Motorized spindle intelligent pre-tightening force control method and system based on multi-sensor data fusion

Abstract

The invention provides a motorized spindle intelligent pre-tightening force control method and system based on multi-sensor data fusion. The motorized spindle intelligent pre-tightening force control method comprises the steps that state parameters of a motorized spindle are obtained; according to the state parameters of the motorized spindle, a target pre-tightening force is obtained through a pre-tightening force optimization decision model; and the target pre-tightening force is compared with the current pre-tightening force, a control instruction is generated, a pre-tightening force executing mechanism is driven to act, and the actual pre-tightening force is adjusted to the target pre-tightening force. The technical problems that the pre-tightening force of the motorized spindle cannot be adjusted in real time under the dynamic working condition, and the adjusting precision and adaptability are poor are solved.

Description

Technical Field

[0001] This invention relates to the field of spindle machining, and more specifically, to an intelligent preload control method and system for electric spindles based on multi-sensor data fusion. Background Technology

[0002] In high-end CNC equipment, the electric spindle is a core functional component, and its performance directly affects the accuracy and efficiency of the machining process. To ensure that the electric spindle has good rigidity and rotational accuracy, bearing preload technology is widely used in the electric spindle structure. The setting of bearing preload plays a crucial role in spindle performance: if the preload is insufficient, the spindle rigidity decreases, thus affecting machining accuracy; if the preload is too large, it will cause increased internal friction in the bearing, resulting in increased temperature rise and accelerated wear, which in turn affects the service life and operational reliability of the spindle.

[0003] In existing technologies, bearing preload is typically set once during the assembly stage of the electric spindle and remains constant throughout its service life. However, during actual operation, the operating conditions (such as speed and load) of the electric spindle frequently change dynamically. Under high speed or heavy load conditions, the bearings generate a large amount of heat due to continuous friction, causing thermal expansion and deformation of the shaft system. This alters the bearing clearance, resulting in the actual preload deviating from the initial set value. This deviation can lead to phenomena such as "preload relaxation" or "preload over-tightening," which not only reduces the rigidity of the spindle system but may also accelerate temperature rise and wear, making it difficult to simultaneously meet the rigidity and temperature control requirements during high-speed machining.

[0004] To address these issues, some existing technologies have proposed adjustable preload solutions, such as dynamic control of preload through hydraulic adjustment devices or electromagnetic mechanisms. However, these solutions generally rely on open-loop adjustment of a single operating parameter (such as rotational speed), lacking comprehensive perception and feedback control of the multi-dimensional operating states of the spindle (such as thermal deformation, load changes, runout, etc.). This results in low adjustment accuracy and adaptability to operating conditions, making it difficult to meet the needs of refined management of spindle performance under complex and dynamic operating conditions.

[0005] For example, Chinese patent application number CN201610081550.X discloses an intelligent electric spindle control method. This method, by configuring an intelligent preload adjustment device, a temperature sensor, a dynamic torque detection system, and a runout detection system, and combining this with a controller to process the sensor data, achieves real-time regulation of preload, coolant flow rate, and output torque, thus possessing a certain degree of intelligent control capability. However, this solution still suffers from problems such as complex system integration and limited response rate, and there is still room for improvement in high-speed, high-precision machining scenarios. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this invention is to provide an intelligent preload control method and system for electric spindles based on multi-sensor data fusion.

[0007] According to one aspect of the present invention, an intelligent preload control method for electric spindles based on multi-sensor data fusion includes: Step S1: Obtain the state parameters of the electric spindle; Step S2: Based on the state parameters of the electric spindle, obtain the target preload through the preload optimization decision model; Step S3: Compare the target preload with the current preload, generate a control command, drive the preload actuator to adjust the actual preload to the target preload.

[0008] Preferably, step S1 includes: acquiring the state parameters of the electric spindle based on a clock synchronization mechanism, storing them in a data buffer, and reading data from the buffer for processing.

[0009] Preferably, the preload optimization decision model in step S2 is used to perform the following steps: Sub-step S2.1: Obtain preset empirical constants, which include temperature threshold T0, preload increment ΔF, basic preload F0, basic load P0, load factor a, basic rotational speed N0, and rotational speed factor b; Sub-step S2.2: Obtain real-time data, including temperature T, load P, rotational speed N, and current preload F; Sub-step S2.3: Determine if the temperature T exceeds the temperature threshold T0. If it does, reduce the preload, i.e., reduce the target preload F. t It is set to F-ΔF; if it does not exceed this value, the preload factor c is calculated, and the target preload F is determined. t Set as c F0, where the preload factor c is calculated using the following formula:

[0010] In the formula, a is the load factor, N0 is the basic speed, N is the speed, P is the load, P0 is the basic load, and b is the speed factor. Sub-step S2.4: Output the target preload based on the judgment result.

[0011] Preferably, the state parameters of the electric spindle include load torque, rotational speed, bearing temperature, and current preload.

[0012] Preferably, the clock synchronization mechanism employs hardware triggering or a high-precision network clock protocol.

[0013] According to another aspect of the present invention, an intelligent preload control system for electric spindles based on multi-sensor data fusion includes: Module M1: Acquires the status parameters of the electric spindle; Module M2: Based on the state parameters of the electric spindle, the target preload is obtained through a preload optimization decision model; Module M3: Compares the target preload with the current preload, generates a control command, drives the preload actuator to adjust the actual preload to the target preload.

[0014] Preferably, the preload optimization decision model in module M2 is used to perform the following steps: Submodule M2.1: Obtain preset empirical constants, including temperature threshold T0, preload increment ΔF, basic preload F0, basic load P0, load factor a, basic speed N0, and speed factor b; Submodule M2.2: Acquires real-time data, including temperature T, load P, rotational speed N, and current preload F; Submodule M2.3: Determines whether the temperature T exceeds the temperature threshold T0. If it does, the preload is reduced, i.e., the target preload F is reduced. t It is set to F-ΔF; if it does not exceed this value, the preload factor c is calculated, and the target preload F is determined. t Set as c F0, where the preload factor c is calculated using the following formula:

[0015] In the formula, a is the load factor, N0 is the basic speed, N is the speed, P is the load, P0 is the basic load, and b is the speed factor. Submodule M2.4: Outputs the target preload based on the judgment result.

[0016] Preferably, the module M1 includes: Based on the clock synchronization mechanism, the state parameters of the electric spindle are obtained and stored in the data buffer, and the data is read from the buffer for processing.

[0017] Preferably, the state parameters of the electric spindle include load torque, rotational speed, bearing temperature, and current preload.

[0018] Preferably, the clock synchronization mechanism employs hardware triggering or a high-precision network clock protocol.

[0019] Compared with the prior art, the present invention has the following beneficial effects: Full-state perception and intelligent decision-making: By integrating multi-dimensional information such as load, speed, temperature and preload, the system achieves comprehensive perception of the electric spindle's operating state and makes intelligent optimization decisions on the preload based on this, overcoming the limitations of single-parameter control.

[0020] Dynamic Adaptation and Optimal Performance: It can dynamically adjust the preload according to real-time working conditions, ensuring rigidity under high load and suppressing temperature rise at high speed, thus achieving the best match between rigidity and thermal balance performance of the electric spindle across the entire working range.

[0021] High reliability and safety: Through real-time temperature monitoring and overheat protection logic, it effectively prevents bearing burn-out or failure caused by improper preload, significantly improving the operational reliability and service life of the electric spindle.

[0022] High-efficiency data processing: The clock synchronization mechanism and the "producer-consumer" data interaction mode adopted effectively ensure the real-time performance, synchronization and system stability of multi-source heterogeneous signal processing, laying the foundation for precise control. Attached Figure Description

[0023] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a block diagram of an intelligent preload control system for an electric spindle based on multi-sensor data fusion, provided in Embodiment 2. Figure 2 This is a flowchart illustrating the calculation of the target preload force as described in this invention. Figure 3 This is a logical schematic diagram of the preload optimization decision model described in this invention. Detailed Implementation

[0024] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0025] Example 1: This embodiment provides an intelligent preload control method for electric spindles based on multi-sensor data fusion. This method monitors the operating status of the electric spindle in real time and uses data collected by multiple sensors to input into the preload optimization decision model. Under the premise of meeting the requirements of spindle stiffness and machining accuracy, the preload is dynamically adjusted according to different working conditions to achieve intelligent control that reduces bearing temperature rise, extends spindle life and improves machining stability.

[0026] Step S1: Obtain the state parameters of the electric spindle.

[0027] Step S1 includes: acquiring the state parameters of the electric spindle based on the clock synchronization mechanism, storing them in the data buffer, and reading data from the buffer for processing.

[0028] The status parameters of the electric spindle include load torque, speed, bearing temperature, and current preload.

[0029] The clock synchronization mechanism employs hardware triggering or a high-precision network clock protocol.

[0030] Based on the above scheme, by establishing a unified clock synchronization mechanism, the consistency of data from different sensors in the time dimension can be ensured, enabling accurate fusion of multi-source data. Furthermore, by caching and uniformly processing the collected load torque, speed, bearing temperature, and current preload, misjudgments caused by data delays or asynchrony can be effectively avoided, improving the accuracy and reliability of subsequent preload decisions.

[0031] Step S2: Based on the state parameters of the electric spindle, obtain the target preload through the preload optimization decision model.

[0032] It should be noted that, as Figure 2 As shown, the preload optimization decision model in step S2 is used to perform the following steps: Sub-step S2.1: Obtain preset empirical constants, which include temperature threshold T0, preload increment ΔF, basic preload F0, basic load P0, load factor a, basic rotational speed N0, and rotational speed factor b; Sub-step S2.2: Obtain real-time data, including temperature T, load P, rotational speed N, and current preload F; Sub-step S2.3: Determine if the temperature T exceeds the temperature threshold T0. If it does, reduce the preload, i.e., reduce the target preload F. t It is set to F-ΔF; if it does not exceed this value, the preload factor c is calculated, and the target preload F is determined. t Set as c F0, where the preload factor c is calculated using the following formula:

[0033] In the formula, a is the load factor, N0 is the basic speed, N is the speed, P is the load, P0 is the basic load, and b is the speed factor. Sub-step S2.4: Output the target preload based on the judgment result.

[0034] It is understood that the preload optimization decision model in module M2 is used to perform the following steps: Submodule M2.1: Obtain preset empirical constants, including temperature threshold T0, preload increment ΔF, basic preload F0, basic load P0, load factor a, basic speed N0, and speed factor b; Submodule M2.2: Acquires real-time data, including temperature T, load P, rotational speed N, and current preload F; Submodule M2.3: Determines whether the temperature T exceeds the temperature threshold T0. If it does, the preload is reduced, i.e., the target preload F is reduced. t It is set to F-ΔF; if it does not exceed this value, the preload factor c is calculated, and the target preload F is determined. t Set as c F0, where the preload factor c is calculated using the following formula:

[0035] In the formula, a is the load factor, N0 is the basic speed, N is the speed, P is the load, P0 is the basic load, and b is the speed factor. Submodule M2.4: Outputs the target preload based on the judgment result.

[0036] Based on the above scheme, by establishing a mapping relationship between preload and operating condition variables, the decision model can dynamically adjust the preload under different machining conditions, so that it meets both the machining rigidity requirements and the spindle temperature control and reliability objectives. The model uses load and speed as the basis for operating condition identification and temperature information as a protective judgment condition, making the preload control decision adaptive and safe, thereby improving the stability and lifespan of the electric spindle under complex operating conditions.

[0037] Step S3: Compare the target preload with the current preload, generate a control command, drive the preload actuator to adjust the actual preload to the target preload.

[0038] Based on the above scheme, by comparing the deviation between the target preload and the current preload, the control system can generate adjustment commands in real time, enabling the preload actuator to automatically complete compensation or release actions. This closed-loop adjustment mechanism ensures that the actual preload can quickly and stably track the target value output by the decision model, thereby achieving intelligent adaptive control of the electric spindle preload and guaranteeing the efficiency and safety of the machining process.

[0039] Example 2: like Figure 1 As shown, an intelligent preload control system for electric spindles based on multi-sensor data fusion is presented. The system architecture of this invention includes a sensing module, a data acquisition and processing module, a preload optimization decision model, a preload actuator, and a human-machine interface.

[0040] In practical implementation, the temperature sensor is preferably a PT100 platinum resistance thermometer, embedded in the outer ring of the front and rear bearings of the electric spindle or in the nearest position to the housing; the current sensor is installed on the power line of the spindle drive motor; and the pressure sensor is installed on the force-bearing surface of the preload application structure. All sensors are triggered for acquisition by a unified synchronization signal, and the data is transmitted to the data processing module via a high-speed fieldbus.

[0041] The data acquisition and processing module runs a real-time operating system and adopts a "producer-consumer" software architecture. The data acquisition thread, acting as the "producer," writes timestamped sensor data into a buffer. The control algorithm thread, acting as the "consumer," reads the latest set of synchronized data from the buffer and performs calculations.

[0042] like Figure 3 As shown, the preload optimization decision model can be an empirical formula, a smart controller based on fuzzy logic, or a neural network model trained with a large amount of experimental data. Taking the empirical formula as an example, its input variables are load torque, speed, bearing temperature, and current preload, and its output variable is the target preload.

[0043] After calculating the target preload, the system compares it with the current preload and generates a control signal to drive the hydraulic servo valve or electromagnetic actuator to adjust the preload to the target value. The entire control process is completed within milliseconds, achieving online real-time adjustment.

[0044] The present invention also provides a method and system for intelligent preload control of electric spindles based on multi-sensor data fusion. The method and system for intelligent preload control of electric spindles based on multi-sensor data fusion can be implemented by executing the process steps of the method for intelligent preload control of electric spindles based on multi-sensor data fusion. That is, those skilled in the art can understand the method for intelligent preload control of electric spindles based on multi-sensor data fusion as a preferred embodiment of the method and system for intelligent preload control of electric spindles based on multi-sensor data fusion.

[0045] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0046] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A multi-sensor data fusion-based intelligent pre-tightening force control method for an electric spindle, characterized in that, The method comprises the following steps: Step S1: obtaining state parameters of the electric spindle; Step S2: obtaining a target pre-tightening force through a pre-tightening force optimization decision model according to the state parameters of the electric spindle; Step S3: comparing the target pre-tightening force with a current pre-tightening force, generating a control instruction, and driving a pre-tightening force actuator to act, so as to adjust an actual pre-tightening force to the target pre-tightening force.

2. The method of claim 1, wherein, The step S1 comprises: Based on a clock synchronization mechanism, the state parameters of the electric spindle are obtained and stored in a data buffer, and data in the buffer is read for processing.

3. The method of claim 1, wherein, The pre-tightening force optimization decision model in the step S2 is used to execute the following steps: Sub-step S2.1: obtaining preset empirical constants, the empirical constants comprising a temperature threshold T0, a pre-tightening force increment ΔF, a basic pre-tightening force F0, a basic load P0, a load factor a, a basic speed N0, and a speed factor b; Sub-step S2.2: obtaining real-time data, the real-time data comprising a temperature T, a load P, a speed N, and a current pre-tightening force F; Sub-step S2.3: judging whether the temperature T exceeds a temperature threshold value T0, and if so, a pre-tightening force reduction, i.e. a target pre-tightening force F t is set to F - ΔF; If not exceeded, the calculation of the pretension factor c is performed, the target pretension F t is set to c F0, wherein the pretension factor c is calculated according to the following formula: In the formula, a is a load factor, N0 is a basic speed, N is a speed, P is a load, P0 is a basic load, and b is a speed factor; Sub-step S2.4: outputting the target pre-tightening force according to a judgment result.

4. The method of claim 3, wherein, The state parameters of the electric spindle comprise a load torque, a speed, a bearing temperature, and a current pre-tightening force.

5. The method of claim 1, wherein, The clock synchronization mechanism adopts a hardware trigger or a high-precision network clock protocol.

6. A multi-sensor data fusion based intelligent pre-tightening force control system for an electric spindle, characterized in that, The method comprises the following steps: Module M1: obtaining state parameters of the electric spindle; Module M2: obtaining a target pre-tightening force through a pre-tightening force optimization decision model according to the state parameters of the electric spindle; Module M3: comparing the target pre-tightening force with a current pre-tightening force, generating a control instruction, and driving a pre-tightening force actuator to act, so as to adjust an actual pre-tightening force to the target pre-tightening force.

7. The method of claim 6, wherein, The pre-tightening force optimization decision model in the module M2 is used to execute the following steps: Sub-module M2.1: obtaining preset empirical constants, the empirical constants comprising a temperature threshold T0, a pre-tightening force increment ΔF, a basic pre-tightening force F0, a basic load P0, a load factor a, a basic speed N0, and a speed factor b; Sub-module M2.2: obtaining real-time data, the real-time data comprising a temperature T, a load P, a speed N, and a current pre-tightening force F; Submodule M2.3: Determine whether the temperature T exceeds a temperature threshold value T0, and if so, reduce the pretensioning force, i.e. the target pretensioning force F t is set to F - ΔF; If not exceeded, the calculation of the pretension factor c is performed, the target pretension F t is set to c F0, wherein the pretension factor c is calculated according to the following formula: In the formula, a is a load factor, N0 is a basic speed, N is a speed, P is a load, P0 is a basic load, and b is a speed factor; Sub-module M2.4: outputting the target pre-tightening force according to a judgment result.

8. The system of claim 1, wherein, The module M1 comprises: Based on a clock synchronization mechanism, the state parameters of the electric spindle are obtained and stored in a data buffer, and data in the buffer is read for processing.

9. The system of claim 3, wherein, The state parameters of the electric spindle comprise a load torque, a speed, a bearing temperature, and a current pre-tightening force.

10. The system of claim 1, wherein, The clock synchronization mechanism adopts a hardware trigger or a high-precision network clock protocol.

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