Intelligent cooling system and method of centerless grinding machine for difficult-to-machine materials

Abstract

The invention relates to the technical field of industrial control, in particular to an intelligent centerless grinding machine cooling system and method for difficult-to-machine materials, and the system comprises a multi-source sensing unit, an edge calculation module, a decision center module, a precise execution module and a digital immune memory bank; compared with the defects that in the prior art, monitoring depends on a single temperature sensor, thermal field sensing is incomplete, thermal damage early warning lags behind and the like, according to the scheme, grinding power frequency spectrum, acoustic emission signals, triaxial vibration and coolant mass spectrum data are collected in real time through a multi-source sensing unit, and a micron-sized temperature field is reconstructed in combination with an edge end space-time diagram convolutional network; full-dimensional monitoring of the grinding thermal-mechanical coupling effect is achieved, and thermal anomaly recognition sensitivity and early warning timeliness are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial control technology, in particular to a centerless grinding machine intelligent cooling system and method for difficult-to-machine materials. BACKGROUND

[0002] As the core process of high-precision shaft part machining, centerless grinding technology plays an irreplaceable role in the manufacturing of high-value-added difficult-to-machine material parts such as aero-engine rotors and aerospace actuators. Such materials usually include nickel-based superalloys, titanium alloys, and ceramic matrix composites. Their high strength and low thermal conductivity characteristics make it easy to accumulate intense friction heat in the contact area during grinding, which can induce thermal damage defects such as workpiece surface burn, micro-cracks, and metallurgical structure phase change. Not only does this cause the parts to be scrapped, but it can also cause catastrophic failures during the service period of the equipment. The traditional cooling scheme relies on experience to set constant flow parameters, which is difficult to adapt to material property fluctuations and dynamic changes in working conditions, and has become a bottleneck problem restricting the reliable manufacturing of high-end equipment.

[0003] The current mainstream technology mainly relies on contact thermocouple temperature measurement and PID feedback control, which has three inherent defects: first, point temperature measurement cannot capture the temperature gradient distribution of the entire grinding zone, leading to local over-temperature detection; second, the pure feedback mechanism has significant hysteresis, and thermal damage has already formed when the temperature is detected to be excessive; third, it lacks intelligent response capability in the face of sudden working conditions such as wheel clogging and coolant degeneration. More seriously, existing intelligent cooling systems rely on pre-set material databases, which frequently fail when processing unknown alloys or when material batch differences occur, and completely ignore the hidden damage risks caused by microstructure evolution. These defects have resulted in a high burn rate of high-temperature alloy materials during grinding.

[0004] The present application proposes an intelligent cooling system based on physical information machine learning, which innovatively constructs a four-dimensional collaborative architecture of "multi-source perception-edge prediction-dynamic decision-immune execution": through a multi-source sensing array, real-time capture of thermal-mechanical-acoustic-chemical multi-physical field signals during grinding; in the edge computing layer, fusion of spatio-temporal graph convolution network and differentiable heat conduction equation to realize dynamic prediction of micron-level temperature field; using a meta-reinforcement learning framework to drive a Pareto-regret value double optimizer to generate a cooling strategy; finally, relying on a digital immune memory bank to realize millisecond-level self-healing response to sudden working conditions. This system is fully compatible with existing machine tool hardware, not only solving the industry problem of precise prevention and control of thermal damage of difficult-to-machine materials, but also creating a paradigm shift from "passive response" to "thermodynamic prophet", providing a revolutionary solution for extreme manufacturing scenarios. SUMMARY

[0005] In order to overcome the problems presented in the background art, the present application proposes a centerless grinding machine intelligent cooling system and method for difficult-to-machine materials.

[0006] The technical scheme of the present application is: a centerless grinding machine intelligent cooling system for difficult-to-machine materials, comprising:

[0007] A multi-source perception unit is used to collect grinding power spectrum, acoustic emission signals, three-axis vibration acceleration, and cooling liquid composition mass spectrum data in real time.

[0008] An edge computing module is used to deploy a spatio-temporal graph convolution network and a physical constraint fusion model at the edge to generate a dynamic temperature field distribution map.

[0009] A decision hub module is used to run a meta-reinforcement learning framework to output a pulse width modulation control strategy.

[0010] A precise execution module is used to perform directional cooling liquid injection through a distributed micro-nozzle array.

[0011] A digital immune memory bank is used to store the mapping relationship between typical working condition fault features and response strategies.

[0012] Preferably, the edge computing module comprises:

[0013] A11: A thermophysical property inversion unit that dynamically inverts the material thermal conductivity and specific heat capacity based on the grinding force frequency domain characteristics, wherein the inversion algorithm satisfies:

[0014] k=f1(∫|F(f)| 2 df);

[0015] Where F(f) is the grinding force frequency domain function, k is the material thermal conductivity, f1 is the frequency domain feature mapping function, and df is the frequency differential unit.

[0016] A12: A phase change risk prediction unit that integrates the Johnson-Mehl-Avrami equation to calculate the microstructure evolution risk level.

[0017] Preferably, the physical constraint fusion model deployed by the edge computing module embeds the Fourier heat conduction law into the neural network training process through a differentiable partial differential solver; and the loss function of the physical constraint fusion model contains a physical law constraint term and a data fitting term, specifically:

[0018] A21: The physical constraint term forces the predicted temperature field to satisfy the heat conduction differential equation.

[0019] A22: The data fitting term ensures that the deviation between the predicted value and the actual measured value of the high-response thermocouple is minimized.

[0020] Preferably, the decision hub module contains a dual optimizer that operates in conjunction, specifically including:

[0021] A31: Pareto optimizer: used to build a three-dimensional target space coordinate system in real time, and dynamically screen the non-inferior solution set based on the elite retention strategy, automatically avoid the risk area of thermal damage when outputting the cooling strategy;

[0022] A32: Regret minimization controller: used to continuously monitor the deviation of actual grinding effect from the theoretical optimal solution, and trigger the strategy gradient back mechanism to update the network weight when the cumulative regret value exceeds the dynamic threshold.

[0023] As preferred, the digital immune memory library specifically includes:

[0024] A41: Fault feature extractor: uses multi-modal fusion technology to generate a 128-dimensional feature vector, which integrates frequency domain energy entropy, wavelet packet decomposition coefficients, and principal component dimension reduction features;

[0025] A42: Strategy matching engine: used to calculate the cosine similarity between real-time fault features and pre-stored templates, and automatically retrieve the coping strategy when the similarity is greater than 0.9, the coping strategy includes cooling parameter adjustment and modification program start.

[0026] As preferred, the precise execution module contains an adaptive PWM controller, whose output pulse width satisfies:

[0027] τ = K p ·|ΔT| + K d ·dΔT / dt;

[0028] Where τ is the pulse width, K p is the proportional gain coefficient, K d is the derivative gain coefficient, |ΔT| is the absolute temperature deviation value, and dΔT / dt is the temperature change rate.

[0029] As preferred, the adaptive PWM controller also includes a dynamic compensation mechanism, and the principle of the dynamic compensation mechanism is:

[0030] S11: intelligently calculate the wear amount through the CCD imaging system;

[0031] S12: automatically adjust the proportional and derivative coefficients according to the real-time wear state of the grinding wheel, specifically:

[0032] A. Initial grinding wheel, i.e. wear amount less than 15%, uses aggressive control mode, proportional coefficient increased by 30%, derivative coefficient decreased by 20%;

[0033] B. Mid-term grinding wheel, i.e. 15%-50% wear, uses balanced control mode, proportional and derivative coefficients remain unchanged

[0034] C. Late grinding wheel, i.e. wear amount greater than 50%, uses conservative control mode, proportional coefficient decreased by 40%, derivative coefficient increased by 35%.

[0035] As preferred, a grinding wheel clogging monitoring module is further included, and the working flow of the grinding wheel clogging monitoring module is as follows:

[0036] S21: analyzing the gray scale distribution of the grinding dust image, and calculating a clogging index Cl=f2(I dark / I total ), wherein f2 is a nonlinear enhancement function, I dark is the total sum of pixels in the grinding dust coverage area, and I total is the total pixels in the effective area of the grinding wheel surface;

[0037] S22: triggering a high-pressure flushing and dressing program when the clogging index Cl is greater than 0.35, wherein the determination strategy of the clogging index Cl is dynamically configured according to the material type:

[0038] High-temperature alloy: Cl threshold value = 0.35 ± 0.05;

[0039] Ceramic matrix composite: Cl threshold value = 0.28 ± 0.03.

[0040] As preferred, the decision-making center module builds a real-time control channel through the OPC-UA protocol, and the specific configuration is as follows:

[0041] A51: realizing 10 ms level data synchronization by using the publish and subscribe mode;

[0042] A52: implementing a triple verification mechanism for instruction transmission. The triple verification mechanism includes CRC32 verification, time sequence stamp verification, and heartbeat packet monitoring.

[0043] As preferred, the intelligent cooling method for the centerless grinding machine for difficult-to-machine materials includes the following steps:

[0044] S31: collecting dynamic data streams in the grinding process in real time through a multi-source sensor array, including power spectrum, acoustic emission signal, vibration acceleration time sequence, and cooling liquid mass spectrum;

[0045] S32: using an adversarial autoencoder and a Wasserstein distance constraint mechanism to perform noise reduction processing on the original data to generate a purified signal vector;

[0046] S33: inputting the purified signal into a spatio-temporal graph convolution network, coupling a physical information constraint module composed of a differentiable partial differential equation solver to output a contact area temperature field distribution map in real time;

[0047] S34: dynamically calculating the thermal physical property parameters of the workpiece material based on the inversion algorithm of the grinding force frequency domain characteristics, and predicting the microstructure evolution risk by combining the Johnson-Mehl-Avrami phase transition kinetics equation;

[0048] S35: Generate cooling strategy by meta-reinforcement learning framework, simultaneously perform Pareto front multi-objective optimization and regret minimization evaluation, and output pulse width modulation control instruction;

[0049] S36: Perform microsecond-level cooling spray control through edge computing device, and trigger digital immune memory library pre-stored strategy to cope with sudden working conditions.

[0050] The beneficial effects of the present application are:

[0051] 1. Compared with the prior art relying on a single temperature sensor for monitoring, there are defects such as incomplete thermal field perception and delayed thermal damage warning. The present scheme realizes full-dimensional monitoring of grinding thermal coupling effect by real-time collection of grinding power spectrum, acoustic emission signal, three-axis vibration and cooling liquid mass spectrum data through multi-source perception unit, and reconstruction of micron-level temperature field through edge temporal-spatial graph convolution network, significantly improving the sensitivity of thermal anomaly identification and the timeliness of early warning.

[0052] 2. Compared with the traditional method which needs to pre-set material parameter library and cannot perceive microstructure damage, the present scheme dynamically analyzes the thermal conductivity and specific heat capacity of the material by using the thermal property inversion algorithm, and integrates the phase change dynamics equation to predict the risk of microstructure evolution, directly identifies the change of material properties according to the dynamic characteristics of the grinding process, effectively avoiding the risk of misjudgment and microstructure phase change damage caused by unknown materials.

[0053] 3. Compared with the pure data-driven model which is easy to violate the laws of thermodynamics and produce distorted prediction, the present scheme embeds the Fourier heat conduction law into the neural network training through the differentiable partial differential solver, forces the prediction results to comply with the physical law constraints, solves the defect of insufficient model generalization ability under few-sample working conditions, and significantly improves the reliability of temperature prediction in complex scenarios such as high temperature and variable working conditions.

[0054] 4. Compared with the fixed weight strategy which is difficult to balance the contradiction between temperature control, cooling consumption and surface quality, the present scheme designs a Pareto optimizer to dynamically select a non-inferior solution set in a three-dimensional target space, simultaneously uses a regret minimization mechanism to real-time correct the strategy behavior deviating from the optimal, realizes adaptive optimization and rapid convergence of the cooling strategy, and completely eliminates the process fluctuations caused by parameter oscillation.

[0055] 5. Compared with manual intervention fault diagnosis which is low in efficiency and high in misjudgment rate, the present scheme constructs a digital immune memory library to pre-store typical fault characteristics and solutions, triggers precise countermeasures through a cosine similarity matching engine in milliseconds, significantly reduces the non-planned downtime and maintenance cost of the equipment, and builds an intelligent operation and maintenance system with self-healing capability. BRIEF DESCRIPTION OF DRAWINGS

[0056] Fig. 1 A constructional schematic view of the intelligent cooling system of the centerless grinding machine for difficult-to-machine materials of the present application is shown.

[0057] Fig. 2 The flowchart of the intelligent cooling method of the centerless grinding machine for difficult-to-machine materials is shown. DETAILED DESCRIPTION

[0058] The application will be further described below in conjunction with the drawings and examples.

[0059] Referring to Figs. 1-2 The application provides an example: an intelligent cooling system for a centerless grinding machine for difficult-to-machine materials, comprising:

[0060] A multi-source perception unit is configured to collect grinding power spectrum, acoustic emission signals, three-axis vibration acceleration, and cooling liquid composition mass spectrum data in real time.

[0061] An edge computing module is configured to deploy a spatiotemporal graph convolution network and a physical constraint fusion model at the edge end to generate a dynamic temperature field distribution map.

[0062] A decision hub module is configured to run a meta-reinforcement learning framework to output a pulse width modulation control strategy.

[0063] A precise execution module is configured to perform directional spraying of the cooling liquid through a distributed micro-nozzle array.

[0064] A digital immune memory bank is configured to store the mapping relationship between typical working condition fault features and coping strategies.

[0065] In this embodiment, the application realizes full-dimensional monitoring of multi-physical fields (force / heat / vibration / chemistry) in the grinding process through the multi-source perception unit, relies on the spatiotemporal graph convolution + physical constraint model to complete 0.1-second-level delay of micron-level temperature field reconstruction at the edge end; combines the meta-reinforcement learning framework to generate a PWM control strategy that takes into account cooling efficiency and resource consumption, and realizes nanoscale precise spraying through a distributed micro-nozzle array; the digital immune memory bank provides pre-stored solutions for 78 typical faults, constructs a full-link closed-loop control of software and hardware collaboration, and significantly improves the thermal damage suppression capability, resource utilization efficiency, and system robustness of difficult-to-machine material grinding.

[0066] Preferably, the edge computing module comprises:

[0067] A11: a thermophysical property inversion unit, which dynamically inverts the material thermal conductivity and specific heat capacity based on the grinding force frequency domain characteristics, wherein the inversion algorithm satisfies:

[0068] k = f1(∫|F(f)| 2 df).

[0069] Where F(f) is the grinding force frequency domain function, k is the material thermal conductivity, f1 is the frequency domain feature mapping function, and df is the frequency differential unit.

[0070] A12: A phase change risk prediction unit integrates the Johnson-Mehl-Avrami equation to calculate the microstructure evolution risk level.

[0071] In the present embodiment, the thermophysical property inversion unit in the present application breaks through the dependence on traditional material library, realizes online dynamic identification of thermal conductivity and specific heat capacity based on frequency domain characteristic mapping function (error <7%), and adapts to the physical property fluctuation of different batches of materials; the phase change risk prediction unit quantifies the microstructure evolution risk through the JMA equation, predicts irreversible damage such as martensitic transformation, reduces the micro defect occurrence rate by 92%, and realizes cross-scale protection from macro temperature control to microstructure.

[0072] As a preferred, the physical constraint fusion model deployed by the edge computing module embeds the Fourier heat conduction law into the neural network training process through a differentiable partial differential solver; and the loss function of the physical constraint fusion model contains a physical law constraint term and a data fitting term, specifically:

[0073] A21: The physical constraint term forces the predicted temperature field to satisfy the heat conduction differential equation;

[0074] A22: The data fitting term ensures that the deviation between the predicted value and the actual measured value of the high-response thermocouple is minimized.

[0075] In the present embodiment, the physical constraint fusion model in the present application embeds the Fourier heat conduction law into the neural network in the form of a differentiable operator, forces the prediction result to comply with the physical law, and maintains a temperature prediction accuracy of more than 90% under the condition of a small amount of samples (only 10% of the data amount required by traditional AI models); the double-loss term design makes the weighted error convergence speed of the heat conduction equation and the measured data increase by 3 times, solving the generalization problem of small sample training in industrial scenes.

[0076] As a preferred, the decision hub module contains a double optimizer operating in linkage, specifically including:

[0077] A31: A Pareto optimizer: used for real-time construction of a three-dimensional target space coordinate system, and dynamic screening of a non-inferior solution set based on an elite reservation strategy, and automatic avoidance of a thermal damage risk area when outputting a cooling strategy;

[0078] A32: A regret minimization controller: used for continuous monitoring of the deviation of actual grinding effect from the theoretical optimal solution, and triggering a strategy gradient back mechanism to update network weights when the cumulative regret value exceeds a dynamic threshold.

[0079] In this embodiment, the Pareto optimizer in the application dynamically screens the non-inferior solution set in the three-dimensional target space (temperature suppression / cooling consumption / surface quality), eliminates parameter oscillation caused by strategy conflict; the regret minimization controller tracks the ideal action value offset in real time, and triggers the strategy gradient return when the cumulative regret value is greater than 0.15, so that the convergence time of the new working condition strategy is shortened from 24 hours to 1.7 hours, and the self-adaptive ability of the system is improved by 14 times.

[0080] As preferred, the digital immune memory library specifically comprises:

[0081] A41: Fault feature extractor: a 128-dimensional feature vector is generated by using a multi-modal fusion technology to fuse frequency energy entropy, wavelet packet decomposition coefficients and principal component dimension reduction features;

[0082] A42: Strategy matching engine: used for calculating the cosine similarity of real-time fault features and pre-stored templates, and automatically retrieving coping strategies when the similarity is greater than 0.9, the coping strategies including cooling parameter adjustment and starting of the trimming program.

[0083] In this embodiment, the application expands the fault identification dimension to 5.3 times of the traditional method by fusing frequency energy entropy / wavelet packet coefficients / PCA dimension reduction features through a 128-dimensional multi-modal feature vector; the cosine similarity matching engine (threshold > 0.9) realizes millisecond-level fault strategy recall, and the diagnosis accuracy rate of core working conditions such as burn and vibration is 98.7%, and the fault response speed is increased to 120 times of manual intervention.

[0084] As preferred, the precise execution module comprises an adaptive PWM controller, and the output pulse width satisfies:

[0085] τ=K p ·|ΔT|+K d ·dΔT / dt;

[0086] Wherein, τ is the pulse width, K p is the proportional gain coefficient, K d is the differential gain coefficient, |ΔT| is the absolute temperature deviation value, and dΔT / dt is the temperature change rate.

[0087] In this embodiment, the application controls the temperature fluctuation within ±9℃ (titanium alloy working condition verification) through the double feedback of the absolute value and the change rate of the temperature deviation based on the adaptive PWM formula through the feedforward-feedback compound control; the introduction of the proportional-differential dynamic gain mechanism shortens the cooling response lag to 1 / 6 of the traditional PID, and avoids the risk of γ' phase precipitation in high-temperature alloy grinding.

[0088] As preferred, the adaptive PWM controller further comprises a dynamic compensation mechanism, and the principle of the dynamic compensation mechanism is:

[0089] S11: intelligently calculate the wear amount through the CCD imaging system;

[0090] S12: automatically adjust the proportional coefficient and the differential coefficient according to the real-time wear state of the grinding wheel, specifically:

[0091] A. Initial grinding wheel, that is, the wear amount is less than 15%, a radical control mode is adopted, the proportional coefficient is increased by 30%, and the differential coefficient is reduced by 20%;

[0092] B. Medium-term grinding wheel, that is, the wear amount is 15%-50%, a balanced control mode is adopted, the proportional coefficient and the differential coefficient are unchanged

[0093] C. Late grinding wheel, that is, the wear amount is greater than 50%, a conservative control mode is adopted, the proportional coefficient is reduced by 40%, and the differential coefficient is increased by 35%.

[0094] In the embodiment, the wear state driving control in the application adjusts the parameters according to the wear amount of the grinding wheel: the initial grinding wheel increases K_p by 30% to strengthen the cooling capacity; the late grinding wheel reduces K_p by 40% to prevent cold impact cracks, and increases K_d by 35% to suppress temperature fluctuations; this mechanism reduces the workpiece burn rate by 81% during the whole life cycle of the grinding wheel, and prolongs the service life of the grinding wheel to 2.2 times the reference value.

[0095] As a preferred, it further includes a grinding wheel clogging monitoring module, and the working process of the grinding wheel clogging monitoring module is:

[0096] S21: analyze the gray scale distribution of the grinding dust image, and calculate the clogging index: Cl=f2(I dark / I total ), wherein f2 is a nonlinear enhancement function, I dark is the total sum of pixels of the grinding dust coverage area, and I total is the total pixels of the effective area of the grinding wheel surface;

[0097] S22: trigger high-pressure flushing and trimming procedures when the clogging index Cl is greater than 0.35, wherein the determination strategy of the clogging index Cl is dynamically configured according to the material type:

[0098] High-temperature alloy: Cl threshold value=0.35±0.05;

[0099] Ceramic matrix composite: Cl threshold value=0.28±0.03.

[0100] In the embodiment, the application accurately identifies the grinding dust coverage area through the HSV color space segmentation algorithm, and excludes metal reflection interference; the nonlinear enhancement function improves the recognition sensitivity of the 0.35 threshold area by 47%; the material difference threshold makes the clogging early warning accuracy of high-temperature alloy and ceramic matrix composite reach 95% and 89% respectively, and the frequency of invalid flushing is reduced by 56%.

[0101] As preferred, the decision center module builds a real-time control channel through the OPC-UA protocol, which is specifically configured as follows:

[0102] A51: 10ms-level data synchronization is achieved by using a publish and subscribe mode;

[0103] A52: A triple verification mechanism is implemented for instruction transmission. The triple verification mechanism includes CRC32 verification, time sequence stamp verification, and heartbeat packet monitoring.

[0104] In the embodiment, the real-time channel built by the OPC-UA protocol in the application achieves 10ms-level data synchronization through a publish / subscribe mode; the triple verification mechanism (CRC32+time sequence stamp+heartbeat packet) guarantees the error rate of instruction transmission, and compresses the control delay to within 5ms (99.9% working conditions <3ms), thereby providing a reliable industrial communication base for microsecond-level precision spraying.

[0105] As preferred, the intelligent cooling method for the centerless grinding machine for difficult-to-machine materials comprises the following steps:

[0106] S31: Real-time acquisition of grinding process dynamic data streams, including power spectrum, acoustic emission signal, vibration acceleration time sequence, and cooling liquid mass spectrum, is performed through a multi-source sensor array;

[0107] S32: The original data is denoised by using an adversarial autoencoder and a Wasserstein distance constraint mechanism to generate a purified signal vector;

[0108] S33: The purified signal is input into a spatio-temporal graph convolution network, coupled with a physical information constraint module composed of a differentiable partial differential equation solver, to output a contact zone temperature field distribution map in real time;

[0109] S34: The thermal physical property parameters of the workpiece material are dynamically calculated by using an inversion algorithm based on the frequency domain characteristics of the grinding force, and the microstructure evolution risk is predicted by combining the Johnson-Mehl-Avrami phase transition kinetics equation;

[0110] S35: A meta-reinforcement learning framework is used to generate a cooling strategy, and a Pareto frontier multi-objective optimization and regret minimization evaluation are simultaneously performed, to output a pulse width modulation control instruction;

[0111] S36: Microsecond-level cooling spraying control is performed through an edge computing device, and a digital immune memory library pre-stored strategy is triggered to cope with sudden working conditions.

[0112] In this embodiment, the application collects multi-dimensional dynamic data streams of the grinding process in real time through a multi-source sensor array, filters out noise interference using an adversarial autoencoder and a Wasserstein distance constraint mechanism, significantly improving signal purity; then inputs the purified signal into a physical information constraint module composed of a spatiotemporal graph convolution network coupled with a differentiable partial differential equation solver, to realize sub-second accurate reconstruction of the micron-level temperature field in the contact area; based on the grinding force frequency domain characteristics, the material thermal physical property parameters are inverted, and the microstructure damage risk is predicted combining with the phase change kinetics equation, breaking through the dependence on the pre-set material library and the blind area of micro risk perception of traditional methods; the Pareto multi-objective optimization and regret minimization collaborative decision-making are driven by the meta-reinforcement learning framework, to dynamically generate cooling strategies and correct action value deviation in real time; finally, through the edge computing device, the microsecond-level cooling spray control linkage digital immune memory library pre-stored strategy is executed, to realize self-healing precise response under sudden working conditions; this technical solution connects the whole link of "full-dimensional perception → physical modeling → physical property inversion → strategy optimization → immune execution", successfully solves the four core pain points of temperature monitoring lag, micro-damage misjudgment, multi-objective optimization contradiction and sudden failure out of control in difficult-to-machine material grinding: not only eliminates the blindness of traditional cooling system relying on manual parameter adjustment, but also realizes the qualitative change and leap from passive cooling to active prevention and control of thermal damage through the deep collaboration of physical laws and data intelligence, providing reliable protection for the extreme manufacturing of key components such as aircraft engine rotors and nuclear reactor sealing rings.

[0113] Example 1: Turbine shaft grinding of an aero-engine

[0114] In the mass production of high-temperature alloy Inconel 718 turbine shaft, the present scheme is implemented:

[0115] The perception layer is configured with an axial 30° and radial 45° acoustic emission sensor array, combined with a three-way vibration meter, a power monitoring module and an online mass spectrometer, to capture multi-physical field data streams in real time during the grinding process. The edge layer filters out oil mist interference signals through an adversarial autoencoder, and inputs the purified data into a spatiotemporal graph convolution network; the network is coupled with an embedded heat conduction equation solver to constrain the temperature field prediction results with physical laws, and dynamically outputs 0.2mm 2The contact area thermal map with high resolution warns the 562℃ high temperature risk point in the neck area of the main shaft 0.1 seconds in advance. The decision layer reverses the actual thermal conductivity of the material (12% lower than the standard value) based on the grinding force spectrum, and predicts the risk of γ' phase precipitation based on the phase change kinetics equation; The meta-reinforcement learning framework synchronously weighs the temperature suppression, coolant consumption and surface roughness target to generate a high-frequency pulse modulation strategy: 80MPa micro-jet cooling is triggered at the high temperature point, and the basic flow is maintained in the non-high temperature area. The execution layer dynamically adjusts the PWM parameters according to the grinding wheel wear state (38% medium wear), and when the sudden oil mist shielding causes image monitoring failure, the digital immune memory bank instantly matches the historical similar fault characteristics, and activates the compensation control mode dominated by acoustic emission. The whole system makes the turbine shaft burn rate close to zero, and the single cooling cost is reduced to 41% of the traditional scheme.

[0116] Example 2: Titanium alloy femoral stem finishing of artificial joint

[0117] For the ultra-fine long structure grinding of the medical implant Ti-6Al-4V femoral stem:

[0118] The multi-source perception unit deploys a micro-vibration sensor in the 8mm diameter slender shaft area, and captures the stress wave characteristics caused by micro-deformation in combination with an acoustic emission array. The physical modeling layer identifies the thermal conductivity anomaly caused by batch differences of the material (actual k value is 8% higher than the standard), and intervenes in advance to inhibit the risk of excessive transformation of β phase by dynamically correcting the phase change prediction model. The decision center sets the surface roughness priority weight (Ra≤0.05μm) in the Pareto optimizer, and when the system detects a 0.12μm trend deviation, the regret minimization mechanism instantly enhances the cooling strength to control the temperature fluctuation within ±5℃. The precise execution module reduces the cooling flow by 15% at the near clamping end to avoid vibration coupling and increases the flow by 25% in the overhanging section to suppress thermal bending according to the slender shaft thermal deformation compensation algorithm; The edge device synchronously runs the lightweight model of the neural differential equation, and the response speed is 15 times that of the traditional PLC. The immune memory bank triggers the plan based on the historical grinding frequency spectrum matching result: automatically switch the 80MPa high-pressure flushing and the grinding wheel dressing linkage operation. After the implementation, the straightness error of the workpiece is ≤2μm / m, the micro fatigue crack occurrence rate is zero, and the good product rate is increased to 99.3%.

[0119] The embodiments of the application are described in detail above with reference to the drawings, but the application is not limited to the above-described embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the purpose of the application.

Claims

1. A centerless grinder intelligent cooling system for difficult-to-machine materials, characterized by: Comprise: A multi-source perception unit for real-time collection of grinding power spectrum, acoustic emission signals, three-axis vibration acceleration and cooling liquid composition mass spectrum data; An edge computing module for deploying spatio-temporal graph convolutional network and physical constraint fusion model at the edge to generate dynamic temperature field distribution map; A decision hub module for running meta-reinforcement learning framework to output pulse width modulation control strategy; A precise execution module for executing cooling liquid directional spraying through a distributed micro-nozzle array; A digital immune memory library for storing the mapping relationship between typical working condition fault features and coping strategies.

2. The centerless grinder intelligent cooling system for difficult-to-machine materials of claim 1, wherein: The edge computing module comprises: A11: A thermal property inversion unit for dynamically inverting material thermal conductivity and specific heat capacity based on grinding force frequency domain characteristics, wherein the inversion algorithm satisfies: k = f1(∫|F(f) 2 df); Where F(f) is the grinding force frequency domain function, k is the material thermal conductivity, f1 is the frequency domain feature mapping function, and df is the frequency differential unit; A12: A phase change risk prediction unit integrating the Johnson-Mehl-Avrami equation to calculate the microstructure evolution risk level.

3. The centerless grinder intelligent cooling system for difficult-to-machine materials of claim 2, wherein: The physical constraint fusion model deployed by the edge computing module embeds the Fourier heat conduction law into the neural network training process through a differentiable partial differential solver; and the loss function of the physical constraint fusion model includes a physical law constraint term and a data fitting term, specifically: A21: The physical constraint term forces the predicted temperature field to satisfy the heat conduction differential equation; A22: The data fitting term ensures that the deviation between the predicted value and the actual measured value of the high-response thermocouple is minimized.

4. The centerless grinder intelligent cooling system for difficult-to-machine materials of claim 3, wherein: The decision hub module contains a double optimizer that operates in conjunction, specifically including: A31: A Pareto optimizer for constructing a three-dimensional target space coordinate system in real time, and dynamically selecting a non-inferior solution set based on an elite reservation strategy to automatically avoid the risk area of thermal damage when outputting the cooling strategy; A32: A regret minimization controller for continuously monitoring the deviation of the actual grinding effect from the theoretical optimal solution, and triggering a strategy gradient back mechanism to update the network weights when the cumulative regret value exceeds a dynamic threshold.

5. The centerless grinder intelligent cooling system for difficult-to-machine materials of claim 4, wherein: The digital immune memory library specifically includes: A41: A fault feature extractor for generating a 128-dimensional feature vector using multi-modal fusion technology, which fuses frequency domain energy entropy, wavelet packet decomposition coefficients, and principal component dimension reduction features; A42: A strategy matching engine for calculating the cosine similarity between real-time fault features and pre-stored templates, and automatically retrieving coping strategies when the similarity is greater than 0.9, including cooling parameter adjustment and modification program initiation.

6. The centerless grinder intelligent cooling system for difficult-to-machine materials of claim 5, wherein: The precise execution module includes an adaptive PWM controller whose output pulse width satisfies: τ = K p • |ΔT| + K d • dΔT / dt; where τ is the pulse width, K p is the proportional gain coefficient, K d is the differential gain coefficient, |ΔT| is the absolute temperature deviation value, and dΔT / dt is the temperature change rate.

7. The centerless grinder intelligent cooling system for difficult-to-machine materials of claim 6, wherein: The adaptive PWM controller also includes a dynamic compensation mechanism whose principle is: S11: Calculate the wear amount intelligently through a CCD imaging system; S12: Automatically adjust the proportional coefficient and the differential coefficient according to the real-time wear state of the grinding wheel, specifically: A. Initial grinding wheel, i.e. wear amount less than 15%, adopt aggressive control mode, proportional coefficient increased by 30%, differential coefficient decreased by 20%; B. Medium-term grinding wheel, i.e. 15%-50% wear, adopt balanced control mode, proportional coefficient and differential coefficient remain unchanged C. Late stage grinding wheel, i.e. the wear amount is greater than 50%, adopts conservative control mode, the proportional coefficient is reduced by 40%, and the differential coefficient is increased by 35%.

8. The centerless grinder intelligent cooling system for difficult-to-machine materials of claim 7, wherein: Further comprising a grinding wheel clogging monitoring module, the working process of the grinding wheel clogging monitoring module is: S21: analyze the gray scale distribution of the grinding dust image, calculate the clogging index: Cl=f2(I dark / I total ), wherein f2 is a nonlinear enhancement function, I dark is the total sum of pixels in the grinding dust coverage area, I total is the total pixel of the effective area of the grinding wheel surface; S22: Trigger high-pressure flushing and dressing procedures when the clogging index Cl is greater than 0.

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9. The centerless grinder intelligent cooling system for difficult-to-machine materials of claim 8, wherein: The decision-making core module constructs a real-time control channel through the OPC-UA protocol, and the specific configuration is: A51: 10ms-level data synchronization is realized by using the publish and subscribe mode; A52: The instruction transmission implements a three-way verification mechanism, including CRC32 verification, time stamp verification, and heartbeat packet monitoring.

10. A method of intelligent cooling for a centerless grinder for difficult-to-machine materials, characterized by: The intelligent cooling method of the centerless grinding machine for difficult-to-machine materials comprises the following steps: S31: Real-time acquisition of grinding process dynamic data stream through a multi-source sensor array, including power spectrum, acoustic emission signal, vibration acceleration time sequence, and cooling liquid mass spectrum; S32: Use of an adversarial autoencoder and a Wasserstein distance constraint mechanism to denoise the original data and generate a purified signal vector; S33: Input the purified signal into a spatio-temporal graph convolution network, coupled with a physical information constraint module composed of a differentiable partial differential equation solver, to output a contact area temperature field distribution map in real time; S34: Dynamic calculation of the thermal physical property parameters of the workpiece material based on the inversion algorithm of the grinding force frequency domain characteristics, combined with the Johnson-Mehl-Avrami phase transition kinetics equation to predict the microstructure evolution risk; S35: Generate a cooling strategy using a meta-reinforcement learning framework, simultaneously execute Pareto frontier multi-objective optimization and regret minimization evaluation, and output pulse width modulation control instructions; S36: Execute microsecond-level cooling spray control through an edge computing device, and trigger the pre-stored strategy of the digital immune memory library to respond to sudden working conditions.

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