifically includes: S41: The real-time processing status is displayed to the operator through the AR device, and a corresponding processing strategy is generated according to the real-time processing status.

[0048] Specifically, by wearing AR smart glasses or using mobile devices with augmented reality functions, operators can see a visual interface of key data in the processing process, including parameters such as material hardness, tool load, and processing temperature. These data will be intuitively displayed in the form of charts or dynamic indicators in the operator's field of view, helping them to understand the current processing status in real time. At the same time, the system will automatically generate processing strategies based on these real-time data, prompting the operator how to adjust the current processing, for example, when to increase the amount of coolant injection, when to reduce the tool load, etc. The AR device will also provide a historical record of the processing status, and the operator can compare the current status with the historical status to determine the changing trend in the processing process.

[0049] S42: Obtain the operator's operating instructions, and adjust the processing strategy or fine-tune the system parameters in real time according to the operating instructions.

[0050] Specifically, the operator can directly issue operating instructions through the AR device, such as adjusting the torque output, changing the coolant flow rate or the working status of the heating device. The operator can input through voice commands, gesture control or touch screen, and the system will make real-time adjustments according to the operator's instructions. After receiving the instructions, the system will immediately calculate the new parameters and apply the adjusted processing strategy to the current processing process. For example, if the operator finds that the tool load is too high, he can use the AR device instruction to reduce the output power of the load system, and the system will respond quickly and implement adjustments. This interactive method greatly improves the operator's control flexibility over the processing process and reduces the errors that may be caused by manual intervention.

[0051] In one embodiment, if Figure 6 As shown, the titanium alloy processing machine tool control method also includes: S50: Based on the historical data collected during the processing, the historical material hardness, historical tool load and historical temperature changes in the historical data are analyzed through a self-learning algorithm to identify trends and anomalies in the historical data.

[0052] Specifically, the system automatically records the data such as material hardness, tool load and temperature changes collected during each processing, and inputs this data into the self-learning algorithm for analysis. The self-learning algorithm compares historical data under different processing tasks and analyzes the commonalities and changing trends in the data. For example, when the system finds that the hardness changes of a batch of materials are abnormal, the self-learning algorithm will identify this abnormality and make adjustment suggestions. During the analysis process, the algorithm will also continuously optimize the processing process to ensure that similar situations can be better dealt with in future processing. Through this automated trend analysis, the system can predict future processing needs and actively adjust processing parameters to improve the system's adaptability.

[0053] S60: Optimizing a parameter analysis function in a predictive torque vector control algorithm according to the analysis results to optimize future torque adjustment parameters.

[0054] Specifically, based on the analysis results of the self-learning algorithm, the parameter analysis function in the predictive torque vector control algorithm is dynamically adjusted. Specifically, the algorithm optimizes the parameter settings of the torque adjustment according to the load changes, material hardness fluctuations and temperature change trends identified in the historical data. For example, if the system detects that in some cases, the material hardness fluctuates greatly and the tool load continues to increase, the algorithm will automatically adjust the weight of the parameter analysis function to ensure that in future processing, the torque output can more accurately match the actual needs. This optimization can not only reduce processing errors, but also improve overall production efficiency and extend the service life of the equipment.

[0055] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0056] In one embodiment, a titanium alloy processing machine tool control device is provided, and the titanium alloy processing machine tool control device corresponds to the titanium alloy processing machine tool control method in the above embodiment. Figure 7 As shown, the titanium alloy processing machine tool control device includes a data acquisition module, a torque adjustment module, a temperature monitoring module, and an AR interaction module. The detailed description of each functional module is as follows: The data acquisition module is used to collect material hardness information, tool load data and temperature changes during titanium alloy processing in real time, and upload the material hardness information, tool load data and temperature changes to the cloud computing platform through the Internet of Things hub; The torque adjustment module is used to compare the tool load data with the preset torque range based on the material hardness and tool load data, using a predictive torque vector control algorithm, calculate the required torque value in real time, and adjust the output torque of the processing machine tool according to the required torque value; The temperature monitoring module is used to monitor the temperature change in real time. When the temperature change exceeds the preset temperature fluctuation range, the temperature adjustment instruction is triggered, and the cooling device or heating device of the processing area is adjusted according to the temperature adjustment instruction; The AR interaction module is used to obtain the real-time processing status through the AR device, and obtain the operator's operating instructions in real time, and make secondary adjustments based on the operating instructions.

[0057] Optionally, the torque regulation module specifically includes: The parameter analysis submodule is used to analyze the material hardness information and tool load data one by one through the predictive torque vector control algorithm to obtain the corresponding parameter fluctuation trend; The correlation model submodule is used to establish a correlation model based on material hardness and tool load based on the parameter fluctuation trend, determine the deviation value between the current processing state and the preset ideal state, and obtain the required torque value based on the deviation value.

[0058] Optionally, the parameter analysis submodule specifically includes: A data processing unit, used for collecting a continuous data stream of material hardness information and tool load data in real time, and performing denoising processing on the continuous data stream; The trend calculation unit is used to analyze the denoised material hardness information and tool load data respectively, and determine the corresponding material hardness fluctuation trend and tool load change trend by weighted average calculation.

[0059] Optionally, the temperature monitoring module specifically includes: The temperature monitoring submodule is used to monitor the temperature of the processing area through an optical fiber sensor and calculate the temperature change within a preset time period in real time based on the temperature; The temperature adjustment submodule is used to activate the cooling device or the heating device through the temperature adjustment instruction to keep the temperature of the processing area stable when the temperature change exceeds the preset temperature fluctuation range; The temperature control submodule is used to stop the adjustment operation and continuously monitor the temperature change when the temperature change reaches the temperature fluctuation range.

[0060] Optionally, the AR interaction module specifically includes: The AR display submodule is used to display the real-time processing status to the operator through the AR device and generate the corresponding processing strategy according to the real-time processing status; The instruction adjustment submodule is used to obtain the operator's operating instructions and adjust the processing strategy or fine-tune the system parameters in real time according to the operating instructions.

[0061] Optionally, the titanium alloy processing machine tool control method further includes: A data analysis module is used to analyze historical material hardness, historical tool load and historical temperature changes in historical data based on historical data collected during the processing through a self-learning algorithm to identify trends and anomalies in the historical data; The algorithm optimization module is used to optimize the parameter analysis function in the predictive torque vector control algorithm according to the analysis results to optimize the future torque adjustment parameters.

[0062] The specific definition of the titanium alloy processing machine tool control device can be found in the above-mentioned definition of the titanium alloy processing machine tool control method, which will not be repeated here. Each module in the above-mentioned titanium alloy processing machine tool control device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0063] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a titanium alloy processing machine tool control method is implemented.

[0064] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Collect material hardness information, tool load data and temperature changes during titanium alloy processing in real time, and upload the material hardness information, tool load data and temperature changes to the cloud computing platform through the Internet of Things hub; According to the material hardness and tool load data, a predictive torque vector control algorithm is used to compare the tool load data with the preset torque range, calculate the required torque value in real time, and adjust the output torque of the processing machine tool according to the required torque value; Monitor the temperature change in real time. When the temperature change exceeds the preset temperature fluctuation range, trigger the temperature adjustment instruction and adjust the cooling device or heating device in the processing area according to the temperature adjustment instruction. The real-time processing status and the operator's operating instructions are obtained through AR devices, and secondary adjustments are made based on the operating instructions.

[0065] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Collect material hardness information, tool load data and temperature changes during titanium alloy processing in real time, and upload the material hardness information, tool load data and temperature changes to the cloud computing platform through the Internet of Things hub; According to the material hardness and tool load data, a predictive torque vector control algorithm is used to compare the tool load data with the preset torque range, calculate the required torque value in real time, and adjust the output torque of the processing machine tool according to the required torque value; Monitor the temperature change in real time. When the temperature change exceeds the preset temperature fluctuation range, trigger the temperature adjustment instruction and adjust the cooling device or heating device in the processing area according to the temperature adjustment instruction. The real-time processing status and the operator's operating instructions are obtained through AR devices, and secondary adjustments are made based on the operating instructions.

[0066] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0067] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by 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.

[0068] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A titanium alloy processing machine tool control method, characterized in that: The titanium alloy processing machine tool control method comprises: Collect material hardness information, tool load data and temperature changes during titanium alloy processing in real time, and upload the material hardness information, tool load data and temperature changes to the cloud computing platform through the Internet of Things hub; According to the material hardness and tool load data, a predictive torque vector control algorithm is used to compare the tool load data with a preset torque range, calculate a required torque value in real time, and adjust the output torque of the processing machine tool according to the required torque value; Monitor the temperature change in real time, and when the temperature change exceeds a preset temperature fluctuation range, trigger a temperature adjustment instruction, and adjust a cooling device or a heating device in the processing area according to the temperature adjustment instruction; The real-time processing status is obtained through the AR device, and the operator's operating instructions are obtained in real time, and secondary adjustments are made according to the operating instructions.

2. The titanium alloy processing machine tool control method according to claim 1, characterized in that: The method uses a predictive torque vector control algorithm based on the material hardness and tool load data to compare the tool load data with a preset torque range and calculate the required torque value in real time, specifically including: By using a predictive torque vector control algorithm, the material hardness information and the tool load data are analyzed one by one to obtain corresponding parameter fluctuation trends; Based on the parameter fluctuation trend, a correlation model based on material hardness and tool load is established to determine the deviation value between the current processing state and the preset ideal state, and the required torque value is obtained according to the deviation value.

3. The titanium alloy processing machine tool control method according to claim 2, characterized in that: The material hardness information and the tool load data are analyzed one by one to obtain corresponding parameter fluctuation trends, specifically including: Collecting the continuous data stream of the material hardness information and the tool load data in real time, and performing denoising processing on the continuous data stream; The denoised material hardness information and the tool load data are analyzed respectively, and the corresponding material hardness fluctuation trend and tool load change trend are determined by weighted average calculation.

4. The titanium alloy processing machine tool control method according to claim 1, characterized in that: The real-time monitoring of the temperature change, triggering a temperature adjustment instruction when the temperature change exceeds a preset temperature fluctuation range, and adjusting the cooling or heating device of the processing area according to the temperature adjustment instruction specifically includes: Monitor the temperature of the processing area through an optical fiber sensor, and calculate the temperature change within a preset time period in real time based on the temperature; When it is detected that the temperature change exceeds a preset temperature fluctuation range, the cooling device or the heating device is activated through the temperature adjustment instruction to keep the temperature of the processing area stable; When the temperature change reaches the temperature fluctuation range, the adjustment operation is stopped and the temperature change is continuously monitored.

5. The titanium alloy processing machine tool control method according to claim 1, characterized in that: The real-time processing status is obtained through the AR device, and the operation instruction of the operator is obtained in real time, and the secondary adjustment is performed according to the operation instruction, specifically including: The real-time processing status is displayed to the operator through the AR device, and a corresponding processing strategy is generated according to the real-time processing status; The operation instruction of the operator is obtained, and the processing strategy is adjusted in real time or the system parameters are fine-tuned according to the operation instruction.

6. The titanium alloy processing machine tool control method according to claim 1, characterized in that: The titanium alloy processing machine tool control method further includes: Based on the historical data collected during the processing, a data analysis is performed on the historical material hardness, historical tool load and historical temperature changes in the historical data through a self-learning algorithm to identify trends and anomalies in the historical data; The parameter analysis function in the predictive torque vector control algorithm is optimized according to the analysis results to optimize future torque adjustment parameters.

7. A titanium alloy processing machine tool control device, characterized in that: The titanium alloy processing machine tool control device comprises: A data acquisition module is used to collect material hardness information, tool load data and temperature changes in the titanium alloy processing process in real time, and upload the material hardness information, tool load data and temperature changes to the cloud computing platform through the Internet of Things hub; A torque adjustment module, for comparing the tool load data with a preset torque range using a predictive torque vector control algorithm based on the material hardness and tool load data, calculating a required torque value in real time, and adjusting the output torque of the processing machine tool according to the required torque value; A temperature monitoring module is used to monitor the temperature change in real time, and when the temperature change exceeds a preset temperature fluctuation range, trigger a temperature adjustment instruction, and adjust a cooling device or a heating device in a processing area according to the temperature adjustment instruction; The AR interaction module is used to obtain the real-time processing status through the AR device, and obtain the operator's operation instructions in real time, and perform secondary adjustments according to the operation instructions.

8. The titanium alloy processing machine tool control device according to claim 7, characterized in that: The torque adjustment module specifically includes: A parameter analysis submodule, for analyzing the material hardness information and the tool load data one by one through a predictive torque vector control algorithm to obtain corresponding parameter fluctuation trends; The correlation model submodule is used to establish a correlation model based on material hardness and tool load based on the parameter fluctuation trend, determine the deviation value between the current processing state and the preset ideal state, and obtain the required torque value according to the deviation value.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the titanium alloy processing machine tool control method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the titanium alloy processing machine tool control method according to any one of claims 1 to 6 are implemented.

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