Method and apparatus for suppressing temperature drift in low-torque servo modules

Abstract

This invention relates to a method for suppressing temperature drift in a low-torque servo module, comprising the following steps: measuring the coefficient of thermal expansion of each component within the module based on different operating temperature ranges and the torque load variation curve, obtaining the coefficient of thermal expansion of each component; estimating the dimensional change of each component using the coefficient of thermal expansion, obtaining a set of estimated dimensional change values ​​for each component; calculating the change in fit clearance between each component based on the set of estimated dimensional change values, obtaining the change in fit clearance for each component; analyzing the overall stiffness change of the low-torque servo module using the change in fit clearance, obtaining a set of overall stiffness change values ​​for the module; and formulating a temperature drift suppression strategy for the low-torque servo module based on the set of overall stiffness change values ​​for the module, thus solving the technical problem that temperature fluctuations can cause changes in the torque load inside the module.

Description

Technical Field

[0001] This invention relates to the field of servo module technology, and in particular to a method and apparatus for suppressing temperature drift in low-torque servo modules. Background Technology

[0002] In the field of industrial automation, low-torque servo modules are widely used in scenarios with extremely high precision requirements, such as semiconductor manufacturing and precision assembly. However, with the increasing complexity of application scenarios, the issue of performance stability under different operating temperatures has become increasingly prominent. Temperature fluctuations can cause changes in the torque load inside the module, thereby affecting the control accuracy and reliability of the entire system.

[0003] When the operating environment temperature changes, the different components within the module have varying coefficients of thermal expansion due to their different material properties. This can cause changes in component dimensions, further altering the clearances between components. For example, in high-temperature environments, some metal components may increase in size due to thermal expansion, leading to smaller clearances and even jamming; while in low-temperature environments, component contraction may increase clearances, generating additional vibration and noise. All of these factors severely affect the normal operation of the module.

[0004] More importantly, changes in component fit clearances can have a cascading effect on the overall stiffness of the low-torque servo module. Instability in overall stiffness can prevent the module from accurately outputting the preset torque during operation, leading to issues such as positional deviations and speed fluctuations. Existing technologies struggle to comprehensively and accurately suppress temperature drift when dealing with these complex temperature-induced variations, resulting in increased product defect rates and higher equipment maintenance costs. Therefore, an effective temperature drift suppression method is urgently needed to address these problems. Summary of the Invention

[0005] The main objective of this invention is to provide a method and apparatus for suppressing temperature drift in low-torque servo modules, thereby solving the technical problem that temperature fluctuations can cause changes in the torque load inside the module.

[0006] To achieve the above objectives, the present invention provides a method for suppressing temperature drift in a low-torque servo module, comprising the following steps:

[0007] Obtain the torque load variation curves of the low-torque servo module under different operating ambient temperature ranges;

[0008] The thermal expansion coefficients of each component in the module were measured based on different operating temperature ranges and the torque load variation curves to obtain the thermal expansion coefficients of each component.

[0009] Under different operating temperature ranges, the dimensional change of each component is estimated by combining the thermal expansion coefficient of each component, and a set of estimated dimensional change of each component is obtained.

[0010] Based on the estimated set of changes in the dimensions of each component, the change in the fit clearance between each component is calculated to obtain the change in the fit clearance of each component.

[0011] By utilizing the variation in the fit clearance of each component, an overall stiffness variation analysis of the low-torque servo module is performed to obtain the overall stiffness variation set of the module;

[0012] A temperature drift suppression strategy for the low-torque servo module is formulated based on the overall stiffness variation set of the module.

[0013] Furthermore, the thermal expansion coefficients of each component within the module are measured based on different operating temperature ranges and the torque load variation curves to obtain the thermal expansion coefficients of each component, including:

[0014] Based on different operating temperature ranges and the torque load variation curves, the operating temperature ranges of each component in the module are divided to obtain a set of operating temperature ranges for each component, and the set of key temperature points for each component in the set of operating temperature ranges for each component is determined.

[0015] The dimensions of each component are measured by the key temperature point set of each component, and the dimensions of each component after temperature change are compared and measured based on the corresponding dimensions of each component to obtain a comparison set of the size changes of each component.

[0016] Based on the comparison set of size changes of each component, the thermal expansion direction of each component is analyzed, and the thermal expansion vector of each component is constructed according to the thermal expansion direction of each component to obtain the thermal expansion vector set of each component;

[0017] The thermal expansion coefficient of each component is calculated using the thermal expansion vector set of each component.

[0018] Furthermore, under different operating temperature ranges, the dimensional change of each component is estimated based on its thermal expansion coefficient, resulting in a set of estimated dimensional changes for each component, including:

[0019] Under different operating temperature ranges, the thermal stability of each component material is analyzed based on the thermal expansion coefficient of each component to obtain the thermal stability set of each component material. Then, thermal stress limit analysis is performed on the thermal stability set of each component material to obtain the thermal stress limit set of each component.

[0020] The maximum permissible strain, elastic deformation limit, and plastic deformation threshold of each component are determined based on the thermal stress limit set of each component.

[0021] By using the thermal stress limit set of each component, the temperature-sensitive region of each component is identified, the temperature-sensitive region set of each component is obtained, and the thermal deformation sensitive direction set of each component is determined based on the temperature-sensitive region set of each component.

[0022] Based on the set of heat deformation sensitive directions, the heat deformation constraint factors of each component are determined to obtain the set of heat deformation constraint factors for each component. The dimensional changes of each component are estimated based on the maximum allowable strain, elastic deformation limit, plastic deformation threshold and the set of heat deformation constraint factors for each component.

[0023] Furthermore, based on the set of heat deformation sensitive directions, the heat deformation constraint factors for each component are determined to obtain a set of heat deformation constraint factors for each component, including:

[0024] Vector decomposition is performed on the thermal deformation sensitive direction set of each component to obtain the thermal deformation sensitive direction component set of each component, and correlation analysis is performed on the thermal deformation sensitive direction component set of each component to obtain the thermal deformation sensitive direction correlation coefficient set of each component.

[0025] Set a threshold for the correlation coefficient set of the thermal deformation sensitive direction of each component, obtain the thermal deformation influence state identifier of each component, and filter the thermal deformation sensitive direction component set of each component based on the thermal deformation influence state identifier to obtain the thermal deformation sensitive direction component filtering set of each component.

[0026] Based on the heat deformation sensitive direction component screening set, the physical structure characteristics of each component are analyzed to obtain a physical structure characteristic analysis set, and the stress concentration area of ​​each component is identified based on the physical structure characteristic analysis set.

[0027] Microstructure detection is performed on the stress concentration region to obtain the microstructure set of stress concentration region for each component. Based on the microstructure set of stress concentration region for each component, thermal deformation constraint factor analysis is performed on each component to obtain the thermal deformation constraint factor set for each component.

[0028] Furthermore, the step of calculating the change in fit clearance between components based on the estimated set of changes in the dimensions of each component, and obtaining the change in fit clearance between each component, includes:

[0029] Based on the estimated set of size changes of each component, the assembly positioning method topology identification of each component is performed to obtain the assembly positioning method set of each component, and the assembly constraint geometry modeling of the assembly positioning method set of each component is performed to obtain the assembly constraint set of each component.

[0030] By using the assembly constraint set of each component, the relative position change trend vector of each component is synthesized to obtain the relative position change trend set of each component, and the ideal mating position of each component is determined based on the relative position change trend set of each component to obtain the ideal mating position set of each component.

[0031] The actual mating positions between each component are measured to obtain the set of actual mating positions between each component, and the mating offset between each component is calculated based on the set of ideal mating positions between each component and the set of actual mating positions between each component.

[0032] Using the aforementioned fit offset, the fit clearance variation between each component is analyzed to obtain the fit clearance variation of each component.

[0033] Furthermore, by utilizing the variation in the fit clearance of each component, an overall stiffness variation analysis is performed on the low-torque servo module to obtain a set of overall stiffness variation of the module, including:

[0034] Based on the changes in the fit clearance of each component, the contact state of each component in the low torque servo module is determined to obtain the contact state set of each component, and the contact stress distribution finite element solution is performed on the contact state set of each component to obtain the contact stress distribution set of each component.

[0035] By using the contact stress distribution set of each component, the force transmission path of each component is constructed, and the force equivalent model of the corresponding component is constructed based on the force transmission path of each component.

[0036] Based on the force equivalent model, the overall structural mechanics of the low-torque servo module is decomposed to obtain the module structure decomposition set, and the structural stiffness matrix of the low-torque servo module is constructed according to the module structure decomposition set to obtain the module structure stiffness matrix set.

[0037] Using the module structure stiffness matrix set, the overall stiffness change of the low-torque servo module is predicted and calculated, resulting in the overall stiffness change set of the module.

[0038] Furthermore, the temperature drift suppression strategy for the low-torque servo module based on the overall stiffness variation set of the module includes:

[0039] Based on the overall stiffness variation set of the module, the critical working state of the low torque servo module is identified to obtain the critical working state set of the module, and the safety margin of the critical working state set of the module is evaluated to obtain the safety margin evaluation set of the module.

[0040] The temperature-sensitive components of the low-torque servo module are identified using the module safety margin assessment set to obtain a module temperature-sensitive component set. The temperature adjustment priority of each component is determined based on the module temperature-sensitive component set to obtain a set of temperature adjustment priorities for each component.

[0041] Based on the temperature regulation priority set of each component, temperature regulation means are matched and selected for each component to obtain a temperature regulation means set for each component. Then, the temperature regulation effect of each component is predicted based on the temperature regulation means set of each component to obtain a temperature regulation effect prediction set for each component.

[0042] A set of temperature drift suppression strategies for low-torque servo modules is formulated using the temperature regulation effect prediction set of each component.

[0043] The present invention also provides a low-torque servo module temperature drift suppression device, comprising:

[0044] The acquisition module is used to acquire the torque load variation curves of the low-torque servo module under different operating ambient temperature ranges.

[0045] The measurement module is used to measure the thermal expansion coefficient of each component in the module based on different working environment temperature ranges and the torque load change curve, and to obtain the thermal expansion coefficient of each component.

[0046] The estimation module is used to estimate the size change of each component under different operating temperature ranges, based on the thermal expansion coefficient of each component, and to obtain a set of estimated size changes for each component.

[0047] The calculation module is used to calculate the change in the fit clearance between each component based on the estimated set of changes in the size of each component, and to obtain the change in the fit clearance between each component.

[0048] The analysis module is used to perform overall stiffness variation analysis on the low-torque servo module by utilizing the variation of the fit clearance of each component, and obtain the overall stiffness variation set of the module.

[0049] A formulation module is used to formulate a temperature drift suppression strategy for the low-torque servo module based on the overall stiffness variation set of the module.

[0050] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0051] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0052] This invention provides a method for suppressing temperature drift in a low-torque servo module, comprising the following steps: measuring the coefficient of thermal expansion of each component within the module based on different operating temperature ranges and the torque load variation curve, obtaining the coefficient of thermal expansion of each component; estimating the dimensional change of each component using the coefficient of thermal expansion, obtaining a set of estimated dimensional changes for each component; calculating the change in fit clearance between each component based on the set of estimated dimensional changes for each component, obtaining the change in fit clearance for each component; analyzing the overall stiffness change of the low-torque servo module using the change in fit clearance for each component, obtaining a set of overall stiffness changes for the module; and formulating a temperature drift suppression strategy for the low-torque servo module based on the set of overall stiffness changes for the module. This method solves the technical problem that temperature fluctuations can cause changes in the torque load inside the module, realizes the calculation of fit clearance changes based on dimensional changes, and can guide the optimization of the module's structural design, avoiding the beneficial effects of jamming or loosening caused by temperature changes. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the steps of a low-torque servo module temperature drift suppression method in one embodiment of the present invention;

[0054] Figure 2 This is a structural block diagram of a low-torque servo module temperature drift suppression device in one embodiment of the present invention;

[0055] Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0056] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0058] like Figure 1 As shown, Figure 1 This is a schematic diagram of the steps of a low-torque servo module temperature drift suppression method in one embodiment of the present invention;

[0059] One embodiment of the present invention provides a method for suppressing temperature drift in a low-torque servo module, comprising the following steps:

[0060] Step S1: Obtain the torque load variation curves of the low-torque servo module under different operating ambient temperature ranges.

[0061] Specifically, to obtain the torque load variation curves of a low-torque servo module under different operating temperature ranges, systematic modeling and experimental analysis are required based on its material properties, structural design, and the feedback mechanism of the control system. This process should closely focus on key factors such as the difference in thermal expansion coefficients caused by temperature fluctuations within the module, changes in the clearance between components, and their impact on overall stiffness. By deploying a high-precision temperature sensor network in typical application scenarios such as semiconductor manufacturing or precision assembly, temperature data of key parts of the module, such as bearing housings, reducer housings, and motor housings, can be collected in real time, and the actual torque output value of the module during operation can be recorded simultaneously, thereby constructing a dynamic mapping relationship between ambient temperature and torque load. For example, in high-temperature environments, the thermal expansion of metal components leads to a decrease in the clearance between components, which may cause increased friction and thus an increase in torque load; while in low-temperature environments, component contraction increases the clearance, which may lead to a decrease in transmission efficiency and torque fluctuations. Combining these measured data with theoretical models, curves reflecting the changes in the module's torque response characteristics under different temperature conditions can be plotted, providing a basis for the subsequent development of temperature-compensated control strategies.

[0062] Step S2: Based on different operating temperature ranges and the torque load variation curve, the thermal expansion coefficients of each component in the module are measured to obtain the thermal expansion coefficients of each component.

[0063] Specifically, measuring the coefficient of thermal expansion of each component within the module based on different operating temperature ranges and the torque load variation curves is crucial for accurately understanding the dimensional changes of low-torque servo modules caused by differences in material properties in high-precision applications such as semiconductor manufacturing or precision assembly. This step requires combining the previously obtained torque load variation curves with typical temperature points as test benchmarks. Under laboratory conditions, actual operating conditions are simulated, placing key module components such as gears, shafts, and bearings in a controllable temperature field. High-precision displacement sensors are used to measure their length changes at different temperatures, thereby calculating the coefficient of thermal expansion of each component. This process not only reveals the potential changes in fit clearances between components due to uneven thermal expansion but also provides fundamental parameter support for the subsequent design of thermal compensation structures. For example, in high-temperature environments, if the coefficient of thermal expansion of a metal shaft is significantly higher than that of its mating housing material, it may lead to reduced clearance or even jamming. Conversely, at low temperatures, the clearance may increase, affecting the overall rigidity and control accuracy of the module. Therefore, by systematically measuring and comparing the coefficients of thermal expansion of each component, a scientific basis can be provided for optimizing material selection and structural design, improving the operational stability of the module in complex temperature environments.

[0064] Step S3: Under different operating temperature ranges, based on the thermal expansion coefficients of each component, estimate the dimensional change of each component to obtain a set of estimated dimensional changes for each component.

[0065] Specifically, by estimating the dimensional changes of each component within different operating temperature ranges, based on the coefficients of thermal expansion of each component, this step aims to further quantify the internal structural deformation of the low-torque servo module caused by temperature fluctuations in high-precision applications such as semiconductor manufacturing or precision assembly. This step requires establishing a temperature-deformation mathematical model based on the previously measured coefficients of thermal expansion of each component, combined with the temperature boundary conditions that may be encountered during actual operation. This model predicts and analyzes the dimensional change trends of key internal components such as gears, shafts, bearings, and housings at different temperature points. By substituting the original design dimensions of each component and the corresponding material's coefficient of thermal expansion into the linear expansion formula, and setting typical temperature ranges (e.g., from -20°C to 80°C), the elongation or contraction of each component under temperature changes can be calculated item by item, ultimately forming a set of estimated dimensional changes for all components across multiple temperature zones. For example, in high-temperature environments, if the coefficient of thermal expansion of a metal shaft is greater than that of its mating housing, the shaft's dimensions will increase more rapidly with rising temperatures, potentially leading to a decrease in the mating clearance or even a risk of jamming. Conversely, at low temperatures, the shaft may shrink more, causing increased clearance and resulting in vibration and noise problems. Therefore, systematically predicting the dimensional changes of each component not only helps identify potential mechanical interference or loosening risks but also provides accurate data support for the subsequent design of structures with thermal compensation functions.

[0066] Step S4: Based on the estimated set of changes in the dimensions of each component, calculate the change in the fit clearance between each component to obtain the change in the fit clearance between each component.

[0067] Specifically, based on the estimated set of dimensional changes of each component, the calculation of the fit clearance changes between each component is performed to deeply analyze the dynamic evolution of the internal mechanical structure fit state of the low-torque servo module caused by temperature changes in high-precision applications such as semiconductor manufacturing or precision assembly. This step requires combining the previously obtained estimated set of dimensional changes of each component under different operating temperature ranges, and establishing a fit model between adjacent components based on their assembly relationships and initial design clearances. Mathematical calculations are then used to derive the actual fit clearance changes between components under different temperature conditions. For example, in a high-temperature environment, if the coefficient of thermal expansion of a shaft component is higher than that of its mating bearing or housing component, the shaft's size will increase more than the mating parts as the temperature rises, leading to a reduction in the originally set assembly clearance, or even a risk of jamming. In low-temperature conditions, the shaft may shrink more, causing an increase in the fit clearance, which in turn leads to vibration and noise problems. Therefore, through systematic calculation of the fit clearance changes of each component, the contact state changes of the internal kinematic pairs of the module under complex temperature environments can be accurately assessed, providing key data support for subsequently improving the overall stiffness, stability, and control accuracy of the module.

[0068] Step S5: Analyze the overall stiffness variation of the low-torque servo module by utilizing the changes in the fit clearance of each component, and obtain the overall stiffness variation set of the module.

[0069] Specifically, analyzing the overall stiffness variation of the low-torque servo module by utilizing the changes in the fit clearances of the various components is to further reveal the dynamic response characteristics of its mechanical structure caused by temperature fluctuations in high-precision applications such as semiconductor manufacturing or precision assembly. This step requires establishing a stiffness transmission path that considers the influence of temperature based on the aforementioned calculated data on the changes in the fit clearances of the various components, combined with the contact stiffness model of key kinematic pairs (such as gear pairs and shaft-bearing systems) within the module, and then quantitatively evaluating the trend of overall stiffness variation of the module under different operating ambient temperatures. For example, in a high-temperature environment, if the fit clearance between a shaft and a bearing decreases due to differences in thermal expansion, it will lead to increased contact pressure and improved local stiffness, but may be accompanied by increased friction and nonlinear response; while under low-temperature conditions, the clearance may expand, causing slight loosening between the contact surfaces, reducing overall stiffness and causing positional deviation. By layering these local stiffness disturbances caused by changes in fit clearances onto the entire transmission chain, a set of responses of the overall stiffness of the module as a function of temperature can be formed, i.e., the set of overall stiffness variation of the module. This result not only reflects the mechanical stability of the module under complex temperature change conditions, but also provides a physical basis and parameter reference for the subsequent design of control strategies with temperature adaptive capabilities.

[0070] Step S6: Formulate a temperature drift suppression strategy for the low-torque servo module based on the overall stiffness variation set of the module.

[0071] Specifically, the temperature drift suppression strategy for the low-torque servo module, based on the overall stiffness variation set of the module, aims to effectively suppress mechanical performance fluctuations caused by temperature changes in applications requiring extremely high control precision, such as semiconductor manufacturing or precision assembly. This step requires establishing a dynamic mapping relationship between stiffness, temperature, and control output, based on the previously analyzed response law of the overall module stiffness to temperature changes, and designing a compensation mechanism that can adaptively adjust control parameters accordingly. For example, in a high-temperature environment, if the local stiffness of a key transmission component of the module increases due to a decrease in the fit clearance, the actual output torque will deviate from the set value, thus affecting positioning accuracy. Conversely, in low-temperature conditions, an increase in the fit clearance may cause a decrease in stiffness and increased vibration, further affecting system stability. Therefore, by embedding the overall stiffness variation set of the module as an input variable into the feedforward compensation algorithm of the servo controller, PID parameters can be dynamically adjusted or a neural network model can be introduced for nonlinear correction, enabling the module to maintain consistent control precision and operational stability under different temperature conditions. This temperature drift suppression strategy based on physical characteristics not only improves the robustness of low-torque servo modules in complex temperature change environments, but also provides key technical support for achieving high-precision and high-reliability automated operation.

[0072] In a specific embodiment, the determination of the thermal expansion coefficient of each component within the module based on different operating ambient temperature ranges and the torque load variation curve, to obtain the thermal expansion coefficient of each component, includes:

[0073] Based on different operating temperature ranges and the torque load variation curves, the operating temperature ranges of each component in the module are divided to obtain a set of operating temperature ranges for each component, and the set of key temperature points for each component in the set of operating temperature ranges for each component is determined.

[0074] The dimensions of each component are measured by the key temperature point set of each component, and the dimensions of each component after temperature change are compared and measured based on the corresponding dimensions of each component to obtain a comparison set of the size changes of each component.

[0075] Based on the comparison set of size changes of each component, the thermal expansion direction of each component is analyzed, and the thermal expansion vector of each component is constructed according to the thermal expansion direction of each component to obtain the thermal expansion vector set of each component;

[0076] The thermal expansion coefficient of each component is calculated using the thermal expansion vector set of each component.

[0077] Specifically, measuring the coefficient of thermal expansion of each component within the module based on different operating temperature ranges and the torque load variation curve is a crucial step in improving the temperature stability of low-torque servo modules in high-precision applications such as semiconductor manufacturing or precision assembly. This process first requires a detailed analysis of the temperature distribution that key components (such as gears, shafts, bearings, and housings) within the module may experience during operation, based on the obtained torque load variation curve and the actual operating temperature range of the module. This analysis then divides the specific operating temperature range for each component, forming a set of operating temperature ranges for each component. Subsequently, representative temperature points are selected within the operating temperature range of each component as test benchmarks, i.e., a set of key temperature points for each component is constructed. These key temperature points typically include the initial temperature, intermediate turning points, and extreme high or low temperature points to ensure that the measurement results cover the entire operating temperature range and reflect nonlinear trends. After determining the key temperature points for each component, the module needs to be disassembled into individual components and placed in a temperature-controlled experimental environment. High-precision displacement sensors or laser interferometers are used to measure their actual dimensions at different key temperature points, recording their original dimensions at room temperature and their dimensional changes after heating or cooling at a set temperature. This creates a comparison set of dimensional changes for each component. This process involves measuring not only changes in length but also changes in width, diameter, and other dimensions to comprehensively assess the thermal expansion behavior of the material in different directions. For example, at high temperatures, if the coefficient of thermal expansion of a metal shaft is higher than that of its mating shell material, the shaft's size will increase more than the shell during heating, potentially leading to a reduction in the mating clearance or even jamming. Conversely, at low temperatures, the opposite trend may occur, requiring accurate capture through the dimensional change comparison set. Furthermore, based on this comparison set of dimensional changes for each component, the thermal expansion trends of each component in different directions can be analyzed, identifying their thermal expansion directions. A thermal expansion vector describing their thermal deformation characteristics is then constructed, forming a thermal expansion vector set for each component. This vector not only contains information about the magnitude of the dimensional change but also reflects the directional characteristics of the deformation, serving as a crucial basis for subsequent calculations of the coefficient of thermal expansion. For example, if the outer ring of a bearing expands both radially and axially, but the axial expansion is more significant, its thermal expansion vector will reflect this anisotropic characteristic, providing a more accurate reference for structural design. Ultimately, by using the data from the set of thermal expansion vectors of each component and combining it with the standard thermal expansion formula, the coefficient of thermal expansion of each component can be derived. This parameter is the result of the combined effect of the inherent properties of the material and the geometry of the component, and it is of decisive significance for subsequent optimization of the fit between components within the module, design of thermal compensation structures, and formulation of temperature drift suppression strategies.For example, by introducing these coefficients of thermal expansion into the digital twin model, the control system can predict mechanical deformation caused by temperature changes in real time and adjust the output commands in advance to offset the resulting position deviations and stiffness fluctuations, thereby ensuring that the module can maintain high-precision operation in complex temperature change environments.

[0078] In a specific embodiment, the step of estimating the dimensional change of each component under different operating temperature ranges, based on the thermal expansion coefficients of each component, to obtain a set of estimated dimensional change for each component, includes:

[0079] Under different operating temperature ranges, the thermal stability of each component material is analyzed based on the thermal expansion coefficient of each component to obtain the thermal stability set of each component material. Then, thermal stress limit analysis is performed on the thermal stability set of each component material to obtain the thermal stress limit set of each component.

[0080] The maximum permissible strain, elastic deformation limit, and plastic deformation threshold of each component are determined based on the thermal stress limit set of each component.

[0081] By using the thermal stress limit set of each component, the temperature-sensitive region of each component is identified, the temperature-sensitive region set of each component is obtained, and the thermal deformation sensitive direction set of each component is determined based on the temperature-sensitive region set of each component.

[0082] Based on the set of heat deformation sensitive directions, the heat deformation constraint factors of each component are determined to obtain the set of heat deformation constraint factors for each component. The dimensional changes of each component are estimated based on the maximum allowable strain, elastic deformation limit, plastic deformation threshold and the set of heat deformation constraint factors for each component.

[0083] Specifically, under different operating temperature ranges, the dimensional changes of each component are estimated based on its thermal expansion coefficient, resulting in a set of estimated dimensional changes for each component. This is a crucial analytical step in ensuring the structural stability and control accuracy of low-torque servo modules in high-precision applications such as semiconductor manufacturing or precision assembly. This process first assesses the thermal stability of the materials themselves based on the obtained thermal expansion coefficients of each component and the actual operating temperature range the module may encounter, thus constructing a set of thermal stability data for each component. Further thermal stress limit analysis is then conducted to identify the maximum thermal stress each component can withstand under different temperature conditions, forming a set of thermal stress limits for each component. Through these thermal stress limit sets, key mechanical parameters such as the maximum permissible strain, elastic deformation limit, and plastic deformation threshold of each component during operation can be clearly defined. These parameters directly determine whether the component will experience irreversible deformation or failure when subjected to temperature changes. For example, in high-temperature environments, if the thermal stress of a metal shaft exceeds its material yield strength, plastic deformation may occur, leading to a permanent change in the fit clearance and affecting the overall rigidity of the module. Conversely, in low-temperature conditions, some non-metallic materials may crack or fracture due to increased brittleness, thus requiring precise thermal stress limit analysis for prediction and mitigation. Based on this, the thermal stress limit sets of each component are used to identify the most temperature-sensitive regions within each component, i.e., to determine the temperature-sensitive region set for each component. This allows for analysis of the degree of thermal deformation sensitivity in different directions, ultimately constructing a set of thermal deformation sensitive directions for each component. For instance, a bearing component may be more prone to thermal deformation in the radial direction due to structural limitations, while another housing component may exhibit higher thermal sensitivity in the axial direction. This information is crucial for developing accurate dimensional change prediction models. Subsequently, based on the set of thermal deformation sensitive directions, key external and internal constraints affecting the thermal deformation of each component need to be further identified and quantified, including assembly methods, fixing structures, and the contact state of adjacent components, thereby establishing a set of thermal deformation constraint factors for each component. These constraints significantly affect whether components can freely expand or contract during actual operation, thus determining their actual dimensional changes. For example, in precision assembly scenarios, a gear assembly rigidly fixed between two supports will have its axial expansion restricted, resulting in significant internal thermal stress, potentially leading to localized deformation or even misalignment. Ultimately, by comprehensively considering the maximum permissible strain, elastic deformation limit, plastic deformation threshold, and the set of thermal deformation constraints for each component, a systematic prediction of the dimensional change trends of each component under different operating temperature ranges can be made, forming a predicted set of dimensional changes for each component. This prediction not only reflects the elongation or shortening of each component at typical temperature points but also provides a precise data foundation for subsequent calculations of clearance changes, overall stiffness analysis, and the formulation of temperature drift suppression strategies.For example, in semiconductor manufacturing equipment, if a drive shaft is expected to undergo a length change of 0.02 mm within a temperature range of -10°C to 85°C, and this change is sufficient to affect the positioning accuracy of the wafer handling mechanism, it can be offset by optimizing material selection or introducing a flexible compensation structure, thereby ensuring that the module can maintain high precision and high reliability operation performance throughout the entire temperature range.

[0084] In a specific embodiment, the step of determining the thermal deformation constraint factors for each component based on the set of thermal deformation sensitive directions to obtain a set of thermal deformation constraint factors for each component includes:

[0085] Vector decomposition is performed on the thermal deformation sensitive direction set of each component to obtain the thermal deformation sensitive direction component set of each component, and correlation analysis is performed on the thermal deformation sensitive direction component set of each component to obtain the thermal deformation sensitive direction correlation coefficient set of each component.

[0086] A threshold is set for the correlation coefficient set of the thermal deformation sensitive direction of each component to obtain the thermal deformation influence state identifier of each component, and the thermal deformation direction component set of each component is filtered based on the thermal deformation influence state identifier to obtain the thermal deformation direction component filtering set of each component.

[0087] Based on the thermal deformation direction component screening set, the physical structure characteristics of each component are analyzed to obtain a physical structure characteristic analysis set, and the stress concentration area of ​​each component is identified based on the physical structure characteristic analysis set.

[0088] Microstructure detection is performed on the stress concentration region to obtain the microstructure set of stress concentration region for each component. Based on the microstructure set of stress concentration region for each component, thermal deformation constraint factor analysis is performed on each component to obtain the thermal deformation constraint factor set for each component.

[0089] Specifically, determining the thermal deformation constraint factors for each component based on the aforementioned set of thermal deformation sensitive directions, and obtaining a set of thermal deformation constraint factors for each component, is an important technical path to improve the structural stability and temperature adaptability of low-torque servo modules in high-precision application scenarios such as semiconductor manufacturing or precision assembly. The core of this step lies in starting from the macroscopic thermal deformation sensitive directions, combining microscopic material properties and structural features, to systematically identify and quantify the key external and internal constraint factors affecting the thermal deformation of each component within the module under temperature changes. Specifically, after obtaining the aforementioned set of thermal deformation sensitive directions, the set is first vector-decomposed to reveal the degree of thermal deformation sensitivity of each component in different spatial dimensions (such as axial, radial, and circumferential), thus forming a set of thermal deformation sensitive direction components for each component. Based on this, further correlation analysis is conducted, that is, the linear relationship strength between each directional component is calculated using statistical methods to generate a set of correlation coefficients for the thermal deformation sensitive directions of each component. These correlation coefficients reflect the consistency and coupling of the thermal deformation behavior of the components in multidimensional space. For example, a bearing ring may exhibit a highly correlated thermal expansion trend in the axial and radial directions, while another housing component may exhibit independent deformation modes in different directions. Subsequently, a threshold is set for the correlation coefficient set of the thermal deformation sensitive directions of each component to distinguish which directional components are significantly correlated and which are relatively independent. This generates a thermal deformation influence state identifier for each component, which marks whether each directional component has a significant impact on the overall thermal deformation behavior. Based on this identifier, the directional components that play a dominant role in the thermal deformation of the components are selected, forming a thermal deformation directional component screening set for each component, providing key input for subsequent in-depth analysis. Next, based on the thermal deformation directional component screening set, physical structural feature analysis is performed on each component, focusing on its geometry, support method, fixed boundary conditions, and contact relationship with other components, thereby establishing a physical structural feature analysis set. This process includes not only identifying structural information such as the component's own dimensions, slot distribution, and wall thickness variations, but also considering its assembly position and stress environment within the module. For example, in a precision assembly scenario, a drive shaft may be unable to freely extend or retract due to rigid fixation at both ends, resulting in a strong restriction on its thermal deformation in a specific direction. This constraint will directly affect its thermal stress distribution and deformation trend. Furthermore, based on the aforementioned set of physical structural features, stress concentration regions within each component are identified. These regions are typically areas of increased local stress caused by structural abrupt changes (such as steps, holes, transition fillets, etc.) or material inhomogeneities. Through finite element simulation or experimental testing, these regions can be accurately located, and their response behavior under thermal loads can be modeled and analyzed. Finally, the identified stress concentration regions are subjected to microstructural probing to obtain information on their grain orientation, phase composition, interface bonding state, and other microscopic aspects, thus forming a set of microstructures of stress concentration regions for each component.These microstructural features are crucial for understanding thermal deformation mechanisms because they determine the material's response behavior when heated. For example, some metallic materials may recrystallize or undergo phase transformation at high temperatures, thereby altering their thermal expansion properties. Based on the microstructural set of stress concentration regions for each component, further analysis of thermal deformation constraint factors is conducted. Taking into account material properties, geometry, boundary conditions, and microstructure, a set of thermal deformation constraint factors for each component is ultimately constructed. This set of thermal deformation constraint factors not only includes the main external constraints affecting component thermal deformation (such as assembly gaps, support stiffness, and interference from adjacent components) but also covers internal factors (such as material inhomogeneity, microscopic defects, and crystal orientation), forming the basis for developing accurate dimensional change prediction models and subsequent temperature drift suppression strategies. For example, in semiconductor manufacturing equipment, if the thermal deformation direction of a drive shaft is mainly concentrated in the axial direction and its two ends are rigidly constrained, some thermal stress can be released by introducing flexible connection structures or adjusting assembly tolerances, thereby avoiding positional deviations and control instability problems caused by thermal deformation. Therefore, by systematically identifying and modeling the constraints of thermal deformation, the operational stability and control accuracy of low-torque servo modules under complex temperature variations can be effectively improved.

[0090] In a specific embodiment, the step of calculating the change in the fit clearance between each component based on the estimated set of changes in the dimensions of each component, and obtaining the change in the fit clearance of each component, includes:

[0091] Based on the estimated set of size changes of each component, the assembly positioning method topology identification of each component is performed to obtain the assembly positioning method set of each component, and the assembly constraint geometry modeling of the assembly positioning method set of each component is performed to obtain the assembly constraint set of each component.

[0092] By using the assembly constraint set of each component, the relative position change trend vector of each component is synthesized to obtain the relative position change trend set of each component, and the ideal mating position of each component is determined based on the relative position change trend set of each component to obtain the ideal mating position set of each component.

[0093] The actual mating positions between each component are measured to obtain the set of actual mating positions between each component, and the mating offset between each component is calculated based on the set of ideal mating positions between each component and the set of actual mating positions between each component.

[0094] Using the aforementioned fit offset, the fit clearance variation between each component is analyzed to obtain the fit clearance variation of each component.

[0095] Specifically, calculating the variation in fit clearance between components based on the estimated set of dimensional changes of each component is a crucial analytical step in ensuring the operational stability and control accuracy of low-torque servo modules in high-precision applications such as semiconductor manufacturing or precision assembly. This process requires starting from the assembly relationships of the components within the module, combining their thermal deformation characteristics and structural constraints, to systematically identify and quantify the evolution of the fit state caused by temperature changes. Specifically, after obtaining the aforementioned estimated set of dimensional changes of each component, the first step is to perform topological identification of the assembly positioning methods between the components within the module, that is, to clarify the installation position, connection method, and relative motion relationship between each component and other components in the overall structure, thereby forming a set of assembly positioning methods for each component. For example, in a precision assembly device, a gear may be fixed to the drive shaft by an axial keyway and limited by an end cover, while the bearing is nested in the housing hole and subjected to axial compression by the front and rear flanges. This assembly information constitutes a complex mutual constraint relationship between the components. Based on this, further assembly constraint geometric modeling is performed on the assembly positioning method set of each component to establish a mathematical model reflecting the spatial relationship between the components under actual assembly conditions, ultimately generating an assembly constraint set for each component. This assembly constraint set not only includes physical constraint types such as rigid connections, sliding fits, and interference fits between components, but also reflects the direction and degree of freedom restriction, which is an important foundation for subsequent analysis of relative position change trends. Subsequently, using the assembly constraint set of each component as input parameters, a vector synthesis analysis of the relative position change trends of each component caused by dimensional changes under different working environmental temperatures is carried out. That is, the deformation direction and magnitude of each component due to thermal expansion or contraction are vectorized and superimposed, considering the limiting effect of its assembly boundary conditions on thermal deformation, thereby obtaining the relative displacement trend of each component under temperature change conditions, forming a set of relative position change trends for each component. For example, in a high-temperature environment, if the coefficient of thermal expansion of a certain shaft component is higher than that of its mating shell, the elongation of the shaft will be restricted by the shell, resulting in a slight axial displacement of the shaft end relative to the shell end face. The direction and magnitude of this displacement are included in the set of relative position change trends of the component. Furthermore, based on the set of relative position change trends of each component, the ideal mating positions of each component under different temperature conditions can be derived. That is, without considering any errors or nonlinear factors, the optimal contact and meshing state that the components should maintain forms the ideal mating position set for each component. This ideal position set provides a theoretical benchmark for subsequent evaluation of the actual mating state. Next, the actual operating state of the module at typical temperature points needs to be measured to obtain the true mating position data between each component, thereby constructing the actual mating position set between each component.This process typically utilizes high-precision laser rangefinders, capacitive displacement sensors, or optical microscopes to simulate real-world working conditions in an experimental environment, recording the actual contact states of key mating interfaces, such as the meshing depth of gear pairs and the contact pressure distribution between shafts and bearings. Based on this, the ideal mating position set of each component is compared with the actual mating position set between the components, and the deviation value between the two is calculated, thus obtaining the mating offset between each component. This mating offset reflects the degree to which the component deviates from the ideal assembly state under temperature changes and is the core basis for analyzing the change in mating clearance. Finally, using the mating offset, a dynamic analysis of the mating clearance between each component is performed, comprehensively considering the influence of component material properties, structural characteristics, and assembly methods, to calculate the actual changes in the mating clearance between each component under different temperature conditions, forming the results of the mating clearance changes for each component. For example, in high-temperature environments, if a shaft component increases in size due to thermal expansion, but its mating housing expands less due to different materials, the originally set 0.01mm gap may shrink to 0.002mm, or even cause slight interference. In low-temperature environments, the gap at the same mating interface may expand to 0.018mm, increasing vibration and noise, and affecting the overall stiffness and control stability of the module. In summary, by deeply applying the estimated set of component size changes, combined with a series of technical processes such as assembly positioning method identification, assembly constraint modeling, relative position change trend vector synthesis, comparison of ideal and actual mating positions, and calculation of mating offset, it is possible to systematically and accurately predict and evaluate the changes in mating gaps of low-torque servo modules under complex temperature environments. This analysis process not only provides a physical basis for subsequent stiffness change modeling and temperature drift suppression strategy formulation, but also lays a solid foundation for improving the long-term operational reliability of the module in high-precision scenarios such as semiconductor manufacturing.

[0096] In a specific embodiment, the step of using the variation in the fit clearance of each component to perform an overall stiffness variation analysis on the low-torque servo module, and obtaining the overall stiffness variation set of the module, includes:

[0097] Based on the changes in the fit clearance of each component, the contact state of each component in the low torque servo module is determined to obtain the contact state set of each component, and the contact stress distribution finite element solution is performed on the contact state set of each component to obtain the contact stress distribution set of each component.

[0098] By using the contact stress distribution set of each component, the force transmission path of each component is constructed, and the force equivalent model of the corresponding component is constructed based on the force transmission path of each component.

[0099] Based on the force equivalent model, the overall structural mechanics of the low-torque servo module is decomposed to obtain the module structure decomposition set, and the structural stiffness matrix of the low-torque servo module is constructed according to the module structure decomposition set to obtain the module structure stiffness matrix set.

[0100] Using the module structure stiffness matrix set, the overall stiffness change of the low-torque servo module is predicted and calculated, resulting in the overall stiffness change set of the module.

[0101] Specifically, the analysis of the overall stiffness change of the low-torque servo module by utilizing the changes in the fit clearances of the various components to obtain the overall stiffness change set of the module is a core step in maintaining the control accuracy and operational stability of the module in high-precision application scenarios such as semiconductor manufacturing or precision assembly. This step, based on the aforementioned results of the changes in the fit clearances of the various components, starts from microscopic contact behavior and combines macroscopic structural mechanics modeling to systematically evaluate the trend of internal stiffness fluctuations caused by temperature changes. Specifically, after obtaining the data on the changes in the fit clearances of the various components, the contact state between the key components within the low-torque servo module must first be determined. This involves judging the actual contact mode under different operating temperatures based on whether the clearance is positive, zero, or negative (i.e., whether there is interference fit), thereby constructing a set of contact states for each component. For example, in a high-temperature environment, if a shaft component's thermal expansion coefficient is higher than that of its mating housing, resulting in a reduced fit clearance or even interference, the contact state of that component will change from "loose" to "tight contact," while under low-temperature conditions, the opposite trend may occur. The dynamic changes in this contact state directly affect the load transmission path and local stiffness characteristics within the module. Based on this, a finite element method for contact stress distribution is further performed on the contact state sets of each component to obtain the actual contact area and stress distribution of each component under different temperature conditions, ultimately forming a contact stress distribution set for each component. This process typically employs a nonlinear contact finite element method, considering the effects of material nonlinearity, geometric nonlinearity, and boundary condition variations to accurately simulate the pressure distribution, friction effects, and minute slippage behavior between contact surfaces. For example, in a precision transmission mechanism, if the contact area between the bearing raceway and rolling elements decreases due to changes in the fit clearance, it will lead to local stress concentration, affecting the module's dynamic response and fatigue life. Subsequently, based on the contact stress distribution sets of each component, the force transmission path between each component can be further constructed, that is, the load transmission route from the input end to the output end can be clarified, and the main force directions and points of application between the components inside the module at different temperatures can be identified accordingly. This process allows for the establishment of an equivalent stress model for each component under temperature variations. This model not only reflects the stiffness characteristics of the component itself but also embodies its interaction with adjacent components, thus forming an equivalent stress model. Next, based on this equivalent stress model, the entire low-torque servo module undergoes a comprehensive structural mechanics decomposition. This involves breaking down the complex module system into several sub-structural units with clearly defined stress characteristics, thereby constructing a module structural decomposition set. This decomposition process includes not only geometric division but also emphasizes the mechanical coupling relationships between functional modules, such as the load transfer tasks undertaken by key components like the drive motor, reducer, and output shaft, and their mutual influence mechanisms.Furthermore, based on the module structure decomposition set, the structural stiffness matrix of each sub-structural unit is established, and these sub-units are combined into the overall structural stiffness matrix of the module through assembly, ultimately forming a module structural stiffness matrix set. This stiffness matrix set describes the module's response capability to external loads in different degrees of freedom directions and is the basis for evaluating its static and dynamic performance. For example, in a precision positioning platform, if the clearance between the output shaft and the guide sleeve decreases due to increased temperature, resulting in an increase in its displacement stiffness in the Y direction, this change will be reflected in the structural stiffness matrix as an increase in the Y-direction stiffness coefficient. Finally, using the module structural stiffness matrix set, combined with the module's boundary conditions and external load input at typical temperature points, the structural stiffness is predicted and calculated. Taking into account factors such as material nonlinearity, contact nonlinearity, and geometric deformation, the overall stiffness variation trend of the low-torque servo module in different operating temperature ranges is accurately evaluated, thereby generating a module overall stiffness variation set. This set of changes not only includes the stiffness values ​​of the module in each degree of freedom direction, but also reflects its stiffness distribution characteristics in multi-dimensional space. It serves as a crucial basis for subsequently developing temperature drift suppression strategies and optimizing control algorithm parameters. For example, in semiconductor manufacturing equipment, if the overall stiffness of a drive unit decreases by 12% within a temperature range of -10℃ to 85℃, it may lead to micrometer-level positional shifts during wafer handling, affecting processing accuracy. In this case, a feedforward compensation control strategy based on the stiffness change set can be introduced to dynamically adjust the gain parameters of the servo system, ensuring that the module maintains consistent motion accuracy and stability under different temperature conditions. In summary, through in-depth application of the variation in the fit gaps between components, combined with a series of technical processes such as contact state determination, finite element analysis of contact stress, force transmission path construction, establishment of equivalent force models, structural mechanics decomposition, and stiffness matrix modeling, it is possible to systematically and accurately predict and evaluate the overall stiffness changes of low-torque servo modules under complex temperature environments. This analysis process not only provides theoretical support for improving the operational reliability of the module in high-precision scenarios but also lays a solid foundation for realizing its intelligent temperature compensation control.

[0102] In a specific embodiment, the step of formulating a temperature drift suppression strategy for the low-torque servo module based on the overall stiffness variation set of the module includes:

[0103] Based on the overall stiffness variation set of the module, the critical working state of the low torque servo module is identified to obtain the critical working state set of the module, and the safety margin of the critical working state set of the module is evaluated to obtain the safety margin evaluation set of the module.

[0104] The temperature-sensitive components of the low-torque servo module are identified using the module safety margin assessment set to obtain a module temperature-sensitive component set. The temperature adjustment priority of each component is determined based on the module temperature-sensitive component set to obtain a set of temperature adjustment priorities for each component.

[0105] Based on the temperature regulation priority set of each component, temperature regulation means are matched and selected for each component to obtain a temperature regulation means set for each component. Then, the temperature regulation effect of each component is predicted based on the temperature regulation means set of each component to obtain a temperature regulation effect prediction set for each component.

[0106] A set of temperature drift suppression strategies for low-torque servo modules is formulated using the temperature regulation effect prediction set of each component.

[0107] Specifically, the temperature drift suppression strategy for the low-torque servo module based on the overall stiffness variation set of the module is a key closed-loop control step to maintain the control accuracy and operational stability of the module in high-precision application scenarios such as semiconductor manufacturing or precision assembly. This process requires starting from the trend of the overall stiffness of the module changing with temperature, identifying its extreme states under different working environments, and combining the temperature sensitivity and adjustability of the components to systematically construct a set of targeted and prioritized temperature drift suppression strategies. Specifically, after obtaining the aforementioned constructed overall stiffness variation set of the module, it is first necessary to identify the critical working states that the module may enter under different working environment temperature ranges. That is, by setting a threshold for the decrease or increase of module stiffness, it is determined whether it is close to the design-allowed limit performance point, thereby forming a set of critical working states of the module. For example, in a high-temperature environment, if a drive shaft causes a decrease in the fit clearance and an increase in contact stress due to thermal expansion, resulting in an abnormal increase in local stiffness, causing the module to exhibit response lag or oscillation when performing high-precision positioning tasks, then this state is marked as one of the critical working states of the module. Based on this, a safety margin assessment is further performed on the critical operating state set of the module. This involves comparing the actual stiffness of the module with the design target stiffness, and considering factors such as material strength limits, structural bearing capacity, and the fault tolerance of the control system, to calculate the safety margin values ​​of the module under different temperature conditions, ultimately forming a module safety margin assessment set. This assessment result not only reflects the stability level of the module under extreme temperatures but also provides a basis for subsequent identification of temperature-sensitive components. Subsequently, using the data from the module safety margin assessment set, the influence of each component inside the low-torque servo module under different temperature conditions is analyzed, identifying those components that contribute significantly to the overall stiffness change of the module, thus constructing a set of temperature-sensitive components. These components typically include key moving pairs or supporting structures such as bearings, gears, output shafts, and housings, whose thermal deformation behavior directly determines the dynamic response characteristics of the module. For example, in precision assembly scenarios, a reducer housing with a large coefficient of thermal expansion undergoes significant deformation during temperature rise, leading to changes in gear meshing clearance, thus becoming a key factor affecting the overall stiffness of the module. Furthermore, based on the set of temperature-sensitive components in the module, the temperature regulation priority of each component is determined. This involves comprehensively considering factors such as its impact on overall stiffness, adjustability (e.g., ease of integrating heating / cooling devices), response speed, and coupling effects on other parts of the module. Each component is assigned a temperature regulation priority value, thus forming a set of component temperature regulation priorities. For example, if the stiffness change of a certain bearing has the greatest impact on the overall stiffness of the module, and rapid temperature control can be achieved by arranging a micro-heating film on its outer ring, then its regulation priority will be higher than that of the housing component, which can only be cooled by air.Next, based on the temperature regulation priority set for each component, suitable temperature regulation methods are matched to each component, including but not limited to active heating (such as PTC heating film, resistance wire), passive cooling (such as thermally conductive silicone, phase change materials), local constant temperature cover, and forced heat dissipation by micro fans, thereby constructing a set of temperature regulation methods for each component. This process also needs to consider the module's spatial layout, power consumption limitations, and response time requirements to ensure the selected methods are feasible in terms of physical space and engineering implementation. Subsequently, for each regulation method in the set of temperature regulation methods for each component, temperature regulation effect prediction is carried out. That is, through experimental testing or finite element simulation, the ability to correct the component's temperature field distribution, dimensional changes, and fit clearance under typical temperature conditions is predicted, thereby establishing a set of predicted temperature regulation effects for each component. For example, for a bearing component using a micro heating film, after the heating function is activated in a -10℃ low-temperature environment, its surface temperature can rise to 25℃ within 30 seconds, allowing the fit clearance to return to the room temperature design value, effectively improving the overall rigidity and control accuracy of the module. Finally, using the predicted temperature regulation effects of each component, combined with the overall stiffness change trend of the module, safety margin assessment results, and component adjustment priority information, a complete set of low-torque servo module temperature drift suppression strategies was formulated. This strategy set not only includes temperature regulation action commands for different temperature ranges (such as "start heating" and "enhance heat dissipation"), but also covers the corresponding control logic (such as adaptive PID parameter adjustment and feedforward compensation factor update) and execution sequence arrangement to ensure that the module maintains stable mechanical performance and control response under complex temperature variations. For example, in semiconductor manufacturing equipment, when the overall stiffness of the module is detected to decrease beyond a set threshold due to temperature rise, the control system can automatically trigger the heating compensation mechanism of the highest-priority bearing component, while simultaneously adjusting the proportional gain of the servo controller to offset the displacement deviation caused by the decrease in stiffness, thereby ensuring that the repeatability of the wafer handling mechanism is not affected. In summary, by deeply applying the overall stiffness variation set of the module and combining a series of technical processes such as critical operating state identification, safety margin assessment, temperature-sensitive component screening, temperature regulation priority ranking, regulation method matching, and effect prediction, a systematic and precise temperature drift suppression strategy for low-torque servo modules suitable for complex temperature environments can be formulated. This strategy not only improves the operational reliability and control accuracy of the module in high-precision scenarios such as semiconductor manufacturing, but also provides a solid technical foundation for its intelligent temperature compensation control.

[0108] The above describes the method for suppressing temperature drift in a low-torque servo module according to an embodiment of the present invention. The following describes the device for suppressing temperature drift in a low-torque servo module according to an embodiment of the present invention. Please refer to [link / reference]. Figure 2 One embodiment of the low-torque servo module temperature drift suppression device in this invention includes:

[0109] The acquisition module 21 is used to acquire the torque load change curves of the low torque servo module under different operating ambient temperature ranges.

[0110] Measurement module 22 is used to measure the thermal expansion coefficient of each component in the module based on different working environment temperature ranges and the torque load change curve, and to obtain the thermal expansion coefficient of each component.

[0111] The estimation module 23 is used to estimate the size change of each component under different working environment temperature ranges, based on the thermal expansion coefficient of each component, to obtain a set of estimated size changes of each component.

[0112] Calculation module 24 is used to calculate the change in the fit clearance between each component based on the estimated set of changes in the size of each component, and to obtain the change in the fit clearance between each component.

[0113] Analysis module 25 is used to perform overall stiffness variation analysis on the low torque servo module by utilizing the variation of the fit clearance of each component, and obtain the overall stiffness variation set of the module;

[0114] The formulation module 26 is used to formulate a temperature drift suppression strategy for the low-torque servo module based on the overall stiffness variation set of the module.

[0115] In this embodiment, the specific implementation of each unit in the above device embodiment is described in the above method embodiment, and will not be repeated here.

[0116] Reference Figure 3 This invention also provides a computer device whose internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0117] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0118] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0119] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0120] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0121] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A low-torque servo module temperature drift suppression method, characterized by, The method comprises the following steps: Obtaining a torque load change curve of a low-torque servo module corresponding to different working environment temperature ranges; Based on the different working environment temperature ranges and the torque load change curve, the thermal expansion coefficients of each component in the module are determined to obtain the thermal expansion coefficients of each component; Under different working environment temperature ranges, the size change of each component is estimated based on the thermal expansion coefficients of each component to obtain a size change estimation set of each component; Based on the size change estimation set of each component, the fitting gap change between each component is calculated to obtain the fitting gap change of each component; Based on the fitting gap change of each component, the overall stiffness change of the low-torque servo module is analyzed to obtain a module overall stiffness change set; Based on the module overall stiffness change set, a temperature drift suppression strategy of the low-torque servo module is formulated; The size change estimation set of each component is obtained by estimating the size change of each component under different working environment temperature ranges and combining the thermal expansion coefficients of each component. Under different working environment temperature ranges, the material thermal stability of each component is analyzed based on the thermal expansion coefficients of each component to obtain a material thermal stability set of each component, and a thermal stress limit analysis is performed on the material thermal stability set of each component to obtain a thermal stress limit set of each component; Based on the thermal stress limit set of each component, the maximum allowable strain, elastic deformation limit, and plastic deformation threshold of each component are determined; Based on the thermal stress limit set of each component, the temperature sensitive region of each component is identified to obtain a temperature sensitive region set of each component, and a thermal deformation sensitive direction set of each component is determined based on the temperature sensitive region set of each component; Based on the thermal deformation sensitive direction set, the thermal deformation constraint factors of each component are determined to obtain a thermal deformation constraint factor set of each component, and the size change of each component is estimated based on the maximum allowable strain, elastic deformation limit, plastic deformation threshold, and thermal deformation constraint factor set of each component.

2. The low-torque servo module temperature drift suppression method of claim 1, wherein, Based on the different working environment temperature ranges and the torque load change curve, the thermal expansion coefficients of each component in the module are determined to obtain the thermal expansion coefficients of each component. Based on the different working environment temperature ranges and the torque load change curve, the working temperature intervals of each component in the module are divided to obtain a working temperature interval set of each component, and a key temperature point set of each component in the working temperature interval set is determined; Based on the size change comparison set of each component, the thermal expansion direction of each component is analyzed, and the thermal expansion vector of each component is constructed based on the thermal expansion direction of each component to obtain a thermal expansion vector set of each component; Based on the thermal expansion vector set of each component, the thermal expansion coefficients of each component are calculated to obtain the thermal expansion coefficients of each component. Based on the thermal deformation sensitive direction set, the thermal deformation constraint factors of each component are determined to obtain a thermal deformation constraint factor set of each component.

3. The low-torque servo module temperature drift suppression method of claim 1, wherein, ​ vector decomposition is performed on the set of directions sensitive to thermal deformation of each component to obtain a set of components of the direction sensitive to thermal deformation, and correlation analysis is performed on the set of components of the direction sensitive to thermal deformation to obtain a set of correlation coefficients of the direction sensitive to thermal deformation of each component; A threshold value of the set of correlation coefficients of the direction sensitive to thermal deformation of each component is set to obtain a thermal deformation impact state identifier of each component, and a set of thermal deformation direction components of each component is screened based on the thermal deformation impact state identifier to obtain a set of thermal deformation direction components of each component. Based on the set of thermal deformation direction components, physical structure feature analysis is performed on each component to obtain a set of physical structure feature analysis, and a stress concentration area of each component is identified based on the set of physical structure feature analysis. Microstructure detection is performed on the stress concentration area to obtain a set of microstructures of the stress concentration area of each component, and thermal deformation constraint factor analysis is performed on each component based on the set of microstructures of the stress concentration area of each component to obtain a set of thermal deformation constraint factors of each component.

4. The low-torque servo module temperature drift suppression method of claim 1, wherein, Based on the set of estimated size change amounts of each component, the fitting gap change between each component is calculated to obtain the fitting gap change of each component, including: Based on the set of estimated size change amounts of each component, a set of assembly positioning modes of each component is identified, and a set of assembly constraints of each component is obtained by assembly constraint geometric modeling on the set of assembly positioning modes of each component; Based on the set of relative position change trends of each component, the ideal fitting position of each component is determined to obtain a set of ideal fitting positions of each component; The actual fitting position between each component is measured to obtain a set of actual fitting positions between each component, and the fitting offset between each component is calculated based on the set of ideal fitting positions of each component and the set of actual fitting positions between each component; Using the fitting offset, the fitting gap change between each component is analyzed to obtain the fitting gap change of each component.

5. The low-torque servo module temperature drift suppression method of claim 1, wherein, Based on the set of fitting gap changes of each component, the contact state of each component in the low-torque servo module is determined to obtain a set of contact states of each component, and a set of contact stress distributions of each component is obtained by finite element solution of the contact stress distribution of the set of contact states of each component; Based on the set of contact stress distributions of each component, the force transmission path of each component is constructed, and the equivalent stress model of the corresponding component is constructed based on the force transmission path of each component; Based on the equivalent stress model, the overall structure mechanics of the low-torque servo module is decomposed to obtain a set of module structure decompositions, and a set of module structure stiffness matrices is obtained by constructing the structure stiffness matrix of the low-torque servo module according to the set of module structure decompositions; Using the set of module structure stiffness matrices, the overall stiffness change of the low-torque servo module is predicted and calculated to obtain a set of module overall stiffness changes. ​ 6. The low-torque servo module temperature drift suppression method of claim 1, wherein, The temperature drift suppression strategy of the low-torque servo module is formulated based on the module overall stiffness change set, including: Based on the module overall stiffness change set, the critical working state of the low-torque servo module is identified to obtain a module critical working state set, and the safety margin of the module critical working state set is evaluated to obtain a module safety margin evaluation set; Through the module safety margin evaluation set, the temperature-sensitive components of the low-torque servo module are identified to obtain a module temperature-sensitive component set, and the temperature adjustment priority of each component is determined based on the module temperature-sensitive component set to obtain a component temperature adjustment priority set; Based on the component temperature adjustment priority set, the temperature adjustment means of each component is matched and selected to obtain a component temperature adjustment means set, and the temperature adjustment effect of each component is estimated according to the component temperature adjustment means set to obtain a component temperature adjustment effect estimation set; The temperature drift suppression strategy of the low-torque servo module is formulated using the component temperature adjustment effect estimation set.

7. A low-torque servo module temperature drift suppression device, characterized by, It includes: The acquisition module is used to acquire the torque load change curve of the low-torque servo module corresponding to different working environment temperature ranges; The measurement module is used to measure the thermal expansion coefficient of each component in the module based on different working environment temperature ranges and the torque load change curve to obtain the thermal expansion coefficient of each component; The estimation module is used to estimate the size change of each component under different working environment temperature ranges in combination with the thermal expansion coefficient of each component to obtain a component size change estimation set; The calculation module is used to calculate the fitting gap change between each component based on the component size change estimation set to obtain the fitting gap change of each component; The analysis module is used to analyze the overall stiffness change of the low-torque servo module using the fitting gap change of each component to obtain a module overall stiffness change set; The formulation module is used to formulate the temperature drift suppression strategy of the low-torque servo module based on the module overall stiffness change set; The estimation module is used to estimate the size change of each component under different working environment temperature ranges in combination with the thermal expansion coefficient of each component to obtain a component size change estimation set, including: Under different working environment temperature ranges, the material thermal stability of each component is analyzed in combination with the thermal expansion coefficient of each component to obtain a component material thermal stability set, and the thermal stress limit of the component material thermal stability set is analyzed to obtain a component thermal stress limit set; Based on the component thermal stress limit set, the maximum allowable strain, elastic deformation limit and plastic deformation threshold of each component are determined; Through the component thermal stress limit set, the temperature-sensitive region of each component is identified to obtain a component temperature-sensitive region set, and the thermal deformation sensitive direction set of each component is determined based on the component temperature-sensitive region set; Based on the thermal deformation sensitive direction set, the thermal deformation constraint factor of each component is determined to obtain a component thermal deformation constraint factor set, and the size change of each component is estimated according to the maximum allowable strain, elastic deformation limit, plastic deformation threshold and component thermal deformation constraint factor set.

8. A computer device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

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