A robot body intelligence harmonic reducer reliability analysis method

Abstract

The application discloses a kind of robot body intelligence harmonic reducer reliability analysis method, to solve the problem that existing harmonic reducer reliability analysis ignores load uncertainty, does not consider the influence of intelligent sensor installation and the model is low with actual degree of fit.The method comprises: the component model of flexspline, rigid wheel, wave generator and intelligent film sensor is established;The tooth engagement of flexspline and rigid wheel, the cementation of flexspline and sensor, the contact of flexspline and wave generator are simplified in model;Variable load samples are obtained by sampling in the range of [0, 30] N / m using DOE optimal Latin hypercube design;Variable load is input to dynamic model for simulation;Reliability is evaluated based on transmission accuracy, tooth wear, strain and backlash index.The application improves the accuracy and practicality of intelligent harmonic reducer reliability analysis, and is suitable for the whole life cycle state perception demand in the intelligent scene of robot body.

Description

Technical Field

[0001] This invention relates to the field of reliability analysis technology for core transmission components of industrial robots, specifically a reliability analysis method for a robot-embedded intelligent harmonic reducer. Background Technology

[0002] Harmonic reducers, as core transmission components of high-end equipment such as industrial robots, aerospace equipment, and medical devices, are mainly composed of flexible gears, rigid gears, and wave generators. They transmit power and motion by achieving relative tooth misalignment between the teeth through the controllable elastic deformation of the flexible gears. They have advantages such as compact structure, large transmission ratio, high precision, and adaptability to sealed / vacuum environments. More than 70% of their applications are concentrated in industrial robot joints.

[0003] However, harmonic reducers are prone to two types of failures during long-term operation: performance degradation failure, manifested as decreased transmission accuracy and reduced stiffness; and strength failure, typically manifested as fatigue fracture of the flexspline. Both types of failures can lead to poor robot performance or sudden malfunctions, failing to meet the reliable operation requirements of over 10,000 hours, especially for harmonic reducer models equipped with intelligent sensors, whose service life is even shorter. To solve this problem, reliability analysis using simulation technology is needed to assist in performance optimization, but existing technologies have the following key drawbacks:

[0004] State perception and monitoring lag: For robotic systems with limited intelligent sensor data acquisition capabilities, traditional monitoring methods (such as external vibration monitoring) cannot capture the internal stress distribution and deformation state of the harmonic reducer in real time, resulting in delayed fault warnings; for example... Figure 1 As shown, although some solutions add intelligent thin-film sensors (to collect stress and strain) to the surface of the flexible wheel, the sensors are fixed by adhesive bonding. The mechanical properties of the adhesive layer will significantly affect the overall reliability of the harmonic reducer, and the existing technology has not carried out reliability analysis for this "flexible wheel-sensor" coupling structure.

[0005] The simulation is out of touch with the actual working conditions: existing reliability simulations are mostly based on ideal working conditions (constant load) and do not fully consider the load fluctuations in the actual operation of the robot (such as sudden load changes when grasping heavy objects, dynamic loads when switching trajectories). They cannot meet the needs of the robot's embodied intelligence to perceive the state of the core transmission components throughout the entire life cycle, resulting in insufficient engineering applicability of the simulation results.

[0006] Poor model simplification: The existing dynamic model simplifies "tooth meshing" and "component contact" in ways that differ greatly from the actual structure (such as ignoring friction and treating the adhesive layer as rigid). In particular, the model simplification of the adhesive area of ​​the "flexible wheel-smart sensor" does not consider the elasticity and damping characteristics of the adhesive layer, which further reduces the accuracy of reliability analysis.

[0007] Therefore, there is an urgent need for a reliability analysis method for harmonic reducers that can take into account load uncertainty, adapt to the structure of smart sensor mounting, and have a model that fits reality, so as to meet the reliability assessment needs in robot embodied intelligence scenarios.

[0008] The above content is supplemented by adding the phrase "especially for harmonic reducer models equipped with intelligent sensors, the service life is even lower," which is more in line with the research on intelligent harmonic reducers that this method is based on, and thus is added to the above content. Summary of the Invention

[0009] The core objective of this invention is to provide a reliability analysis method for a robot-embodied intelligent harmonic reducer. Through a complete process design of "component modeling - contact simplification - load sampling - simulation analysis - reliability assessment", it solves the defects of the prior art.

[0010] To achieve the above objectives, the present invention employs the following technical means:

[0011] A reliability analysis method for a robot-embodied intelligent harmonic reducer includes the following steps:

[0012] S1: Establish a model of the harmonic reducer components and construct three-dimensional models of the flexible wheel, rigid wheel, wave generator, and intelligent thin-film sensor respectively. The rigid wheel adopts a rigid body model and is set to be fixed. The flexible wheel, wave generator, and intelligent thin-film sensor all adopt flexible body models to simulate actual deformation. The intelligent thin-film sensor is used to collect stress and strain data of the flexible wheel and is bonded to the surface of the flexible wheel with a specific material.

[0013] S2: Simplify model contact relationships. The contact relationships between the component models are simplified, specifically as follows:

[0014] S21: The meshing contact between the flexible wheel and the rigid wheel is replaced by a spring-damped friction model;

[0015] S22: The adhesive contact between the flexible wheel and the smart thin film sensor divides the adhesive area into 6 independent regions, each of which is replaced by a spring damping model;

[0016] S23: The contact between the flexible wheel and the wave generator is replaced by a spring-damped model;

[0017] A rigid-flexible coupled dynamic model is constructed using the above simplification.

[0018] S3: Obtain Uncertain Load Samples

[0019] Using the optimal Latin hypercube design method in experimental design, variable load samples are extracted within the load range of [0, 30] N / m. The sampling formula is as follows: ;

[0020] in, For the first The first sample One load parameter, For the first The value of the permutation function of the column. A random number within the interval [0, 1] This represents the total number of samples; this design ensures that the sample points are evenly distributed across the load design space, guaranteeing the statistical validity of the samples.

[0021] S4: Perform dynamic simulation

[0022] The variable load sample obtained in step S3 is used as input and loaded into the wave generator in the rigid-flexible coupling dynamic model. The dynamic simulation is started to obtain the stress, strain, transmission error and wear data during the operation of the harmonic reducer.

[0023] S5: Assess Reliability

[0024] Set safety thresholds for transmission accuracy, tooth wear, strain magnitude, and backlash. Compare the simulation data from step S4 with the safety thresholds to complete the reliability analysis of the harmonic reducer.

[0025] Preferably, in step S1, the material of the intelligent thin-film sensor is selected to be a flexible sensing material that is compatible with the deformation of the flexure, and the specific material adhesive is a structural adhesive with fatigue resistance and low elastic modulus to adapt to the periodic deformation requirements of the harmonic reducer.

[0026] Preferably, in step S22, the division of the bonding area is based on the deformation law of the flexible wheel, with 6 independent areas evenly distributed along the circumference of the flexible wheel, and the spring damping parameter of each area is calibrated according to the mechanical properties of the bonding material.

[0027] Preferably, in step S3, the total number of samples The value range is 30~100, and the statistical representativeness of the sample is verified by analysis of variance to ensure coverage of fluctuation scenarios within the load range of [0, 30] N / m.

[0028] Preferably, in step S4, the dynamic simulation is performed using multibody dynamics software, and the nonlinearity of the elastic deformation of the flexible wheel, the impact effect of tooth meshing, and the damping dissipation of the adhesive region are taken into account during the simulation.

[0029] Preferably, in step S5, the safety threshold is set according to the application scenario requirements of the harmonic reducer.

[0030] The present invention has the following beneficial effects:

[0031] 1. Adapted to intelligent harmonic reducer structure: For the first time, a reliability analysis was conducted on the special structure of "intelligent thin film sensor added to the surface of flexible wheel". The analysis focuses on the impact of adhesive layer on reliability, filling the gap in the existing technology for intelligent sensing coupling structure analysis. The analysis results are more in line with the state perception needs of robot embodied intelligence.

[0032] 2. Improve the model's fit with reality: The simplification of "tooth meshing", "adhesion", and "contact" is based on the mechanical properties of materials and the actual assembly relationship. In particular, the adhesive area is divided into 6 regions and replaced with spring damping, which not only retains the elasticity and damping characteristics of the adhesive layer, but also avoids the computational redundancy of the full 3D adhesive model, reducing the simulation error to less than 10%.

[0033] 3. Considering load uncertainty: The optimal Latin hypercube design is used to obtain variable load samples, covering load fluctuation scenarios in actual robot operation, which solves the limitations of traditional ideal working condition simulation and makes the reliability analysis results more meaningful for engineering guidance.

[0034] 4. Comprehensive evaluation dimensions: Reliability is evaluated from four dimensions: "transmission accuracy (performance), tooth wear (life), strain (strength), and backlash (accuracy retention)," which can better reflect the full life cycle status of the harmonic reducer compared to a single dimension evaluation. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the structure of an existing intelligent harmonic reducer;

[0036] Figure 2 This is an overall flowchart of the reliability analysis method of the present invention;

[0037] Figure 3 This is a schematic diagram of the dynamic model of the intelligent harmonic reducer of the present invention;

[0038] Figure 4 This is a schematic diagram of the load sample distribution for the optimal Latin hypercube design of this invention. Detailed Implementation

[0039] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] like Figure 2-4 As shown, a reliability analysis method for a robot-embedded intelligent harmonic reducer includes the following steps:

[0041] S1: Establish a model of the harmonic reducer components and construct three-dimensional models of the flexible wheel, rigid wheel, wave generator, and intelligent thin-film sensor respectively. The rigid wheel adopts a rigid body model and is set to be fixed. The flexible wheel, wave generator, and intelligent thin-film sensor all adopt flexible body models to simulate actual deformation. The intelligent thin-film sensor is used to collect stress and strain data of the flexible wheel and is bonded to the surface of the flexible wheel with a specific material.

[0042] The material selection of the intelligent thin film sensor is a flexible sensing material that matches the deformation compatibility of the flex wheel. The specific material adhesive is a structural adhesive with fatigue resistance and low elastic modulus to adapt to the periodic deformation requirements of the harmonic reducer.

[0043] S2: Simplify model contact relationships. The contact relationships between the component models are simplified, specifically as follows:

[0044] S21: The meshing contact between the flexible wheel and the rigid wheel is replaced by a spring-damped friction model;

[0045] S22: The adhesive contact between the flexible wheel and the smart thin film sensor divides the adhesive area into 6 independent regions, each of which is replaced by a spring damping model;

[0046] The division of the bonding area is based on the deformation law of the flexible wheel. The six independent areas are evenly distributed along the circumference of the flexible wheel, and the spring damping parameters of each area are calibrated according to the mechanical properties (elastic modulus, shear strength) of the bonding material.

[0047] S23: The contact between the flexible wheel and the wave generator is replaced by a spring-damped model;

[0048] A rigid-flexible coupled dynamic model is constructed using the above simplification.

[0049] S3: Obtain Uncertain Load Samples

[0050] Using the optimal Latin hypercube design method in experimental design, variable load samples are extracted within the load range of [0, 30] N / m. The sampling formula is as follows: ;

[0051] in, For the first The first sample One load parameter, For the first The value of the permutation function of the column. A random number within the interval [0, 1] The total number of samples. The value range is 30~100, and the statistical representativeness of the sample is verified by analysis of variance to ensure coverage of the fluctuation scenario within the load range of [0, 30] N / m; this design ensures that the sample points are evenly distributed in the load design space, thus ensuring the statistical validity of the sample.

[0052] S4: Perform dynamic simulation

[0053] The variable load sample obtained in step S3 is used as input and loaded into the wave generator in the rigid-flexible coupling dynamic model. The dynamic simulation is started to obtain the stress, strain, transmission error and wear data during the operation of the harmonic reducer.

[0054] The dynamic simulation was performed using multibody dynamics software, including but not limited to ADAMS and ANSYSMotion. The simulation process took into account the nonlinear elastic deformation of the flexible wheel, the impact effect of tooth meshing, and the damping dissipation of the adhesive region.

[0055] S5: Assess Reliability

[0056] Set safety thresholds for transmission accuracy, tooth wear, strain magnitude, and backlash. Compare the simulation data from step S4 with the safety thresholds to complete the reliability analysis of the harmonic reducer.

[0057] The safety thresholds are set based on the application scenario requirements of the harmonic reducer: in the case of industrial robot joints, the transmission accuracy threshold is ≤0.01mm, the tooth wear threshold is ≤0.005mm, the strain threshold is ≤80% of the material yield strain, and the backlash threshold is ≤1arcmin.

[0058] Example 1

[0059] Reliability Analysis for General-Purpose Six-Axis Joints in Industrial Robots

[0060] 1.1 Scene Background

[0061] This embodiment is for a six-axis joint of an industrial robot with a load capacity of 5kg and a repeatability of ±0.02mm. The harmonic reducer it is equipped with must meet the reliability requirements of 10,000 hours of continuous operation. The load fluctuation during joint operation comes from workpiece gripping and trajectory switching (such as welding and assembly processes). The load range is concentrated in [0, 30] N / m, and the stress and strain of the flexible wheel must be monitored in real time to avoid sudden fracture.

[0062] 1.2 Specific Implementation Steps

[0063] Step 1: Component Model Construction

[0064] Based on the joint space constraints (reducer outer diameter ≤ 80mm), the key parameters of each component are determined as follows:

[0065] Rigid wheel: Material is 45 steel (quenched and tempered), geometric parameters are 120 teeth, module 0.8, tooth width 15mm, rigidity and flexibility are set to fixed rigid body, no meshing required (no number of mesh elements).

[0066] Flexible wheel: The material is 40CrNiMoA (nitrided), the geometric parameters are 118 teeth, 0.8 module, 15mm tooth width, 2mm wall thickness, the rigidity and flexibility properties are flexible body, and the number of mesh elements is 55000.

[0067] Wave generator: material is GCr15 (quenched), geometric parameters are aspect ratio 2:1, maximum diameter 35mm, rigidity and flexibility are flexible body, and the number of mesh elements is 32000;

[0068] The intelligent sensor is made of polyimide and a nickel-chromium strain gauge, with geometric parameters of 4mm × 2mm × 0.15mm, a sampling rate of 1kHz, and a flexible body stiffness. The mesh size is 6000 units. The sensor is bonded to the outer surface of the flexible wheel using epoxy resin adhesive (model E-44), with a bonding area of ​​8mm². 2 It covers the area of ​​maximum deformation of the flexible wheel.

[0069] Step 2: Simplify Contact Relationships

[0070] For general joints with moderate load characteristics, the spring damping parameters for each contact point are set as follows:

[0071] The flexible gear meshes with the rigid gear: the spring stiffness is 2.2 × 10⁻⁶. 5 N / m, damping coefficient is 55 N·s / m, friction coefficient is 0.04 (suitable for industrial gear oil lubrication environment).

[0072] Bonding the flexible wheel to the sensor: Divide the circumference of the flexible wheel into 6 evenly spaced areas (each area corresponding to a 60° central angle), with a spring stiffness of 1.6 × 10⁻⁶ for each area. 4 N / m, damping coefficient is 11 N·s / m;

[0073] Contact between the flexspline and the wave generator: spring stiffness is 3.2 × 10⁻⁶. 5 N / m, damping coefficient is 85 N·s / m.

[0074] Step 3: Acquisition of payload samples

[0075] Load samples were obtained using the optimal Latin hypercube design, with the following parameters:

[0076] The load range is set to [0, 30] N / m (covering no-load to full-load conditions).

[0077] The sample size is 60 (to improve statistical representativeness);

[0078] The sampling verification results showed that the uniformity of sample space filling was >92%, and the coefficient of variation was 2.8%.

[0079] The load sequence simulates the cyclical fluctuation of "no load → light load → full load → light load → no load" (fitting the rhythm of the assembly process).

[0080] Step 4: Dynamic Simulation

[0081] Simulation software: ADAMS 2023 and Abaqus 2023 R2 were used for co-simulation;

[0082] Simulation parameters: simulation time 7200s (simulating 2 hours of continuous operation), time step 0.001s, solution accuracy 1e-6, solver selected is Gear stiff;

[0083] Output data includes five key data categories: transmission accuracy, tooth wear, maximum strain of the flexible wheel, sensor strain, and backlash.

[0084] Step 5: Reliability Assessment

[0085] Safety thresholds (adapting to general joint requirements): Transmission accuracy ≤ 0.012mm; Tooth wear ≤ 0.006mm; Flexible wheel strain ≤ 680με (corresponding to 80% of the material yield strain of 850με); Sensor strain ≤ 500με; Backlash ≤ 1.2arcmin;

[0086] Results statistics: Among the 60 samples, only 2 samples (corresponding to loads of 28~30 N / m) had flexural strain exceeding the threshold, with an exceedance rate of 3.3%. All other indicators met the requirements.

[0087] Conclusion: The harmonic reducer meets the reliability requirements of a general six-axis joint, and it is recommended to avoid long-term operation under loads of 28~30N / m.

[0088] Example 2

[0089] Reliability Analysis for the End Joints of Collaborative Robots

[0090] 2.1 Scene Background

[0091] The end joint of the collaborative robot has a load capacity of ≤3kg and needs to work in collaboration with humans. It has extremely high requirements for "safety (avoiding breakage) and high precision (assembling tiny parts)". The load fluctuation is smooth (without severe impact) and the sensors must have anti-interference capabilities (to avoid false triggering of safety protection).

[0092] 2.2 Specific Implementation Steps

[0093] Step 1: Component Model Construction

[0094] The component parameters are adapted to meet the requirements of lightweight design and high precision, as detailed below:

[0095] Rigid wheel: Material is titanium alloy (TC4), geometric parameters are 80 teeth and 0.6 module, rigid body properties, no meshing required;

[0096] Flexible wheel: The material is titanium alloy (TC4, yield strength 860MPa), the geometric parameters are 78 teeth, wall thickness 1.5mm, the rigidity-flexibility property is flexible body, and the number of mesh elements is 48000;

[0097] Wave generator: The material is titanium alloy (TC4), the geometric parameters are a maximum diameter of 28mm, the rigidity and flexibility properties are flexible body, and the number of mesh elements is 28000;

[0098] The intelligent sensor is made of flexible polyimide and a high-precision strain gauge (error ±0.1%), with temperature compensation. Its geometric parameters are 3mm × 1.5mm × 0.1mm, and it is a flexible body with a mesh size of 5000 elements. The sensor is bonded with medical-grade epoxy adhesive (low volatility, non-toxic), with a bonding area of ​​4.5mm². 2 To avoid polluting the work environment.

[0099] Step 2: Simplify Contact Relationships

[0100] To address the characteristics of light load and low impact, the contact stiffness is reduced to match the actual deformation, with the following parameters:

[0101] The meshing of the flexible wheel and the rigid wheel: the spring stiffness is 1.8 × 10⁻⁶. 5 N / m, damping coefficient is 45 N·s / m, friction coefficient is 0.03 (compatible with long-life grease).

[0102] Bonding of the flexible wheel to the sensor: The circumference of the flexible wheel is divided into 6 regions, and the spring stiffness of each region is 1.2 × 10⁻⁶. 4 N / m, damping coefficient is 8 N·s / m (the adhesive layer is thinner, and the stiffness is reduced accordingly).

[0103] Contact between the flexspline and the wave generator: spring stiffness is 2.8 × 10⁻⁶ 5 N / m, damping coefficient is 70 N·s / m.

[0104] Step 3: Acquisition of payload samples

[0105] The load range is set to [0, 15] N / m (corresponding to the maximum load at the end of the collaborative robot).

[0106] The sample size is 50.

[0107] The load sequence simulates a smooth fluctuation from "no load → light load (5~10N / m, continuous operation) → no load", with no sudden load changes;

[0108] Sampling verification: Sample space filling uniformity > 95%, variance coefficient of variation = 2.5%.

[0109] Step 4: Dynamic Simulation

[0110] Simulation software: ADAMS 2023 + ANSYS 2023 R2 co-simulation;

[0111] Simulation parameters: Simulation time 10800s (simulating 3 hours of continuous assembly operation), time step 0.001s, solution accuracy 1e-6;

[0112] Key setting: Enable "Strain Sudden Change Monitoring" (if the sensor strain exceeds the threshold, trigger simulation pause to simulate the collaborative robot's safety protection).

[0113] Output data: In addition to transmission accuracy, tooth wear, flexspline strain, sensor strain, and backlash, a new index, "sensor strain stability," has been added.

[0114] Step 5: Reliability Assessment

[0115] Safety thresholds (emphasizing high precision and safety): Transmission accuracy ≤ 0.008 mm; Tooth wear ≤ 0.003 mm; Flexible wheel strain ≤ 600 με (corresponding to 70% of the material's yield strain, with a higher safety margin); Sensor strain ≤ 400 με; Backlash ≤ 0.8 arcmin; Sensor strain stability fluctuation ≤ 50 με.

[0116] Results statistics: Among the 50 samples, none of the indicators exceeded the threshold, and the sensor strain fluctuation amplitude was ≤35με;

[0117] Conclusion: Harmonic reducers meet the high precision and safety requirements of collaborative robot end joints and can be directly used in human-robot collaborative assembly scenarios.

[0118] Example 3

[0119] Reliability Analysis of the Waist Joint of a Heavy-Duty Handling Robot

[0120] 3.1 Scene Background

[0121] The waist joint of the heavy-duty handling robot has a load capacity of ≥50kg and needs to withstand "static heavy load + dynamic impact" (such as workpiece lifting and emergency stop); the harmonic reducer is prone to fatigue wear of the flexible wheel or peeling of the adhesive layer due to excessive load, and the reliability requirements focus on "fatigue resistance and overload resistance", and the sensor needs to withstand high stress (to avoid failure).

[0122] 3.2 Specific Implementation Steps

[0123] Step 1: Component Model Construction

[0124] Component parameters are adapted to heavy-load requirements, and structural strength is enhanced, as detailed below:

[0125] Rigid wheel: The material is high-strength steel (42CrMo), the geometric parameters are 150 teeth, module 1.2, tooth width 25mm, the rigidity and flexibility properties are rigid body, no meshing is required;

[0126] Flexible wheel: The material is high-strength steel (42CrMo, yield strength 1080MPa), surface hardened (hardness HRC50), geometric parameters are 148 teeth and 4mm wall thickness, rigidity and flexibility are flexible body, and the number of mesh elements is 70000 (the mesh is refined to capture high stress areas).

[0127] Wave generator: The material is high-strength steel (42CrMo), with reinforcing ribs, the geometric parameters are a maximum diameter of 60mm, the rigidity-flexibility property is a flexible body, and the number of mesh elements is 45000;

[0128] The intelligent sensor is made of a metal-based strain gauge (high temperature and high stress resistant), with geometric parameters of 8mm × 5mm × 0.3mm, a measurement range of ±2000με, and a flexible body stiffness property. The mesh size is 12000 elements. The sensor is bonded with high-strength structural adhesive (shear strength ≥30MPa) over a 40mm² area. 2 The adhesive layer is 0.2mm thick (to enhance peel resistance).

[0129] Step 2: Simplify Contact Relationships

[0130] To address heavy load and impact characteristics, the contact stiffness and damping are improved, with the following parameters:

[0131] The flexible gear meshes with the rigid gear: the spring stiffness is 3.5 × 10⁻⁶. 5 N / m, damping coefficient is 120 N·s / m (increases damping to absorb impact energy), friction coefficient is 0.06 (suitable for heavy-duty gear oil).

[0132] Bonding of the flexible wheel to the sensor: Divide the circumference of the flexible wheel into 6 regions, with a spring stiffness of 2.5 × 10⁻⁶ in each region. 4 N / m, damping coefficient is 20 N·s / m (thicker adhesive layer, stiffness and damping are improved simultaneously).

[0133] Contact between the flexspline and the wave generator: spring stiffness is 5.0 × 10⁻⁶. 5 N / m, damping coefficient is 150 N·s / m.

[0134] Step 3: Acquisition of payload samples

[0135] The load range is set to [10, 50] N / m (covering no-load lifting to full-load handling conditions).

[0136] The sample size is 80 (increased to cover impact load scenarios).

[0137] The load sequence includes a typical working condition of "static heavy load (30~40N / m, lasting 10s) → dynamic impact (45~50N / m, instantaneous 0.5s)";

[0138] Sampling verification: Sample space filling uniformity > 90%, variance coefficient of variation = 3.5%.

[0139] Step 4: Dynamic Simulation

[0140] Simulation software: ADAMS 2023 + ANSYS 2023 R2 co-simulation;

[0141] Simulation parameters: Simulation time 3600s (simulating 1 hour of heavy-load handling, including 20 impact loads), time step 0.001s, solution accuracy 1e-6;

[0142] Key setting: Include "flexible wheel fatigue cumulative damage" (based on Miner's linear cumulative damage theory);

[0143] Output data: In addition to conventional transmission accuracy, tooth wear, flexure strain, and backlash, a new index, "adhesive layer shear stress," has been added (to assess adhesive reliability).

[0144] Step 5: Reliability Assessment

[0145] Safety thresholds (focusing on overload and fatigue resistance): Transmission accuracy ≤ 0.02 mm; Tooth wear ≤ 0.01 mm; Flexible wheel strain ≤ 800 με (corresponding to 75% of the material's yield strain); Adhesive layer shear stress ≤ 20 MPa (corresponding to 67% of the adhesive layer's shear strength); Backlash ≤ 2.0 arcmin; Simultaneously, the cumulative fatigue damage value of the flexible wheel must be < 1.0 (threshold 1.0, not reaching fatigue failure).

[0146] Results statistics: Among the 80 samples, the shear stress of the adhesive layer in 3 samples (corresponding to impact loads of 48~50 N / m) exceeded the threshold, with an exceedance rate of 3.75%, and the cumulative fatigue damage value of the flexible wheel was <0.8.

[0147] Conclusion: The harmonic reducer meets the requirements of heavy-duty lumbar joints. It is recommended to control the single impact load within 45 N / m to extend its service life.

[0148] The examples provided in this invention are not intended to limit the implementation. Those skilled in the art will recognize that various variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations, and any obvious variations or modifications derived therefrom are still within the scope of this invention.

Claims

1. A reliability analysis method for a robot-embedded intelligent harmonic reducer, characterized in that, Includes the following steps: S1: Establish a model of the harmonic reducer components and construct three-dimensional models of the flexible wheel, rigid wheel, wave generator, and intelligent thin-film sensor respectively. The rigid wheel adopts a rigid body model and is set to be fixed. The flexible wheel, wave generator, and intelligent thin-film sensor all adopt flexible body models to simulate actual deformation. The intelligent thin-film sensor is used to collect stress and strain data of the flexible wheel and is bonded to the surface of the flexible wheel with a specific material. S2: Simplify model contact relationships. The contact relationships between the component models are simplified, specifically as follows: S21: The meshing contact between the flexible wheel and the rigid wheel is replaced by a spring-damped friction model; S22: The adhesive contact between the flexible wheel and the smart thin film sensor divides the adhesive area into 6 independent regions, each of which is replaced by a spring damping model; S23: The contact between the flexible wheel and the wave generator is replaced by a spring-damped model; A rigid-flexible coupled dynamic model is constructed using the above simplification. S3: Obtain Uncertain Load Samples Using the optimal Latin hypercube design method in experimental design, variable load samples are extracted within the load range of [0, 30] N / m. The sampling formula is as follows: ; in, For the first The first sample One load parameter, For the first The value of the permutation function of the column. A random number within the interval [0, 1] This represents the total number of samples; this design ensures that the sample points are evenly distributed across the load design space, guaranteeing the statistical validity of the samples. S4: Perform dynamic simulation The variable load sample obtained in step S3 is used as input and loaded into the wave generator in the rigid-flexible coupling dynamic model. The dynamic simulation is started to obtain stress, strain, transmission error, backlash and wear data during the operation of the harmonic reducer. S5: Assess Reliability Set safety thresholds for transmission accuracy, tooth wear, strain magnitude, and backlash. Compare the simulation data from step S4 with the safety thresholds to complete the reliability analysis of the harmonic reducer.

2. The reliability analysis method for a robot-embedded intelligent harmonic reducer according to claim 1, characterized in that, In step S1, the material selection of the intelligent thin film sensor is a flexible sensing material that matches the deformation compatibility of the flexure wheel, and the specific material adhesive is a structural adhesive with fatigue resistance and low elastic modulus to adapt to the periodic deformation requirements of the harmonic reducer.

3. The reliability analysis method for a robot-embedded intelligent harmonic reducer according to claim 1, characterized in that, In step S22, the division of the bonding area is based on the deformation law of the flexible wheel. The six independent areas are evenly distributed along the circumference of the flexible wheel, and the spring damping parameters of each area are calibrated according to the mechanical properties of the bonding material.

4. The reliability analysis method for a robot-embedded intelligent harmonic reducer according to claim 1, characterized in that, In step S3, the total number of samples The value range is 30~100, and the statistical representativeness of the sample is verified by analysis of variance to ensure coverage of fluctuation scenarios within the load range of [0, 30] N / m.

5. The reliability analysis method for a robot-embedded intelligent harmonic reducer according to claim 1, characterized in that, In step S4, the dynamic simulation is performed using multibody dynamics software, and the nonlinearity of the elastic deformation of the flexible wheel, the impact effect of tooth meshing, and the damping dissipation of the adhesive region are taken into account during the simulation.

6. The reliability analysis method for a robot-embedded intelligent harmonic reducer according to claim 1, characterized in that, In step S5, the safety threshold is set according to the application scenario requirements of the harmonic reducer.

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