PEEK material composite polishing method and system based on multi-process cooperation

Abstract

The invention relates to the technical field of material polishing, and discloses a PEEK material composite polishing method and system based on multi-process collaboration.The method comprises the steps that surface detection is conducted on a PEEK material, and crystallinity gradient distribution information is obtained; according to the crystallinity gradient distribution information, process parameter calculation is conducted on the crystalline area and the amorphous area, and a polishing process parameter combination is obtained; inputting the polishing process parameter combination into multi-process collaborative composite polishing equipment, and carrying out gradient interface strengthening polishing on the PEEK material to obtain an interface strengthening polishing semi-finished product; and dynamically adjusting the process parameters of the semi-finished interface strengthening and polishing product to obtain the finished interface strengthening and polishing product, so that accurate division of a crystallization area and an amorphous area of the PEEK material and differential process parameter calculation are realized, and the composite polishing precision of the PEEK material is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of material polishing, in particular to a PEEK material composite polishing method and system based on multi-process cooperation. BACKGROUND

[0002] PEEK material has excellent high-temperature resistance, chemical stability and mechanical strength, but its complex molecular structure also brings technical challenges to surface processing.

[0003] Existing PEEK material polishing techniques mainly use polishing methods based on single-target optimization, which have major limitations in dealing with PEEK's unique low-surface-energy interface bonding and crystalline-amorphous region difference modification. Traditional polishing processes usually use uniform process parameters to treat the entire material surface, which cannot differentiate between the different characteristics of PEEK's crystalline and amorphous regions. At the same time, existing technologies lack precise control means for intermolecular π-π interaction forces, making it difficult to accurately control molecular chain orientation during polishing, affecting the final interface quality and performance stability. SUMMARY

[0004] The present application provides a PEEK material composite polishing method and system based on multi-process cooperation, which realizes accurate division of PEEK material crystalline and amorphous regions and differential process parameter calculation, improving the precision of PEEK material composite polishing.

[0005] The first aspect of the present application provides a PEEK material composite polishing method based on multi-process cooperation, which comprises: Surface detection of PEEK material to obtain crystallinity gradient distribution information; Process parameter calculation for crystalline and amorphous regions according to the crystallinity gradient distribution information to obtain polishing process parameter combinations; Input the polishing process parameter combinations into a multi-process cooperative composite polishing device to perform gradient interface strengthening polishing on the PEEK material to obtain an interface strengthening polishing semi-finished product; Dynamic adjustment of process parameters for the interface strengthening polishing semi-finished product to obtain an interface strengthening polishing finished product.

[0006] In combination with the first aspect, in the first implementation manner of the first aspect of the present application, the surface detection of PEEK material to obtain crystallinity gradient distribution information comprises: Raman spectrum scanning of PEEK material surface to obtain vibration peak intensity data; Based on the vibration peak intensity data, the PEEK material surface is subjected to benzene ring distance measurement calculation to obtain interaction region distribution data; The interaction region distribution data is input into an X-ray photoelectron spectroscopy device to detect the surface electron binding energy of the PEEK material, and carbon atom density distribution data is obtained; According to the carbon atom density distribution data, the PEEK material is subjected to molecular chain orientation degree gradient calculation to obtain interface characteristic data; Based on the interface characteristic data, the PEEK material is subjected to differential scanning calorimetry detection and infrared spectrum detection to obtain crystallinity gradient distribution information.

[0007] In combination with the first aspect, in a second implementation manner of the first aspect of the present application, the differential scanning calorimetry detection and infrared spectrum detection of the PEEK material based on the interface characteristic data to obtain the crystallinity gradient distribution information, comprises: The interface characteristic data is input into a differential scanning calorimetry device to detect the glass transition temperature and melting temperature of the PEEK material, and thermodynamic parameter data is obtained; Based on the thermodynamic parameter data, the PEEK material is subjected to crystallinity calculation to obtain crystallinity distribution data; According to the crystallinity distribution data, the PEEK material is subjected to infrared spectrum scanning detection to obtain functional group density distribution data; Based on the crystallinity distribution data and the functional group density distribution data, crystalline-amorphous region difference gradient calculation is performed to obtain the crystallinity gradient distribution information.

[0008] In combination with the first aspect, in a third implementation manner of the first aspect of the present application, the process parameter calculation of the crystalline region and the amorphous region based on the crystallinity gradient distribution information to obtain the polishing process parameter combination, comprises: Based on the crystallinity gradient distribution information, the PEEK material is subjected to region division to obtain distribution position data of the crystalline region and the amorphous region; According to the distribution position data, the crystalline region and the amorphous region are subjected to differential calculation of mechanical polishing parameters, chemical polishing parameters and plasma polishing parameters, respectively, to obtain regional process parameter data; The regional process parameter data is input into an NSGA-III multi-objective genetic algorithm to perform Pareto optimal solution calculation of interface bonding strength, surface roughness and molecular chain orientation degree, and a polishing process parameter combination is obtained.

[0009] In a fourth implementation form of the first aspect, the inputting the area process parameter data into the NSGA-III multi-objective genetic algorithm to perform calculation of a Pareto optimal solution of the interfacial bonding strength, the surface roughness and the molecular chain orientation degree, and obtaining a polishing process parameter combination, includes: generating initial population data based on the area process parameter data and the NSGA-III multi-objective genetic algorithm. performing three-objective fitness function calculation of the interfacial bonding strength, the surface roughness and the molecular chain orientation degree according to the initial population data, and obtaining population fitness numerical data. performing evolution operation of selection, crossover and mutation on the population fitness numerical data by inputting the population fitness numerical data into a genetic algorithm evolution device, and obtaining evolution population data. performing Pareto front screening based on the evolution population data, and obtaining the polishing process parameter combination.

[0010] In a fifth implementation form of the first aspect, the inputting the polishing process parameter combination into the multi-process synergistic composite polishing device to perform gradient interfacial strengthening polishing on the PEEK material, and obtaining an interfacial strengthening polishing semi-finished product, includes: performing process distribution on mechanical polishing parameters, chemical polishing parameters and plasma polishing parameters based on the polishing process parameter combination, and obtaining synergistic parameter distribution data. performing glass transition temperature interval control on the PEEK material according to the synergistic parameter distribution data, and obtaining temperature control parameter data. inputting the temperature control parameter data into the multi-process synergistic composite polishing device, and performing synergistic polishing of mechanical polishing, chemical polishing and plasma polishing on the PEEK material, and obtaining multi-process synergistic polishing data. performing gradient interfacial strengthening of aromatic molecular chain rearrangement on the PEEK material based on the multi-process synergistic polishing data, and obtaining the interfacial strengthening polishing semi-finished product.

[0011] In a sixth implementation form of the first aspect, the performing process parameter dynamic adjustment on the interfacial strengthening polishing semi-finished product, and obtaining an interfacial strengthening polishing finished product, includes: performing real-time monitoring of π-π interaction force on the interfacial strengthening polishing semi-finished product based on an atomic force microscope, and obtaining π-π interaction force monitoring data. performing deviation amount calculation based on the π-π interaction force monitoring data and a target π-π interaction force value, and obtaining π-π interaction force deviation calculation data. The PID feedback regulation is performed on the pi-pi interaction force deviation calculation data to obtain process parameter dynamic adjustment data. The polishing pressure, the rotation speed and the polishing liquid flow are dynamically adjusted in real time based on the process parameter dynamic adjustment data to obtain the interface reinforced polishing product.

[0012] In a seventh implementation form of the first aspect, the PID feedback regulation is performed on the pi-pi interaction force deviation calculation data to obtain process parameter dynamic adjustment data, including: The proportional term product calculation is performed based on the pi-pi interaction force deviation calculation data and a proportional coefficient to obtain proportional control component data; The integral term calculation is performed on a historical deviation cumulative value and an integral coefficient based on the pi-pi interaction force deviation calculation data to obtain integral control component data; The differential term calculation of a deviation change rate is performed based on the pi-pi interaction force deviation calculation data and a differential coefficient to obtain differential control component data; The PID control amount synthesis calculation of weighted summation is performed based on the proportional control component data, the integral control component data and the differential control component data to obtain process parameter dynamic adjustment data.

[0013] In an eighth implementation form of the first aspect, the PEEK material composite polishing method based on multi-process cooperation further includes: The molecular chain orientation degree gradient detection is performed on the interface reinforced polishing product to obtain molecular chain orientation degree detection data; The surface roughness and interface bonding strength detection is performed on the interface reinforced polishing product based on the molecular chain orientation degree detection data to obtain surface quality index detection data; The performance comprehensive analysis is performed based on the molecular chain orientation degree detection data and the surface quality index detection data to obtain comprehensive performance analysis data; The interface failure prediction and quality evaluation grade determination are performed based on the comprehensive performance analysis data to obtain polishing quality verification results.

[0014] The second aspect of the present application provides a PEEK material composite polishing system based on multi-process cooperation, including: The surface detection module is configured to perform surface detection on the PEEK material to obtain crystallinity gradient distribution information. The process parameter calculation module is configured to perform process parameter calculation on the crystalline region and the amorphous region respectively based on the crystallinity gradient distribution information to obtain polishing process parameter combination. Interface strengthening polishing module, used for combining the polishing process parameters into a multi-process synergistic composite polishing equipment, performing gradient interface strengthening polishing on the PEEK material, and obtaining an interface strengthening polishing semi-finished product; Parameter dynamic adjustment module, used for performing process parameter dynamic adjustment on the interface strengthening polishing semi-finished product, and obtaining an interface strengthening polishing finished product.

[0015] Compared with the prior art, the present application has the following beneficial effects: a three-dimensional gradient interface distribution model for PEEK material is established by detecting the pi-pi stacking state through Raman spectroscopy and analyzing the electron binding energy through X-ray photoelectron spectroscopy, which can accurately describe the spatial distribution evolution law of aromatic molecular chains, the interface bonding strength, surface roughness and molecular chain orientation degree are simultaneously optimized by using the NSGA-III multi-objective genetic algorithm, and through the solution of the Pareto optimal solution set, the technical problem that the performance of other targets is reduced due to the over-optimization of a target in the traditional method is effectively solved. Based on the dual molecular recognition technology of differential scanning calorimetry and infrared spectroscopy, the accurate division and differential process parameter calculation of the crystalline region and amorphous region of the PEEK material are realized, and the technical problem that the traditional unified processing method cannot adapt to the complex structural characteristics of the PEEK material is solved. Through the synergistic effect of mechanical polishing, chemical polishing and plasma polishing, combined with the accurate control of the glass transition temperature interval, the ordered rearrangement and gradient interface strengthening of the aromatic molecular chains of the PEEK material are realized, and the technical limitation of low efficiency of the traditional sequential multi-process processing is overcome. The change of pi-pi interaction force is monitored in real time by using an atomic force microscope, and a PID feedback control algorithm is used to realize the dynamic optimization and adjustment of the process parameters in the polishing process, ensuring the stability and consistency of the sub-molecular level interface precision control. Through gradient detection of the molecular chain orientation degree and comprehensive analysis of the surface quality index, a multi-objective performance evaluation and interface failure prediction mechanism is established, which provides a systematic technical guarantee for the polishing quality control of the PEEK material. By accurately controlling the aromatic functional group density and the gradient distribution of crystallinity, the surface performance of the PEEK material is significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] The structure, proportion, size and the like shown in the drawings of the specification are only used to cooperate with the content disclosed in the specification, to be understood and read by those skilled in the art, and do not have technical significance to limit the conditions that the application can be implemented. Any modification of the structure, change of the proportion relationship or adjustment of the size, without affecting the effect that the application can produce and the purpose that the application can achieve, should still fall within the scope that the disclosed technology can cover.

[0018] Figure 1 is a flowchart of a PEEK material composite polishing method based on multi-process cooperation provided by an embodiment of the application; Figure 2 is a structural schematic block diagram of a PEEK material composite polishing system based on multi-process cooperation provided by an embodiment of the application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0020] The flowchart shown in the drawings is only an example for illustration, and does not necessarily include all the contents and operations / steps, nor does it necessarily be executed in the described order. For example, some operations / steps can be decomposed, combined or partially merged, so that the actual execution order can be changed according to the actual situation.

[0021] It should also be understood that the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in the specification and the appended claims of the application, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0022] It should be further understood that the term "and / or" used in the specification and the appended claims of the application means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. Please refer to Figure 1 One embodiment of the PEEK material composite polishing method based on multi-process cooperation in the embodiments of the application includes: Step 100, surface detection is performed on the PEEK material to obtain crystallinity gradient distribution information; It can be understood that the execution subject of the present application can be a PEEK material composite polishing system based on multi-process cooperation, and can also be a terminal or a server, and the specific embodiments are not limited herein. The server is taken as an example for description of the embodiments of the present application.

[0023] Specifically, the PEEK material surface is collected by high-resolution two-dimensional spectrum scanning technology through Raman spectrum scanning technology, and the characteristic vibration peaks of the aromatic benzene ring region are focused, especially the intensity change of the benzene ring C=C stretching vibration peak at 1600cm -1 The change of the vibration peak intensity data reflects the local density distribution and orientation state of the aromatic molecular chain, and the average distance between the benzene rings is quantitatively derived by analyzing the vibration peak intensity data. The distance between the benzene rings is calculated based on the π-π stacking model, combined with the approximate derivation of molecular dynamics, to determine the range of the center-to-center distance of the aromatic ring, which is usually concentrated in the range of 3.4 angstrom to 3.8 angstrom, and the distribution data of the benzene ring interaction region are obtained, which are directly related to the local order of the π-π conjugated structure and the molecular chain arrangement state inside the material. The interaction region distribution data are input into the X-ray photoelectron spectroscopy (XPS) equipment for fine electron structure analysis, and the electron binding energy of the C1s peak position is detected. By finely distinguishing the C1s signal near 284.8eV, the density distribution of the surface aromatic carbon atom is quantitatively obtained. Since the aromatic carbon atom is a key component of the main chain structure of the PEEK material molecular chain, the carbon atom density distribution not only reflects the arrangement order of the molecular chain in the surface layer, but also provides important spatial resolution information for the molecular level interface characteristics. Based on the spatial distribution data of the carbon atom density, combined with the benzene ring distance information derived by Raman spectrum, a gradient distribution model of the molecular chain orientation degree is established, and an exponential function is used to approximate the decreasing trend of the molecular chain orientation degree from the surface to the inside. The orientation degree of the surface layer is usually in the range of 85%-95%, the sub-surface layer is in the range of 60%-75%, and the deep layer is in the range of 40%-55%, forming a continuous gradient interface characteristic data. Based on the interface characteristic data, the PEEK material is detected by differential scanning calorimetry to determine the glass transition temperature and melting temperature of the material, and the absolute value of the crystallinity is obtained by quantitative calculation of the melting enthalpy. Combined with the infrared spectrum detection of the C=O stretching vibration peak at 1650cm -1 and the aromatic ring skeleton vibration peak signal intensity at 1490cm⁻¹, the spatial characteristics of the aromatic functional group density distribution are refined. Based on the cross-checking of DSC and FTIR data, the crystalline region with a crystallinity higher than 40% and the amorphous region with a crystallinity lower than 35% are divided, and the consistency of the molecular chain orientation and the functional group distribution is verified by the intensity change of the infrared characteristic peak, and finally the complete crystallinity gradient distribution information of the PEEK material from the surface to the inside is formed.

[0024] The interface characteristic data is input into a differential scanning calorimetry analysis device, and the glass transition temperature (Tg) and melting temperature (Tm) of the PEEK material are detected by relying on high-sensitivity heat flow change detection technology. By monitoring the relationship curve between the heat flow and the temperature of the sample during the heating process in real time, the key signals of the physical changes of the PEEK material during the heating process are extracted, wherein the Tg generally appears near 143°C as a baseline shift, and the Tm appears near 334°C as an obvious endothermic peak. By integrating the endothermic peak area, the crystallinity of each detection point is quantitatively calculated in combination with the known standard melting enthalpy value, and a set of thermodynamic parameter data covering different depths from the surface to the interior of the PEEK is obtained. Based on the thermodynamic parameter data, the melting enthalpy data corresponding to each depth is compared with the theoretical melting enthalpy value of the completely crystalline state by using a standard crystallinity calculation formula, and the percentage of crystallinity of each detection position is derived to form a set of continuous crystallinity distribution data. In order to refine the molecular structure level information, the set of crystallinity distribution data is used in combination with the infrared spectrum analysis result, so that after the differential scanning calorimetry analysis is completed, the surface and subsurface of the PEEK material are scanned and detected by using the Fourier transform infrared spectrum technology, and the absorption intensities of the C=O stretching vibration peak at 1650 cm⁻¹ and the aromatic ring skeleton vibration peak at 1490 cm⁻¹ are analyzed. By integrating and normalizing the intensities of the characteristic peaks, the density distribution data of the aromatic functional groups at each detection point are obtained. Based on the crystallinity distribution data and the functional group density distribution data, the difference gradient of the crystalline and amorphous regions is calculated by mathematical modeling, the piecewise linear fitting or nonlinear gradient interpolation is used, the change trend graph of the functional group density in different crystallinity intervals is constructed, and the interface characteristic gradient of each region is further extracted. In the region with a crystallinity higher than 40%, a higher density of aromatic functional groups is arranged, and a stronger π-π conjugation effect is exhibited. In the region with a crystallinity lower than 35%, the functional group density is relatively sparse, and the molecular chain orientation degree is also obviously reduced. By comparing the gradient changes of the two types of regions in terms of the crystallinity and the functional group density, the continuous evolution process of the internal structure of the PEEK material from the surface to the deep layer is quantitatively described, and the crystallinity gradient distribution information is obtained.

[0025] Step 200, respectively calculating process parameters of the crystalline region and the amorphous region according to the crystallinity gradient distribution information to obtain a polishing process parameter combination; Specifically, based on the crystallinity gradient distribution information, the surface and near-surface layer of PEEK material are spatially divided. Through numerical segmentation and spatial recognition algorithms, the material is divided into high-crystallinity regions and low-crystallinity amorphous regions, forming a set of distribution location data covering the entire surface. For the divided crystalline and amorphous regions, targeted process parameters are set according to their microstructural characteristics and physicochemical differences. For crystalline regions, due to their regular molecular chain arrangement, high density, and strong wear resistance, finer diamond abrasives are preferentially selected for mechanical polishing, and the grinding pressure is controlled in a low range to avoid excessive surface damage. At the same time, a low-concentration (0.1%-0.3%) fluorinated surfactant solution is used for surface modification treatment during chemical polishing to protect the ordered molecular chain arrangement of crystalline regions, and low-energy (100-200W) argon plasma polishing is used to promote the repair of surface micro-defects. For amorphous regions, due to their disordered molecular chain arrangement, low density, and relative softness, more efficient removal and surface reshaping are required. Therefore, mechanical polishing parameters are set with slightly larger abrasive particles (0.5-1.0 μm) and grinding pressure increased to a medium range. Chemical polishing uses a relatively high concentration (0.3%-0.5%) of surfactant solution to enhance the activation and uniform dissolution of amorphous segments. Simultaneously, plasma polishing power is increased to 200-300W to strengthen the surface reshaping effect. This differentiated parameter setting fully utilizes the differences in physical properties and chemical reactivity between crystalline and amorphous regions, maximizing the surface modification effect. The region process parameter data is input into the NSGA-III multi-objective genetic algorithm for multi-objective optimization. As a new generation of multi-objective optimization algorithm, NSGA-III, based on dominance relationships and reference point settings, can effectively find Pareto optimal solutions among multiple conflicting objectives. The optimization process uses three core evaluation indicators: interfacial bonding strength, surface roughness, and molecular chain orientation. Interfacial bonding strength is primarily influenced by aromatic π-π interactions, surface roughness reflects the microscopic smoothness after polishing, and molecular chain orientation directly relates to the ordered arrangement of molecular chains on the material's surface. By dynamically adjusting crossover and mutation parameters during genetic evolution, the optimal solution set is gradually approximated. In the optimization process, the population size is set to 200 individuals, the number of generations to 500, the crossover probability to 0.9, and the mutation probability to 0.1. A high-dimensional reference point guides the multi-objective search, ensuring that while improving interfacial bonding strength, surface roughness is reduced and the ordered arrangement of molecular chains is enhanced. Through this optimization system, a set of process parameters is obtained, including the optimal combination of polishing time, grinding pressure, rotational speed, polishing slurry flow rate, and plasma power.

[0026] Based on the regional process parameter data, the coding of population individuals and the generation of initial population are carried out. According to the particle size and pressure parameters of mechanical polishing, the active agent concentration and treatment time parameters of chemical polishing, the power and action time parameters of plasma polishing, each parameter is regarded as a gene locus, and these multi-dimensional continuous process parameters are composed of population individuals in the form of vectors through real number coding. Then, based on the uniform distribution or Latin hypercube sampling method, the initial population data covering the entire process parameter space is generated, and the population size is set to 200 individuals to ensure that the algorithm has good diversity and coverage in the initial search. For each individual in the initial population, the fitness value is calculated according to the set three-objective fitness function. The fitness function is designed around the three core performance indicators of interface bonding strength, surface roughness and molecular chain orientation degree. The interface bonding strength is modeled and derived through π-π interaction, the surface roughness reflects the micro flatness based on the AFM characterization data after polishing, and the molecular chain orientation degree reflects the molecular ordered arrangement degree through the intensity ratio of the orientation peak of the surface Raman spectrum. Through normalization processing and weight allocation, it is ensured that each objective function is evaluated in the same dimension, and the three-objective fitness value data corresponding to each population individual is calculated to form a multi-objective performance evaluation matrix covering the initial population. The fitness value data is input into the genetic algorithm evolution module, and the evolution mechanism is set according to the NSGA-III algorithm. The selection, crossover and mutation operations are carried out in turn. The selection operation adopts the elite strategy based on non-dominated sorting and crowding comparison, and the Pareto advantage individuals are preferentially retained; the crossover operation adopts simulated binary crossover (SBX) to ensure that the offspring are uniformly distributed in the parent parameter space neighborhood, improve the search efficiency and the diversity of solutions; the mutation operation adopts the polynomial mutation method to fine-tune the process parameters in a small probability disturbance manner to avoid falling into local optimum. Through continuous selection, crossover and mutation operations, the evolutioner constantly generates new evolution population data, and the real optimal solution set is approached through generation evolution. After a certain number of evolutions are completed, the dominance relationship judgment and reference point distance calculation are carried out based on the current evolution population data, and the Pareto front is screened. The specific method is to perform non-dominated sorting on all evolution individuals, divide them into different Pareto layers according to the dominance relationship, and calculate the nearest distance from each individual to the reference point according to the pre-set reference point set, and preferentially retain the individuals with closer distance to the reference point and uniform distribution, so as to ensure that the interface bonding strength is maximized while the surface roughness is minimized and the molecular chain orientation degree is optimized. The optimal individual set screened from the Pareto front is the polishing process parameter combination under multi-objective optimization, which covers the optimal matching configuration of mechanical, chemical and plasma process parameters.

[0027] Step 300, input the polishing process parameter combination into the multi-process collaborative composite polishing equipment to perform gradient interface strengthening polishing on the PEEK material to obtain an interface strengthening polishing semi-finished product; Specifically, based on the polishing process parameter combination, the specific parameters of the three process modules of mechanical polishing, chemical polishing and plasma polishing are allocated. For the mechanical polishing process, the optimal particle size range (usually 0.25 μm to 1.0 μm) and grinding pressure (controlled between 0.1 MPa and 0.8 MPa) are extracted from the parameter combination, while a reasonable rotation speed interval (100 rpm to 500 rpm) is set to ensure that the material surface can uniformly remove micron-level protrusions; for chemical polishing, the concentration range (0.1%-0.5%) of fluorine-containing surfactant solution and immersion time are set according to the combined parameters to ensure that the surface functional groups are activated and modified in a mild environment; as for plasma polishing, the discharge power (100 W to 300 W) of argon plasma and the processing time are set according to the combined results to realize the self-organization repair of surface micro defects by adjusting the ion bombardment energy. Through systematic allocation of parameters of each module, a synergistic parameter allocation data covering different surface area characteristics is formed. After completing the parameter allocation, the glass transition temperature (Tg) interval control of the processing temperature of PEEK material is performed according to the synergistic parameter allocation data. Since the Tg of PEEK material is about 143°C, and the molecular chain has the best local activity when it is close to or slightly higher than Tg, the temperature in the actual polishing process is accurately controlled within the range of Tg±10°C, i.e. 133°C to 153°C. Therefore, through the integrated heating control module, the substrate temperature during polishing is dynamically adjusted, and the infrared temperature sensor is used to monitor the surface temperature change in real time, and the closed-loop temperature adjustment is performed according to the synergistic parameter allocation data to obtain temperature control parameter data covering each process stage, so that the material surface is in the most suitable temperature environment for molecular chain reconstruction during polishing. The above temperature control parameter data is input into the multi-process synergistic composite polishing equipment, which relies on the highly integrated multi-module synergistic working capability to start the alternative synergistic polishing process of mechanical polishing, chemical polishing and plasma polishing. During the operation of the equipment, the polishing sequence is set, the action time ratio is adjusted, and the pressure, flow and energy input are simultaneously controlled to realize the deep integration and dynamic switching of the three processes. Mechanical polishing is used for preliminary roughness reduction, chemical polishing is used for surface activation treatment, and plasma polishing is used for surface micro area repair and energy balance treatment in the last stage. The multi-process synergistic polishing data records the surface state change, temperature change curve and surface energy density evolution in each stage. After the multi-process synergistic polishing is completed, the reordering behavior of the aromatic molecular chains on the surface of the PEEK material is analyzed and controlled based on the accumulated synergistic polishing data. Since the molecular chains have a certain activity at a temperature close to Tg, combined with the physical shear force and chemical environmental stimulation in the polishing process, the aromatic molecular chains can be oriented and rearranged, forming a molecular chain orientation gradient that gradually decreases from the surface to the interior.By rearranging the molecular chains, the π-π interaction force between the material surface and the polishing medium is enhanced, the interface bonding strength is improved, and the spatial distribution of the molecular chain end functional groups is improved, further reducing the surface energy, improving the wear resistance and chemical stability. The interface strengthening polishing semi-finished product with ordered surface molecular chain arrangement, enhanced bonding force and significantly reduced roughness is obtained.

[0028] Step 400, dynamically adjusting the process parameters of the interface strengthening polishing semi-finished product to obtain the interface strengthening polishing finished product.

[0029] Specifically, the surface of the interface-strengthening polishing semi-finished product is scanned and detected by an atomic force microscope (AFM), focusing on the change of the π-π interaction force between aromatic molecular chains, and the local interaction force of the surface molecular chains is accurately measured through a force-distance curve to capture the small change of the interaction force in the range of 0.1 nN to 10 nN in real time. With the high resolution of the AFM, continuous and stable monitoring data of the π-π interaction force can be obtained at the nanoscale, which truly reflects the degree of orientation and rearrangement of the surface molecular chains of the semi-finished product and the interface bonding state. The real-time monitoring data of the π-π interaction force is compared and analyzed with the target value of the π-π interaction force set in advance, and the target value is set in the interval range of 2.5±0.5 nN, which not only ensures the strong bonding force between the aromatic molecular chains, but also prevents the increase of surface fragility caused by excessive compaction. Through real-time comparison of the monitoring data and the target value, the deviation amount is calculated to obtain the π-π interaction force deviation calculation data reflecting the difference between the current surface state and the target state. According to the obtained π-π interaction force deviation calculation data, a proportional-integral-derivative (PID) feedback control algorithm is used to dynamically adjust the process parameters. The PID controller takes the deviation amount as the input, and performs proportional adjustment, cumulative adjustment and change rate adjustment according to the proportional coefficient (Kp=0.8), the integral coefficient (Ki=0.3) and the differential coefficient (Kd=0.1) respectively, and outputs the dynamic adjustment data of the process parameters through the comprehensive weighting of the three adjustment results. The dynamic adjustment data of the process parameters are applied to the control of the key process parameters in the actual polishing process, mainly including the grinding pressure, the rotation speed and the polishing liquid flow. For the grinding pressure, when the π-π interaction force is low, the grinding pressure is appropriately increased to 0.8 MPa of the set upper limit to enhance the local arrangement density of the molecular chains; when the interaction force is high, the pressure is appropriately reduced to prevent excessive accumulation of the molecular chains. For the rotation speed, the adjustment range is 100 rpm to 500 rpm according to the deviation amount, the low speed is suitable for the force enhancement stage, and the high speed is suitable for the force release and surface uniformization stage. As for the polishing liquid flow, it is dynamically adjusted in the range of 10 mL / min to 50 mL / min, and the change of the liquid flow can adjust the shear force between the polishing liquid and the surface to further fine-tune the arrangement state of the molecular chains. Through this series of real-time, dynamic and closed-loop parameter adjustment process, the orientation degree and interface bonding performance of the molecular chains on the surface of the semi-finished product are continuously optimized, and finally the interface strengthening effect reaches the ideal state, the surface roughness is further reduced, the molecular chain orientation gradient is more reasonable, and the π-π interaction force is stably maintained within the target range, so that the interface-strengthening polishing finished product is obtained.

[0030] The proportional control component directly reflects the current error size on the immediate impact of the adjusting system output, and is the basic part of the adjusting response. The main role of the proportional term is to directly amplify the deviation, so that when the π-π interaction force deviation is large, the control quantity can quickly respond and quickly make preliminary adjustment to the process parameters to realize fast error convergence. On the basis of proportional control, in order to eliminate the long-term cumulative deviation, the integral control component calculation is introduced. The calculation process of the integral term is based on the cumulative integration of the historical deviation value of the π-π interaction force deviation calculation data, and the accumulated total deviation is multiplied by the pre-set integral coefficient Ki to obtain the integral control component data. The introduction of the integral term can effectively avoid the long-term existence of small steady-state error of the system. Through the cumulative effect of the historical deviation, the correction trend is continuously introduced into the adjusting quantity, so that even a small residual deviation can be gradually eliminated under the promotion of the integral, and the interface bonding strength in the polishing process is stably tended to the target value interval set. At the same time, in order to improve the sensitive response ability of the system to the deviation change trend, the differential control component calculation is based on the change rate of the π-π interaction force deviation calculation data. The first order derivative of the deviation data in time sequence is calculated, that is, the change rate of the deviation is obtained, and the differential coefficient Kd is multiplied by the set to obtain the differential control component data. The main role of the differential term is to predict the future deviation trend. When the deviation is detected to have a trend of intensification or weakening, it can respond in advance to suppress the overshoot or oscillation of the system, improve the smoothness of the process parameter adjustment and the dynamic stability of the system. According to the weighted sum of the proportional control component data, the integral control component data and the differential control component data, the PID control quantity synthesis calculation is carried out to form the final PID control output value, that is, the process parameter dynamic adjustment data.

[0031] The molecular chain orientation degree gradient of the polished finished product surface is detected by Raman spectrum polarization analysis technology. The incident polarized light beam is used to perform spectrum scanning in different directions on the PEEK surface, and the aromatic benzene ring skeleton vibration peak at 1490 cm -1The polarization intensity variation at each position is quantified by calculating the ratio of parallel polarization to vertical polarization light intensity at each position, and the molecular chain orientation degree of the surface and near-surface layer is quantified. By detecting the surface at different depths in layers, the gradient distribution data of the molecular chain orientation degree with depth, i.e. the molecular chain orientation degree detection data, is obtained. This data reflects the trend that the order of the distribution of aromatic molecular chains decreases from the surface layer to the inside, and is an important quantitative index of the interface strengthening effect. Based on the above molecular chain orientation degree detection data, in order to more comprehensively evaluate the surface performance of the interface strengthening polishing finished product, the surface roughness and interface bonding strength are detected. The surface roughness detection is performed by atomic force microscopy (AFM), and the three-dimensional surface topography information at the nanoscale is obtained, and the Ra value of each detection area is calculated to obtain the surface roughness distribution; the interface bonding strength detection is performed by nanoindentation technology combined with peeling experiment, and the interface bonding energy and peeling strength are quantified, and the stability of the π-π interaction force is indirectly reflected by measuring the dissociation force between molecular layers. The surface roughness and interface bonding strength data obtained by these detections are integrated to form the surface quality index detection data. According to the molecular chain orientation degree detection data and the surface quality index detection data, performance comprehensive analysis is performed. Through multivariate correlation analysis of the gradient distribution of the molecular chain orientation degree, the surface roughness and the interface bonding strength, the internal relationship between the order degree of the molecular chain, the surface topography characteristics and the interface bonding performance is revealed. Based on the multivariate linear regression or support vector machine regression model, a quantitative mapping relationship between the molecular chain orientation degree and the surface performance index is established, the synergistic evolution law of each performance index is mined, and comprehensive performance analysis data is obtained. Based on the comprehensive performance analysis data, interface failure prediction and quality evaluation grade determination are performed. Through the failure prediction model constructed by combining historical data, such as the interface failure probability prediction system based on random forest or neural network, the possible interface failure probability under future service conditions is predicted according to the current molecular chain orientation gradient, surface roughness and interface bonding strength, and the prediction result is compared with the preset quality grade standard, and the quality grade of the interface strengthening polishing finished product is divided according to the comprehensive performance index, such as being divided into four levels of excellent, good, medium and poor, and finally the polishing quality verification result is formed.

[0032] In the embodiment of the application, by detecting the π-π stacking state through Raman spectroscopy and analyzing the electron binding energy through X-ray photoelectron spectroscopy, a three-dimensional gradient interface distribution model for PEEK material is established, which can accurately describe the spatial distribution evolution law of aromatic molecular chains, and the NSGA-III multi-objective genetic algorithm is used to simultaneously optimize the interface bonding strength, surface roughness and molecular chain orientation degree, and through the solution of the Pareto optimal solution set, the technical problem that the performance of other targets is reduced due to the excessive optimization of a target in the traditional method is effectively solved. Based on the dual molecular recognition technology of differential scanning calorimetry and infrared spectroscopy detection, the accurate division of the crystalline region and amorphous region of the PEEK material and the differential process parameter calculation are realized, and the technical problem that the traditional unified processing method cannot adapt to the complex structure characteristics of the PEEK material is solved. Through the synergistic effect of mechanical polishing, chemical polishing and plasma polishing three processes, combined with the accurate control of the glass transition temperature interval, the ordered rearrangement and gradient interface strengthening of the aromatic molecular chains of the PEEK material are realized, and the technical limitation of the low efficiency of the traditional sequential multi-process processing is overcome. The change of the π-π interaction force is monitored in real time by using an atomic force microscope, and a PID feedback control algorithm is used to realize the dynamic optimization and adjustment of the process parameters in the polishing process, so as to ensure the stability and consistency of the sub-molecular level interface precision control. Through the gradient detection of the molecular chain orientation degree and the comprehensive analysis of the surface quality index, a multi-objective performance evaluation and interface failure prediction mechanism is established, which provides a systematic technical support for the polishing quality control of the PEEK material. By accurately controlling the aromatic functional group density and the crystallinity gradient distribution, the surface performance of the PEEK material is significantly improved.

[0033] In a specific embodiment, the process of step 100 can specifically include the following steps: Raman spectroscopy scanning is performed on the surface of the PEEK material to obtain vibration peak intensity data; Based on the vibration peak intensity data, the inter-ring distance of the PEEK material surface is calculated to obtain interaction region distribution data; The interaction region distribution data is input into the X-ray photoelectron spectroscopy equipment to detect the surface electron binding energy of the PEEK material, and carbon atom density distribution data is obtained; According to the carbon atom density distribution data, the molecular chain orientation degree gradient of the PEEK material is calculated to obtain interface characteristic data; Based on the interface characteristic data, differential scanning calorimetry detection and infrared spectroscopy detection are performed on the PEEK material to obtain crystallinity gradient distribution information.

[0034] Specifically, a high-resolution Raman spectrometer was used to scan the surface of PEEK material globally. By adjusting the laser wavelength and power density, high signal-to-noise ratio spectral data was collected without damaging the surface structure of the material. During the scanning process, attention was paid to the aromatic structure characteristic vibration peaks, especially the benzene ring skeleton vibration peak at 1490 cm -1 and the 1600 cm -1The C=C bond stretching vibration peaks of the PEEK surface were recorded, and the vibration peak intensity data of each micro-region position were obtained. The vibration peak intensity data were processed by spectral intensity distribution imaging to achieve a preliminary quantitative description of the molecular arrangement order of the PEEK surface. The changes in the vibration peak intensity are directly related to the stacking state of the aromatic benzene ring and reflect the spatial variation trend of the molecular chain orientation. Based on the changes in the peak position and intensity of the Raman spectrum, the calculation of the benzene ring spacing was performed. The distance calculation formula based on the π-π interaction model was used to link the local vibration intensity and the molecular packing density. Combined with the existing molecular mechanics simulation results, the aromatic ring center spacing was calculated, which was usually in the range of 3.4 to 3.8 angstroms. Through this method, the vibration peak intensity data of each micro-region were converted into average distance information between benzene rings, and the distribution data of the interaction region were obtained. By constructing a two-dimensional or three-dimensional distribution map, the spatial variation characteristics of the stacking and arrangement state of the aromatic molecular chain in different regions were displayed. The interaction region distribution data were input into the X-ray photoelectron spectroscopy (XPS) equipment for fine detection of the surface element chemical state and binding energy. During the XPS detection process, the high-energy resolution analysis condition was set to focus on the measurement of the C1s electron spectrum signal, and multi-peak fitting processing was performed. The aromatic carbon peak distribution near 284.8 eV was particularly focused on. By analyzing the area ratio of the C1s main peak and its shoulder peak, the carbon atom density per unit area was calculated. Because the change in the aromatic carbon atom density is related to the degree of benzene ring π-π stacking, the carbon atom density distribution data can be used as a direct representation of the molecular chain arrangement and binding state. Combined with the XPS spatial scanning mode, the carbon atom density distribution map with spatial resolution was obtained, which further verified and supplemented the molecular packing information obtained by Raman spectrum analysis. After obtaining the carbon atom density distribution data, the molecular chain orientation degree gradient calculation was performed based on the data. By comparing the carbon density variation rate at different depths or different surface positions, the gradient distribution of the molecular chain orientation in space was derived. The exponential decay model was used to fit the variation trend of the carbon atom density from the surface to the interior, and the gradient distribution characteristics of the molecular chain orientation degree from 85%-95% high orientation degree on the surface to 60%-75% medium orientation degree in the interior and then to 40%-55% low orientation degree in the deep layer were obtained. Through the molecular chain orientation degree gradient model, the variation law of the molecular chain orientation in the longitudinal direction was described, and the uniformity of the orientation degree in the lateral local region was analyzed to obtain the interface characteristic data with three-dimensional spatial characteristics. Based on the above interface characteristic data, in order to reveal the distribution characteristics of the crystallinity and amorphousness inside the PEEK material, the joint analysis of differential scanning calorimetry (DSC) detection and Fourier transform infrared spectroscopy (FTIR) detection was performed. The DSC equipment was used to measure the heat flow of the PEEK sample at a heating rate of 10°C / min, and the glass transition temperature (Tg) and melting temperature (Tm) variation curves were obtained.The crystallinity distribution of the local region is quantitatively calculated by integrating the area of the melting endothermic peak, combined with the known theoretical melting enthalpy value of the completely crystalline PEEK material. At the same time, the local thermal analysis of different depth positions is realized by cooperating with the micro-zone DSC probe technology, so as to obtain the crystallinity change data of each layer from the surface to the interior. The crystallinity distribution data formed thereby can accurately reflect the spatial distribution characteristics of the crystalline and amorphous structures of the surface and near-surface regions of the material. While performing thermal analysis, micro-zone infrared absorption detection of the PEEK material is performed using FTIR spectroscopy, with particular attention to the intensity changes of the C=O stretching vibration peak at 1650 cm. -1 and the benzene ring skeleton vibration peak at 1490 cm -1 The distribution of the aromatic functional group density at different spatial positions is quantitatively evaluated by calculating the integral area of the characteristic absorption peaks of different regions and combining normalization processing. The change of the functional group density is directly related to the crystallinity. The region with high crystallinity has higher functional group density and more regular molecular chain arrangement, and vice versa, indicating that the molecular arrangement in the amorphous region is disordered. By matching and analyzing the functional group density distribution data obtained by FTIR detection and the crystallinity distribution data obtained by DSC detection, the crystallinity gradient distribution map of the PEEK material can be more accurately drawn.

[0035] In a specific embodiment, the process of obtaining the crystallinity gradient distribution information of the PEEK material based on the interface feature data by performing differential scanning calorimetry detection and infrared spectrum detection can specifically include the following steps: The interface feature data is input into a differential scanning calorimetry device to detect the glass transition temperature and melting temperature of the PEEK material, and obtain thermodynamic parameter data; The crystallinity of the PEEK material is calculated based on the thermodynamic parameter data to obtain crystallinity distribution data; The PEEK material is scanned and detected by infrared spectrum based on the crystallinity distribution data to obtain functional group density distribution data; The crystallinity-noncrystallinity region difference gradient calculation is performed based on the crystallinity distribution data and the functional group density distribution data to obtain the crystallinity gradient distribution information.

[0036] Specifically, based on the gradient of molecular chain orientation and the distribution information of aromatic functional group density, representative interface characteristic regions are selected for sample preparation. Micro samples are collected from different depths of the PEEK material surface to the interior, ensuring that the collected samples can reflect the spatial gradient characteristics of molecular chain orientation and density distribution. Then these samples are standardized to ensure consistent thermal history, to exclude structural deviations caused by differences in previous processing technology, and then these pretreated samples are placed in a differential scanning calorimeter (DSC) with a heating rate of 10°C / min to ensure accurate recording and analysis of heat flow changes. During the DSC test, by recording the heat flow curve in the sample endothermic and exothermic process in real time, the glass transition temperature (Tg) and melting temperature (Tm) of the PEEK material are detected, in which the glass transition temperature is represented as the baseline shift, and the melting temperature appears as a clear endothermic peak. By integrating the melting endothermic peak, the melting enthalpy (ΔHm) value corresponding to the peak is obtained, combined with the known standard melting enthalpy value of the ideal complete crystalline state of PEEK material (taken as 130 J / g), the crystallinity (Xc) is calculated by the following formula: Xc=(ΔHm / ΔH0)×100%. Through this step, the crystallinity data of each sample at a specific depth position is obtained, and a set of crystallinity distribution data covering different depth levels of the PEEK material is formed, reflecting the spatial variation trend of the crystalline and amorphous structure from the surface to the deep layer of the material. According to the crystallinity distribution data, the PEEK material is detected by infrared spectrum scanning. Infrared spectrum detection can provide molecular level functional group information by analyzing the characteristic absorption peaks of molecular groups in the material, especially for the change of aromatic group and carbonyl functional group density, which has very high sensitivity. In the specific detection process, Fourier transform infrared spectrometer (FTIR) is used to scan the sample, focusing on the C=O stretching vibration peak at 1650 cm -1 and the 1490 cm -1The aromatic ring skeleton vibration peak of PEEK is obtained by normalizing the integral area of the absorption peak and combining the standard sample for quantitative analysis to obtain the functional group density distribution data at different depth positions. Based on the crystallinity distribution data and the functional group density distribution data, the difference gradient of the crystalline-amorphous region is calculated to quantitatively describe the spatial distribution characteristics and transition law of the crystalline region and the amorphous region in the PEEK material. By establishing a piecewise function model based on the spatial coordinates, the crystallinity and functional group density data at each depth position are jointly fitted, and a gradient change model is constructed by using piecewise linear regression or polynomial interpolation method, and its derivative is calculated to obtain the gradient change rate of the crystalline-amorphous region interface transition zone. By analyzing the gradient change curve, the high crystalline region with a crystallinity higher than 40%, the amorphous region with a crystallinity lower than 35% and the transition zone are identified, and the change range of the molecular chain arrangement regularity is further confirmed based on the change trend of the functional group density. The joint gradient analysis method of crystallinity and functional group density can effectively describe the transition process from the high-regularity crystalline region to the low-regularity amorphous region, and clearly define the width and change characteristics of the interface region, forming the crystallinity gradient distribution information.

[0037] In a specific embodiment, the process of performing step 200 can specifically include the following steps: Based on the crystallinity gradient distribution information, the PEEK material is divided into regions to obtain the distribution position data of the crystalline region and the amorphous region; According to the distribution position data, the crystalline region and the amorphous region are respectively subjected to differential calculation of mechanical polishing parameters, chemical polishing parameters and plasma polishing parameters to obtain regional process parameter data; The regional process parameter data is input into the NSGA-III multi-objective genetic algorithm to calculate the Pareto optimal solution of the interface bonding strength, surface roughness and molecular chain orientation degree, and obtain the polishing process parameter combination.

[0038] Specifically, the PEEK material is divided into regions based on the crystallinity gradient distribution information. The material surface is divided into crystalline and amorphous regions according to the set threshold value using a spatial layering algorithm or clustering method. Regions with a crystallinity higher than 40% are defined as high crystallinity regions, while regions with a crystallinity lower than 35% are defined as amorphous regions. A reasonable transition interval is set between the two to eliminate boundary effects and ensure the continuity and physical reasonableness of the division. Through regional division, the distribution location data is obtained. After obtaining the distribution location data of the crystalline and amorphous regions, based on the local crystallinity and microstructure differences of the material, the process parameters of the two types of regions are calculated differently. For the high crystallinity region, due to its regular molecular chain arrangement, high density, and large surface hardness, the wear resistance and corrosion resistance are relatively stronger. Therefore, small diamond abrasive particles with a particle size range of 0.25 μm to 0.5 μm should be used in the mechanical polishing stage, and the grinding pressure should be set to a lower interval, such as 0.2 MPa to 0.4 MPa, to prevent the generation of local surface micro-cracks due to excessive mechanical stress. In the chemical polishing stage, a low concentration (0.1%-0.3%) fluorine-containing surfactant solution is used to control the reaction activity and avoid excessive corrosion of the ordered segments in the crystalline region, maintaining the integrity of the surface structure. In the plasma polishing stage, low-power (100 W to 150 W) argon plasma is selected for short-term action to fine-tune the surface micro-defects and promote the activation of molecular chain end groups, improving the local interfacial bonding capacity. For the amorphous region, due to its disordered molecular chain arrangement, low density, and soft surface, the mechanical properties are relatively weak. Therefore, larger diamond particles with a particle size of 0.7 μm to 1.0 μm should be appropriately used in the mechanical polishing stage, and the grinding pressure should be increased to 0.5 MPa to 0.7 MPa to increase the removal efficiency while ensuring surface flatness. In the chemical polishing stage, a high concentration (0.3%-0.5%) fluorine-containing surfactant solution is used to enhance the chemical action on the amorphous segments and improve the surface activation degree. In the plasma polishing stage, the power needs to be increased to 200 W to 300 W, and the treatment time needs to be lengthened to effectively remove potential surface defects in the amorphous region and promote surface reconstruction and molecular chain orientation. By differentiating the parameters of mechanical polishing, chemical polishing, and plasma polishing, the optimal surface modification effect can be achieved for different regions based on their microstructure characteristics, and a set of process parameter data covering all regions is obtained. The regional process parameter data is input into the NSGA-III multi-objective genetic algorithm for comprehensive optimization to simultaneously satisfy the collaborative improvement of the interfacial bonding strength, surface roughness, and molecular chain orientation. As a new generation of multi-objective evolutionary algorithm, NSGA-III algorithm uses a multi-objective optimization mechanism based on dominance relation and reference points, which can effectively handle the solution set distribution problem in high-dimensional complex target space.In a specific implementation, each regional process parameter is encoded as an individual gene sequence, and real number encoding is used to standardize process variables such as particle size, pressure, polishing time, polishing liquid concentration, and plasma power, and an initial population is constructed based on this. The population size is set to 200, the maximum evolution generation is set to 500, the crossover probability is set to 0.9, and the mutation probability is set to 0.1, to ensure population diversity and promote global exploration of the search space. For each individual, multi-objective performance evaluation is performed according to the set fitness function. The interface bonding strength fitness is calculated by molecular dynamics simulation combined with experimental verification to calculate the size of the aromatic π-π interaction force, the surface roughness fitness is calculated by simulating the Ra value of the polished surface three-dimensional profile, and the molecular chain orientation degree is quantitatively evaluated by combining molecular orientation simulation with actual Raman spectrum intensity ratio. Through non-dominated sorting of the three target fitness, and density estimation and selection based on the preset reference point, high-quality individuals that are located on the Pareto frontier and are uniformly distributed are preferentially retained, and gene recombination and fine-tuning are performed using simulated binary crossover and polynomial mutation operations, thereby continuously evolving and optimizing the population. The final Pareto optimal solution set obtained after multiple generations of evolution contains a series of process parameter combinations that achieve the best compromise between maximizing interface bonding strength, minimizing surface roughness, and optimizing molecular chain orientation.

[0039] In a specific embodiment, the process of inputting regional process parameter data into the NSGA-III multi-objective genetic algorithm to perform Pareto optimal solution calculation of interface bonding strength, surface roughness, and molecular chain orientation to obtain a polishing process parameter combination can include the following steps: Based on the regional process parameter data and the NSGA-III multi-objective genetic algorithm, population individual encoding and initial population generation are performed to obtain initial population data; According to the initial population data, three-objective fitness function calculation is performed on the interface bonding strength, surface roughness, and molecular chain orientation to obtain population fitness numerical data; The population fitness numerical data is input into the genetic algorithm evolution to perform selection, crossover, and mutation evolution operations to obtain evolution population data; Based on the evolution population data, Pareto frontier screening is performed based on dominance relationship judgment and reference point distance calculation to obtain a polishing process parameter combination.

[0040] Specifically, the regional process parameter data is structured. For different crystalline and amorphous regions of PEEK material, seven key process parameters are extracted, including mechanical polishing particle size, polishing pressure, polishing speed, chemical polishing solution concentration, immersion time, plasma polishing power, and action time. These parameters are normalized to the range [0, 1] to avoid the influence of different dimensions on subsequent genetic operations. Based on the standardized regional process parameters, each individual is encoded using real number encoding, and a combination of process parameters corresponds to a chromosome in the population, forming a gene sequence containing multi-dimensional continuous variables. A certain number of initial population individuals are generated uniformly in the parameter space by Latin hypercube sampling or uniform random sampling. The population size is set to 200 to ensure the diversity and distribution of the population in the initial stage, and the initial population data covering a wide solution space is obtained. The fitness of each individual is evaluated. The design of the fitness function focuses on the three key objectives of interface bonding strength, surface roughness, and molecular chain orientation. For interface bonding strength, the size of the aromatic π-π interaction force is calculated based on molecular dynamics simulation or existing experimental models, and the interface bonding energy density or fracture toughness is quantified. Surface roughness is predicted by fitting the experimental or simulation relationship model between polishing process parameters and surface Ra value, and the smaller the Ra value, the higher the fitness. The molecular chain orientation is quantified by analyzing the temperature and shear force field during polishing and combining the Raman polarized spectrum orientation intensity ratio. The higher the orientation, the more ordered the molecular chain arrangement, and the higher the fitness score. After standardizing the three performance index values of each individual, the three-objective fitness function output is formed, and the fitness value data matrix of the entire initial population is obtained as the basis for genetic algorithm evolution. The population fitness value data is input into the NSGA-III multi-objective genetic algorithm evolution module, and the genetic evolution process is started. First, selection is performed based on the non-dominated sorting principle to classify the population and select Pareto superior individuals. The distribution density of the selected individuals in the solution space is ensured by combining the crowding distance or reference point, and premature convergence to local optimum is avoided. Then, crossover is performed using the simulated binary crossover (SBX) method to recombine the parent individuals and generate new offspring individuals. The crossover probability is generally set to 0.9 to ensure genetic diversity, and the mutation probability is generally set to 0.1 to prevent the population from falling into a local optimum trap.After continuous operation of selection, crossover and mutation, new evolutionary population data is obtained. The new population individuals not only inherit the excellent genes of the parents, but also introduce new genetic diversity, thereby constantly approaching the global optimal solution. After several generations of population evolution, based on the latest evolutionary population data, the dominance relationship is judged and the reference point distance is calculated to screen the optimal solution set of the Pareto front. The population is non-dominantly sorted, and all individuals are divided into different levels according to the dominance relationship. The individuals in the first layer of non-dominant set constitute the preliminary Pareto front. In order to further improve the diversity and uniformity of the solution set, the reference point method is used for distance calculation. According to the preset reference point set uniformly distributed in the target space, the distance of each individual to the nearest reference point is calculated. The individual with a short distance to the reference point is preferentially retained to avoid excessive aggregation of the solution set in a local part of the target space. Through this combination of dominance relationship judgment and reference point distance calculation method, the Pareto optimal solution set that achieves the best balance between the maximum interface bonding strength, the minimum surface roughness and the optimal molecular chain orientation can be effectively selected, and a series of polishing process parameter combinations are finally formed.

[0041] In a specific embodiment, the process of performing step 300 can specifically include the following steps: Based on the polishing process parameter combination, the mechanical polishing parameters, chemical polishing parameters and plasma polishing parameters are allocated to the process to obtain collaborative parameter allocation data; According to the collaborative parameter allocation data, the glass transition temperature interval control of the PEEK material is performed to obtain temperature control parameter data; The temperature control parameter data is input into the multi-process collaborative composite polishing equipment, and the PEEK material is subjected to collaborative polishing of mechanical polishing, chemical polishing and plasma polishing to obtain multi-process collaborative polishing data; Based on the multi-process collaborative polishing data, the gradient interface strengthening of the aromatic molecular chain rearrangement of the PEEK material is performed to obtain the interface strengthened polishing semi-finished product.

[0042] Specifically, the polishing process parameters are assigned based on the polishing process parameter combination. The core parameters in the polishing parameter combination, such as the mechanical polishing particle size, the polishing pressure, the rotation speed, the chemical polishing solution concentration, the immersion time, the plasma polishing power and the action time, are classified and arranged according to their respective process requirements. The process parameters of mechanical polishing, chemical polishing and plasma polishing are respectively assigned to the corresponding modules to form a systematic collaborative parameter assignment data. In this process, the mechanical polishing parameters are used to determine the removal rate and surface topography control in the polishing process. The particle size is set between 0.25 μm and 1.0 μm to adjust the polishing accuracy. The pressure is set in the range of 0.2 MPa to 0.8 MPa to adjust the mechanical removal depth. The rotation speed is set between 100 rpm and 500 rpm, which directly affects the shear force and heat distribution on the material surface. The chemical polishing parameters mainly control the molecular chain end group activation and surface free energy reduction process by adjusting the concentration of fluorine-containing surfactant solution and the action time. The solution concentration is set in the range of 0.1%-0.5%, and the immersion time is set between 30 seconds and 120 seconds according to the crystallinity region. The plasma polishing parameters are set by setting the power of argon plasma between 100 W and 300 W and the action time between 60 seconds and 300 seconds to repair micro defects and rearrange molecular chains in different regions. After forming the collaborative parameter assignment data, in order to ensure that the molecular chains have a moderate thermal activation state during the polishing process, the glass transition temperature interval control is performed based on the assignment data. Since the glass transition temperature Tg of PEEK material is about 143°C, the molecular chains have the best local activity at a temperature close to or slightly higher than Tg, so the surface temperature of the substrate during polishing needs to be controlled in the range of Tg±10°C, i.e. between 133°C and 153°C. By calculating the total heat input in the process based on the collaborative parameter assignment data, considering the mechanical friction heat, chemical reaction heat and plasma bombardment energy, combining the heat flux density and substrate thermal diffusivity parameters, the total energy input required for the target temperature interval is derived, and the temperature control parameter data is formed by dynamically adjusting the process parameters in different process stages. The temperature control parameter data and the collaborative parameter assignment data are input into the multi-process collaborative composite polishing equipment. The equipment integrates mechanical polishing unit, chemical polishing unit and plasma polishing unit, and has the ability of independent controllable process module and self-adaptive scheduling process. In the specific operation process, the mechanical polishing module is started to perform preliminary flattening treatment under the control of particle size, pressure and speed parameters to remove surface macro machining marks and preliminarily adjust the surface roughness. Then switch to the chemical polishing module to immerse in the pre-set concentration of fluorine-containing active agent solution to activate the surface molecular chain end group, promote the surface energy reduction and molecular chain flexibility improvement. Finally, switch to the plasma polishing module to produce a moderate local reconstruction effect on the surface micro area through argon plasma bombardment and heating to further repair micro defects and promote molecular chain directional rearrangement.The whole process dynamically adjusts the process time and energy input of each module through the process scheduler, realizes seamless connection and fine control of each polishing stage, forms continuous and stable multi-process collaborative polishing data, records the changes of key parameters such as temperature curve, energy input, surface stress state and other key parameters of each stage. After the completion of multi-process collaborative polishing, based on the cumulative polishing data and temperature control data, the rearrangement of aromatic molecular chains on the surface of PEEK material is promoted, and the goal of gradient interface strengthening is realized. Due to the sufficient local fluidity of molecular chains in the temperature interval close to Tg, combined with the coupling effect of mechanical shear force and plasma energy, the aromatic molecular chains can be ordered under the action of external force, and a gradient structure with decreasing molecular chain orientation degree from the surface layer to the subsurface layer is gradually formed. By controlling the surface stress release rate and heat input gradient, the problem of surface brittleness caused by excessive accumulation of chain segments is effectively avoided, and the spatial order and interface bonding strength of molecular chains are improved. Through this series of multi-process collaborative polishing and dynamic temperature control means, a continuous gradient interface structure from the high orientation surface layer to the low orientation interior is formed on the surface of PEEK material.

[0043] In a specific embodiment, the process of performing step 400 can specifically include the following steps: Based on the atomic force microscope, the interface strengthening polishing semi-finished product is monitored in real time for π-π interaction force to obtain π-π interaction force monitoring data; Based on the π-π interaction force monitoring data and the target π-π interaction force value, deviation amount calculation is performed to obtain π-π interaction force deviation calculation data; PID feedback control is performed on the π-π interaction force deviation calculation data to obtain process parameter dynamic adjustment data; Based on the process parameter dynamic adjustment data, the grinding pressure, rotation speed and polishing liquid flow are dynamically adjusted in real time to obtain the interface strengthening polishing finished product.

[0044] Specifically, a set of atomic force microscope (AFM) detection system with high sensitivity and high spatial resolution is built, and suitable working mode is selected according to the surface characteristics of PEEK material. The elastic constant of the probe is set in the range of 0.01 N / m to 0.1 N / m by using force-distance curve mode or force modulation imaging mode, to ensure sufficient response capability to the intermolecular interaction force change of nanonewton level. By scanning the surface of the interface reinforced and polished semi-finished product in a lattice, a small positive indentation force is applied at each scanning point, and the mechanical response data generated during contact and separation of the probe and the material surface are recorded, and the π-π interaction force between aromatic molecular chains is obtained in real time. During the scanning process, the atomic force microscope controls the probe to contact and stretch on different micro areas of the surface periodically, and combines the variation characteristics of the force-distance curve to quantify the π-π interaction force at each position, and the detection range is controlled between 0.1 nN and 10 nN. By summarizing and statistically analyzing the data of multiple micro areas, the spatial distribution map of the π-π interaction force is generated, and the average π-π interaction force of the overall surface is extracted, and the π-π interaction force monitoring data with time sequence characteristics is obtained. These monitoring data reflect the ordering of the molecular chain arrangement on the surface of the polished semi-finished product, and can also reveal the spatial uniformity of the interface bonding performance. The real-time π-π interaction force monitoring data is compared with the preset target π-π interaction force value, and the target value is set in the range of 2.5±0.5 nN, which can ensure the ordering of the molecular chain arrangement and avoid the increase of the surface structure brittleness due to excessive compaction. By analyzing the difference between each real-time monitoring value and the target value, the deviation between the current surface state and the target interface performance is calculated in real time. The specific calculation method is to subtract the real-time π-π interaction force value from the target value, and keep the sign to distinguish the deviation direction, to obtain the instantaneous deviation data; at the same time, by introducing time sequence integral and differential operation, the cumulative amount and instantaneous change rate of historical deviation are calculated respectively, so as to form the π-π interaction force deviation calculation data. The PID feedback control is carried out on the π-π interaction force deviation calculation data, and then the process parameters in the polishing process are adjusted in real time. The PID controller takes the π-π interaction force deviation as the input signal, and adjusts according to the proportional (Kp), integral (Ki) and differential (Kd) three control components, wherein the proportional term forms a quick response by directly multiplying the deviation, the integral term realizes the steady-state error elimination by accumulating the history of the deviation, and the differential term predicts the future trend based on the deviation change rate to prevent the system from overshooting or oscillating. By weighting and summing the calculation results of the three parts, the comprehensive control output, that is, the dynamic adjustment data of the process parameters, is obtained. According to the system response requirements, the proportional coefficient Kp is set between 0.8 and 1.2, the integral coefficient Ki is set between 0.3 and 0.5, and the differential coefficient Kd is set between 0.05 and 0.1, to ensure that the system has good response speed, strong stability and anti-disturbance ability.The key parameters of the polishing equipment are dynamically adjusted in real time based on the process parameter dynamic adjustment data of the PID controller output. The polishing pressure, as the main control quantity in the mechanical polishing process, directly affects the compaction degree of the surface micro area and the local arrangement behavior of the molecular chain. When the π-π interaction force is lower than the target value, the polishing pressure is appropriately increased to enhance the surface shear force and heat input and promote the rearrangement of the molecular chain. Conversely, when the interaction force is too high, the polishing pressure is reduced to prevent excessive accumulation of the molecular chain. The rotation speed affects the surface shear stress distribution and local temperature rise speed. The rotation speed is appropriately increased or decreased according to the deviation size, and the adjustment range is set to 100 rpm to 500 rpm. The polishing liquid flow control adjusts the stability of the surface reaction environment and the shear stress transmission efficiency. The flow range is set to 10 mL / min to 50 mL / min. Increasing the flow rate can increase the polishing medium refresh rate and reduce local stress concentration. Conversely, the local shear effect is enhanced. Through real-time dynamic adjustment of the three key process parameters of the polishing pressure, the rotation speed and the polishing liquid flow, the polishing process can be self-adaptively optimized according to the real-time changes of the surface molecular chain arrangement state, and dynamically approaches the preset interface strengthening target. With the continuous operation of the PID feedback system, the deviation of the surface π-π interaction force gradually converges and finally stabilizes in the target interval, ensuring that the interface molecular chain has a highly ordered arrangement state, and the surface binding energy reaches the best level. At the same time, the surface roughness after polishing is also controlled in the ideal range, and finally the interface strengthening polishing finished product is obtained.

[0045] In a specific embodiment, the process of performing step PID feedback control on the π-π interaction force deviation calculation data to obtain process parameter dynamic adjustment data can specifically include the following steps: Perform proportional term product calculation based on the π-π interaction force deviation calculation data and the proportional coefficient to obtain proportional control component data; According to the π-π interaction force deviation calculation data, the integral term calculation is performed on the historical deviation cumulative value and the integral coefficient to obtain the integral control component data; Based on the π-π interaction force deviation calculation data and the differential coefficient, the differential term calculation of the deviation change rate is performed to obtain the differential control component data; According to the proportional control component data, the integral control component data and the differential control component data, the PID control quantity synthesis calculation of weighted summation is performed to obtain the process parameter dynamic adjustment data.

[0046] Specifically, the π-π interaction force monitoring data is compared with the set target π-π interaction force value in real time, and the instantaneous deviation data at each moment is calculated. The deviation data is a signed continuous variable, and a positive value indicates that the current actual interaction force is lower than the target value, and a negative value indicates that it is higher than the target value. The proportional term product calculation is performed based on the π-π interaction force deviation calculation data and the proportional coefficient. The control idea of the proportional term (P term) is to produce a rapid response through the immediate amplification of the deviation amount. The calculation formula of the proportional control component is P=Kp×e(t), where e(t) is the deviation value at the current moment, and Kp is the set proportional coefficient. The proportional control component data is obtained by multiplying the current deviation data by Kp. The role of proportional control is to quickly produce a large adjustment amount when the deviation is large, so as to quickly bring the system to the target state, and when the deviation is small, the adjustment amount is also reduced, maintaining the sensitivity of the system response. In order to prevent the proportional coefficient from being too large to cause system oscillation or too small to cause slow response, Kp is set between 0.8 and 1.2, and is adjusted according to the dynamic characteristics of the polishing process. After the proportional control component calculation is completed, the integral term accumulation calculation is performed on the π-π interaction force deviation calculation data. The control idea of the integral term (I term) is to eliminate the long-term existing steady-state error, and gradually correct the system output by accumulating the deviation value over time, so that the system can accurately converge to the set target after a certain period of time. The calculation formula of the integral control component is I=Ki×∫e(t)dt, and in actual calculation, the discrete integral method is used, that is, the historical deviation is added and multiplied by the integral coefficient Ki. In order to avoid the phenomenon of integral saturation, that is, the integral accumulation is too much to cause excessive adjustment of the system, Ki is set between 0.3 and 0.5. The integral control component data is obtained by adding the historical deviation data. The introduction of the integral term makes it possible for the system to continuously approach the zero deviation state over time even if there is a small steady-state deviation under proportional control, ensuring that the π-π interaction force of the interface strengthening polishing product is long-term stable within the target interval. After the integral control component calculation is completed, the differential term calculation is performed, and the control idea of the differential term (D term) is to predict the trend of the deviation change, so as to adjust the system output in advance and prevent the system from overshooting or oscillating due to rapid change of the deviation. The calculation formula of the differential control component is D=Kd×de(t) / dt, and in actual calculation, the discrete differential is used, that is, the difference between the current deviation value and the deviation value at the last moment is divided by the time interval Δt, which is approximately equal to the rate of change of the deviation, and then multiplied by the differential coefficient Kd to obtain the differential control component data. In order to ensure that the differential term can effectively suppress the unstable factors in the system response process, Kd is set between 0.05 and 0.1. The introduction of the differential control component makes the system sensitive to the acceleration trend of the deviation change, and can suppress it in advance when the rate of change of the deviation is detected to be accelerated, reducing system oscillation and improving the dynamic stability of the system.The PID control amount synthesis calculation is performed according to the proportional control component data, the integral control component data and the differential control component data, and process parameter dynamic adjustment data is obtained.

[0047] In a specific embodiment, the PEEK material composite polishing method based on multi-process synergy further comprises the following steps: The interface strengthening polishing product is subjected to molecular chain orientation degree gradient detection to obtain molecular chain orientation degree detection data; The interface strengthening polishing product is subjected to surface roughness and interface bonding strength detection based on the molecular chain orientation degree detection data to obtain surface quality index detection data; Performance comprehensive analysis is performed according to the molecular chain orientation degree detection data and the surface quality index detection data to obtain comprehensive performance analysis data; Interface failure prediction and quality evaluation grade determination are performed based on the comprehensive performance analysis data to obtain polishing quality verification results.

[0048] Specifically, for the surface of the polishing product, high-precision Raman spectrum polarization analysis technology is used for molecular chain orientation degree gradient detection. The polarization characteristics of incident light are used in combination with the Raman active vibration peak 1490 cm-1 of the aromatic molecular chain of the PEEK material to obtain the molecular chain orientation degree gradient detection data. -1The response change on the (benzene ring skeleton vibration) is measured and compared by measuring the Raman scattering intensity at different orientation angles, and the polarization intensity ratio (I∥ / I⊥) is calculated, wherein I∥ is the scattering intensity under parallel polarized light, and I⊥ is the scattering intensity under perpendicular polarized light. By taking different depth levels of continuous measurement points, combined with micro-erosion or ion beam tomography technology, the change curve of the molecular chain orientation degree at different depths from the surface to the interior is obtained, forming the molecular chain orientation degree gradient data, describing the continuous change rule of the surface order degree from high orientation to low orientation. Based on the molecular chain orientation degree detection data, the surface roughness and interface bonding strength of the polished finished product are detected. The surface roughness detection adopts atomic force microscopy to scan the three-dimensional morphology, obtains the nanoscale fluctuation characteristics of the surface, and forms the surface roughness distribution graph through statistical analysis of Ra value (arithmetic average roughness) and Rq value (root mean square roughness). At the same time, the interface bonding strength detection adopts nanoindentation technology combined with micro area tensile stripping experiment, measures the energy density or fracture toughness required for molecular layer dissociation by applying a controlled loading force to the surface layer, and then deduces the actual performance strength of the aromatic π-π interaction force. The two groups of data respectively reflect the performance of the interface strengthened polished finished product in terms of macroscopic surface morphology and planar bonding energy, and the detection data of the surface quality index covering the entire surface of the finished product is obtained by normalizing and standardizing the detection data. The performance comprehensive analysis is completed by fusing the molecular chain orientation degree detection data and the surface quality index detection data. Through the establishment of a multiple regression analysis model or a prediction model based on machine learning methods (such as support vector regression SVR or random forest regression), the correlation between the molecular chain orientation degree gradient and the surface roughness and interface bonding strength is quantitatively analyzed. The high and low of the orientation degree gradient directly affects the uniformity of the interface bonding strength and the surface energy distribution, and the surface roughness is related to the local defect concentration. Through model training and data regression, a quantitative coupling relationship between the molecular chain orientation degree, the surface roughness and the bonding strength is established, and the detection data is further mapped to the overall comprehensive performance score to form comprehensive performance analysis data. Based on the comprehensive performance analysis data, interface failure prediction and quality evaluation grade determination are carried out. The interface failure prediction is based on the comprehensive performance score and historical failure sample data, and uses logistic regression analysis (Logistic Regression) or a deep learning model (such as a neural network) to predict the failure probability, and by learning the corresponding relationship between different performance combination features and failure modes, the possible hidden defects and failure risks of the interface are identified in advance. If the molecular chain orientation degree gradient fluctuates greatly, or the surface roughness abnormally increases, or the local interface bonding strength is lower than the safety threshold, the prediction model gives a higher failure probability, thereby triggering an early warning.The quality evaluation grade determination is based on the clear grading standard set according to the comprehensive performance index, for example, according to the uniformity of the molecular chain orientation degree, the surface roughness distribution range, and the average and fluctuation of the interface bonding strength, the finished product is divided into four grades of excellent, good, medium and poor, the excellent grade corresponds to high interface bonding strength, low surface roughness and good gradient continuity of the molecular chain orientation degree, the medium and poor grades gradually show the trend of performance decline and failure risk increase. Combined with the failure prediction result and the quality grade determination standard, the polishing quality verification result is formed.

[0049] The PEEK material composite polishing method based on multi-process cooperation in the embodiments of the application is described above, and the PEEK material composite polishing system based on multi-process cooperation in the embodiments of the application is described below. Please refer to Figure 2 An embodiment of the PEEK material composite polishing system based on multi-process cooperation in the embodiments of the application includes: A surface detection module 11 is configured to perform surface detection on the PEEK material to obtain crystallinity gradient distribution information. A process parameter calculation module 12 is configured to perform process parameter calculation on the crystalline region and the amorphous region respectively according to the crystallinity gradient distribution information to obtain a polishing process parameter combination. An interface strengthening polishing module 13 is configured to input the polishing process parameter combination into a multi-process cooperative composite polishing device to perform gradient interface strengthening polishing on the PEEK material to obtain an interface strengthening polishing semi-finished product. A parameter dynamic adjustment module 14 is configured to perform process parameter dynamic adjustment on the interface strengthening polishing semi-finished product to obtain an interface strengthening polishing finished product.

[0050] Through the synergistic cooperation of the above-mentioned various components, through the detection of the π-π stacking state by Raman spectroscopy and the analysis of the electron binding energy by X-ray photoelectron spectroscopy, a three-dimensional gradient interface distribution model for PEEK materials is established, which can accurately describe the spatial distribution evolution law of the aromatic molecular chain. The NSGA-III multi-objective genetic algorithm is used to simultaneously optimize the interface bonding strength, surface roughness and molecular chain orientation degree. Through the solution of the Pareto optimal solution set, the technical problem of the decline of other target performance caused by the excessive optimization of a certain target in the traditional method is effectively solved. Based on the dual molecular recognition technology of differential scanning calorimetry and infrared spectroscopy detection, the accurate division and differentiated process parameter calculation of the crystalline region and amorphous region of PEEK materials are realized, and the technical problem that the traditional unified processing method cannot adapt to the complex structural characteristics of PEEK materials is solved. Through the synergistic effect of mechanical polishing, chemical polishing and plasma polishing three processes, combined with the accurate control of the glass transition temperature interval, the ordered rearrangement and gradient interface strengthening of the aromatic molecular chain of PEEK materials are realized, and the technical limitation of low efficiency of the traditional sequential multi-process processing is overcome. The change of π-π interaction force is monitored in real time by atomic force microscopy, and the PID feedback control algorithm is used to realize the dynamic optimization and adjustment of the process parameters in the polishing process, ensuring the stability and consistency of the sub-molecular level interface precision control. Through the gradient detection of the molecular chain orientation degree and the comprehensive analysis of the surface quality index, a multi-objective performance evaluation and interface failure prediction mechanism is established, which provides a systematic technical support for the polishing quality control of PEEK materials. Through the accurate control of the aromatic functional group density and the gradient distribution of the crystallinity, the surface performance of PEEK materials is significantly improved.

[0051] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, system and unit described above can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

[0052] The integrated unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0053] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not intended to limit the present application; even though the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can be made to the technical solutions recorded in the foregoing embodiments, or equivalent replacements can be made to some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A PEEK material composite polishing method based on multi-process synergy, characterized in that, The application relates to a PEEK material interface strengthening polishing method. The application comprises the following steps: surface detection of the PEEK material to obtain crystallinity gradient distribution information; process parameter calculation of crystalline regions and amorphous regions according to the crystallinity gradient distribution information to obtain polishing process parameter combinations; input of the polishing process parameter combinations into a multi-process synergistic composite polishing device to perform gradient interface strengthening polishing on the PEEK material to obtain interface strengthening polishing semi-finished products; 2. The multi-process synergy based PEEK material composite polishing method according to claim 1, wherein, dynamic adjustment of process parameters of the interface strengthening polishing semi-finished products to obtain interface strengthening polishing finished products. The surface detection of the PEEK material to obtain crystallinity gradient distribution information comprises the following steps: Raman spectrum scanning of the PEEK material surface to obtain vibration peak intensity data; interaction region distribution data calculation based on the vibration peak intensity data to measure the distance between benzene rings on the PEEK material surface; surface electron binding energy detection of the PEEK material by inputting the interaction region distribution data into an X-ray photoelectron spectroscopy device to obtain carbon atom density distribution data; interface characteristic data calculation of the PEEK material based on the carbon atom density distribution data to calculate the molecular chain orientation degree gradient of the PEEK material; 3. The multi-process synergy based PEEK material composite polishing method according to claim 2, wherein, differential scanning calorimetry detection and infrared spectrum detection of the PEEK material based on the interface characteristic data to obtain crystallinity gradient distribution information. The differential scanning calorimetry detection and infrared spectrum detection of the PEEK material based on the interface characteristic data to obtain crystallinity gradient distribution information comprises the following steps: glass transition temperature and melting temperature detection of the PEEK material by inputting the interface characteristic data into a differential scanning calorimetry device to obtain thermodynamic parameter data; crystallinity calculation of the PEEK material based on the thermodynamic parameter data to obtain crystallinity distribution data; infrared spectrum scanning detection of the PEEK material according to the crystallinity distribution data to obtain functional group density distribution data; 4. The multi-process synergy based PEEK material composite polishing method according to claim 1, wherein, crystalline-amorphous region difference gradient calculation based on the crystallinity distribution data and the functional group density distribution data to obtain crystallinity gradient distribution information. The process parameter calculation of crystalline regions and amorphous regions according to the crystallinity gradient distribution information to obtain polishing process parameter combinations comprises the following steps: region division of the PEEK material based on the crystallinity gradient distribution information to obtain distribution position data of the crystalline regions and the amorphous regions; differential calculation of mechanical polishing parameters, chemical polishing parameters and plasma polishing parameters of the crystalline regions and the amorphous regions according to the distribution position data to obtain regional process parameter data; 5. The multi-process synergy based PEEK material composite polishing method according to claim 4, wherein, Pareto optimal solution calculation of interface bonding strength, surface roughness and molecular chain orientation degree of the regional process parameter data by inputting the regional process parameter data into an NSGA-III multi-objective genetic algorithm to obtain polishing process parameter combinations. The Pareto optimal solution calculation of interface bonding strength, surface roughness and molecular chain orientation degree of the regional process parameter data by inputting the regional process parameter data into an NSGA-III multi-objective genetic algorithm to obtain polishing process parameter combinations comprises the following steps: Based on the area process parameter data and the NSGA-III multi-objective genetic algorithm, population individual coding and initial population generation are performed to obtain initial population data; According to the initial population data, three-objective fitness function calculation is performed on the interfacial bonding strength, surface roughness and molecular chain orientation degree to obtain population fitness numerical data; The population fitness numerical data is input into a genetic algorithm evolutioner for evolution operations of selection, crossover and mutation to obtain evolution population data; Based on the evolution population data, a Pareto front screening is performed through dominance relationship judgment and reference point distance calculation to obtain a polishing process parameter combination.

6. The multi-process synergy based PEEK material composite polishing method according to claim 1, wherein, The polishing process parameter combination is input into a multi-process collaborative polishing equipment to perform gradient interfacial strengthening polishing on the PEEK material to obtain an interfacial strengthening polishing semi-finished product, including: Based on the polishing process parameter combination, process distribution is performed on mechanical polishing parameters, chemical polishing parameters and plasma polishing parameters to obtain collaborative parameter distribution data; According to the collaborative parameter distribution data, glass transition temperature interval control is performed on the PEEK material to obtain temperature control parameter data; The temperature control parameter data is input into the multi-process collaborative polishing equipment to perform collaborative polishing of mechanical polishing, chemical polishing and plasma polishing on the PEEK material to obtain multi-process collaborative polishing data; Based on the multi-process collaborative polishing data, gradient interfacial strengthening of aromatic molecular chain rearrangement is performed on the PEEK material to obtain the interfacial strengthening polishing semi-finished product.

7. The multi-process synergy based PEEK material composite polishing method according to claim 1, wherein, The interfacial strengthening polishing semi-finished product is subjected to process parameter dynamic adjustment to obtain an interfacial strengthening polishing finished product, including: Based on an atomic force microscope, real-time monitoring of π-π interaction force is performed on the interfacial strengthening polishing semi-finished product to obtain π-π interaction force monitoring data; Based on the π-π interaction force monitoring data and a target π-π interaction force value, deviation calculation is performed to obtain π-π interaction force deviation calculation data; PID feedback regulation is performed on the π-π interaction force deviation calculation data to obtain process parameter dynamic adjustment data; Based on the process parameter dynamic adjustment data, real-time dynamic adjustment is performed on the grinding pressure, rotation speed and polishing liquid flow to obtain the interfacial strengthening polishing finished product.

8. The multi-process synergy based PEEK material composite polishing method according to claim 7, wherein, The PID feedback regulation on the π-π interaction force deviation calculation data to obtain process parameter dynamic adjustment data includes: Based on the π-π interaction force deviation calculation data and a proportional coefficient, proportional term product calculation is performed to obtain proportional control component data; According to the π-π interaction force deviation calculation data, integral term calculation is performed on a historical deviation cumulative value and an integral coefficient to obtain integral control component data; Based on the π-π interaction force deviation calculation data and a differential coefficient, differential term calculation is performed on a deviation change rate to obtain differential control component data; PID control amount synthesis calculation is performed on the proportional control component data, the integral control component data and the differential control component data through weighted summation to obtain process parameter dynamic adjustment data.

9. The multi-process synergy based PEEK material composite polishing method according to claim 1, wherein, The PEEK material composite polishing method based on multi-process collaboration further includes: The interface reinforced polishing finished product is subjected to molecular chain orientation degree gradient detection to obtain molecular chain orientation degree detection data; The interface reinforced polishing finished product is subjected to surface roughness and interface bonding strength detection based on the molecular chain orientation degree detection data to obtain surface quality index detection data; Comprehensive performance analysis is performed according to the molecular chain orientation degree detection data and the surface quality index detection data to obtain comprehensive performance analysis data; Interface failure prediction and quality evaluation grade determination are performed based on the comprehensive performance analysis data to obtain polishing quality verification results.

10. A multi-process synergy based PEEK material composite polishing system, characterized in that, A method for performing the multi-process synergistic PEEK material composite polishing method according to any one of claims 1-9, comprising: a surface detection module for detecting the surface of the PEEK material to obtain crystallinity gradient distribution information; a process parameter calculation module for calculating process parameters for the crystalline region and the amorphous region respectively according to the crystallinity gradient distribution information to obtain polishing process parameter combinations; an interface reinforced polishing module for inputting the polishing process parameter combinations into a multi-process synergistic composite polishing device to perform gradient interface reinforced polishing on the PEEK material to obtain an interface reinforced polishing semi-finished product; a parameter dynamic adjustment module for dynamically adjusting process parameters for the interface reinforced polishing semi-finished product to obtain an interface reinforced polishing finished product.

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