A selenium drum recycling replacement system and control method

Abstract

The application relates to the technical field of selenium drum recycling, and particularly discloses a selenium drum recycling and replacing system and a control method. First, sub-surface defects and local mechanical properties of waste metal core substrates are quantitatively characterized through microwave resonance and nano-pressing multi-source fusion detection, and substrate state quantitative data sets are generated. The data sets and target coating parameters are input to perform multi-physical field transient simulation, and interface residual stress in a regeneration process is predicted. Based on the prediction result, a dynamic temperature control sequence and a gradient material ratio scheme are reversely solved. In a vacuum coating environment, active stress compensation coating is realized, transient thermal excitation modal testing is performed on the regenerated selenium drum, high-frequency dynamic characteristics of the regenerated selenium drum are extracted and compared with the simulation prediction value, and simulation model parameters are optimized according to deviation data feedback, so that a closed-loop control is formed, and the interface bonding strength and long-term imaging reliability of the regenerated selenium drum are improved.

Description

Technical Field

[0001] This invention relates to the field of toner cartridge recycling technology, specifically to a toner cartridge recycling and replacement system and control method. Background Technology

[0002] As a core imaging component, the recycling and reuse of printer toner cartridges is of great significance for saving resources and reducing electronic waste pollution. Currently, the industry's commonly used recycling processes mainly focus on disassembling, cleaning, refilling, and replacing easily damaged parts (such as drum cores and magnetic rollers) of used toner cartridges. For the recycling of the most valuable and technologically advanced photosensitive drum (i.e., the metal core substrate and its photoconductive coating), the usual methods are to directly replace it with a brand new drum core or to simply clean the surface of the old drum core and then recoat it with photoconductive material.

[0003] Existing toner cartridge regeneration technology cannot overcome the unpredictable interfacial residual stress caused by the material property mismatch between the historical micro-fatigue of the waste metal core substrate and the newly coated photoconductive layer. This residual stress is gradually released or redistributed during the subsequent service of the regenerated toner cartridge due to environmental thermal cycling and mechanical stress loading, resulting in the initiation, propagation, and even local peeling of micro-cracks in the photoconductive coating. The external manifestation is random, delayed imaging defects (such as ghost stripes and periodic faint spots) on the printed image that cannot be reproduced or traced by conventional means. This seriously damages the long-term service reliability and product consistency of the regenerated toner cartridge, becoming a technical bottleneck restricting high-quality regeneration and recycling. Summary of the Invention

[0004] The purpose of this invention is to provide a toner cartridge recycling and replacement system and control method to solve the problems mentioned above.

[0005] The objective of this invention can be achieved through the following technical solutions: A method for controlling the recycling and replacement of toner cartridges includes the following steps: S1: Scan the metal core substrate of the cleaned waste toner cartridge to obtain fatigue characteristic data and geometric deformation data of the surface and subsurface of the metal core substrate. Through multi-source information fusion processing, generate a unique substrate state quantification dataset of the metal core substrate. S2: Combine the substrate state quantification dataset with the material parameters and preset process parameters of the target photoconductive layer to perform transient simulation analysis with multi-physics coupling, and output regenerative stress field prediction data to characterize the interface stress state between the metal core substrate and the target photoconductive layer under the preset process. S3: In a vacuum coating environment, a dynamic temperature control sequence is obtained, and dynamic temperature control is implemented on the metal core substrate according to the dynamic temperature control sequence; based on the gradient transition layer composition planning, a gradient transition layer with continuously changing composition and the final target photoconductive layer are sequentially deposited on the surface of the metal core substrate to complete the drum regeneration. S4: In a vacuum deposition environment, a pre-set compensation temperature control scheme is used to regulate the temperature field of the metal substrate according to the pre-set compensation temperature control scheme; based on the gradient material ratio scheme, a functional gradient transition layer with continuously changing composition and a target photoconductive coating are sequentially deposited on the surface of the metal substrate. S5: Collect actual deformation or stress response data of the regenerated drum and compare it with the regenerated stress field prediction data. If the comparison result does not meet the preset standard, the actual collected data is fed back to S2 as a new input to iteratively update the regenerated stress field prediction data and the adaptive compensation process parameter set until the comparison result meets the standard.

[0006] As a further aspect of the present invention: S1 specifically includes: By using a microwave resonant probe to scan along the axial and circumferential directions of a metal core substrate, and capturing the offset between the resonant frequency and the quality factor, first-type fatigue characteristic data characterizing the distribution of subsurface micro-defects are obtained. Based on the defect area indicated by the first type of fatigue characteristic data, a probe is used to perform fixed-point testing to obtain the load-displacement curves of the material in the defect area at multiple indentation depths, from which the second type of fatigue characteristic data characterizing the local mechanical property degradation is extracted. The first type of fatigue feature data and the second type of fatigue feature data are registered and fused in spatial coordinates, and combined with the geometric deformation data of the metal core substrate surface synchronously acquired by the optical profilometer, to jointly generate a substrate state quantification dataset.

[0007] As a further aspect of the present invention: S2 specifically includes: Based on the data that centrally characterizes the local mechanical property degradation of materials using the substrate state quantification dataset, the local yield function of the metal core substrate at different axial and circumferential positions under thermal cycling conditions is calculated to generate microscopic plastic evolution prediction data. Using microscopic plastic evolution prediction data as input for non-uniform material properties, the equivalent structural stiffness of the metal core substrate under the constraint of the coating fixture is corrected to obtain time-varying structural constraint characterization data. Using time-varying structural constraint characterization data as dynamic boundary conditions, and combined with the material parameters of the target photoconductive layer, we perform iterative calculations of the energy field coupling thermal conduction, material phase transformation and elastoplastic deformation until the interface energy density distribution tends to stabilize. Based on the stable energy field distribution, the stress gradient in the thickness direction between the metal core substrate and the target photoconductive layer is inverted, and the regenerative stress field prediction data is output.

[0008] As a further aspect of the present invention: the inversion process of the stress gradient is as follows: Based on a stable energy field distribution, the elastic energy density component and plastic dissipation energy density component are extracted at the interface between the metal core substrate and the target photoconductive layer and at multiple discrete depth nodes inside the layer. Based on the elastic energy density components at each node and the acoustoelastic relationship of the material, the relative offset of the equivalent elastic constant caused by residual stress at the corresponding node is calculated. Based on the relative offset of the equivalent elastic constants and the spatial gradient of the plastic dissipation energy density component, the stress tensor components that vary continuously along the thickness direction are reconstructed, and the stress gradient is obtained.

[0009] As a further aspect of the present invention: S3 specifically includes: Based on the dynamic temperature control sequence, independent infrared radiation power spectra for different axial zones of the metal core substrate are decoupled. Based on the independent infrared radiation power spectrum, multiple independent and controllable infrared radiation units are used to perform non-contact heating on the corresponding axial partitions of the rotating metal core substrate, and the surface dynamic temperature data of each partition is obtained in real time through non-contact infrared temperature measurement. The surface dynamic temperature data is compared with the set value in the dynamic temperature control sequence in real time. By adjusting the power of the infrared radiation unit, the surface dynamic temperature data tracks the set value in real time, forming a closed-loop temperature control.

[0010] As a further aspect of the present invention: the decoupling of independent infrared radiation power spectra for different axial zones of the metal core substrate specifically includes: Based on the target temperature sequence of each axial zone in the dynamic temperature control sequence and the thermal properties of the metal core substrate, the theoretical instantaneous heat flux density required for each zone to reach the target temperature is calculated. Based on the rotational speed and orientation of the metal core substrate in the vacuum coating environment, the theoretical instantaneous heat flux density is converted into a periodic equivalent heat flux density modulated by the rotation period. Using the periodic equivalent heat flux density as the desired value and taking into account the radial and axial thermal conduction coupling effects between adjacent partitions, the set of power commands that each infrared radiation unit should output is solved by inversion calculation. The power command set of all partitions is integrated to form the independent infrared radiation power spectrum covering the entire process cycle.

[0011] As a further aspect of the present invention: S4 specifically includes: Based on the gradient material ratio scheme, the real-time power ratio change curves of multiple sputtering targets are analyzed, and power ratio control timing instructions are generated. Based on the power ratio control timing command, multiple sputtering targets are driven to work simultaneously, and the emission spectrum of the plasma in the deposition area is collected in real time using a spectral probe placed near the metal substrate. The intensity ratios of characteristic spectral lines of different elements are extracted from the emission spectrum, and the intensity ratios are compared in real time with the expected values ​​in the power ratio control timing command to generate element ratio deviation signals. Based on the element ratio deviation signal, the power of each sputtering target is adjusted so that the actual chemical composition of the deposited film continuously tracks the gradient material ratio scheme.

[0012] As a further aspect of the present invention: S5 specifically includes: A transient thermal shock excitation with a known amplitude and frequency range was applied to the regenerated drum, and the residual vibration response spectrum of the metal core substrate under transient thermal shock excitation was collected using a non-contact vibration measurement device. From the residual vibration response spectrum, the high-order modal frequencies and damping ratios that are directly related to the interface bonding state between the target photoconductive layer and the metal core substrate are separated to generate interface dynamic mechanical characteristic data. The interface dynamic mechanical characteristic data and the theoretical dynamic response at the corresponding position in the regenerative stress field prediction data are compared point by point, and the quantitative deviation between the two in characteristic frequency and energy dissipation rate is calculated to generate energy field deviation data. Based on the energy field deviation data, the correction amount needed to adjust the local dynamic stiffness and gradient transition layer damping characteristics of the metal core substrate in subsequent simulations is derived in reverse, and the correction amount is output as feedback data.

[0013] A toner cartridge recycling and replacement system includes: The substrate state quantification module scans the metal core substrate of the cleaned waste toner cartridge to obtain fatigue characteristic data and geometric deformation data of the surface and subsurface of the metal core substrate. Through multi-source information fusion processing, a unique substrate state quantification dataset of the metal core substrate is generated. The stress field simulation and prediction module combines the substrate state quantification dataset with the material parameters and preset process parameters of the target photoconductive layer to perform transient simulation analysis with multi-physics coupling, and outputs regenerative stress field prediction data to characterize the interface stress state between the metal core substrate and the target photoconductive layer under the preset process. The adaptive coating control module acquires a dynamic temperature control sequence in a vacuum coating environment and implements dynamic temperature control on the metal core substrate according to the dynamic temperature control sequence; based on the gradient transition layer composition planning, it sequentially deposits a gradient transition layer with continuously changing composition and the final target photoconductive layer on the surface of the metal core substrate to complete the drum regeneration. The deposition process execution module, in a vacuum deposition environment, presets a compensation temperature control scheme and regulates the temperature field of the metal substrate according to the preset compensation temperature control scheme; based on the gradient material ratio scheme, it sequentially deposits a functional gradient transition layer with continuously varying composition and a target photoconductive coating on the surface of the metal substrate. The closed-loop verification and iterative optimization module collects actual deformation or stress response data of the regenerated drum and compares it with the regenerated stress field prediction data. If the comparison result does not meet the preset standard, the actual collected data is fed back to the stress field simulation prediction module as a new input to iteratively update the regenerated stress field prediction data and the adaptive compensation process parameter set until the comparison result meets the standard.

[0014] The beneficial effects of this invention are: (1) This invention achieves individualized and quantitative characterization of the subsurface fatigue state of the substrate through multi-source fusion detection (microwave resonance, nano-indentation), and based on this, accurately predicts the interface stress evolution under specific processes using multi-physics coupling simulation. Furthermore, through an "adaptive stress compensation coating" process (such as dynamic non-uniform temperature control and gradient transition layer deposition), the adverse internal stress caused by the historical state of the substrate is actively and accurately offset. Finally, the effectiveness of stress compensation is ensured through closed-loop verification based on vibration modes. This technological leap from "passive masking" to "active prediction and compensation" extends the reliable service life of regenerated toner cartridges.

[0015] (2) This invention constructs a data closed loop throughout the entire process: from the initial quantitative data of the substrate state, to the stress prediction data generated by simulation, to the real-time monitoring data of the deposition process (temperature, spectrum), and finally to the performance verification data of the finished product. Each process decision (such as temperature control curve, target power) is driven and optimized by the data of the previous step, rather than a fixed program. In particular, through the feedback iteration of the S5 step, the simulation model can continuously self-correct, and the process parameters can be continuously optimized, forming a self-learning and adaptive intelligent manufacturing system. This not only ensures the high consistency of the quality of substrate regeneration in different batches and in different states, but also makes the full life cycle data of each regenerated toner cartridge traceable, providing a solid data foundation for quality analysis and continuous process improvement. From an economic point of view, although the initial investment in testing and simulation increases, the resulting increase in yield, decrease in rework rate, and enhanced product premium make the overall cost-effectiveness better than traditional methods. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2This is a system block diagram of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 As shown, this invention provides a control method for the recycling and replacement of toner cartridges, comprising the following steps: S1: Scan the metal core substrate of the cleaned waste toner cartridge to obtain fatigue characteristic data and geometric deformation data of the surface and subsurface of the metal core substrate. Through multi-source information fusion processing, generate a unique substrate state quantification dataset of the metal core substrate. S2: Combine the substrate state quantification dataset with the material parameters and preset process parameters of the target photoconductive layer to perform transient simulation analysis with multi-physics coupling, and output regenerative stress field prediction data to characterize the interface stress state between the metal core substrate and the target photoconductive layer under the preset process. S3: In a vacuum coating environment, a dynamic temperature control sequence is obtained, and dynamic temperature control is implemented on the metal core substrate according to the dynamic temperature control sequence; based on the gradient transition layer composition planning, a gradient transition layer with continuously changing composition and the final target photoconductive layer are sequentially deposited on the surface of the metal core substrate to complete the drum regeneration. S4: In a vacuum deposition environment, a pre-set compensation temperature control scheme is used to regulate the temperature field of the metal substrate according to the pre-set compensation temperature control scheme; based on the gradient material ratio scheme, a functional gradient transition layer with continuously changing composition and a target photoconductive coating are sequentially deposited on the surface of the metal substrate. S5: Collect actual deformation or stress response data of the regenerated drum and compare it with the regenerated stress field prediction data. If the comparison result does not meet the preset standard, the actual collected data is fed back to S2 as a new input to iteratively update the regenerated stress field prediction data and the adaptive compensation process parameter set until the comparison result meets the standard.

[0020] In S1, microwave resonant scanning is performed to acquire subsurface defect data. The metal core substrate is mounted on a fixture capable of precise rotation and axial movement. A frequency-adjustable microwave resonant probe is used, whose electromagnetic field depth covers the subsurface region of the substrate. The probe is controlled to scan point-by-point along the axial and circumferential directions of the substrate. At each scanning point, the offset of the resonant frequency and the offset of the quality factor when the probe couples with a defect-free reference substrate at that point are recorded. The resonant frequency offset is obtained by calculating the difference between the resonant frequency value of the current scanning point and the pre-stored standard resonant frequency value of a defect-free substrate of the same model at the same location. The quality factor offset is obtained by calculating the ratio of the measured quality factor value of the current scanning point to the pre-stored standard quality factor value, and then subtracting one. The two offset data of all scanning points, combined with their corresponding three-dimensional spatial coordinates, are organized into the first type of fatigue characteristic data, which is a dataset containing position information, frequency offset, and quality factor offset.

[0021] Targeted nanoindentation tests were performed to quantify local mechanical property degradation. Based on the first type of fatigue characteristic data, coordinate regions where both frequency offset and quality factor offset exceeded set thresholds were identified and marked as suspected defect areas. Using a nanoindentation probe equipped with a Berkovich indenter, indentation tests were performed at the center point of each marked region. During the indentation process, the indenter was controlled to load at a constant rate to three different preset depth values. After holding the load at each depth value for a period of time, the load was unloaded, and the load and displacement data during this process were fully recorded to obtain a load-displacement curve. The curve was analyzed, and the slope in the middle of the loading phase was extracted as the gradient value at that depth. The gradient value was calculated by linearly fitting the load-displacement curve data within a specified load range; the slope of the fitted line was the gradient value. The gradient values ​​obtained at the three different indentation depths were correlated with the coordinate positions to form the second type of fatigue characteristic data.

[0022] Multi-source data registration and fusion are performed. While acquiring the geometric deformation data by using an optical profilometer to collect the three-dimensional morphology of the substrate surface, the coordinate system of the optical scan is simultaneously recorded. By setting three non-collinear reference points on the surface of the substrate under test, a unified spatial mapping relationship is established between the microwave scanning coordinate system, the nano-indentation test coordinate system, and the optical profilometer coordinate system, completing the registration of the first type of fatigue characteristic data, the second type of fatigue characteristic data, and the geometric deformation data in spatial coordinates. During data fusion, for points where the spatial coordinates coincide after registration, the frequency offset in the first type of fatigue characteristic data, the gradient value at a moderate indentation depth in the second type of fatigue characteristic data, and the local curvature value in the geometric deformation data are weighted and summed according to preset weighting coefficients to generate a comprehensive state index value. The weighting coefficients are pre-calibrated based on the signal-to-noise ratio of each data source. Finally, the comprehensive state index values, original characteristic data, and coordinate information corresponding to all spatial coordinate points are integrated to form the substrate state quantification dataset.

[0023] In S2, firstly, based on the data characterizing the local mechanical property degradation of the material in the substrate state quantification dataset, the local yield function of the metal core substrate at different locations is calculated, generating microscopic plastic evolution prediction data. The substrate state quantification dataset contains a comprehensive state index value corresponding to each coordinate point, which is negatively correlated with the degree of degradation of the material's yield strength. A mapping relationship between local yield strength and comprehensive state index value is established. This relationship is obtained through prior experimental calibration: uniaxial tensile tests are performed on samples with different known fatigue states to obtain their yield strength, and their comprehensive state index values ​​are calculated simultaneously. The two data are then fitted using a second-order polynomial least-squares method, and the resulting fitting relationship is the mapping relationship. For each finite element of the metal core substrate in the simulation, the corresponding comprehensive state index value is obtained from the quantification dataset based on its spatial coordinates, and the current yield strength value of the element is calculated through the mapping relationship. The calculation method for the local yield function is as follows: at each simulation time step, based on the element's current Mises equivalent stress, the calculated yield strength value, and the material hardening modulus, it is determined whether it has entered the plastic state and the increment of plastic strain. The Mises equivalent stress is calculated based on the components of the stress tensor of the element. The predicted yield strength of all elements and the plastic strain increment at each time step are organized into microscopic plastic evolution prediction data.

[0024] Secondly, the microscopic plastic evolution prediction data is used as a non-uniform material property to correct the equivalent structural stiffness of the metal core substrate under fixture constraints, thus obtaining time-varying structural constraint characterization data. The metal core substrate is discretized into a three-dimensional solid element mesh, and the regions at both ends that contact the simulated coating fixture are defined as constraint boundaries. The yield strength values ​​of each element obtained in the previous step are assigned to the corresponding element's material properties. At each simulation time step, the plastic state of each element is checked: if an element enters plasticity, the elastic modulus value of that element in the current direction is dynamically reduced according to its plastic strain increment and the tangent modulus method in plasticity theory. The reduction magnitude is proportional to the magnitude of the plastic strain increment, and the proportionality coefficient is calibrated through uniaxial cyclic plasticity tests of the material. Based on the updated elastic modulus of each element, the overall stiffness matrix of the entire substrate is reassembled, and the overall deformation field and internal nodal reactions of the substrate are obtained by solving the equilibrium equations under fixture displacement constraints. The deformation field and nodal reaction force data together constitute time-varying structural constraint characterization data reflecting the time-varying mechanical state of the substrate.

[0025] Next, using time-varying structural constraint characterization data as dynamic boundary conditions, and combining the material parameters of the target photoconductive layer, an iterative calculation of the energy field coupled with thermal conduction, material phase transformation, and elastoplastic deformation is performed. The calculation is performed iteratively in each time step: First, based on the preset coating process temperature curve, the heat conduction process from the environment to the substrate and coating is calculated, and the temperature field of all units is updated; Second, based on the updated temperature field, it is determined whether the target photoconductive layer material has undergone a phase transition. If the unit temperature exceeds its phase transition initiation temperature, the latent heat of phase transition and the volume fraction increment of phase transition within this time step are calculated, and the material properties of the unit (such as the coefficient of thermal expansion and the elastic modulus) are updated. Specifically, this includes: determining whether the current temperature of the unit is within the interval formed by its phase transition initiation temperature and phase transition end temperature; if it is within the interval, the volume fraction of phase transition at the end of the current time step is calculated according to the phase transition kinetic model. This calculation uses the difference between the current temperature value and the phase transition characteristic temperature, multiplied by the phase transition rate constant, and then multiplied by the time step size. This product is then added to the volume fraction of the previous step, but the total sum does not exceed one; the volume fraction increment is the volume fraction at the end of the current step minus the volume fraction of the previous step. Subsequently, the latent heat of phase change released or absorbed within this time step is calculated. Its value is equal to the product of the latent heat of phase change per unit mass of the material, the material density, the unit volume, and the increment of the phase change volume fraction. Finally, based on the updated phase change volume fraction, the material properties of the unit are updated using a weighted average: its equivalent thermal expansion coefficient is equal to the thermal expansion coefficient of the matrix material multiplied by one minus the current volume fraction, plus the thermal expansion coefficient of the new phase material multiplied by the current volume fraction; its equivalent elastic modulus is also updated according to this linear mixing rule. In the third step, the updated temperature field and phase change volume fraction are applied as thermal loads and phase change strain loads to the structural mechanics analysis. Simultaneously, the time-varying structural constraint characterization data from the current time step are imported as displacement and force boundary conditions to solve for the new stress and strain fields. These three processes are iterated until a preset convergence threshold is met.

[0026] Finally, based on the stable energy field distribution, the stress gradient in the thickness direction between the metal core substrate and the target photoconductive layer is inverted. The inversion process is as follows: In the energy field results that tend to stabilize, a series of nodes are selected at equal intervals along the thickness direction perpendicular to the interface, from the interior of the metal substrate, through the interface, and down to the interior of the photoconductive coating. For each node, two energy components are extracted from its element results: one is the elastic strain energy density, which is half of the dot product of the element stress tensor and the elastic strain tensor; the other is the plastic dissipation energy density increment, which is the dot product of the element stress tensor and the plastic strain increment tensor within that time step. Based on the elastic strain energy density of each node and the acoustoelastic relationship of the material, the relative shift of the equivalent elastic constant caused by residual stress is calculated. The acoustoelastic relationship is expressed as follows: the square of the ultrasonic longitudinal wave velocity in the material is linearly related to the hydrostatic pressure stress component. The method for calculating the relative shift of the equivalent elastic constant is to divide the elastic strain energy density of the node by the product of the material density and the square of the longitudinal wave velocity in the stress-free state, and then multiply by a calibration coefficient determined by the third elastic constant of the material. After obtaining the equivalent elastic constant relative offset sequence of a series of nodes along the thickness direction, the first derivative of this sequence with respect to the thickness coordinate is calculated, thus obtaining its spatial gradient. Simultaneously, the first derivative of the plastic dissipation energy density increment sequence with respect to the thickness coordinate is calculated. The reconstruction of each component of the stress tensor is accomplished through iterative inversion: first, an initial stress distribution is assumed, and its predicted elastic constant offset gradient and plastic work gradient are calculated and compared with the two gradient values ​​obtained from simulation; the assumed stress distribution is continuously adjusted using the gradient descent method until the root mean square error between its predicted gradient value and the simulated gradient value is less than a preset threshold. The final stress distribution that continuously varies along the thickness is the stress gradient. Integrating the stress data from all time steps and spatial locations constitutes the regenerative stress field prediction data.

[0027] In S3, firstly, based on the dynamic temperature control sequence, independent infrared radiation power spectra for different axial partitions of the metal core substrate are decoupled. The dynamic temperature control sequence includes target surface temperature values ​​for multiple independent partitions along the axial direction of the metal core substrate at various time points within the process cycle. The first step of the decoupling process is to calculate the required theoretical instantaneous heat flux density based on the target temperature time series of each partition. For each partition, at each calculation time step, the theoretical instantaneous heat flux density is equal to the product of the density of the metal material in that partition, its specific heat capacity, and the rate of change of the target temperature with respect to time. The rate of change of the target temperature with respect to time is obtained by numerically differentiating its target temperature time series curve. The second step of the decoupling process is to perform heat flux density conversion. Considering that the metal core substrate rotates uniformly around its axis within the vacuum chamber, and the fixed heating area of ​​each infrared radiation unit periodically sweeps across the substrate surface, the calculated theoretical instantaneous heat flux density acting on the fixed spatial partition is converted into an equivalent heat flux density that periodically changes with time and acts on the corresponding spatial angular domain of the fixed radiation unit. The conversion is based on the substrate's rotational speed and the axial projection width of the radiating element: the equivalent heat flux density equals the theoretical value during the time the radiating element sweeps across the corresponding partition, and is zero when sweeping other partitions. The third step of the decoupling process is to invert and calculate the power command of the infrared radiating element. The periodic equivalent heat flux density obtained in the previous step is used as the desired input. A comprehensive heat transfer model describing infrared radiation, substrate surface absorption, and heat conduction between the substrate interior and adjacent partitions is established. This model is discretized into a system of linear equations based on time steps and spatial grids. By solving the inverse problem of this system of equations, a set of power output sequences of infrared radiating elements is found such that the overall deviation between the substrate surface heat flux distribution predicted by the model from this power sequence and the desired periodic equivalent heat flux density sequence is minimized. This solution is performed iteratively using the least squares method until the root mean square value of the deviation is less than 10 watts per square meter. The final step of the decoupling process is integration, which summarizes and aligns the power output time series of each infrared radiating element obtained from the inversion calculation throughout the entire process cycle to form the independent infrared radiation power spectrum.

[0028] Secondly, dynamic temperature control is implemented based on independent infrared radiation power spectra. Within the vacuum coating chamber, multiple independent infrared radiation units are arranged parallel to the axis of the metal core substrate, each unit roughly corresponding to an axial partition of the substrate. The power supply of each radiation unit receives commands from the power spectrum and adjusts its output power in real time. Simultaneously, non-contact infrared temperature probes are paired with each radiation unit, pointing towards the corresponding partitions on the substrate surface. Each probe has a data sampling period of 100 milliseconds, measuring the average temperature of the substrate surface within its field of view in real time, and outputting dynamic surface temperature data.

[0029] Finally, closed-loop temperature control is executed to track the setpoint. This process is continuous within each control cycle, which is set to 500 milliseconds. At the beginning of each control cycle, the latest surface dynamic temperature data collected by the infrared temperature probes of each zone at the current moment is read. This data is compared with the target temperature setpoint of the corresponding zone at the current moment in the dynamic temperature control sequence, and the temperature deviation is calculated. Then, a proportional-integral-derivative (PID) control algorithm is used to process this deviation. The proportional, integral, and derivative coefficients are pre-tuned according to the thermal response time constant of the substrate in each zone. The output of the control algorithm is the percentage of the power adjustment required by each infrared radiation unit in the current control cycle. This percentage adjustment is applied to the pre-stored baseline power value in the independent infrared radiation power spectrum at the current moment, generating a real-time corrected power execution command, which is immediately sent to the power supply of the corresponding infrared radiation unit. Through this cycle of real-time measurement, comparison, calculation, and adjustment, the surface dynamic temperature data of each zone closely tracks the setpoint in the dynamic temperature control sequence, achieving precise and non-uniform dynamic temperature control of the metal core substrate.

[0030] In step S4, firstly, based on the gradient material ratio scheme, the real-time power ratio variation curves of multiple sputtering targets are analyzed, generating power ratio control timing instructions. The gradient material ratio scheme defines, in the form of a data table, the continuous variation trajectory of the atomic percentage of different chemical elements in the desired thin film throughout the entire deposition period. For example, for a gradient layer transitioning from metallic aluminum to a target photoconductive material (such as amorphous selenium), the scheme specifies a function where the percentage of aluminum smoothly decreases from 100% to 0% over time, while the percentage of selenium smoothly increases from 0% to 100% over time. The analysis process converts this compositional variation trajectory into proportional requirements for the output power of multiple sputtering targets (such as pure aluminum targets and pure selenium targets). The conversion is based on the deposition rate curves of each target under specific process gas pressures and target-substrate distances. These curves are obtained through prior experimental calibration and record the deposition rate values ​​of each target on the substrate at different sputtering powers. For each control time point (with a 1-second time interval), based on the percentage of the target element in the gradient material ratio scheme at that moment and the deposition rate curve of each target, a set of linear equations is solved to calculate the sputtering power required for each target at that time, so that the weighted sum of the deposition rates of each target exactly reaches the target composition. The power values ​​of each target calculated at all time points are arranged in chronological order to generate a power ratio control timing instruction, which is a data stream containing a timestamp, target number, and set power value.

[0031] Secondly, the sputtering process is driven and spectral monitoring is performed according to the power ratio control timing command. At least two independent DC or RF sputtering targets are installed in the vacuum deposition chamber, each aligned with the rotating metal substrate. The power controller of each target receives and executes the corresponding power setting value in the power ratio control timing command. Simultaneously, a spectral probe consisting of an optical fiber collector is installed near the substrate deposition area within the chamber, with its field of view aligned with the plasma glow region. This probe is connected to a spectrometer, whose acquisition period is set to 200 milliseconds. During the deposition process, the spectrometer continuously acquires the emission spectrum of the plasma, covering a wavelength range from 200 nanometers to 800 nanometers, and outputs the spectral intensity data in real time according to wavelength channels.

[0032] Next, the intensity ratios of elemental characteristic spectral lines are extracted from the emission spectrum, and a deviation signal is generated. Each frame of emission spectrum data acquired in real time is processed. First, the peak positions of characteristic spectral lines belonging to different target elements are identified; for example, the characteristic spectral line of aluminum is located at 396.2 nm, and the characteristic spectral line of selenium is located at 196.1 nm. Characteristic spectral line identification is accomplished by peak matching between the current spectrum and a pre-stored standard elemental emission spectrum database. Then, the key intensity ratios used for monitoring are calculated, such as the ratio of the peak intensity of the selenium characteristic spectral line at 196.1 nm to the peak intensity of the aluminum characteristic spectral line at 396.2 nm. The moving average of this ratio within the current time window (e.g., the most recent second) is calculated. Simultaneously, based on the power setting values ​​of each target material in the current power ratio control timing command and the pre-calibrated "power ratio-deposition composition-plasma spectral line intensity ratio" relationship model, the theoretical value of the expected spectral intensity ratio at that moment is calculated. The difference between the calculated real-time moving average of the spectral intensity ratio and the calculated theoretical expected value is used as the elemental ratio deviation signal.

[0033] Finally, the sputtering target power is adjusted based on the elemental ratio deviation signal to achieve composition tracking. A proportional-integral controller is established to process the elemental ratio deviation signal. The controller's input is a continuous deviation signal, and its output is a real-time correction to the "slave target" power (e.g., selenium target power) set in the power ratio control timing command. The proportional coefficient and integral time constant are tuned according to the process response delay (mainly plasma stabilization time and deposition film thickness accumulation time). Specifically, the adjustment process is as follows: every 500 milliseconds, the controller calculates once, and its output value is the sum of the proportional and integral terms. The proportional term is the current deviation signal multiplied by the proportional coefficient (e.g., 0.5). The integral term is the sum of all deviation signal values ​​over a past period (e.g., 10 seconds) multiplied by the integral coefficient (e.g., 0.05). The power correction output by the controller is superimposed on the "slave target" set power value corresponding to the current moment in the power ratio control timing command to form the final real-time execution power of the target, which is immediately sent to its power controller. Through this closed-loop control, the actual chemical composition of the deposited film can be corrected in real time, thereby continuously and accurately tracking the preset gradient material ratio scheme.

[0034] In S5, firstly, a transient thermal shock excitation is applied to the regenerated drum unit, and the residual vibration response spectrum is acquired. The regenerated drum unit (i.e., the metal core substrate coated with a gradient transition layer and a target photoconductive layer) is mounted on a vibration isolation test bench, simulating the actual constraint state inside a printer. A high-power pulsed laser is used, with the laser beam focused into a 2 mm diameter spot via an optical system and projected onto the center of one end face of the metal core substrate. The energy density of the laser pulse is controlled at 50 millijoules per square centimeter, and the pulse width is 10 nanoseconds, thus applying a transient thermal shock excitation with a known amplitude and frequency range (mainly energy distribution within 100 kHz). Simultaneously, a laser Doppler vibrometer is used, with its measurement beam focused on the center point of the other end face of the metal core substrate. From the start of laser pulse triggering, the vibrometer continuously acquires the velocity response time-domain signal at this measurement point at a sampling rate of 1 MHz for 10 milliseconds. The velocity time-domain signal is converted to the frequency domain by a fast Fourier transform to obtain the velocity amplitude spectrum in the frequency range from 0 Hz to 500 kHz, which is the residual vibration response spectrum.

[0035] Secondly, higher-order modal frequencies and damping ratios are separated from the residual vibration response spectrum to generate dynamic mechanical characteristic data of the interface. The obtained residual vibration response spectrum is analyzed to identify all obvious resonance peaks. Through extensive testing of intact interface samples, a database was established that identifies which specific modal frequencies (fifth order and above) and their corresponding mode shapes are most sensitive to minute changes in the interface bonding state. Based on this database, resonance peaks of 2 to 4 target higher-order modes are located and extracted from the current response spectrum. For each target resonance peak, its modal frequency value is taken as the frequency value corresponding to that peak. Its modal damping ratio is calculated using the half-power bandwidth method on the data near the resonance peak: find the frequency points on both sides where the amplitude of the resonance peak drops to the peak value divided by the square root of two, calculate the difference between these two frequency points, and then divide this difference by twice the modal frequency value; the result is the damping ratio of that mode. The extracted frequency values ​​and damping ratios of each target mode, along with their corresponding modal order identifiers, are collectively organized into the interface dynamic mechanical feature data.

[0036] Next, the interface dynamic mechanical characteristic data is compared with the theoretical response in the regenerative stress field prediction data to generate energy field deviation data. The regenerative stress field prediction data includes theoretical modal frequencies and theoretical modal damping ratios for the same modal order, calculated based on the simulation model. For each mode in the interface dynamic mechanical characteristic data, the absolute difference between its actual measured frequency and the corresponding theoretical frequency is calculated to obtain the frequency deviation value. Simultaneously, the relative difference between its actual measured damping ratio and the corresponding theoretical damping ratio is calculated (i.e., the difference between the actual value and the theoretical value divided by the theoretical value) to obtain the damping ratio deviation value. Then, the quantization deviation in energy dissipation rate is calculated: for each mode, the theoretical energy dissipation rate is equal to two times pi, multiplied by the theoretical frequency of that mode, and multiplied by the theoretical damping ratio of that mode. The actual energy dissipation rate is equal to two times pi, multiplied by the measured frequency of that mode, and multiplied by the measured damping ratio of that mode. The absolute value of the difference between the two is the energy dissipation rate deviation for that mode. The frequency deviation, damping ratio deviation, and energy dissipation rate deviation of all target modes are summarized to form the energy field deviation data.

[0037] Finally, the correction amounts for the simulation model parameters are derived in reverse based on the energy field deviation data. A parameter sensitivity matrix is ​​established, obtained through extensive parameter perturbation analysis of the simulation model in the early stages. Each row of the matrix corresponds to a target mode, and each column corresponds to a simulation model parameter to be corrected, including the local dynamic elastic modulus correction coefficient for different axial partitions of the metal core substrate, and the damping loss factor correction coefficient for the gradient transition layer material. The element values ​​of the matrix represent the relative change in the corresponding modal frequency or damping ratio caused by a small unit change in a certain model parameter. Using the frequency deviation and damping ratio deviation in the energy field deviation data as target vectors, the required correction amounts for each model parameter are derived by solving the least squares solution of the linear equation system with the sensitivity matrix as coefficients. If the absolute value of the frequency deviation is greater than 5 Hz or the absolute value of the relative deviation of the damping ratio is greater than 20%, the correction amount is considered effective. The effective correction amount, including specific parameter identifiers (such as "correction coefficient for elastic modulus of the third axial zone") and correction values ​​(such as "multiplied by 0.95"), is output as feedback data to guide the updating of simulation model parameters in the next execution of step S2.

[0038] The simulation model is constructed as follows: Based on the three-dimensional geometric dimensions of the waste toner cartridge metal core substrate that has been cleaned and for which a substrate state quantification dataset has been obtained, a precise solid geometric model is established in the simulation software. The comprehensive state index values ​​of each spatial coordinate point in the substrate state quantification dataset are transformed into the yield strength and hardening parameters of the corresponding material location through a pre-calibrated mapping relationship, and assigned as non-uniform elastoplastic material properties to each finite element element after the geometric model is divided. The material parameters of the target photoconductive layer (including elastic modulus, coefficient of thermal expansion, phase transition temperature) are then... The model assigns geometry to the coating (temperature and latent heat); the preset process parameters (including the curve of coating temperature changing with time, vacuum chamber ambient temperature, and the constraint position of the substrate in the fixture) are used as the loads and boundary conditions for simulation; transient analysis is performed by solving the heat conduction equation, phase transformation dynamics equation and elastoplastic constitutive equation by coupling; the final output of the model is the regenerative stress field prediction data covering the entire process time history and refined to the interface between the coating and the substrate and each internal unit node, specifically including the stress tensor components, strain tensor components and temperature history data of each node.

[0039] Please see Figure 2 As shown, a toner cartridge recycling and replacement system includes: The substrate state quantification module scans the metal core substrate of the cleaned waste toner cartridge to obtain fatigue characteristic data and geometric deformation data of the surface and subsurface of the metal core substrate. Through multi-source information fusion processing, a unique substrate state quantification dataset of the metal core substrate is generated. The stress field simulation and prediction module combines the substrate state quantification dataset with the material parameters and preset process parameters of the target photoconductive layer to perform transient simulation analysis with multi-physics coupling, and outputs regenerative stress field prediction data to characterize the interface stress state between the metal core substrate and the target photoconductive layer under the preset process. The adaptive coating control module acquires a dynamic temperature control sequence in a vacuum coating environment and implements dynamic temperature control on the metal core substrate according to the dynamic temperature control sequence; based on the gradient transition layer composition planning, it sequentially deposits a gradient transition layer with continuously changing composition and the final target photoconductive layer on the surface of the metal core substrate to complete the drum regeneration. The deposition process execution module, in a vacuum deposition environment, presets a compensation temperature control scheme and regulates the temperature field of the metal substrate according to the preset compensation temperature control scheme; based on the gradient material ratio scheme, it sequentially deposits a functional gradient transition layer with continuously varying composition and a target photoconductive coating on the surface of the metal substrate. The closed-loop verification and iterative optimization module collects actual deformation or stress response data of the regenerated drum and compares it with the regenerated stress field prediction data. If the comparison result does not meet the preset standard, the actual collected data is fed back to the stress field simulation prediction module as a new input to iteratively update the regenerated stress field prediction data and the adaptive compensation process parameter set until the comparison result meets the standard.

[0040] The working principle of this invention is as follows: First, quantitative non-destructive testing is performed on the metal core substrate of waste toner cartridges using a combination of microwave resonant scanning and nano-indentation testing. This yields quantitative data on the distribution of subsurface micro-defects and the degradation of local mechanical properties. This data is then spatially registered and fused with surface geometric deformation data to generate a non-uniform material property dataset reflecting the fatigue state of the substrate. Subsequently, using this dataset as input, and combining the target photoconductive layer material parameters and process parameters, a transient multiphysics simulation involving heat conduction, material phase transformation, and elastoplastic deformation coupling is performed to predict the distribution of residual stress at the interface during the regeneration process. Based on this prediction, a dynamic temperature control sequence and gradient material ratio scheme are solved in reverse. In a vacuum coating environment, a closed-loop control system with multi-zone independent infrared temperature control and real-time spectral monitoring is used to implement non-uniform dynamic heating of the metal substrate and precisely deposit a gradient transition layer with continuously varying composition and the target photoconductive coating. Finally, transient thermal shock is applied to the regenerated toner cartridge and its vibration response spectrum is collected. High-order modal parameters are extracted as actual interface state characteristics and compared with the theoretical characteristics predicted by simulation. Based on the deviation data, the material parameters of the simulation model are deduced and corrected in reverse, forming a closed-loop control process of "detection-simulation-manufacturing-verification-iterative optimization", thereby realizing high-quality regeneration of toner cartridges based on individual substrate state prediction and stress compensation.

[0041] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for controlling the recycling and replacement of toner cartridges, characterized in that, Includes the following steps: S1: Scan the metal core substrate of the cleaned waste toner cartridge to obtain fatigue characteristic data and geometric deformation data of the surface and subsurface of the metal core substrate. Through multi-source information fusion processing, generate a unique substrate state quantification dataset of the metal core substrate. S2: Combine the substrate state quantification dataset with the material parameters and preset process parameters of the target photoconductive layer to perform transient simulation analysis with multi-physics coupling, and output regenerative stress field prediction data to characterize the interface stress state between the metal core substrate and the target photoconductive layer under the preset process. S3: In a vacuum coating environment, a dynamic temperature control sequence is obtained, and dynamic temperature control is implemented on the metal core substrate according to the dynamic temperature control sequence; based on the gradient transition layer composition planning, a gradient transition layer with continuously changing composition and the final target photoconductive layer are sequentially deposited on the surface of the metal core substrate to complete the drum regeneration. S4: In a vacuum deposition environment, a pre-set compensation temperature control scheme is used to regulate the temperature field of the metal core substrate according to the pre-set compensation temperature control scheme. Based on the gradient material ratio scheme, a functional gradient transition layer with continuously varying composition and a target photoconductive layer are sequentially deposited on the surface of the metal core substrate. S5: Collect actual deformation or stress response data of the regenerated drum and compare it with the regenerated stress field prediction data. If the comparison result does not meet the preset standard, the actual collected data is fed back to S2 as a new input to iteratively update the regenerated stress field prediction data and the adaptive compensation process parameter set until the comparison result meets the standard.

2. The method for controlling the recycling and replacement of toner cartridges according to claim 1, characterized in that, S1 specifically includes: By using a microwave resonant probe to scan along the axial and circumferential directions of a metal core substrate, and capturing the offset between the resonant frequency and the quality factor, first-type fatigue characteristic data characterizing the distribution of subsurface micro-defects are obtained. Based on the defect area indicated by the first type of fatigue characteristic data, a probe is used to perform fixed-point testing to obtain the load-displacement curves of the material in the defect area at multiple indentation depths, from which the second type of fatigue characteristic data characterizing the local mechanical property degradation is extracted. The first type of fatigue feature data and the second type of fatigue feature data are registered and fused in spatial coordinates, and combined with the geometric deformation data of the metal core substrate surface synchronously acquired by the optical profilometer, to jointly generate a substrate state quantification dataset.

3. The method for controlling the recycling and replacement of toner cartridges according to claim 1, characterized in that, S2 specifically includes: Based on the data that centrally characterizes the local mechanical property degradation of materials using the substrate state quantification dataset, the local yield function of the metal core substrate at different axial and circumferential positions under thermal cycling conditions is calculated to generate microscopic plastic evolution prediction data. Using microscopic plastic evolution prediction data as input for non-uniform material properties, the equivalent structural stiffness of the metal core substrate under the constraint of the coating fixture is corrected to obtain time-varying structural constraint characterization data. Using time-varying structural constraint characterization data as dynamic boundary conditions, and combined with the material parameters of the target photoconductive layer, we perform iterative calculations of the energy field coupling thermal conduction, material phase transformation and elastoplastic deformation until the interface energy density distribution tends to stabilize. Based on the stable energy field distribution, the stress gradient in the thickness direction between the metal core substrate and the target photoconductive layer is inverted, and the regenerative stress field prediction data is output.

4. The method for controlling the recycling and replacement of toner cartridges according to claim 3, characterized in that, The inversion process of the stress gradient is as follows: Based on a stable energy field distribution, the elastic energy density component and plastic dissipation energy density component are extracted at the interface between the metal core substrate and the target photoconductive layer and at multiple discrete depth nodes inside the layer. Based on the elastic energy density components at each node and the acoustoelastic relationship of the material, the relative offset of the equivalent elastic constant caused by residual stress at the corresponding node is calculated. Based on the relative offset of the equivalent elastic constants and the spatial gradient of the plastic dissipation energy density component, the stress tensor components that vary continuously along the thickness direction are reconstructed, and the stress gradient is obtained.

5. The method for controlling the recycling and replacement of toner cartridges according to claim 1, characterized in that, S3 specifically includes: Based on the dynamic temperature control sequence, independent infrared radiation power spectra for different axial zones of the metal core substrate are decoupled. Based on the independent infrared radiation power spectrum, multiple independent and controllable infrared radiation units are used to perform non-contact heating on the corresponding axial partitions of the rotating metal core substrate, and the surface dynamic temperature data of each partition is obtained in real time through non-contact infrared temperature measurement. The surface dynamic temperature data is compared with the set value in the dynamic temperature control sequence in real time. By adjusting the power of the infrared radiation unit, the surface dynamic temperature data tracks the set value in real time, forming a closed-loop temperature control.

6. The method for controlling the recycling and replacement of toner cartridges according to claim 5, characterized in that, The decoupling of independent infrared radiation power spectra for different axial zones of the metal core substrate specifically includes: Based on the target temperature sequence of each axial zone in the dynamic temperature control sequence and the thermal properties of the metal core substrate, the theoretical instantaneous heat flux density required for each zone to reach the target temperature is calculated. Based on the rotational speed and orientation of the metal core substrate in the vacuum coating environment, the theoretical instantaneous heat flux density is converted into a periodic equivalent heat flux density modulated by the rotation period. Using the periodic equivalent heat flux density as the desired value and taking into account the radial and axial thermal conduction coupling effects between adjacent partitions, the set of power commands that each infrared radiation unit should output is solved by inversion calculation. The power command set of all partitions is integrated to form the independent infrared radiation power spectrum covering the entire process cycle.

7. The method for controlling the recycling and replacement of toner cartridges according to claim 1, characterized in that, S4 specifically includes: Based on the gradient material ratio scheme, the real-time power ratio change curves of multiple sputtering targets are analyzed, and power ratio control timing instructions are generated. Based on the power ratio control timing command, multiple sputtering targets are driven to work simultaneously, and the emission spectrum of the plasma in the deposition area is collected in real time using a spectral probe placed near the metal core substrate. The intensity ratios of characteristic spectral lines of different elements are extracted from the emission spectrum, and the intensity ratios are compared in real time with the expected values ​​in the power ratio control timing command to generate element ratio deviation signals. Based on the element ratio deviation signal, the power of each sputtering target is adjusted so that the actual chemical composition of the deposited film continuously tracks the gradient material ratio scheme.

8. The method for controlling the recycling and replacement of toner cartridges according to claim 1, characterized in that, S5 specifically includes: A transient thermal shock excitation with a known amplitude and frequency range was applied to the regenerated drum, and the residual vibration response spectrum of the metal core substrate under transient thermal shock excitation was collected using a non-contact vibration measurement device. From the residual vibration response spectrum, the high-order modal frequencies and damping ratios that are directly related to the interface bonding state between the target photoconductive layer and the metal core substrate are separated to generate interface dynamic mechanical characteristic data. The interface dynamic mechanical characteristic data and the theoretical dynamic response at the corresponding position in the regenerative stress field prediction data are compared point by point, and the quantitative deviation between the two in characteristic frequency and energy dissipation rate is calculated to generate energy field deviation data. Based on the energy field deviation data, the correction amount needed to adjust the local dynamic stiffness and gradient transition layer damping characteristics of the metal core substrate in subsequent simulations is derived in reverse, and the correction amount is output as feedback data.

9. A toner cartridge recycling and replacement system, characterized in that, A method for controlling the recycling and replacement of a toner cartridge according to any one of claims 1-8 includes: The substrate state quantification module scans the metal core substrate of the cleaned waste toner cartridge to obtain fatigue characteristic data and geometric deformation data of the surface and subsurface of the metal core substrate. Through multi-source information fusion processing, a unique substrate state quantification dataset of the metal core substrate is generated. The stress field simulation and prediction module combines the substrate state quantification dataset with the material parameters and preset process parameters of the target photoconductive layer to perform transient simulation analysis with multi-physics coupling, and outputs regenerative stress field prediction data to characterize the interface stress state between the metal core substrate and the target photoconductive layer under the preset process. The adaptive coating control module acquires a dynamic temperature control sequence in a vacuum coating environment and implements dynamic temperature control on the metal core substrate according to the dynamic temperature control sequence; based on the gradient transition layer composition planning, it sequentially deposits a gradient transition layer with continuously changing composition and the final target photoconductive layer on the surface of the metal core substrate to complete the drum regeneration. The deposition process execution module, in a vacuum deposition environment, presets a compensation temperature control scheme and regulates the temperature field of the metal core substrate according to the preset compensation temperature control scheme; based on the gradient material ratio scheme, it sequentially deposits a functional gradient transition layer and a target photoconductive layer with continuously varying composition on the surface of the metal core substrate. The closed-loop verification and iterative optimization module collects actual deformation or stress response data of the regenerated drum and compares it with the regenerated stress field prediction data. If the comparison result does not meet the preset standard, the actual collected data is fed back to the stress field simulation prediction module as a new input to iteratively update the regenerated stress field prediction data and the adaptive compensation process parameter set until the comparison result meets the standard.

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