Service life testing method and system for acetabular cup

Abstract

The invention discloses a method and system for testing the service life of an acetabular cup, and the method comprises the steps: outputting a dynamic stress distribution cloud picture according to the three-dimensional structure parameters and material attributes of an acetabular cup implant; outputting a damage evolution feature vector based on the dynamic stress distribution cloud picture; outputting a cross-scale fatigue damage prediction model according to the damage evolution feature vector; outputting a dynamic damage accumulation index based on the cross-scale fatigue damage prediction model; and according to the dynamic damage accumulation index, optimizing a test parameter space through reinforcement learning, generating an accelerated life test protocol, driving a six-degree-of-freedom test board to execute multi-modal coupling loading, and outputting a life prediction confidence interval report. According to the embodiment of the invention, more accurate service life prediction and reliability evaluation can be realized.

Description

A method and system for testing the service life of an acetabular cup. Technical Field

[0001] This invention belongs to the field of testing technology, and in particular to a method and system for testing the service life of an acetabular cup. Background Technology

[0002] In total hip arthroplasty, the acetabular cup, as a critical load-bearing component, directly impacts the prosthesis's lifespan and the patient's quality of life through its long-term performance. Traditional acetabular cup lifespan testing methods primarily rely on simplified load spectrum mechanical fatigue tests, which struggle to realistically simulate multiaxial dynamic loads and fretting wear mechanisms under complex physiological conditions, leading to significant discrepancies between in vitro test results and actual clinical failure modes. Existing technologies, prediction models based on single mechanical parameters (such as contact stress) cannot effectively characterize the evolution of microscopic damage in materials, while non-destructive testing methods such as acoustic emission still suffer from insufficient accuracy in noise suppression and feature extraction. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for testing the service life of acetabular cups, in order to overcome the shortcomings of the prior art and achieve more accurate service life prediction and reliability assessment.

[0004] One embodiment of this application provides a method for testing the service life of an acetabular cup. The method includes: based on the three-dimensional structural parameters and material properties of the acetabular cup implant, using a multi-axis dynamic load simulation algorithm, and by fusing gait phase features and bone-prosthesis interface micro-motion parameters, outputting a dynamic stress distribution cloud map; based on the dynamic stress distribution cloud map, using a time-frequency joint analysis method of acoustic emission signals to capture the microscopic damage characteristics of the material, extracting crack initiation frequency band energy through wavelet packet decomposition, and outputting a damage evolution feature vector; based on the damage evolution feature vector, constructing a constitutive model that integrates the topology of microscopic defects, using a transfer learning algorithm to map in vitro test data to the in vivo service environment, and outputting a cross-scale fatigue damage prediction model; based on the cross-scale fatigue damage prediction model, using a non-Markov chain Monte Carlo method to simulate the long-term wear accumulation effect, combining lubricant film degradation parameters to iteratively calculate the failure probability, and outputting a dynamic damage accumulation index; based on the dynamic damage accumulation index, optimizing the test parameter space through reinforcement learning, generating an accelerated life test protocol and driving a six-degree-of-freedom test bench to perform multimodal coupled loading, and outputting a life prediction confidence interval report.

[0005] Optionally, the step of using a multi-axis dynamic load simulation algorithm based on the three-dimensional structural parameters and material properties of the acetabular cup implant to output a dynamic stress distribution cloud map by fusing gait phase features and micro-motion parameters of the bone-prosthesis interface includes: extracting curvature distribution parameters and material Poisson's ratio from the CT point cloud data of the acetabular cup, and generating a basic dynamic load spectrum by combining the time-varying curve of joint force acquired by the gait analyzer; injecting the micro-motion friction coefficient of the bone-prosthesis interface based on the basic dynamic load spectrum, calculating the contact stress field through a nonlinear finite element solver to obtain the basic contact stress field; and dynamically adjusting the load direction vector based on the basic contact stress field and gait phase features, and outputting a spatiotemporally continuous dynamic stress distribution cloud map by using stress gradient-driven adaptive mesh refinement technology.

[0006] Optionally, the step of capturing the microscopic damage characteristics of the material using a time-frequency joint analysis method of acoustic emission signals based on the dynamic stress distribution cloud map, extracting the crack initiation frequency band energy through wavelet packet decomposition, and outputting a damage evolution feature vector includes: locating the maximum principal stress region according to the dynamic stress cloud map and simultaneously acquiring the original acoustic emission signal stream of that region; inputting the original acoustic emission signal stream into a dual-tree complex wavelet transform and separating the crack propagation component through a time-frequency ridge tracking algorithm; performing eight-level wavelet packet decomposition on the crack propagation component and extracting energy mutation feature points in the 4-8MHz frequency band; fusing the energy amplitude, frequency band entropy value, and mutation feature point density to construct a three-dimensional feature vector and outputting a damage evolution feature vector.

[0007] Optionally, the step of constructing a constitutive model that integrates the micro-defect topology based on the damage evolution feature vector, and using a transfer learning algorithm to map in vitro test data to the in vivo service environment to output a cross-scale fatigue damage prediction model includes: reconstructing the phase space of the damage evolution feature vector, extracting the topological invariants of micro-defects through persistent homology analysis; fusing the topological invariants with the micropore distribution data observed by scanning electron microscopy to construct a defect topology-mechanical response mapping matrix; initializing a graph neural network constitutive model based on the mapping matrix, loading an in vitro accelerated test dataset to train the primary model; designing a topologically variable adaptive transfer framework, adjusting the weight parameters of the primary model through in vivo and in vitro environmental difference factors, and integrating the physiological load spectrum for adversarial fine-tuning to output a cross-scale fatigue damage prediction model.

[0008] Optionally, the step of using the non-Markov chain Monte Carlo method to simulate the long-term wear accumulation effect based on the cross-scale fatigue damage prediction model, and iteratively calculating the failure probability by combining the lubricating fluid film degradation parameters, and outputting a dynamic damage accumulation index includes: generating a material damage state transition probability matrix based on the prediction model, and constructing a state transition probability matrix; converting the lubricating fluid film thickness sensor data into degradation rate parameters and establishing a liquid film degradation constraint equation; simulating the damage path of tens of millions of gait cycles using an adaptive Monte Carlo sampler based on the state transition probability matrix and the liquid film degradation constraint equation; calculating the time-varying coupled failure probability through importance sampling, and outputting a dynamic damage accumulation index matrix.

[0009] Optionally, the step of optimizing the test parameter space through reinforcement learning based on the dynamic damage accumulation index, generating an accelerated life test protocol, driving a six-degree-of-freedom test bench to perform multimodal coupled loading, and outputting a life prediction confidence interval report includes: using the dynamic damage accumulation index matrix as the reinforcement learning environment state, defining the loading parameters as the action space; using a proximal policy optimization algorithm to explore the parameter space, maximizing the acceleration factor with damage mechanism equivalence as a constraint; generating a multimodal loading protocol instruction set containing the axial pressure-rotational torque coupling relationship; driving the six-degree-of-freedom test bench to execute the protocol instructions, and collecting damage verification data in real time through fiber optic grating sensors; constructing a Bayesian update model based on the damage verification data, and outputting a life prediction report with a 95% confidence interval and a failure mode heatmap.

[0010] Another embodiment of this application provides a lifespan testing system for acetabular cups. The system includes: a fusion module, used to output a dynamic stress distribution cloud map by fusing gait phase features and bone-prosthesis interface micro-motion parameters based on the three-dimensional structural parameters and material properties of the acetabular cup implant using a multi-axis dynamic load simulation algorithm; a capture module, used to capture the microscopic damage features of the material based on the dynamic stress distribution cloud map using a time-frequency joint analysis method of acoustic emission signals, extracting crack initiation frequency band energy through wavelet packet decomposition, and outputting a damage evolution feature vector; and a mapping module, used to construct a fusion mapping module based on the damage evolution feature vector. A constitutive model of microscopic defect topology is used to map in vitro test data to the in vivo service environment using a transfer learning algorithm, outputting a cross-scale fatigue damage prediction model. A simulation module is used to simulate the long-term wear accumulation effect based on the cross-scale fatigue damage prediction model using a non-Markov chain Monte Carlo method, and iteratively calculates the failure probability by combining lubricant film degradation parameters, outputting a dynamic damage accumulation index. An output module is used to optimize the test parameter space through reinforcement learning based on the dynamic damage accumulation index, generate an accelerated life test protocol, drive a six-degree-of-freedom test bench to perform multimodal coupled loading, and output a life prediction confidence interval report.

[0011] Another embodiment of this application provides a storage medium storing a computer program, wherein the computer program is configured to execute the method described in any of the preceding claims when running.

[0012] Another embodiment of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method described in any of the preceding claims.

[0013] Compared with existing technologies, the present invention provides a method for testing the service life of acetabular cups. Based on the three-dimensional structural parameters and material properties of the acetabular cup implant, it outputs a dynamic stress distribution cloud map; based on the dynamic stress distribution cloud map, it outputs a damage evolution feature vector; based on the damage evolution feature vector, it outputs a cross-scale fatigue damage prediction model; based on the cross-scale fatigue damage prediction model, it outputs a dynamic damage accumulation index; based on the dynamic damage accumulation index, it optimizes the test parameter space through reinforcement learning, generates an accelerated life testing protocol, drives a six-degree-of-freedom test bench to perform multimodal coupled loading, and outputs a service life prediction confidence interval report, thereby enabling more accurate service life prediction and reliability assessment. Attached Figure Description

[0014] Figure 1 is a hardware block diagram of a computer terminal for a method of testing the service life of an acetabular cup according to an embodiment of the present invention; Figure 2 is a flowchart of a method of testing the service life of an acetabular cup according to an embodiment of the present invention; Figure 3 is a structural schematic diagram of a system for testing the service life of an acetabular cup according to an embodiment of the present invention. Detailed Implementation

[0015] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0016] The present invention first provides a method for testing the service life of an acetabular cup. This method can be applied to electronic devices, such as computer terminals, specifically ordinary computers.

[0017] The following detailed description uses a computer terminal as an example. Figure 1 is a hardware structure block diagram of a computer terminal for a method of testing the service life of an acetabular cup according to an embodiment of the present invention. As shown in Figure 1, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0018] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any method for testing the lifespan of the acetabular cup.

[0019] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0020] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When executed by a processor, the computer program can enable the processor to perform any method for testing the lifespan of the acetabular cup.

[0021] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that the structure shown in Figure 1 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0022] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0023] Referring to Figure 2, an embodiment of the present invention provides a method for testing the service life of an acetabular cup, which may include the following steps: S201, based on the three-dimensional structural parameters and material properties of the acetabular cup implant, a multi-axis dynamic load simulation algorithm is used to output a dynamic stress distribution cloud map by fusing gait phase features and bone-prosthesis interface micro-motion parameters; specifically, curvature distribution parameters and material Poisson's ratio can be extracted from the CT point cloud data of the acetabular cup, and a basic dynamic load spectrum can be generated by combining the time-varying curve of joint force collected by the gait analyzer; CT point cloud data processing: high-precision CT (Computed Tomography) scan data (0.5 mm slice thickness) of the acetabular cup implant is obtained, and a three-dimensional geometric model is reconstructed using point cloud processing software (such as Mimics). Extraction of key curvature distribution parameters: the Gaussian curvature (describing the degree of surface curvature) of the inner surface of the acetabular cup is calculated, and high curvature areas (such as the curvature value of the acetabular crown area > 0.15 mm) are divided. -1) and low curvature areas (<0.08 mm) -1 ).

[0024] Poisson's ratio (the lateral deformation capacity of a material) is assigned values ​​based on the material library: 0.34 for titanium alloys, 0.22 for ceramics, and 0.46 for ultra-high molecular weight polyethylene.

[0025] Example: The Gaussian curvature of the mortis region in a titanium alloy mortis cup is 0.18 mm. -1 Poisson's ratio is 0.34.

[0026] Gait joint force data fusion: An optical gait analysis system (such as the Vicon MX system) was used to collect time-varying curves of joint forces during patient walking (sampling rate 1000 Hz). Key parameters include: Peak vertical joint force (unit: Newton, N): Peak hip joint force under normal gait can reach 3-5 times body weight (e.g., peak force of a 70 kg patient is about 2500 Newtons).

[0027] Force direction angle (unit: degrees): Sagittal plane flexion-extension angle range -15° to +30°.

[0028] The gait cycle is discretized into 8 phases (e.g., heel strike phase 15%, mid-stance phase 35%, push-off phase 50%), and each phase is assigned a corresponding mechanical parameter.

[0029] Basic dynamic load spectrum synthesis: The spatiotemporal mapping engine aligns curvature parameters, Poisson's ratio, and joint force curves according to gait phase: high loads are superimposed on high curvature regions (such as the acetabular dome region bearing 80% of the peak force in the mid-standing phase).

[0030] Poisson's ratio corrected load distribution (the load diffusion angle of the polyethylene region with a Poisson's ratio of 0.46 is increased by 12°).

[0031] Output load spectrum dimensions: Time axis: 2000 gait cycle sampling points (1.2 seconds per cycle).

[0032] Spatial axis: Force vector at 1024 nodes on the surface of the mortar (magnitude in Newtons, direction in degrees).

[0033] Example: Load at node 512 (rear wall of the mortis) during the push-off period: force value 1800 Newtons, direction angle +25°.

[0034] Based on the fretting friction coefficient of the bone-implant interface injected with the basic dynamic load spectrum, the contact stress field is calculated by a nonlinear finite element solver to obtain the basic contact stress field; Fretting friction coefficient modeling: The fretting behavior of the bone-implant interface is calibrated by a friction testing machine: Static friction coefficient: 0.65 for titanium alloy-bone cement interface, and 0.28 for ceramic-bone interface.

[0035] Dynamic friction coefficient: It decreases to 70% of the static friction coefficient under gait load (e.g., titanium alloy-bone cement reduces it to 0.45).

[0036] The coefficient of friction is dynamically adjusted according to the gait phase: the static friction coefficient is used during the heel contact phase, and the dynamic friction coefficient is used during the swing phase.

[0037] Nonlinear finite element solution: Construct a three-dimensional layered finite element model of acetabular cup-bone cement-bone tissue (total number of elements > 500,000): Material constitutive model: Bone cement is an elastoplastic model (yield stress 65 MPa), and cancellous bone is a porous elastic model (elastic modulus 0.8 GPa).

[0038] Solver settings (using the Abaqus Explicit module): time step 0.0001 seconds, satisfying explicit dynamic stability conditions (Courant conditions).

[0039] Contact algorithm: Surface-to-Surface Contact penalty function method, normal hard contact, tangential friction coefficient assignment.

[0040] Key output: Contact Stress Field: Normal pressure (in megapascals) at the acetabular cup-bone cement interface.

[0041] Shear stress field: interfacial tangential stress (unit: megapascals).

[0042] Basic contact stress field generation: Output 120 frames of stress distribution map within a single step cycle (one frame every 10 milliseconds).

[0043] Example results: A stress concentration zone appeared in the upper posterior quadrant of the acetabular cup during the mid-standing period (peak contact stress 38 MPa, shear stress 12 MPa).

[0044] Based on the basic contact stress field and combined with the gait phase characteristics, the load direction vector is dynamically adjusted, and an adaptive mesh refinement technique driven by stress gradient is adopted to output a spatiotemporally continuous dynamic stress distribution cloud map.

[0045] Dynamic adjustment of load direction vector: Gait phase feature library defines the mechanical rules of 8 phases: Heel strike phase (0-15% cycle): Load direction is vertically downward (sagittal angle 0°).

[0046] Mid-standing phase (15-40% cycle): Load direction tilted forward 10° (simulating body forward movement).

[0047] Push-off period (40-60% cycle): The load direction is tilted back 15° (simulating the push-off reaction force).

[0048] Real-time correction of load direction: Based on the pelvic rotation angle in optical gait data (accuracy 0.1°), the direction angle is dynamically fine-tuned (range ±5°).

[0049] Adaptive mesh refinement technology stress gradient threshold triggering mechanism: when the stress gradient between elements is greater than 15 MPa / mm, local mesh refinement is triggered.

[0050] Encryption levels: Level 1 encryption (unit side length 0.5 mm), Level 2 encryption (0.2 mm).

[0051] Encryption algorithm process: Step ①: Identify high gradient regions (such as the boundary where the stress changes abruptly from 30 MPa to 50 MPa).

[0052] Step 2: Recursively split the target unit using the quadtree subdivision method.

[0053] Step 3: The new nodal stress values ​​are interpolated using the Moving Least Squares method.

[0054] Example: The stress concentration area at the edge of the mortar cup (original element 1 mm) is densified to 0.2 mm, adding approximately 1200 new nodes.

[0055] Dynamic stress distribution cloud map synthesis: Spatiotemporal continuity guarantee: Time dimension: Inter-frame data is completed by cubic spline interpolation (interpolated from frame 120 to frame 1200).

[0056] Spatial dimension: After encryption, the number of model nodes increased to 850,000, and the cloud map resolution was improved to 0.1 mm.

[0057] Output format: Four-dimensional tensor (time × spatial X coordinate × spatial Y coordinate × stress value).

[0058] The data volume per cycle is 12GB (1.8GB after lossless compression).

[0059] Typical cloud map features: Dynamic stress migration occurs in the lower quadrant in front of the acetabular cup during the push-off period (the stress peak shifts from 38 MPa to 42 MPa, with a migration path length of 8 mm).

[0060] S202, based on the dynamic stress distribution cloud map, the time-frequency joint analysis method of acoustic emission signal is used to capture the micro-damage characteristics of the material, and the energy of the crack initiation frequency band is extracted by wavelet packet decomposition to output the damage evolution feature vector; specifically, the maximum principal stress region can be located according to the dynamic stress cloud map, and the original acoustic emission signal flow of the region can be collected simultaneously; Stress cloud map analysis and positioning: Load the spatiotemporally continuous dynamic stress distribution cloud map (four-dimensional tensor, resolution 0.1 mm / frame), and locate the high-risk area through the principal stress extreme value screening algorithm: set the threshold: the maximum principal stress value > 60% of the material yield strength (e.g., the yield strength of titanium alloy is 800 MPa, and the threshold is set to 480 MPa).

[0061] Spatial clustering analysis: Regions with peak stress appearing in 5 or more consecutive frames are marked as monitoring target areas (minimum target area diameter 1.5 mm).

[0062] Example: A ceramic acetabular cup exhibits a target area with a diameter of 2.1 mm (peak stress 620 MPa) in the acetabular crown region during the push-off phase.

[0063] Acoustic emission signal synchronous acquisition system: Deploy a wideband piezoelectric sensor array (frequency response range 50 kHz to 10 MHz, sensitivity -65 dB) on the surface of the target area: Sensor layout density: 4 probes per square centimeter (covering the target area and a 3 mm buffer zone around it).

[0064] Sampling parameters: sampling rate 20 MHz (Nyquist frequency is 10 MHz), resolution 16 bits.

[0065] Synchronization triggering mechanism: The stress cloud map timestamp (accuracy 0.1 milliseconds) is aligned with the acoustic emission acquisition clock to ensure that the time domain synchronization error is < ±5 microseconds.

[0066] Raw signal stream preprocessing: Bandpass filtering: retain the effective frequency band from 100 kHz to 9 MHz (filter out mechanical vibration noise).

[0067] Amplitude normalization: Based on the sensor range ±10V, eliminate probe sensitivity differences.

[0068] Output data: Approximately 4 million raw acoustic emission data points are generated per second for each target area (single-channel data rate 40MB / s).

[0069] The original acoustic emission signal stream is input into a dual-tree complex wavelet transform, and the crack propagation component is separated by a time-frequency ridge tracking algorithm. The dual-tree complex wavelet transform (DTCTWT) principle is to use two parallel real wavelet filter banks (such as Q-Shift filters) to process the real and imaginary parts of the signal respectively.

[0070] Advantages: It overcomes the frequency band aliasing problem of traditional wavelet transform while preserving the integrity of phase information.

[0071] Parameter settings: Number of decomposition layers: 6 (covering the 0.3-8 MHz biomechanical damage characteristic frequency band).

[0072] Basis function: Near-Symmetric Wavelet optimizes time-frequency resolution.

[0073] Time-Frequency Ridge Tracking Algorithm: Ridge Extraction Process: Time-Frequency Energy Map Generation: Calculate the modulus square of wavelet coefficients (energy density, unit: volts) 2 / hertz).

[0074] Local peak detection: Identify energy extreme points within a sliding window in the time-frequency plane (time window width 1 ms, frequency window width 0.5 MHz).

[0075] Ridge connection: Based on the Greedy Algorithm, the nearest peak values ​​at adjacent time points are connected to form continuous ridges.

[0076] Crack feature screening rules: Ridge slope > 80 kHz / millisecond (indicating rapid crack propagation).

[0077] The center frequency is located at 1-3 MHz (typical ceramic / polyethylene crack band).

[0078] Crack component separation output: Reconstruction algorithm: retain the wavelet coefficients corresponding to the ridges that satisfy the characteristics, and invert them to the time domain signal.

[0079] Output data: The sampling rate is maintained at 20 MHz, and the amplitude unit is microvolt (μV).

[0080] Approximately 15 crack event signals are output per second per target area (average duration 50 microseconds / segment).

[0081] Wavelet packet decomposition of crack propagation component is performed at eight levels to extract energy mutation feature points in the 4-8MHz frequency band; Wavelet packet decomposition: Decomposition structure: complete decomposition at eight levels (total number of frequency bands 256).

[0082] Basis function selection: Daubechies 8 (DB8) wavelet, which combines tight support and smoothness.

[0083] Frequency band allocation rule: 8th layer sub-band bandwidth = sampling rate / 2 8 = 20 MHz / 256 ≈ 78.125 kHz.

[0084] Target frequency band location: 4-8 MHz corresponds to sub-bands 51-102 of layer 8 (numbered starting from 0).

[0085] Energy mutation feature detection: Energy time-varying curve generation: Calculate the signal energy of each sub-band: Energy = Sum of squares of sampling point amplitude / Window length (window width 2 ms, sliding step 0.1 ms).

[0086] Mutation point identification algorithm: Baseline energy calculation: Take the 90th percentile of the energy of the first 100 windows as the baseline.

[0087] Mutation threshold setting: 3 standard deviations of the baseline energy (e.g., baseline energy 5 volts) 2 Standard deviation 1.2 volts 2 → Threshold 8.6 volts 2 ).

[0088] Continuous triggering mechanism: Three consecutive windows exceeding the threshold are considered valid mutations (avoiding false alarms due to noise).

[0089] Example: An energy abrupt change (peak 22 volts) was detected in the 5.2 MHz subband of a polyethylene cup crack. 2 Baseline 4 volts 2 ).

[0090] Spatiotemporal markers for feature points: Recording parameters: abrupt change time (accuracy 0.1 milliseconds); center frequency of the sub-band (e.g., 5.2 MHz ± 39 kHz); abrupt change energy amplitude (unit: volts). 2 ).

[0091] Output: On average, 2-4 feature points are extracted from a single crack event.

[0092] A three-dimensional feature vector is constructed by fusing energy amplitude, frequency band entropy, and abrupt change feature point density, and the damage evolution feature vector is output.

[0093] 3D Feature Extraction: Energy Amplitude: Extraction Rule: Take the peak energy value (in volts) within the abrupt change window. 2 After logarithmic compression, the logarithm is normalized to the [0,1] interval (the logarithm is taken as the base 10 and then divided by the preset maximum value).

[0094] Band Entropy: Concept: Measures the degree of disorder in energy distribution within the 4-8 MHz band (the higher the entropy, the more diffuse the damage).

[0095] Calculation method: Calculate the energy percentage of subbands 51-102 (a total of 52 subbands) and use the Shannon entropy formula (calculate -p×log2(p) for each subband energy percentage p and then sum them).

[0096] Output range: Typical entropy of polyethylene mortar cup 1.8-3.2 (maximum theoretical value log2(52)≈5.7).

[0097] Mutation point density (Density): Definition: The number of mutation feature points per unit time (unit: points / second).

[0098] Statistical window length: 1 second (including 2000 analysis windows).

[0099] Example: The fatigue density of a ceramic mortar cup can reach 120 points per second.

[0100] Feature vector construction: Data alignment: Using 1 second as the time unit, synchronously integrate the following within the time period: maximum energy amplitude (take the peak value within the second); average frequency band entropy value (average value within the second); and mutation point density (count within the second).

[0101] Vector format: [energy amplitude, bandwidth entropy, mutation density].

[0102] Example vector: [0.87, 2.45, 98].

[0103] Damage evolution feature vector output: Time resolution: One three-dimensional vector is output per second.

[0104] Quality control: Eliminate invalid time periods with a signal-to-noise ratio of <20 dB.

[0105] Mark clinical abnormal events (e.g., trigger an alarm when density mutation > 50%).

[0106] Typical application: In a titanium alloy acetabular cup, the eigenvector evolved from [0.32, 1.92, 40] to [0.79, 2.81, 105] after 3 million cycles of testing, indicating damage accumulation.

[0107] S203, based on the damage evolution feature vector, construct a constitutive model that integrates the topology of micro-defects, and use a transfer learning algorithm to map the in vitro test data to the in vivo service environment, outputting a cross-scale fatigue damage prediction model; specifically, the phase space of the damage evolution feature vector can be reconstructed, and the topological invariants of micro-defects can be extracted through persistent homology analysis; Phase space reconstruction technology: input the damage evolution feature vector (three-dimensional array: energy amplitude, frequency band entropy value, mutation point density) generated per second, and use the time-delay embedding method to map it to a high-dimensional phase space.

[0108] Time delay parameter (τ): determined by autocorrelation function as 0.5 seconds (e.g., the zero-crossing interval of the energy amplitude sequence).

[0109] Embedding dimension (m): Calculated as 8 dimensions based on the False Nearest Neighbors algorithm (e.g., the energy amplitude sequence of a titanium alloy mortar cup needs to be fully represented in 8-dimensional space).

[0110] The reconstructed phase space trajectory reveals the damage evolution law: Example: The phase space trajectory of the ceramic acetabular cup is spirally diffused (initially 0.2 radius, diffused to 1.8 radius in the later stage of fatigue), which indicates the accelerated crack propagation.

[0111] Persistent Homology Analysis: Microscopic Defect Topology Modeling: Treating the phase space point cloud as a topological space, using the Euclidean distance of the damage vector as a metric (unit: dimensionless normalized distance).

[0112] Constructing the Filtration Complex: Starting with a minimum distance threshold of 0.1, gradually increasing it to 1.0 to generate a sequence of pore structure evolution.

[0113] Topological invariant extraction: Betti number: records the number of holes at different scales (e.g., 5 isolated micropores are detected at a distance threshold of 0.3, and 2 crack-connected channels appear at a distance threshold of 0.6).

[0114] Persistence Barcode: Quantifies the lifespan of a hole (e.g., a micro-hole is generated at a distance of 0.25 from a threshold and disappears at 0.68, with a lifespan of 0.43).

[0115] Output key topological invariants: hole generation / death threshold, Betti number sequence, and the persistence value of the longest surviving hole (e.g., 0.75).

[0116] The topological invariants are fused with the micropore distribution data observed by scanning electron microscopy to construct a defect topology-mechanical response mapping matrix; Multi-source data alignment and fusion: Scanning electron microscopy (SEM) data acquisition: Electron microscopy scanning (5 nm resolution) is performed on the acetabular cup slices after in vitro testing to identify the location and size of micropores (such as the coordinates of pores with diameters of 50-200 micrometers).

[0117] Generating a thermal map of micropore distribution: spatial resolution 0.1 mm × 0.1 mm (quantification of the number of pores per unit area, with a peak area of ​​35 pores / square millimeter).

[0118] Topological-geometric association rule: Map the location of holes in topological invariants to the physical coordinate system of the electron microscope image (e.g., the hole group corresponding to the phase space distance threshold of 0.3 is located at electron microscope coordinates X: 1.2mm, Y: 3.5mm).

[0119] Establish a dual verification mechanism: the peak area of ​​the Betti number must coincide with the dense area of ​​holes in the electron microscope (positional deviation tolerance ±0.05 mm).

[0120] Defect topology-mechanical response mapping matrix construction: Mechanical response label definition: According to the dynamic stress cloud icon, the stress concentration factor of each region is labeled (e.g., stress value of 38 MPa in the pore area vs. 28 MPa in the matrix area).

[0121] Mark the location of crack initiation (e.g., the first crack appears in a region with a stress concentration factor > 1.5).

[0122] Matrix generation algorithm: Divide the surface of the acetabular cup into grid cells (0.5 mm × 0.5 mm).

[0123] Each cell is assigned the following values: topological features: Betti number, hole persistence value.

[0124] Geometric characteristics: pore density, maximum pore diameter.

[0125] Mechanical response: local stress peak, crack initiation markers.

[0126] The mapping relationship is learned through the Random Forest Regression model, generating a 200×200 dimensional matrix (covering 100% of the surface area).

[0127] Example output: When the Betti number is greater than 8 and the porosity is greater than 30 pores / mm², the predicted stress concentration factor is greater than 1.6 (measured error < 7%).

[0128] The graph neural network constitutive model is initialized based on the mapping matrix, and the primary model is trained by loading an external accelerated test dataset; Graph Neural Network (GNN) architecture design: Graph structure definition: Node: Each grid cell (including topological, geometric, and mechanical features).

[0129] Edge: Adjacent elements are connected (elements with a distance of <1 mm are connected by an edge, and the weight is positively correlated with the stress gradient between elements).

[0130] Message Passing Mechanism: Node Feature Update: Aggregate the stress and porosity features of neighborhood cells (such as the aggregation depth of 3-layer neighborhood).

[0131] Edge feature update: weighted transfer of stress gradient values ​​(e.g., edge weights with gradients > 15 MPa / mm are increased by 50%).

[0132] Output layer: Predicts the damage evolution rate of each unit (unit: microcrack propagation rate in micrometers per 10,000 cycles).

[0133] In vitro accelerated test dataset training: Dataset construction: Collect ultra-accelerated test data (load is 3 times that in vivo, number of cycles 2 million): Input: dynamic load spectrum, lubrication conditions (friction coefficient 0.5), temperature (40℃).

[0134] Output: Crack length from micro-CT scan, pore distribution from electron microscopy, and acoustic emission damage vector.

[0135] Sample size: 60 sets of test data for acetabular cups made of different materials / structures (30 sets of titanium alloy, 20 sets of ceramic, and 10 sets of polyethylene).

[0136] Training process: Pre-training: Initialize GNN weights using a mapping matrix (learning rate 0.001, batch size 32).

[0137] Fine-tuning: Inject an in vitro dataset and use the Adaptive Moment Estimation (Adam) optimizer to minimize the prediction error.

[0138] Verification: Reserve 20% of the test set to evaluate the accuracy of crack propagation rate prediction (average error of 12% for titanium alloy group and 8% for ceramic group).

[0139] We designed a topology-variable adaptive migration framework, which adjusts the weight parameters of the primary model by in vivo and in vitro environmental differences, and integrates physiological load spectrum for adversarial fine-tuning, outputting a cross-scale fatigue damage prediction model.

[0140] Topology-Equivariant Domain Adaptation: Quantification of differences between in vivo and in vitro environments: Mechanical difference factor: Peak gait load in vivo 2500 Newtons vs. 3500 Newtons in in vitro testing.

[0141] Biochemical differences: in vivo lubricant viscosity 15 mPa·s vs. in vitro artificial lubricant 8 mPa·s.

[0142] Topological conservation constraints: ensure that the distribution of Betti number of micropores remains unchanged before and after migration (e.g., the connectivity of the pores remains unchanged).

[0143] Adaptive weight adjustment: differential factor encoding: converting mechanical and biochemical parameters into 32-dimensional feature vectors.

[0144] Graph neural network insertion domain classifier: distinguishes between in vivo and in vitro data features.

[0145] By using a gradient reversal layer, the classifier is tricked into learning domain-invariant features.

[0146] Physiological load spectrum fusion and counter-adjustment: Physiological load spectrum integration: Real gait data: Joint force curves (frequency 0.5-2 Hz) for 6 actions such as walking and climbing stairs.

[0147] Segmented load spectrum injection: The single-step period is discretized into 100 time points, and the boundary conditions are dynamically updated.

[0148] Adversarial training mechanism: Generator: Topologically equivariant GNN model (predicts damage evolution).

[0149] Discriminator: Convolutional Neural Network (determines whether the prediction results match physiological and environmental characteristics).

[0150] Training objective: The crack propagation path output by the generator must deceive the discriminator (false positive rate > 70%).

[0151] Fine-tuned output: The cross-scale fatigue damage prediction model achieves 89% accuracy on the in vivo environment validation set (compared to 72% for the non-transfer model).

[0152] Supports long-term prediction (>10 million cycles), with crack length prediction error <0.1 mm.

[0153] S204, based on the aforementioned multi-scale fatigue damage prediction model, a non-Markov chain Monte Carlo method is used to simulate the long-term wear accumulation effect. The failure probability is calculated iteratively by combining lubricant film degradation parameters, and a dynamic damage accumulation index is output. Specifically, a material damage state transition probability matrix can be generated based on the prediction model, and a state transition probability matrix can be constructed. The construction of the material damage state transition probability matrix is ​​based on the output of the multi-scale fatigue damage prediction model. First, the acetabular cup material is divided into micro-state units (e.g., 1 mm). 3 The mesh defines five damage states for each element: S0 (no damage), S1 (micropore initiation), S2 (microcrack propagation), S3 (local plastic deformation), and S4 (macroscopic failure). State transition probabilities are derived statistically from an in vitro accelerated testing dataset: for example, when an element is in state S1, after 1 million gait cycles, there is a 60% probability of remaining in state S1, a 30% probability of upgrading to S2, and a 10% probability of downgrading to S0 (self-healing effect).

[0154] Key parameters were calibrated through in-situ observation using scanning electron microscopy: Micropore density (MPD): number of micropores per unit area (cells / mm²) 2 In in vitro testing, MPD > 50 triggers S1→S2 transition; Plastic strain threshold (PS_th): 80% of the material's yield strength (e.g., 0.25% strain for titanium alloys), exceeding this threshold triggers S2→S3 transition.

[0155] The state transition probability matrix has a dimension of 5×5, and the element values ​​are iteratively optimized using maximum likelihood estimation (MLE). For example, the initial value of the transition probability from S2 to S3 is 0.2, which is corrected to 0.35 after comparison with in vivo and in vitro data. The matrix is ​​finally stored in JSON format for use in Monte Carlo simulations.

[0156] The lubricating fluid film thickness sensor data is converted into a degradation rate parameter, and a fluid film degradation constraint equation is established. The quantification of the lubricating fluid film degradation rate parameter (denoted as DR_lub) requires the fusion of multi-source data: sensor data: real-time monitoring of fluid film thickness (unit μm) by a fiber optic grating sensor, with a sampling frequency of 10kHz; tribological parameters: dynamic friction coefficient (DFC) of the acetabular cup-femoral head interface, calibrated by a pin-disc testing machine (e.g., in the range of 0.05-0.12); biochemical parameters: hyaluronic acid concentration in synovial fluid (HA_conc) (unit mg / mL), with concentrations below 2.0 mg / mL accelerating fluid film degradation.

[0157] The degradation constraint equation is constructed as a piecewise function: when HA_conc≥3.0mg / mL: DR_lub = 0.01 × DFC × (1 - liquid film thickness / initial thickness); when HA_conc<3.0mg / mL: DR_lub = [0.01 + 0.05×(3.0 - HA_conc)] × DFC×(1 - liquid film thickness / initial thickness).

[0158] The equation parameters were fitted to in vitro wear test data using the least squares method. For example, under the conditions of HA_conc=1.5mg / mL and DFC=0.1, the measured liquid film thickness decreased from 10μm to 5μm in 500,000 cycles, with a degradation rate DR_lub=0.1μm / 10,000 cycles.

[0159] Based on the state transition probability matrix and the liquid film degradation constraint equation, an adaptive Monte Carlo sampler is used to simulate the damage path of tens of millions of gait cycles; Non-Markov chain Monte Carlo simulation design: History dependency modeling: Define the state memory window: the current state transition probability is affected by the previous 3 states.

[0160] Example: If the state increases for 3 consecutive times, the probability of transitioning to a higher state in the next iteration increases by 20%.

[0161] The core difference from standard Markov chains is the introduction of time correlation (non-memoryless).

[0162] Adaptive sampling mechanism: Dynamically adjust sampling density: When the simulation path enters a high failure risk area (such as state > 70), the sampling point density is automatically doubled.

[0163] Parallel computing optimization: Deploy a GPU cluster (NVIDIA A100) to simulate 10,000 damage paths simultaneously.

[0164] Damage path simulation process: Single path generation steps: Initialization: State 0 (no damage), liquid film thickness 5.0 micrometers.

[0165] Loop through 10 million steps (simulate 10 million gaits): look up the transition probability matrix based on the current state and randomly jump to a new state (e.g., state 0 → state 1).

[0166] Liquid film degradation calculation: update the thickness according to the constraint equation (e.g., 5.0 → 4.98 micrometers).

[0167] Liquid film rupture detection: When the thickness is <0.5 micrometers, a forced state transition (+5 levels) is triggered.

[0168] Record the complete path: Record the state sequence and liquid film thickness change curve every second.

[0169] Termination conditions: Reaching state 99 (complete failure); or completing 10 million cycles (simulating 10 years of use).

[0170] Output example: A polyethylene acetabular cup failed after 5.6 million cycles (accelerated damage due to liquid film rupture).

[0171] The probability of time-varying coupled failure is calculated by importance sampling, and the dynamic damage accumulation index matrix is ​​output.

[0172] Importance sampling algorithm optimization: Key issue: Direct Monte Carlo simulation requires hundreds of millions of samples to capture rare failure events (such as early fracture probability of 0.1%).

[0173] Sampling strategy improvement: Construct a biased probability distribution: In high-risk areas (states > 60), artificially increase the sampling probability to 10 times the actual probability.

[0174] Reduce the sampling weight to 50% for low-risk areas (status < 30).

[0175] Weight Correction Factor: Each sample is assigned a weight value = actual probability / bias probability.

[0176] Example: A path has a sampling probability of 0.1 under a biased distribution, and an actual probability of 0.01 → weight = 0.1.

[0177] Dynamic damage accumulation index matrix generation: Time-varying failure probability calculation: Statistical analysis of the number of failed paths per million cycles (after weight correction); the proportion of failed paths is 0.3% (after correction) at the 2 millionth cycle; the proportion of failed paths is 18.7% at the 8 millionth cycle.

[0178] Exponential matrix construction: Rows: Material state (levels 0-99); Columns: Time nodes (every 1 million cycles).

[0179] Matrix element values: cumulative failure probability at the corresponding state and time point (range 0.0-1.0).

[0180] Output Example: Dynamic Damage Cumulative Index Matrix (Excerpt): State / Time 2 Million Cycles 5 Million Cycles 10 Million Cycles: State 50 0.003 0.12 0.45; State 70 0.18 0.63 0.98; Engineering Significance: State 70 has a 63% failure probability after 5 million cycles → Design improvements are needed.

[0181] S205, based on the dynamic damage accumulation index, optimize the test parameter space through reinforcement learning, generate an accelerated life test protocol, drive the six-degree-of-freedom test bench to perform multimodal coupled loading, and output a life prediction confidence interval report.

[0182] Specifically, the dynamic damage accumulation index matrix can be used as the state of the reinforcement learning environment, and the loading parameters are defined as the action space; Reinforcement learning environment state construction: Dynamic damage accumulation index matrix parsing: Input a 100×100 dimensional matrix (rows represent material damage levels 0-99, columns represent time nodes every 1 million cycles), and the matrix element values ​​are the cumulative failure probabilities (0.0-1.0).

[0183] State coding rule: Extract high-risk region features: Focus on regions with states ≥70 and failure probability >0.5 (e.g., state 75 has a probability of 0.63 in 6 million cycles).

[0184] Dimensional compression: The matrix is ​​compressed into a 50-dimensional vector using Principal Component Analysis (a dimensionality reduction algorithm) (98% information retention).

[0185] Environment state update mechanism: The state vector is updated once every 100,000 simulations (real-time requirement: delay < 0.1 seconds).

[0186] Example: In the state vector, the ceramic acetabular cup is labeled [0.12, 0.45, ..., 0.89] to represent the coefficients of the first three principal components.

[0187] Action space definition: Loading parameter dimension: Axial pressure (range 0-5000 Newtons, resolution 10 Newtons): simulates the vertical load when a human is standing / walking.

[0188] Rotational torque (range 0-15 Nm, resolution 0.1 Nm): simulates hip joint rotation.

[0189] Loading tilt angle (range 0°-20°, resolution 0.5°): corresponds to different phase states (e.g., tilt angle of 12° when going up stairs).

[0190] Motion space discretization: Discretize the continuous parameters into 200 motion combinations (axial pressure × torque × tilt angle).

[0191] Example action: Action ID 35 = {Pressure 2800 N, Torque 8.5 Nm, Inclination 10°}.

[0192] The proximal policy optimization algorithm is used to explore the parameter space, and the acceleration factor is maximized with the equivalence of the damage mechanism as a constraint. The deployment of the proximal policy optimization algorithm (PPO) is as follows: Policy network architecture: Input layer: 50-dimensional state vector → Hidden layer: 256 neurons (activation function ReLU, i.e., modified linear unit) → Output layer: 200-dimensional action probability distribution.

[0193] Value Network: Independently evaluates the value of a state (128 neurons in the hidden layer).

[0194] Training mechanism: Clipped Objective: Limits the single-step update magnitude (clipping coefficient 0.2) to prevent policy mutation.

[0195] Generalized Advantage Estimation (GAE): Balances immediate rewards with long-term returns (discount factor γ=0.99).

[0196] Constraints and Target Optimization: Damage Mechanism Equivalence Constraint: Microscopic Damage Consistency: The crack propagation path of the accelerated test must have a similarity of >90% to the real physiological environment (by comparing the entropy value of the acoustic radio frequency band).

[0197] Wear morphology matching: the error in the distribution of micropores observed by electron microscopy is <5% (position deviation tolerance ±0.1 mm).

[0198] Acceleration Factor Maximization: Define acceleration factor = actual lifespan / test time (target improvement of 8-12 times).

[0199] Reward function design: Basic reward: +10 points for every 1x increase in acceleration factor.

[0200] Penalty: 50 points will be deducted for each violation of the equivalence constraint (e.g., crack path deviation > 15%).

[0201] Training example: PPO explored action ID 103 (4200 Newtons of pressure + 18° tilt angle) on a titanium alloy mortise, achieving an acceleration factor of 10.5 times and a crack similarity of 92%.

[0202] Generate a multimodal loading protocol instruction set that includes axial pressure-rotational torque coupling; Multimodal instruction set architecture design: Time-load coupling framework: Single protocol cycle: Simulate 10 years of use (10 million cycles), compressed to 120 hours of testing.

[0203] Time slice division: each 0.5 seconds is an instruction unit (a total of 8.64 million units).

[0204] Command element composition: Axial pressure curve: Piecewise linear interpolation (e.g., [0-10 seconds]: 2800→3200 Newtons).

[0205] Torque-angle phase coupling: The rotational torque is dynamically adjusted with the tilt angle (8 Nm at 10° tilt angle and 12 Nm at 15° tilt angle).

[0206] Mutation condition injection: A random shock is inserted every 1000 cycles (pressure increases to 5000 Newtons, simulating a fall).

[0207] Protocol Verification and Optimization: Finite Element Pre-Verification: Perform virtual tests on the instruction set (sampling 1% of the time slice), and calculate the stress distribution using a nonlinear solver.

[0208] Verification point: Peak stress ≤ 80% of the material's yield strength (e.g., the yield strength of cobalt-chromium alloy is 900 MPa → the upper limit of the agreement is 720 MPa).

[0209] Redundant instruction elimination: Merge consecutive identical actions (such as merging pressure stabilization segments into a single instruction), compressing protocol volume by 70%.

[0210] Output example: Protocol P-2024 contains 1.2 million atomic instructions and controls a bandwidth requirement of 15 Mbps (megabits per second).

[0211] The six-degree-of-freedom test bench is driven to execute protocol commands and acquire damage verification data in real time through fiber optic grating sensors. Six-degree-of-freedom test bench control: Hardware configuration: Loading axis: 3 hydraulic servo actuators (stroke ±100 mm, accuracy ±0.01 mm).

[0212] Rotating shaft: 3 torque motors (peak torque 20 Nm, resolution 0.001 Nm).

[0213] Instruction execution flow: Protocol parsing: The instruction set is divided into blocks and transmitted to the test bench controller (1000 instructions per block).

[0214] Closed-loop control: Real-time feedback: Fiber optic sensors monitor the actual load (sampling rate 1 kHz).

[0215] Dynamic calibration: PID control (proportional-integral-derivative control algorithm) is triggered when the pressure deviation is >50 Newtons.

[0216] Safety circuit breaker: Emergency shutdown when stress exceeds the limit by 10% (e.g., if >792 MPa is detected).

[0217] Damage verification data acquisition: Fiber Bragg Grating (FBG) sensor deployment: 32 sensing points (2 mm spacing) were etched on the surface of the acetabular cup with a wavelength resolution of 1 picometer.

[0218] Measurement parameters: strain (range ±5000 micro-strain), temperature (range 0-100℃).

[0219] Key damage feature extraction: Crack propagation rate: located by strain abrupt change points (e.g., a strain step of 0.5% at sensing point 15 → crack extension of 50 micrometers).

[0220] Fretting wear: A 1°C increase in temperature corresponds to a 0.1-micron wear depth (calibration curve).

[0221] Data stream format: Outputs a 32×3 dimensional array (position X, Y, strain value, temperature value) every millisecond.

[0222] A Bayesian update model is constructed based on damage verification data, and a lifetime prediction report and failure mode heatmap with a 95% confidence interval are output.

[0223] Bayesian update model construction: Prior distribution setting: Initial lifetime distribution: based on Monte Carlo simulation results (e.g., titanium alloy mortar cup mean 8.2 million cycles, standard deviation 1.2 million cycles).

[0224] Distribution type: Log-normal distribution (right-skewed characteristics conform to fatigue life law).

[0225] Likelihood function design: Damage consistency factor: Define λ = (measured crack rate / predicted crack rate), λ=1 indicates perfect match.

[0226] Likelihood function: P(measured data|predicted lifetime) ∝ e^(-|λ-1|^2 / 0.05), with a variance of 0.05 used to control sensitivity.

[0227] Posterior distribution calculation: Markov chain Monte Carlo sampling (MCMC, a probabilistic sampling algorithm) is used, with a chain length of 100,000 iterations.

[0228] Example update: Actual crack rate is 15% faster than predicted → Posterior mean lifetime revised to 7.1 million cycles.

[0229] Visualized report generation: Lifespan prediction report: Output 95% confidence interval: Lifespan = 6.8 million to 7.4 million cycles (95% confidence level).

[0230] Critical failure point: Mark the time of the first liquid film rupture (e.g., the 5.1 millionth cycle).

[0231] Failure Mode Heatmap: Spatial Mapping: The surface of the acetabular cup is divided into a 1 mm × 1 mm grid.

[0232] Risk value calculation: Failure probability of each grid = Crack initiation probability × Liquid film degradation coefficient.

[0233] Color coding: Green (probability <10%): Safe zone.

[0234] Red (probability > 60%): High-risk area (e.g., upper quadrant coordinates X: 3.2mm, Y: 4.1mm).

[0235] Example Report: Acetabular Cup Life Prediction Report (95% Confidence Interval): Material: Titanium Alloy TC4 | Protocol Number: P-2024; Predicted Life: 7.02 million cycles [680, 740]; Primary Failure Mode: Micropore connectivity in the upper posterior quadrant (probability 83.5%); Key Risk Point: Coordinates (3.2mm, 4.1mm), failure probability 67.4%.

[0236] As can be seen, based on the three-dimensional structural parameters and material properties of the acetabular cup implant, a dynamic stress distribution cloud map is output; based on the dynamic stress distribution cloud map, a damage evolution feature vector is output; based on the damage evolution feature vector, a cross-scale fatigue damage prediction model is output; based on the cross-scale fatigue damage prediction model, a dynamic damage accumulation index is output; based on the dynamic damage accumulation index, the test parameter space is optimized through reinforcement learning, an accelerated life test protocol is generated, and a six-degree-of-freedom test bench is driven to perform multimodal coupled loading, outputting a life prediction confidence interval report, thereby enabling more accurate lifespan prediction and reliability assessment.

[0237] Another embodiment of the present invention provides a lifespan testing system for acetabular cups. Referring to Figure 3, the system may include: a fusion module 301, used to output a dynamic stress distribution cloud map by fusing gait phase features and bone-prosthesis interface micro-motion parameters based on the three-dimensional structural parameters and material properties of the acetabular cup implant using a multi-axis dynamic load simulation algorithm; a capture module 302, used to capture the microscopic damage features of the material based on the dynamic stress distribution cloud map using a time-frequency joint analysis method of acoustic emission signals, extract the crack initiation frequency band energy through wavelet packet decomposition, and output a damage evolution feature vector; and a mapping module 303, used to map the damage evolution features to... The system constructs a constitutive model that integrates the topology of micro-defects, and uses a transfer learning algorithm to map in vitro test data to the in vivo service environment, outputting a cross-scale fatigue damage prediction model. The simulation module 304 is used to simulate the long-term wear accumulation effect based on the cross-scale fatigue damage prediction model using a non-Markov chain Monte Carlo method, and iteratively calculates the failure probability by combining the lubricating fluid film degradation parameters, outputting a dynamic damage accumulation index. The output module 305 is used to optimize the test parameter space through reinforcement learning based on the dynamic damage accumulation index, generate an accelerated life test protocol, drive a six-degree-of-freedom test bench to perform multimodal coupled loading, and output a life prediction confidence interval report.

[0238] This invention also provides a storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0239] Specifically, in this embodiment, the aforementioned storage medium can be configured to store a computer program for performing the following steps: S201, based on the three-dimensional structural parameters and material properties of the acetabular cup implant, using a multi-axis dynamic load simulation algorithm, by fusing gait phase features and bone-prosthesis interface micro-motion parameters, outputting a dynamic stress distribution cloud map; S202, based on the dynamic stress distribution cloud map, using a time-frequency joint analysis method of acoustic emission signals to capture the microscopic damage characteristics of the material, extracting crack initiation frequency band energy through wavelet packet decomposition, and outputting a damage evolution feature vector; S203, based on the damage evolution feature vector, constructing... A constitutive model integrating microscopic defect topology is used to map in vitro test data to the in vivo service environment using a transfer learning algorithm, outputting a cross-scale fatigue damage prediction model; S204, based on the cross-scale fatigue damage prediction model, a non-Markov chain Monte Carlo method is used to simulate the long-term wear accumulation effect, and the failure probability is calculated iteratively by combining lubricant film degradation parameters, outputting a dynamic damage accumulation index; S205, based on the dynamic damage accumulation index, the test parameter space is optimized through reinforcement learning, an accelerated life test protocol is generated, and a six-degree-of-freedom test bench is driven to perform multimodal coupled loading, outputting a life prediction confidence interval report.

[0240] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0241] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0242] Specifically, in this embodiment, the processor can be configured to execute the following steps via a computer program: S201, based on the three-dimensional structural parameters and material properties of the acetabular cup implant, using a multi-axis dynamic load simulation algorithm, by fusing gait phase features and bone-prosthesis interface micro-motion parameters, outputting a dynamic stress distribution cloud map; S202, based on the dynamic stress distribution cloud map, using a time-frequency joint analysis method of acoustic emission signals to capture the microscopic damage characteristics of the material, extracting crack initiation frequency band energy through wavelet packet decomposition, and outputting a damage evolution feature vector; S203, based on the damage evolution feature vector, constructing a fusion... The constitutive model of the micro-defect topology is used to map the in vitro test data to the in vivo service environment using a transfer learning algorithm, and outputs a cross-scale fatigue damage prediction model; S204, based on the cross-scale fatigue damage prediction model, the long-term wear accumulation effect is simulated using a non-Markov chain Monte Carlo method, and the failure probability is calculated iteratively by combining the lubricating fluid film degradation parameters, and the dynamic damage accumulation index is output; S205, according to the dynamic damage accumulation index, the test parameter space is optimized by reinforcement learning, an accelerated life test protocol is generated, and a six-degree-of-freedom test bench is driven to perform multimodal coupled loading, and a life prediction confidence interval report is output.

[0243] The above description, based on the embodiments shown in the figures, details the structure, features, and effects of the present invention. The above description is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the figures. Any changes made in accordance with the concept of the present invention, or equivalent embodiments modified to have equivalent changes, that do not exceed the spirit covered by the specification and figures, should be within the protection scope of the present invention.

Claims

1. A method for testing the service life of an acetabular cup, characterized in that, The method includes: based on the three-dimensional structural parameters and material properties of the acetabular cup implant, using a multi-axis dynamic load simulation algorithm, and by fusing gait phase features and bone-prosthesis interface micro-motion parameters, outputting a dynamic stress distribution cloud map; based on the dynamic stress distribution cloud map, using a time-frequency joint analysis method of acoustic emission signals to capture the microscopic damage characteristics of the material, extracting crack initiation frequency band energy through wavelet packet decomposition, and outputting a damage evolution feature vector; based on the damage evolution feature vector, constructing a constitutive model that integrates the topology of microscopic defects, using a transfer learning algorithm to map in vitro test data to the in vivo service environment, and outputting a cross-scale fatigue damage prediction model; based on the cross-scale fatigue damage prediction model, using a non-Markov chain Monte Carlo method to simulate the long-term wear accumulation effect, combining the lubricating fluid film degradation parameters to iteratively calculate the failure probability, and outputting a dynamic damage accumulation index; based on the dynamic damage accumulation index, optimizing the test parameter space through reinforcement learning, generating an accelerated life test protocol and driving a six-degree-of-freedom test bench to perform multimodal coupled loading, and outputting a life prediction confidence interval report.

2. The method according to claim 1, characterized in that, The process involves using a multi-axis dynamic load simulation algorithm, based on the three-dimensional structural parameters and material properties of the acetabular cup implant, to output a dynamic stress distribution cloud map by fusing gait phase features and micro-motion parameters of the bone-prosthesis interface. This includes: extracting curvature distribution parameters and material Poisson's ratio from the CT point cloud data of the acetabular cup; combining this with the time-varying joint force curves acquired by the gait analyzer to generate a basic dynamic load spectrum; injecting the micro-motion friction coefficient of the bone-prosthesis interface based on the basic dynamic load spectrum; calculating the contact stress field using a nonlinear finite element solver to obtain the basic contact stress field; and dynamically adjusting the load direction vector based on the basic contact stress field and gait phase features, employing stress gradient-driven adaptive mesh refinement technology to output a spatiotemporally continuous dynamic stress distribution cloud map.

3. The method according to claim 2, characterized in that, The method, based on the dynamic stress distribution cloud map, employs a time-frequency joint analysis of acoustic emission signals to capture the microscopic damage characteristics of the material. It extracts the crack initiation frequency band energy through wavelet packet decomposition and outputs a damage evolution feature vector. This includes: locating the region of maximum principal stress based on the dynamic stress cloud map and simultaneously acquiring the original acoustic emission signal stream of that region; inputting the original acoustic emission signal stream into a dual-tree complex wavelet transform and separating the crack propagation component using a time-frequency ridge tracking algorithm; performing eight-level wavelet packet decomposition on the crack propagation component and extracting energy abrupt change feature points within the 4-8MHz frequency band; and fusing the energy amplitude, frequency band entropy, and density of abrupt change feature points to construct a three-dimensional feature vector and output the damage evolution feature vector.

4. The method according to claim 3, characterized in that, The process involves constructing a constitutive model that integrates the topology of micro-defects based on the damage evolution feature vector, mapping in vitro test data to the in vivo service environment using a transfer learning algorithm, and outputting a cross-scale fatigue damage prediction model. This includes: reconstructing the phase space of the damage evolution feature vector; extracting topological invariants of micro-defects through persistent homology analysis; fusing the topological invariants with micropore distribution data observed by scanning electron microscopy to construct a defect topology-mechanical response mapping matrix; initializing a graph neural network constitutive model based on the mapping matrix; training the primary model by loading an in vitro accelerated test dataset; designing a topologically variable adaptive transfer framework; adjusting the weight parameters of the primary model through in vivo and in vitro environmental differences; and performing adversarial fine-tuning by integrating physiological load spectra to output a cross-scale fatigue damage prediction model.

5. The method according to claim 4, characterized in that, The aforementioned multi-scale fatigue damage prediction model employs a non-Markov chain Monte Carlo method to simulate long-term wear accumulation effects, iteratively calculates the failure probability using lubricant film degradation parameters, and outputs a dynamic damage accumulation index. This includes: generating a material damage state transition probability matrix based on the prediction model; constructing a state transition probability matrix; converting lubricant film thickness sensor data into degradation rate parameters and establishing a liquid film degradation constraint equation; simulating damage paths through millions of gait cycles using an adaptive Monte Carlo sampler based on the state transition probability matrix and the liquid film degradation constraint equation; and calculating the time-varying coupled failure probability through importance sampling, outputting a dynamic damage accumulation index matrix.

6. The method according to claim 5, characterized in that, The process of optimizing the test parameter space through reinforcement learning based on the dynamic damage accumulation index, generating an accelerated life test protocol, driving a six-DOF test bench to perform multimodal coupled loading, and outputting a life prediction confidence interval report includes: using the dynamic damage accumulation index matrix as the reinforcement learning environment state, defining the loading parameters as the action space; exploring the parameter space using a proximal policy optimization algorithm, maximizing the acceleration factor with damage mechanism equivalence as a constraint; generating a multimodal loading protocol instruction set containing the axial pressure-rotational torque coupling relationship; driving the six-DOF test bench to execute the protocol instructions, and collecting damage verification data in real time through fiber optic grating sensors; constructing a Bayesian update model based on the damage verification data, and outputting a life prediction report with a 95% confidence interval and a failure mode heatmap.

7. A lifespan testing system for acetabular cups, characterized in that, The system includes: a fusion module, used to output a dynamic stress distribution cloud map by fusing gait phase features and bone-prosthesis interface micro-motion parameters based on the three-dimensional structural parameters and material properties of the acetabular cup implant; a capture module, used to capture the micro-damage features of the material using a time-frequency joint analysis method of acoustic emission signals based on the dynamic stress distribution cloud map, extracting crack initiation frequency band energy through wavelet packet decomposition, and outputting a damage evolution feature vector; a mapping module, used to construct a constitutive model that fuses the micro-defect topology based on the damage evolution feature vector, and use a transfer learning algorithm to map the in vitro test data to the in vivo service environment, outputting a cross-scale fatigue damage prediction model; a simulation module, used to simulate the long-term wear accumulation effect using a non-Markov chain Monte Carlo method based on the cross-scale fatigue damage prediction model, and iteratively calculate the failure probability by combining lubricant film degradation parameters, outputting a dynamic damage accumulation index; and an output module, used to optimize the test parameter space through reinforcement learning based on the dynamic damage accumulation index, generate an accelerated life test protocol, drive a six-degree-of-freedom test bench to perform multimodal coupled loading, and output a life prediction confidence interval report.

8. The system according to claim 7, characterized in that, The process involves using a multi-axis dynamic load simulation algorithm, based on the three-dimensional structural parameters and material properties of the acetabular cup implant, to output a dynamic stress distribution cloud map by fusing gait phase features and micro-motion parameters of the bone-prosthesis interface. This includes: extracting curvature distribution parameters and material Poisson's ratio from the CT point cloud data of the acetabular cup; combining this with the time-varying joint force curves acquired by the gait analyzer to generate a basic dynamic load spectrum; injecting the micro-motion friction coefficient of the bone-prosthesis interface based on the basic dynamic load spectrum; calculating the contact stress field using a nonlinear finite element solver to obtain the basic contact stress field; and dynamically adjusting the load direction vector based on the basic contact stress field and gait phase features, employing stress gradient-driven adaptive mesh refinement technology to output a spatiotemporally continuous dynamic stress distribution cloud map.

9. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method of any one of claims 1-6 when it is run.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 1-6.

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