Intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation

Abstract

The invention belongs to the technical field of mechanical transmission, and discloses an intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation, and the method comprises the steps: collecting the real-time operation data of a multi-machine cooperation system, the real-time operation data including the gear rotation speed, load, temperature and vibration data; constructing a dynamic model of the transmission gear based on the real-time operation data; calculating a dynamic error of the transmission gear according to the dynamic model; predicting the dynamic error by adopting an intelligent algorithm and generating a compensation parameter; the operation parameters of the transmission gear are adjusted according to the compensation parameters, so that multi-machine high-precision cooperation is achieved; and monitoring the operation state of the multi-machine cooperation system in real time, and dynamically adjusting the compensation parameters according to the monitoring data. According to the method, the operation precision and stability of the multi-machine system are remarkably improved, and the method is suitable for high-precision transmission scenes such as robots, numerical control machine tools and aero-engines and has high practical value and popularization prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical transmission, and more particularly, to an intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation. Background Art

[0002] Multi-machine cooperation systems are increasingly widely used in modern manufacturing industries, especially in high-precision fields such as robots, numerically controlled machine tools, and aero-engines. As a core component, the operating accuracy of transmission gears directly affects the overall performance of the system. However, in actual operation, transmission gears are affected by various external factors, such as load changes, temperature fluctuations, and vibration interference, resulting in the generation of dynamic errors, thereby reducing the accuracy and stability of multi-machine cooperation.

[0003] In the prior art, the compensation for transmission gear errors mainly relies on static or semi-dynamic methods. Static compensation methods reduce initial errors by optimizing gear design or manufacturing processes. For example, the Chinese patent with the publication number CN110568816B proposes a hobbing tooth surface error compensation method and system based on equivalent transmission chain error calculation, but this method is only applicable to fixed working conditions and cannot adapt to dynamic changes during operation. Semi-dynamic compensation methods adjust errors through feedback control, such as real-time correction technology based on PID control, but their adaptability to complex working conditions is insufficient, and the compensation accuracy is difficult to meet the requirements of high-precision cooperation. In addition, the prior art is mostly designed for single equipment and does not fully consider the mutual influence between gears in multi-machine cooperation, resulting in limited compensation effects.

[0004] The deficiencies of the above methods are as follows: First, there is a lack of real-time monitoring and accurate modeling means for dynamic errors, resulting in compensation lags or failures; second, intelligent prediction and optimization technologies are not introduced, making it difficult to cope with error fluctuations under changing working conditions; finally, the applicability of existing methods in multi-machine collaborative scenarios is poor and it is difficult to meet the requirements of modern industry for high-precision cooperation. Therefore, there is an urgent need for a dynamic error correction method for transmission gears that can perform real-time monitoring, intelligent compensation, and is applicable to multi-machine cooperation to improve the overall performance of the system. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and provide an intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation. Through real-time data collection, dynamic model construction, and intelligent algorithm optimization, accurate compensation of transmission gear dynamic errors is achieved, thereby improving the operating accuracy and stability of the multi-machine cooperation system.

[0006] To achieve the above object, the present invention adopts the technical solutions described in claims 1-8:

[0007] An intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation, comprising the following steps:

[0008] Step S1, data collection: Collect the real-time operation data of the multi-machine cooperation system, and the data includes but is not limited to gear rotation speed, load, temperature, and vibration data;

[0009] Step S2, model construction: Based on the collected real-time operation data, construct a dynamic model of the transmission gear;

[0010] Step S3, error calculation: According to the dynamic model, calculate the dynamic error of the transmission gear during operation;

[0011] Step S4, intelligent compensation: Use an intelligent algorithm to predict the dynamic error and generate compensation parameters;

[0012] Step S5, parameter adjustment: According to the compensation parameters, adjust the operation parameters of the transmission gear to achieve high-precision cooperation of multiple machines;

[0013] Step S6, real-time monitoring: Real-time monitor the operation status of the multi-machine cooperation system through a sensor network, and dynamically adjust the compensation parameters according to the monitoring data to adapt to the change of working conditions.

[0014] Further, the specific process of constructing the dynamic model in step S2 includes:

[0015] Step S21, preprocess the collected real-time operation data, including data cleaning to remove noise interference, and normalization to unify the data dimension;

[0016] Step S22, based on the preprocessed data, use the finite element analysis method to construct a mechanical model of the gear, and analyze the stress state of the gear under different working conditions;

[0017] Step S23, combine the geometric parameters of the gear (such as modulus, number of teeth) and material properties (such as elastic modulus, Poisson's ratio) to establish a complete dynamic model.

[0018] Further, the specific process of calculating the dynamic error in step S3 includes:

[0019] Step S31, according to the dynamic model, simulate the operation state of the gear under different rotation speeds, loads, and temperature conditions;

[0020] Step S32, compare the simulation results with the actual operation state, calculate the deviation between the two, and obtain the dynamic error value.

[0021] Further, the intelligent algorithm used in step S4 is an improved genetic algorithm, and its specific implementation includes:

[0022] Step S41: Initialize the population, where each individual in the population is a candidate solution for the compensation parameter, such as the rotational speed adjustment amount or the torque correction value;

[0023] Step S42: Define the fitness function and calculate the fitness of each individual according to the degree of reduction of the dynamic error;

[0024] Step S43: Generate a new generation of population through selection, crossover, and mutation operations;

[0025] Step S44: Repeat the iteration until the convergence condition is met (such as the error is less than the preset threshold), and output the optimal compensation parameter.

[0026] Furthermore, the adjustment of the operating parameters in step S5 includes, but is not limited to, adjusting the gear rotational speed, torque, and lubrication conditions to ensure that the gear operating state matches the cooperation requirements.

[0027] Furthermore, the real-time monitoring in step S6 is realized through a sensor network, and the sensor network includes a rotational speed sensor, a torque sensor, and a temperature sensor, which are respectively used to monitor the rotational speed, force condition, and thermal state of the gear.

[0028] The applicable scope of the method of the present invention includes, but is not limited to, the robot joint drive system, the numerical control machine tool drive system, and the aeroengine drive system, and has strong versatility.

[0029] In addition, the present invention also provides an intelligent transmission gear dynamic compensation system for multi-machine high-precision cooperation, and the system includes:

[0030] Module 1, data acquisition module: used to acquire the real-time operation data of the multi-machine cooperation system;

[0031] Module 2, model construction module: used to construct a dynamic model of the transmission gear based on the real-time operation data;

[0032] Module 3, error calculation module: used to calculate the dynamic error of the transmission gear according to the dynamic model;

[0033] Module 4, intelligent compensation module: used to predict the dynamic error by using an intelligent algorithm and generate compensation parameters;

[0034] Module 5, parameter adjustment module: used to adjust the operating parameters of the transmission gear according to the compensation parameters;

[0035] Module 6, real-time monitoring module: used to monitor the system operation state in real time and dynamically adjust the compensation parameters.

[0036] The technical effects and advantages of an intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation of the present invention:

[0037] Point 1: Through real-time data acquisition and dynamic model construction, the accurate identification of the dynamic error of transmission gears is realized, overcoming the defect that the static compensation method cannot adapt to the operation changes.

[0038] Point 2: An improved genetic algorithm is used for error prediction and compensation, which improves the intelligent level and adaptive ability of the method and has higher accuracy compared with the traditional PID control.

[0039] Point 3: Through parameter adjustment in the multi-machine cooperation scenario, the overall coordination and stability of the system are ensured, filling the gap in the multi-machine application of the existing technology.

[0040] Point 4: The method has strong versatility and can be widely applied to a variety of high-precision transmission systems, providing an efficient solution for modern industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings, where:

[0042] Figure 1 is the overall flow schematic diagram of an intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation according to the present invention;

[0043] Figure 2 is the module structure schematic diagram of an intelligent transmission gear dynamic compensation system for multi-machine high-precision cooperation according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0045] For easy understanding, the specific process of the embodiments of the present invention will be described in detail below:

[0046] Embodiment 1: Specific implementation of the method

[0047] As Figure 1 shown, this embodiment provides an intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation, and the specific steps are as follows:

[0048] Step S1, Data Acquisition:

[0049] In a multi-machine cooperation system, real-time operation data of the transmission gear is collected through a sensor network. The sensor network includes:

[0050] A rotational speed sensor for monitoring the number of revolutions per minute of the gear (unit: rpm);

[0051] A torque sensor for measuring the torque borne by the gear (unit: N·m);

[0052] A temperature sensor for detecting the surface temperature of the gear and the ambient temperature (unit: °C);

[0053] A vibration sensor for recording the vibration frequency and amplitude during the operation of the gear (unit: Hz and mm / s).

[0054] For example, in a certain robot joint transmission system, the rotational speed sensor collects a gear rotational speed of 1200 rpm, the torque sensor measures a torque of 50 N·m, the temperature sensor shows a gear surface temperature of 45 °C, and the vibration sensor records a vibration amplitude of 0.02 mm / s.

[0055] Step S2, Model Construction:

[0056] Based on the collected real-time operation data, a dynamic model of the transmission gear is constructed. The specific process is as follows:

[0057] Step S21, Data Preprocessing:

[0058] The collected data is cleaned to remove outliers caused by sensor failures or noise, such as excluding invalid data with a sudden change in rotational speed to 0; subsequently, normalization processing is performed to map data such as rotational speed and torque to the [0, 1] interval.

[0059] Step S22, Mechanical Modeling:

[0060] Using the finite element analysis method, a three-dimensional model of the gear is established in ANSYS software, and the gear geometric parameters (module m = 2, number of teeth z = 30) and material properties (elastic modulus E = 210 GPa, Poisson's ratio ν = 0.3) are input to simulate the gear stress state.

[0061] Step S23, Dynamic Model Generation:

[0062] Combining the real-time data with the mechanical model, a mathematical model describing the dynamic behavior of the gear is established, such as the relationship between the gear displacement x(t) and time t:

[0063] x(t) = Asin(ωt + φ) + kF(t)

[0064] Among them, A is the vibration amplitude, ω is the angular frequency, φ is the phase, F(t) is the external force function, and k is the stiffness coefficient.

[0065] Step S3, Error calculation:

[0066] Step S31, According to the dynamic model, simulate the operating state of the gear under different working conditions.

[0067] Step S32, For example, under the working condition of a rotational speed of 1200 rpm and a torque of 50 N·m, the simulated gear displacement is 0.015 mm; while the actual measured value is 0.018 mm, and the deviation between the two is the dynamic error Δx = 0.003 mm.

[0068] Step S4, Intelligent compensation:

[0069] Use an improved genetic algorithm to predict and compensate for the dynamic error:

[0070] Step S41, Population initialization:

[0071] Generate 100 candidate solutions, each solution containing a rotational speed adjustment amount Δn and a torque adjustment amount ΔT. For example, Δn ∈ [-50, 50] rpm, ΔT ∈ [-5, 5] N·m;

[0072] Step S42, Fitness calculation:

[0073] Define the fitness function as the amount of error reduction, that is, f = |Δx_before - Δx_after|, and calculate the fitness of each individual;

[0074] Step S43, Genetic operation:

[0075] Select excellent individuals through the tournament selection method, and use single-point crossover (crossover probability 0.8) and Gaussian mutation (mutation probability 0.1) to generate a new population;

[0076] Step S44, Iterative optimization:

[0077] After 50 generations of iteration, when the error Δx is less than 0.001 mm, it converges, and the compensation parameters are output. For example, Δn = 10 rpm, ΔT = 2 N·m.

[0078] Step S5, Parameter adjustment:

[0079] Adjust the operating state of the gear according to the compensation parameters: adjust the rotational speed from 1200 rpm to 1210 rpm, the torque from 50 N·m to 52 N·m, and at the same time increase the lubricating oil flow rate to 0.5 L / min to reduce frictional heat. After adjustment, the dynamic error drops to 0.0008 mm, meeting the high-precision cooperation requirements.

[0080] Step S6, Real-time monitoring:

[0081] Continuously monitor the system operation status through the sensor network. If the rotational speed rises to 1300 rpm and the error increases again, repeat steps S2 - S5 to dynamically adjust the compensation parameters.

[0082] Through the above steps, this embodiment significantly reduces the dynamic error of the robot joint drive system and improves the cooperation accuracy.

[0083] Embodiment 2: Specific implementation of the system

[0084] As Figure 2 shown, this embodiment provides an intelligent transmission gear dynamic compensation system for multi - machine high - precision cooperation, including the following modules:

[0085] Module 1, data acquisition module: Composed of rotational speed, torque, temperature, and vibration sensors, it collects operation data in real - time;

[0086] Module 2, model construction module: Generates a gear dynamic model based on the finite element analysis method and real - time data;

[0087] Module 3, error calculation module: Compares the simulated and actual operation statuses to calculate the dynamic error;

[0088] Module 4, intelligent compensation module: Runs an improved genetic algorithm to generate compensation parameters;

[0089] Module 5, parameter adjustment module: Adjusts operation parameters such as rotational speed and torque according to the compensation parameters;

[0090] Module 6, real - time monitoring module: Continuously monitors the system status and dynamically optimizes the compensation effect.

[0091] When this system is applied in the transmission of a numerically controlled machine tool, the gear error is reduced from 0.005 mm to 0.001 mm, and the machining accuracy is improved by 20%.

[0092] Embodiment 3: Further expansion of the application

[0093] This method and system are further applied to the transmission system of an aero - engine: Under the working conditions of high rotational speed (5000 rpm) and high temperature (120 °C), the error is controlled within 0.002 mm through dynamic compensation, ensuring the stable operation of the engine.

[0094] The above - mentioned is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claimed rights.

[0095] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation, characterized in that, The method includes the following steps: Step S1, collect the real-time operation data of the multi-machine cooperation system, where the real-time operation data includes gear rotation speed, load, temperature, and vibration data; Step S2, construct a dynamic model of the transmission gear based on the real-time operation data; Step S3, calculate the dynamic error of the transmission gear according to the dynamic model; Step S4, use an intelligent algorithm to predict the dynamic error and generate compensation parameters; Step S5, adjust the operation parameters of the transmission gear according to the compensation parameters to achieve high-precision multi-machine cooperation; Step S6, monitor the operation state of the multi-machine cooperation system in real time, and dynamically adjust the compensation parameters according to the monitoring data.

2. The intelligent transmission gear dynamic compensation method for multi-machine high-precision collaboration according to claim 1, wherein The specific construction of the dynamic model of the transmission gear in step S2 includes: Step S21, preprocess the collected real-time operation data, including data cleaning and normalization processing; Step S22, construct a mechanical model of the gear using the finite element analysis method based on the preprocessed data; Step S23, establish the dynamic model in combination with the geometric parameters and material properties of the gear.

3. An intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation according to claim 1, characterized in that The specific calculation of the dynamic error of the transmission gear in step S3 includes: Step S31, simulate the operation state of the gear under different working conditions according to the dynamic model; Step S32, compare the simulated operation state with the actual operation state, and calculate the dynamic error value.

4. An intelligent transmission gear dynamic compensation method for multi-machine high-precision collaboration according to claim 1, characterized in that The intelligent algorithm used in step S4 is an improved genetic algorithm, which specifically includes: Step S41, initialize the population, where the individuals in the population are candidate solutions for the compensation parameters; Step S42, calculate the fitness of each individual according to the reduction degree of the dynamic error; Step S43, generate a new generation of population through selection, crossover, and mutation operations; Step S44, repeat the iteration until the convergence condition is met, and output the optimal compensation parameters.

5. An intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation according to claim 1, characterized in that, Adjusting the operation parameters of the transmission gear in step S5 includes adjusting the gear rotation speed, torque, and lubrication conditions.

6. An intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation according to claim 1, characterized in that The real-time monitoring in step S6 is realized through a sensor network, and the sensor network includes a rotation speed sensor, a torque sensor, and a temperature sensor.

7. An intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation according to claim 1, characterized in that The intelligent transmission gear dynamic compensation method for multi-machine high-precision cooperation is applied to a robot joint transmission system, a numerical control machine tool transmission system, or an aero-engine transmission system.

8. A system for an intelligent transmission gear dynamic compensation method for multi-machine high-precision collaboration according to any one of claims 1-7, characterized in that, The system includes: Module 1, a data acquisition module, for collecting the real-time operation data of the multi-machine cooperation system; Module 2, a model construction module, for constructing a dynamic model of the transmission gear based on the real-time operation data; Module 3, an error calculation module, for calculating the dynamic error of the transmission gear according to the dynamic model; Module 4, an intelligent compensation module, for predicting the dynamic error using an intelligent algorithm and generating compensation parameters; Module 5, a parameter adjustment module, for adjusting the operation parameters of the transmission gear according to the compensation parameters; Module 6, a real-time monitoring module, for monitoring the operation state of the multi-machine cooperation system in real time, and dynamically adjusting the compensation parameters according to the monitoring data.

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