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

Abstract

This invention relates to the field of material polishing technology, and discloses a multi-process synergistic composite polishing method and system for PEEK materials. The method includes: surface inspection of the PEEK material to obtain crystallinity gradient distribution information; calculation of process parameters for crystalline and amorphous regions based on the crystallinity gradient distribution information to obtain a combination of polishing process parameters; inputting the polishing process parameter combination into a multi-process synergistic composite polishing device to perform gradient interface strengthening polishing on the PEEK material to obtain an interface-strengthened polished semi-finished product; and dynamically adjusting the process parameters of the interface-strengthened polished semi-finished product to obtain an interface-strengthened polished finished product. This invention achieves precise division of crystalline and amorphous regions of PEEK materials and differentiated process parameter calculation, improving the accuracy of PEEK material composite polishing.

Description

Technical Field

[0001] This invention relates to the field of material polishing technology, and in particular to a composite polishing method and system for PEEK materials based on multi-process synergy. Background Technology

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

[0003] Existing polishing techniques for PEEK materials primarily employ single-objective optimization-based methods. These methods have significant limitations in addressing the unique low surface energy interfacial bonding and differential modification of crystalline and amorphous regions characteristic of PEEK. Traditional polishing processes typically use uniform process parameters to treat the entire material surface, failing to differentiate treatment based on the different properties of crystalline and amorphous regions in PEEK. Furthermore, current technologies lack precise control over the π-π interactions between aromatic molecules, making it difficult to accurately regulate molecular chain orientation during polishing, thus affecting the final interfacial quality and performance stability. Summary of the Invention

[0004] This invention provides a PEEK material composite polishing method and system based on multi-process synergy. This invention achieves accurate division of crystalline and amorphous regions of PEEK material and calculation of differentiated process parameters, thereby improving the accuracy of PEEK material composite polishing.

[0005] The first aspect of this invention provides a PEEK material composite polishing method based on multi-process synergy, the multi-process synergy PEEK material composite polishing method comprising:

[0006] Surface testing of PEEK material was performed to obtain information on the crystallinity gradient distribution;

[0007] Based on the crystallinity gradient distribution information, process parameters are calculated for the crystalline region and the amorphous region respectively to obtain the polishing process parameter combination;

[0008] The polishing process parameters are input into a multi-process synergistic composite polishing device to perform gradient interface strengthening polishing on the PEEK material, thereby obtaining an interface strengthening polished semi-finished product.

[0009] The process parameters of the interface-strengthened polished semi-finished product are dynamically adjusted to obtain the interface-strengthened polished finished product.

[0010] In conjunction with the first aspect, in a first implementation of the first aspect of the present invention, the step of performing surface detection on the PEEK material to obtain crystallinity gradient distribution information includes:

[0011] Raman spectroscopy was performed on the surface of the PEEK material to obtain vibration peak intensity data;

[0012] Based on the vibration peak intensity data, the distance between benzene rings on the surface of the PEEK material is measured and calculated to obtain the distribution data of the interaction region.

[0013] 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, thereby obtaining carbon atom density distribution data.

[0014] Based on the carbon atom density distribution data, the molecular chain orientation gradient of the PEEK material is calculated to obtain interface characteristic data.

[0015] Based on the interface feature data, differential scanning calorimetry and infrared spectroscopy were performed on the PEEK material to obtain crystallinity gradient distribution information.

[0016] In conjunction with the first aspect, in a second implementation of the first aspect of the present invention, the step of performing differential scanning calorimetry and infrared spectroscopy on the PEEK material based on the interface feature data to obtain crystallinity gradient distribution information includes:

[0017] The interface feature data is input into a differential scanning calorimeter to detect the glass transition temperature and melting temperature of the PEEK material, thereby obtaining thermodynamic parameter data.

[0018] Based on the thermodynamic parameter data, the crystallinity of the PEEK material was calculated to obtain crystallinity distribution data.

[0019] The PEEK material was subjected to infrared spectral scanning detection based on the crystallinity distribution data to obtain functional group density distribution data;

[0020] Based on the crystallinity distribution data and the functional group density distribution data, the differential gradient between crystalline and amorphous regions is calculated to obtain crystallinity gradient distribution information.

[0021] In conjunction with the first aspect, in a third implementation of the first aspect of the present invention, the step of calculating process parameters for the crystalline region and the amorphous region respectively based on the crystallinity gradient distribution information to obtain a combination of polishing process parameters includes:

[0022] Based on the crystallinity gradient distribution information, the PEEK material is divided into regions to obtain the distribution location data of crystalline and amorphous regions.

[0023] Based on the distribution location data, the mechanical polishing parameters, chemical polishing parameters, and plasma polishing parameters of the crystalline region and the amorphous region are calculated separately to obtain the regional process parameter data;

[0024] The process parameter data of the region is input into the NSGA-III multi-objective genetic algorithm to calculate the Pareto optimal solution of interface bonding strength, surface roughness, and molecular chain orientation, so as to obtain the polishing process parameter combination.

[0025] In conjunction with the first aspect, in the fourth implementation of the first aspect of the present invention, the step of inputting the regional process parameter data into the NSGA-III multi-objective genetic algorithm to calculate the Pareto optimal solution for interface bonding strength, surface roughness, and molecular chain orientation to obtain the polishing process parameter combination includes:

[0026] Based on the regional process parameter data and the NSGA-III multi-objective genetic algorithm, individual population encoding and initial population generation are performed to obtain initial population data;

[0027] Based on the initial population data, a three-objective fitness function was calculated for interfacial bonding strength, surface roughness, and molecular chain orientation to obtain population fitness numerical data.

[0028] The population fitness data is input into the genetic algorithm evolver to perform selection, crossover, and mutation evolution operations to obtain the evolved population data.

[0029] Based on the evolutionary population data, Pareto front screening is performed to determine dominance relationships and calculate reference point distances, resulting in a combination of polishing process parameters.

[0030] In conjunction with the first aspect, in a fifth implementation of the first aspect of the present invention, the step of inputting the polishing process parameters into a multi-process collaborative composite polishing device to perform gradient interface strengthening polishing on the PEEK material to obtain an interface strengthening polished semi-finished product includes:

[0031] Based on the aforementioned combination of polishing process parameters, process allocation is performed on mechanical polishing parameters, chemical polishing parameters, and plasma polishing parameters to obtain coordinated parameter allocation data.

[0032] Based on the coordinated parameter allocation data, the glass transition temperature range of the PEEK material is controlled to obtain temperature control parameter data;

[0033] The temperature control parameter data is input into a multi-process synergistic composite polishing device to perform synergistic polishing of the PEEK material by mechanical polishing, chemical polishing, and plasma polishing, thereby obtaining multi-process synergistic polishing data.

[0034] Based on the multi-process synergistic polishing data, the PEEK material is subjected to gradient interface strengthening by rearranging aromatic molecular chains to obtain an interface-strengthened polished semi-finished product.

[0035] In conjunction with the first aspect, in the sixth implementation of the first aspect of the present invention, the step of dynamically adjusting the process parameters of the interface-strengthened polished semi-finished product to obtain the interface-strengthened polished finished product includes:

[0036] Real-time monitoring of π-π interaction forces was performed on the interface-strengthened polished semi-finished product using atomic force microscopy to obtain π-π interaction force monitoring data.

[0037] Based on the π-π interaction force monitoring data and the target π-π interaction force value, the deviation is calculated to obtain the π-π interaction force deviation calculation data.

[0038] PID feedback control is applied to the calculated π-π interaction force deviation data to obtain dynamic adjustment data for process parameters;

[0039] Based on the dynamic adjustment data of the process parameters, the grinding pressure, rotation speed, and polishing fluid flow rate are dynamically adjusted in real time to obtain an interface-strengthened polished finished product.

[0040] In conjunction with the first aspect, in the seventh implementation of the first aspect of the present invention, the step of performing PID feedback control on the π-π interaction force deviation calculation data to obtain dynamic adjustment data of process parameters includes:

[0041] Based on the π-π interaction force deviation calculation data and the proportional coefficient, the proportional term product is calculated to obtain the proportional control component data;

[0042] Based on the π-π interaction force deviation calculation data, the historical cumulative deviation value and integral coefficient are integrated to calculate the integral control component data.

[0043] Based on the π-π interaction force deviation calculation data and differential coefficients, the differential term of the deviation change rate is calculated to obtain the differential control component data;

[0044] The dynamic adjustment data of process parameters is obtained by weighted summation of the proportional control component data, the integral control component data, and the derivative control component data to synthesize the PID control quantity.

[0045] In conjunction with the first aspect, in the eighth implementation of the first aspect of the present invention, the PEEK material composite polishing method based on multi-process synergy further includes:

[0046] The molecular chain orientation gradient detection was performed on the interface-strengthened polished finished product to obtain molecular chain orientation detection data.

[0047] Based on the molecular chain orientation detection data, the surface roughness and interfacial bonding strength of the interface-strengthened polished finished product are detected to obtain surface quality index detection data.

[0048] Based on the molecular chain orientation detection data and the surface quality index detection data, a comprehensive performance analysis is performed to obtain comprehensive performance analysis data.

[0049] Based on the comprehensive performance analysis data, interface failure prediction and quality assessment level determination are performed to obtain polishing quality verification results.

[0050] A second aspect of the present invention provides a PEEK material composite polishing system based on multi-process synergy, the PEEK material composite polishing system based on multi-process synergy includes:

[0051] The surface inspection module is used to inspect the surface of PEEK materials and obtain information on the crystallinity gradient distribution.

[0052] The process parameter calculation module is used to calculate the process parameters for the crystalline region and the amorphous region respectively based on the crystallinity gradient distribution information, so as to obtain the polishing process parameter combination.

[0053] The interface strengthening polishing module is used to input the polishing process parameters into a multi-process collaborative composite polishing equipment to perform gradient interface strengthening polishing on the PEEK material to obtain an interface strengthening polished semi-finished product.

[0054] The parameter dynamic adjustment module is used to dynamically adjust the process parameters of the interface-strengthened polishing semi-finished product to obtain the interface-strengthened polishing finished product.

[0055] Compared with existing technologies, this invention has the following advantages: A three-dimensional gradient interface distribution model for PEEK materials was established by detecting the π-π packing state using Raman spectroscopy and analyzing the electron binding energy using X-ray photoelectron spectroscopy. This model accurately describes the spatial distribution evolution of aromatic molecular chains. The NSGA-III multi-objective genetic algorithm simultaneously optimizes three objectives: interface bonding strength, surface roughness, and molecular chain orientation. By solving the Pareto optimal solution set, the technical problem of over-optimization of one objective leading to a decline in the performance of other objectives in traditional methods is effectively solved. Based on a dual molecular recognition technology using differential scanning calorimetry and infrared spectroscopy, precise division of crystalline and amorphous regions of PEEK materials and calculation of differentiated process parameters are achieved, solving the technical problem that traditional unified processing methods cannot adapt to the complex structural characteristics of PEEK materials. Through the synergistic effect of mechanical polishing, chemical polishing, and plasma polishing, combined with precise control of the glass transition temperature range, the ordered rearrangement of aromatic molecular chains and gradient interface strengthening of PEEK materials are achieved, overcoming the technical limitations of low efficiency in traditional sequential multi-process processing. Atomic force microscopy was used to monitor changes in π-π interaction forces in real time. Combined with a PID feedback control algorithm, dynamic optimization and adjustment of process parameters during polishing were achieved, ensuring the stability and consistency of submolecular level interface precision control. Through molecular chain orientation gradient detection and comprehensive analysis of surface quality indicators, a multi-objective performance evaluation and interface failure prediction mechanism was established, providing systematic technical support for the polishing quality control of PEEK materials. By precisely controlling the density and crystallinity gradient distribution of aromatic functional groups, significant improvements in the surface properties of PEEK materials were achieved. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] The structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0058] Figure 1 This is a schematic flowchart of the PEEK material composite polishing method based on multi-process synergy provided in an embodiment of the present invention;

[0059] Figure 2 This is a schematic block diagram of the structure of the PEEK material composite polishing system based on multi-process synergy provided in the embodiments of the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0062] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0063] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items, and all possible combinations, and includes such combinations. See also Figure 1 One embodiment of the PEEK material composite polishing method based on multi-process synergy in this invention includes:

[0064] Step 100: Perform surface testing on the PEEK material to obtain crystallinity gradient distribution information;

[0065] It is understood that the executing entity of this invention can be a PEEK material composite polishing system based on multi-process collaboration, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be described using a server as an example.

[0066] Specifically, high-resolution two-dimensional spectral acquisition of the PEEK material surface was performed using Raman spectroscopy, with a focus on the characteristic vibrational peaks in the aromatic benzene ring region, especially at 1600 cm⁻¹. -1The intensity variations of the C=C stretching vibration peaks of the benzene rings on both sides are analyzed. These variations reflect the local density distribution and orientation of the aromatic molecular chains. By analyzing this data, the average distance between benzene rings is quantitatively derived. The calculation of the inter-ring distance is based on the π-π packing model, combined with molecular dynamics approximations, to determine the range of aromatic ring center-to-center spacing, typically concentrated in the 3.4 Å to 3.8 Å range. Distribution data of the benzene ring interaction regions are also obtained. These regions are directly related to the local order of the π-π conjugated structure within the material and the molecular chain arrangement. The interaction region distribution data are input into X-ray photoelectron spectroscopy (XPS) for refined electronic structure analysis, focusing on the electron binding energy at the C1s peak. By finely resolving the C1s signal near 284.8 eV, the density distribution of aromatic carbon atoms on the surface is quantitatively obtained. Since aromatic carbon atoms are a key component of the main chain structure of PEEK materials, the carbon atom density distribution not only reflects the orderliness of the molecular chains on the surface but also provides important spatially resolved information for molecular-level interface properties. Based on the spatial distribution data of carbon atom density and the information on benzene ring spacing derived from Raman spectroscopy, a gradient distribution model of molecular chain orientation degree is established. An exponential function is used to approximate the decreasing trend of molecular chain orientation degree from the surface to the interior. Typically, the orientation degree of the surface layer is in the range of 85%-95%, the subsurface layer in the range of 60%-75%, and the deep layer in the range of 40%-55%, forming continuous gradient interface characteristic data. Differential scanning calorimetry is used to detect the glass transition temperature and melting temperature of PEEK material based on the interface characteristic data. The absolute value of crystallinity is obtained through quantitative calculation of the enthalpy of fusion, and infrared spectroscopy is used to detect the crystallinity at 1650 cm⁻¹. -1 The intensity changes of the C=O stretching vibration peak and the aromatic ring skeleton vibration peak at 1490 cm⁻¹ were used to refine the spatial characteristics of the aromatic functional group density distribution. Based on cross-validation of DSC and FTIR data, crystalline regions with a crystallinity higher than 40% and amorphous regions with a crystallinity lower than 35% were identified. The consistency between molecular chain orientation and functional group distribution was verified by the intensity changes of infrared characteristic peaks, ultimately forming complete crystallinity gradient distribution information of PEEK materials from the surface to the interior.

[0067] Interface feature data is input into a differential scanning calorimeter (DSC), and the glass transition temperature (Tg) and melting temperature (Tm) of PEEK material are detected using high-sensitivity heat flow change detection technology. By real-time monitoring of the heat flow versus temperature curve during sample heating, key signals of physical changes in the PEEK material during heating are extracted. Tg typically shows a baseline shift around 143°C, while Tm exhibits a significant endothermic peak around 334°C. By integrating the endothermic peak area and combining it with known standard enthalpy of fusion, the crystallinity at each detection point is quantitatively calculated, resulting in a dataset of thermodynamic parameters covering different depths from the PEEK surface to its interior. Based on this thermodynamic parameter data, the standard crystallinity calculation formula is used to compare the enthalpy of fusion at each depth with the theoretical enthalpy of fusion for a fully crystalline state, deriving the percentage of crystallinity at each detection location and forming a continuous set of crystallinity distribution data. To refine the molecular structure hierarchy information, this set of crystallinity distribution data was combined with infrared spectroscopy analysis results. Therefore, after differential scanning calorimetry (DSC) analysis, Fourier transform infrared spectroscopy was used to scan and detect the surface and subsurface layers of the PEEK material. The absorption intensity of the C=O stretching vibration peak at 1650 cm⁻¹ and the aromatic ring skeleton vibration peak at 1490 cm⁻¹ was analyzed. By integrating and normalizing the intensity of the characteristic peaks, the density distribution data of aromatic functional groups at each detection point was obtained. Based on the crystallinity distribution data and functional group density distribution data, the differential gradient between crystalline and amorphous regions was calculated using mathematical modeling. Piecewise linear fitting or nonlinear gradient interpolation was used to construct a trend map of functional group density variation within different crystallinity ranges, and the interface characteristic gradients of each region were further extracted. In regions with crystallinity higher than 40%, a higher density of aromatic functional groups was observed, exhibiting a strong π-π conjugation effect. In regions with crystallinity lower than 35%, the functional group density was relatively sparse, and the molecular chain orientation was significantly reduced. By comparing the gradient changes in crystallinity and functional group density in the two types of regions, the continuous evolution process of the internal structure of PEEK material from the surface to the deep layer is quantitatively described, and information on the crystallinity gradient distribution is obtained.

[0068] Step 200: Calculate the process parameters for the crystalline region and the amorphous region respectively based on the crystallinity gradient distribution information to obtain the polishing process parameter combination;

[0069] 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.

[0070] Based on regional process parameter data, the individuals in the population are encoded and the initial population is generated. Each parameter is treated as a gene locus, based on parameters such as particle size and pressure for mechanical polishing, surfactant concentration and treatment time for chemical polishing, and power and duration for plasma polishing. These multidimensional continuous process parameters are encoded into vectors to form the population individuals. Then, based on uniform distribution or Latin hypercube sampling methods, initial population data covering the entire process parameter space is generated, with a population size of 200 individuals to ensure good diversity and coverage in the initial search. For each individual in the initial population, the fitness is calculated according to a defined three-objective fitness function. The fitness function design revolves around three core performance indicators: interfacial bonding strength, surface roughness, and molecular chain orientation. Interfacial bonding strength is derived through π-π interaction force modeling; surface roughness reflects microscopic smoothness based on post-polishing AFM characterization data; and molecular chain orientation reflects the degree of molecular ordered arrangement through the ratio of orientation peak intensities in surface Raman spectroscopy. Through normalization and weight allocation, it is ensured that each objective function is evaluated under the same scale, and the three-objective fitness data corresponding to each individual in the population is calculated, forming a multi-objective performance evaluation matrix covering the initial population. The fitness data is input into the genetic algorithm evolutionary module, and selection, crossover, and mutation operations are performed sequentially according to the evolutionary mechanism set by the NSGA-III algorithm. The selection operation adopts an elite strategy based on non-dominated sorting and crowding comparison, prioritizing the retention of Pareto-dominant individuals; the crossover operation adopts simulated binary crossover (SBX) to ensure that offspring are evenly distributed in the neighborhood of the parent's parameter space, improving search efficiency and solution diversity; the mutation operation adopts a polynomial mutation method to fine-tune the process parameters with a small probability perturbation, avoiding getting trapped in local optima. Through continuous selection, crossover, and mutation operations, the evolutionary unit continuously generates new evolutionary population data, evolving generation by generation to approach the true optimal solution set. After completing a certain number of generations of evolution, dominance relationship judgment and reference point distance calculation are performed based on the current evolutionary population data to screen for the Pareto front. The specific method involves non-dominated sorting of all evolved individuals, dividing them into different Pareto layers according to dominance relationships, and calculating the nearest distance from each individual to a pre-defined set of reference points. Individuals that are closer to the reference points and are evenly distributed are prioritized for retention, thus ensuring that while maximizing interfacial bonding strength, surface roughness is minimized and molecular chain orientation is optimized. The optimal set of individuals selected from the Pareto front represents the polishing process parameter combination under multi-objective optimization, covering the optimal matching configuration of mechanical, chemical, and plasma process parameters.

[0071] Step 300: Input the polishing process parameters into the multi-process collaborative composite polishing equipment to perform gradient interface strengthening polishing on the PEEK material to obtain an interface strengthening polished semi-finished product.

[0072] Specifically, based on the combination of polishing process parameters, specific parameters for the three major process modules—mechanical polishing, chemical polishing, and plasma polishing—are allocated. For mechanical polishing, the optimal particle size range (typically 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 range (100 rpm to 500 rpm) is set to ensure uniform removal of micron-sized protrusions from the material surface. For chemical polishing, the concentration range of the fluorinated surfactant solution (0.1%-0.5%) and the wetting time are set according to the combined parameters to ensure that surface functional groups are activated and modified under a mild environment. As for plasma polishing, the discharge power (100 W to 300 W) and processing time of the argon plasma are set based on the combined results. By controlling the ion bombardment energy, self-organized repair of surface micro-defects is achieved. Through the systematic allocation of parameters for each module, synergistic parameter allocation data covering the characteristics of different surface regions is formed. After parameter allocation, the processing temperature of PEEK material is controlled within the glass transition temperature (Tg) range based on the collaborative parameter allocation data. Given that the Tg of PEEK material is approximately 143°C, and that molecular chains exhibit optimal local mobility near or slightly above Tg, the actual polishing temperature is precisely controlled within the range of Tg ± 10°C, i.e., 133°C to 153°C. To this end, an integrated heating control module dynamically adjusts the substrate temperature during polishing, and an infrared temperature sensor monitors surface temperature changes in real time. Closed-loop temperature regulation is performed based on the collaborative parameter allocation data, resulting in temperature control parameters covering all process stages. This ensures the material surface is in the optimal temperature environment for molecular chain reconstruction during polishing. The aforementioned temperature control parameters are input into a multi-process collaborative composite polishing equipment. Leveraging its highly integrated multi-module collaborative working capability, alternating collaborative polishing processes of mechanical polishing, chemical polishing, and plasma polishing are initiated. During equipment operation, the deep integration and dynamic switching of the three processes are achieved by setting the polishing sequence, adjusting the action time ratio, and synchronously controlling the pressure, flow rate, and energy input. Mechanical polishing is used first to reduce roughness, followed by chemical polishing for surface activation, and plasma polishing is used in the final stage for surface micro-area repair and energy balancing. Data from the multi-process synergistic polishing is recorded in real time, showing changes in surface state, temperature curves, and surface energy density evolution at each stage. After the multi-process synergistic polishing is completed, based on the accumulated synergistic polishing data, the focus is on analyzing and controlling the rearrangement behavior of aromatic molecular chains on the PEEK material surface. Because molecular chains possess a certain degree of mobility at temperatures close to Tg, combined with the physical shear forces and chemical stimuli during polishing, directional rearrangement of aromatic molecular chains can be induced, forming a molecular chain orientation gradient that gradually decreases from the surface to the interior.By rearranging the molecular chains, the π-π interaction forces between the material surface and the polishing medium are enhanced, improving the interfacial bonding strength and the spatial distribution of functional groups at the molecular chain ends. This further reduces surface energy and improves wear resistance and chemical stability. A semi-finished product with an interface-strengthened polishing process is obtained, characterized by ordered molecular chains, enhanced bonding strength, and significantly reduced roughness.

[0073] Step 400: Dynamically adjust the process parameters of the interface-strengthened polished semi-finished product to obtain the interface-strengthened polished finished product.

[0074] Specifically, the surface of the interface-reinforced polished semi-finished product is scanned and detected using atomic force microscopy (AFM), focusing on the changes in π-π interactions between aromatic molecular chains. Local forces between surface molecular chains are precisely measured using force-distance curves, capturing minute changes in these forces in real time within the range of 0.1 nN to 10 nN. Utilizing the high resolution of AFM, continuous and stable π-π interaction force monitoring data can be obtained at the nanoscale, accurately reflecting the degree of molecular chain orientation rearrangement and interfacial bonding state on the semi-finished product surface. The real-time π-π interaction force monitoring data is compared and analyzed with a pre-set target π-π interaction force value, set within the range of 2.5 ± 0.5 nN. This ensures sufficiently strong bonding between aromatic molecular chains while preventing increased surface fragility due to excessive compaction. By comparing the monitoring data with the target value in real time, the deviation is calculated, yielding π-π interaction force deviation data reflecting the difference between the current surface state and the target state. Based on 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 as input and performs proportional, cumulative, and rate-of-change adjustments based on the proportional coefficient (Kp=0.8), integral coefficient (Ki=0.3), and derivative coefficient (Kd=0.1), respectively. The output is dynamic adjustment data of the process parameters through a comprehensive weighted sum of these three adjustments. This dynamic adjustment data is applied to the control of key process parameters in the actual polishing process, mainly including grinding pressure, rotation speed, and polishing slurry flow rate. Regarding grinding pressure, when the detected π-π interaction force is low, the grinding pressure is appropriately increased to the upper limit of 0.8 MPa to enhance the local density of molecular chain arrangement; when the force is high, the pressure is appropriately reduced to prevent excessive molecular chain accumulation. For rotation speed, it is adjusted within the range of 100 rpm to 500 rpm based on the deviation. Low speed is suitable for the force enhancement stage, while high speed is suitable for the force release and surface homogenization stage. As for the polishing slurry flow rate, it is dynamically adjusted within the range of 10 mL / min to 50 mL / min. Changes in the flow rate can regulate the shear force between the polishing slurry and the surface, further fine-tuning the molecular chain arrangement. Through this series of real-time, dynamic and closed-loop controlled parameter adjustment processes, the molecular chain orientation degree and interface bonding performance of the semi-finished product surface are continuously optimized, and the interface strengthening effect reaches the ideal state. The surface roughness is further reduced, the molecular chain orientation gradient becomes more reasonable, and the π-π interaction force is stably maintained within the target range, thus obtaining the interface-strengthened polished finished product.

[0075] The proportional control component is calculated by multiplying the real-time detected deviation data with the pre-set proportional coefficient Kp. This multiplication yields the proportional control component data. The proportional control component directly reflects the immediate impact of the current error magnitude on the system output and is the foundation of the control response. The main function of the proportional term is to directly amplify the deviation, enabling the control quantity to respond quickly when the π-π interaction force deviation is large, rapidly adjusting the process parameters to achieve rapid error convergence. Building upon proportional control, an integral control component is introduced to eliminate long-term accumulated deviations. The integral term is calculated by integrating historical deviation values ​​based on the π-π interaction force deviation calculation data, and then multiplying the accumulated deviation amount with the pre-set integral coefficient Ki to obtain the integral control component data. The introduction of the integral term effectively avoids the long-term existence of small steady-state errors in the system. Through the cumulative effect of historical deviations, a corrective trend is continuously introduced into the control quantity, allowing even small residual deviations to be gradually eliminated under the impetus of integration, ensuring that the interface bonding strength during the polishing process stably approaches the set target value range. Meanwhile, to enhance the system's sensitivity to deviation trends, differential control components are calculated based on the rate of change of the deviation calculation data using the π-π interaction force. The rate of change of deviation is obtained by calculating the first derivative of the deviation data over time, and then multiplied by a predefined differential coefficient Kd to obtain the differential control component data. The differential term primarily predicts future deviation trends. When a trend of increasing or decreasing deviation is detected, it can respond in advance, suppressing overshoot or oscillations and improving the smoothness of process parameter adjustments and the dynamic stability of the system. The final PID control output value, i.e., the dynamic adjustment data of the process parameters, is calculated by weighted summation of the proportional control component data, integral control component data, and differential control component data.

[0076] The molecular chain orientation gradient of the polished finished surface was detected using Raman polarization spectroscopy. An incident polarized beam was used to scan the PEEK surface in different directions, with a focus on monitoring the aromatic benzene ring skeletal vibration peak at 1490 cm⁻¹. -1The polarization intensity variation at various locations is calculated by determining the ratio of parallel to perpendicular polarized light intensity to quantify the molecular chain orientation degree of the surface and near-surface layers. Layered detection at different surface depths yields gradient distribution data of molecular chain orientation degree with depth, i.e., molecular chain orientation degree detection data. This data reflects the decreasing orderliness of aromatic molecular chains from the surface to the interior, serving as an important quantitative indicator of interface strengthening effect. Based on the aforementioned molecular chain orientation degree detection data, to more comprehensively evaluate the surface performance of the interface-strengthened polished product, surface roughness and interfacial bonding strength are tested. Surface roughness is detected using atomic force microscopy (AFM) to acquire three-dimensional surface morphology information at the nanoscale, calculating the Ra value of each detection area to obtain the surface roughness distribution. Interfacial bonding strength is detected using nanoindentation technology combined with exfoliation experiments to quantify interfacial bonding energy and exfoliation strength. By measuring the dissociation force between molecular layers, the stability of π-π interactions is indirectly reflected. These obtained surface roughness and interfacial bonding strength data are integrated to form surface quality index detection data. A comprehensive performance analysis is then performed based on the molecular chain orientation degree detection data and the surface quality index detection data. By conducting multivariate correlation analysis on the molecular chain orientation gradient distribution, surface roughness, and interfacial bonding strength, the intrinsic relationship between molecular chain order, surface morphology characteristics, and interfacial bonding performance is revealed. Based on multiple linear regression or support vector machine regression models, a quantitative mapping relationship between molecular chain orientation and surface performance indicators is established, and the synergistic evolution law of each performance indicator is explored to obtain comprehensive performance analysis data. Based on the comprehensive performance analysis data, interface failure prediction and quality assessment level determination are performed. By using a failure prediction model constructed with historical data, such as an interface failure probability prediction system based on random forest or neural networks, the probability of interface failure that may occur under future service conditions is predicted based on the molecular chain orientation gradient, surface roughness, and interfacial bonding strength of the current finished product. Simultaneously, the prediction results are compared with preset quality level standards, and the quality level of the interface-strengthened polished finished product is classified according to the comprehensive performance indicators, for example, into four levels: excellent, good, medium, and substandard, ultimately forming a polishing quality verification result.

[0077] In this invention, a three-dimensional gradient interface distribution model for PEEK materials was established by detecting the π-π packing state using Raman spectroscopy and analyzing the electron binding energy using X-ray photoelectron spectroscopy. This model accurately describes the spatial distribution evolution of aromatic molecular chains. The NSGA-III multi-objective genetic algorithm was used to simultaneously optimize three objectives: interface bonding strength, surface roughness, and molecular chain orientation. By solving the Pareto optimal solution set, the technical problem of over-optimization of one objective leading to a decline in the performance of other objectives, as seen in traditional methods, was effectively solved. Based on a dual molecular recognition technology using differential scanning calorimetry and infrared spectroscopy, precise segmentation of crystalline and amorphous regions of PEEK materials and calculation of differentiated process parameters were achieved, solving the technical problem that traditional uniform processing methods cannot adapt to the complex structural characteristics of PEEK materials. Through the synergistic effect of mechanical polishing, chemical polishing, and plasma polishing, combined with precise control of the glass transition temperature range, the ordered rearrangement of aromatic molecular chains and gradient interface strengthening of PEEK materials were achieved, overcoming the technical limitations of low efficiency in traditional sequential multi-process processing. Atomic force microscopy was used to monitor changes in π-π interaction forces in real time. Combined with a PID feedback control algorithm, dynamic optimization and adjustment of process parameters during polishing were achieved, ensuring the stability and consistency of submolecular level interface precision control. Through molecular chain orientation gradient detection and comprehensive analysis of surface quality indicators, a multi-objective performance evaluation and interface failure prediction mechanism was established, providing systematic technical support for the polishing quality control of PEEK materials. By precisely controlling the density and crystallinity gradient distribution of aromatic functional groups, significant improvements in the surface properties of PEEK materials were achieved.

[0078] In one specific embodiment, the process of performing step 100 may specifically include the following steps:

[0079] Raman spectroscopy was performed on the surface of the PEEK material to obtain vibration peak intensity data;

[0080] Based on the vibration peak intensity data, the distance between benzene rings on the surface of PEEK material was measured and calculated to obtain the distribution data of the interaction region.

[0081] The surface electron binding energy of PEEK material was detected by inputting the data on the distribution of the interaction region into an X-ray photoelectron spectroscopy device to obtain the carbon atom density distribution data.

[0082] Based on the carbon atom density distribution data, the molecular chain orientation gradient of PEEK material was calculated to obtain interface characteristic data.

[0083] Differential scanning calorimetry and infrared spectroscopy were used to detect the crystallinity gradient distribution of PEEK material based on interface feature data.

[0084] Specifically, a high-resolution Raman spectrometer was used to perform a global scan of the PEEK material surface. By adjusting the laser wavelength and power density, high signal-to-noise ratio spectral data were acquired without damaging the material's surface structure. During the scan, attention was paid to the characteristic vibrational peaks of the aromatic structure, especially those located at 1490 cm⁻¹. -1 The benzene ring skeletal vibration peak at 1600 cm⁻¹ and the peak at 1600 cm⁻¹ -1The C=C stretching vibration peaks at each micro-region were recorded, and the intensity data of the vibration peaks at each micro-region were processed by image processing of the spectral intensity distribution to achieve a preliminary quantitative description of the ordered arrangement of molecules on the PEEK surface. These vibration peak intensity variations are directly related to the packing state of aromatic benzene rings, reflecting the spatial variation trend of molecular chain orientation. After obtaining the vibration peak intensity data, the benzene ring spacing was calculated based on the peak positions and intensity variations of the Raman spectrum. Using a distance estimation formula based on the π-π interaction model, the local vibration intensity was linked to the molecular packing density. Combined with existing molecular mechanics simulation results, the central spacing of aromatic rings was estimated, typically clustered in the range of 3.4 to 3.8 Å. This method converts the vibration peak intensity data of each micro-region into average distance information between benzene rings, obtaining the distribution data of the interaction region. By constructing two-dimensional or three-dimensional distribution maps, the spatial variation characteristics of the packing and arrangement state of aromatic molecular chains in different regions are displayed. The interaction region distribution data is input into an X-ray photoelectron spectroscopy (XPS) instrument for fine detection of the surface elemental chemical state and binding energy. During XPS detection, high-energy-resolution analysis conditions were set, with a focus on measuring the C1s electron spectrum signal and performing multi-peak fitting, paying particular attention to the distribution of aromatic carbon peaks around 284.8 eV. The carbon atom density per unit area was calculated by analyzing the area ratio of the C1s main peak and its shoulder peaks. Because the variation in aromatic carbon atom density is related to the degree of π-π packing of benzene rings, the carbon atom density distribution data can serve as a direct characterization of molecular chain arrangement and bonding state. Combined with XPS spatial scanning mode, a spatially resolved carbon atom density distribution map was obtained, further validating and supplementing the molecular packing information obtained from Raman spectroscopy. After obtaining the carbon atom density distribution data, the molecular chain orientation gradient was calculated based on this data. By comparing the rate of change of carbon density at different depths or different surface positions, the spatial orientation gradient distribution of the molecular chains was derived. An exponentially decreasing model was used to fit the carbon atom density variation trend from the surface to the interior, thus obtaining the gradient distribution characteristics of molecular chain orientation, which gradually decreases from 85%-95% high orientation at the surface to 60%-75% medium orientation at the interior, and then to 40%-55% low orientation at the depth. The molecular chain orientation gradient model was used to describe the longitudinal variation of molecular chain orientation and analyze the uniformity of orientation in local transverse regions, obtaining interface characteristic data with three-dimensional spatial features. Based on the above interface characteristic data, in order to reveal the distribution characteristics of crystallinity and amorphousness within the PEEK material, a combined analysis of differential scanning calorimetry (DSC) and Fourier transform infrared spectroscopy (FTIR) was performed. Using a DSC instrument, with a heating rate set at 10°C / min, heat flow measurements were performed on the PEEK sample to obtain the glass transition temperature (Tg) and melting temperature (Tm) variation curves.By integrating the area of ​​the melt endothermic peak and combining it with the known theoretical enthalpy of fusion of fully crystalline PEEK material, the crystallinity distribution in local regions is quantitatively calculated. Simultaneously, micro-area DSC probe technology is used to achieve local thermal analysis at different depths, thereby obtaining data on the crystallinity variations from the surface to the interior layers. The resulting crystallinity distribution data accurately reflects the spatial distribution characteristics of crystalline and amorphous structures on the material surface and near-surface regions. During the thermal analysis, FTIR spectroscopy is used to detect the infrared absorption of PEEK material in micro-areas, with particular attention paid to the 1650 cm⁻¹ region. -1 The C=O stretching vibration peak at 1490 cm⁻¹ and the peak at 1490 cm⁻¹ -1 The intensity variations of the benzene ring skeletal vibration peaks were analyzed. By calculating the integral area of ​​the characteristic absorption peaks in different regions and combining it with normalization, the distribution of aromatic functional group density in different spatial locations was quantitatively evaluated. The variation in functional group density is directly related to crystallinity; regions with high crystallinity have higher functional group density and more orderly molecular chain arrangement, while those with lower crystallinity indicate disordered molecular arrangement in amorphous regions. By matching the functional group density distribution data obtained from FTIR detection with the crystallinity distribution data obtained from DSC detection, a more accurate crystallinity gradient distribution map of PEEK materials was constructed.

[0085] In one specific embodiment, the process of performing differential scanning calorimetry and infrared spectroscopy on PEEK material based on interface feature data to obtain crystallinity gradient distribution information can specifically include the following steps:

[0086] The interface feature data is input into the differential scanning calorimeter to detect the glass transition temperature and melting temperature of the PEEK material, and obtain thermodynamic parameter data.

[0087] Crystallinity of PEEK material was calculated based on thermodynamic parameter data to obtain crystallinity distribution data.

[0088] Infrared spectral scanning was performed on PEEK material based on crystallinity distribution data to obtain functional group density distribution data;

[0089] Based on crystallinity distribution data and functional group density distribution data, the differential gradient between crystalline and amorphous regions is calculated to obtain crystallinity gradient distribution information.

[0090] Specifically, based on the molecular chain orientation gradient and aromatic functional group density distribution information, representative interfacial feature regions were selected for sample preparation. Tiny samples were collected from different depths from the surface to the interior of the PEEK material to ensure that the collected samples could reflect the spatial gradient characteristics of molecular chain orientation and density distribution. These samples were then standardized to ensure consistent thermal histories, eliminating structural deviations caused by differences in previous processing techniques. These pre-treated samples were then 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 DSC testing, the glass transition temperature (Tg) and melting temperature (Tm) of the PEEK material were detected by recording the heat flow curves during the endothermic and exothermic processes in real time. The glass transition temperature showed a baseline shift, while the melting temperature appeared as a distinct endothermic peak. By integrating the melting endothermic peak, the corresponding enthalpy of fusion (ΔHm) is obtained. Combined with the known standard enthalpy of fusion of PEEK material in its ideal fully crystalline state (taken as 130 J / g), the crystallinity (Xc) is calculated using the following formula: Xc = (ΔHm / ΔH0) × 100%. This step yields crystallinity data for each sample at a specific depth, forming a set of crystallinity distribution data covering different depths of the PEEK material, reflecting the spatial variation trend of crystalline and amorphous structures from the surface to the deep layers within the material. Infrared spectroscopy is then performed on the PEEK material based on the crystallinity distribution data. Infrared spectroscopy analysis, by analyzing the characteristic absorption peaks of molecular groups within the material, provides information on functional groups at the molecular level, particularly showing high sensitivity to changes in the density of aromatic and carbonyl functional groups. Specifically, a Fourier transform infrared spectroscopy (FTIR) instrument is used to scan the samples, focusing on the area at 1650 cm⁻¹. -1 The C=O stretching vibration peak and 1490 cm⁻¹ -1The aromatic ring skeletal vibration peaks were analyzed, and the integral area of ​​the absorption peaks was normalized. Combined with standard samples, quantitative analysis was performed to obtain functional group density distribution data at different depths. Based on the crystallinity distribution data and functional group density distribution data, the differential gradient between crystalline and amorphous regions was calculated to quantitatively describe the spatial distribution characteristics and transition laws of crystalline and amorphous regions within the PEEK material. A piecewise function model based on spatial coordinates was established, and the crystallinity and functional group density data at each depth were jointly fitted. A gradient change model was constructed using piecewise linear regression or polynomial interpolation methods, and its derivative was calculated to obtain the gradient change rate of the transition zone at the crystalline-amorphous interface. Analysis of the gradient change curves identified highly crystalline regions (crystallinity above 40%), amorphous regions (crystallinity below 35%), and their transition zones. Furthermore, based on the trend of functional group density changes, the range of variation in molecular chain regularity was confirmed. The combined gradient analysis method of crystallinity and functional group density can effectively describe the transition process from highly regular crystalline regions to less regular amorphous regions, clarify the width and variation characteristics of the interface region, and form crystallinity gradient distribution information.

[0091] In one specific embodiment, the process of performing step 200 may specifically include the following steps:

[0092] Based on the crystallinity gradient distribution information, PEEK material is divided into regions to obtain the distribution location data of crystalline and amorphous regions.

[0093] Based on the distribution location data, the mechanical polishing parameters, chemical polishing parameters, and plasma polishing parameters of the crystalline and amorphous regions were calculated separately to obtain the regional process parameter data.

[0094] The regional process parameter data are input into the NSGA-III multi-objective genetic algorithm to calculate the Pareto optimal solution for interface bonding strength, surface roughness, and molecular chain orientation, thereby obtaining the polishing process parameter combination.

[0095] Specifically, PEEK materials are region-divided based on crystallinity gradient distribution information. Using spatial stratification algorithms or clustering methods, the material surface is divided into two main categories: crystalline and amorphous regions, according to a set threshold. Regions with crystallinity above 40% are defined as high-crystallinity regions, while regions with crystallinity below 35% are defined as amorphous regions. A reasonable transition range is set between the two to eliminate boundary effects and ensure the continuity and physical rationality of the division. Distribution location data is obtained through region division. After obtaining the distribution location data of crystalline and amorphous regions, differentiated process parameters are calculated for these two types of regions based on the differences in local crystallinity and microstructure. For highly crystalline regions, due to their regular molecular chain arrangement, high density, and high surface hardness, their wear resistance and corrosion resistance are relatively stronger. Therefore, in the mechanical polishing stage, diamond abrasive particles with a smaller particle size should be used, with the particle size range controlled between 0.25μm and 0.5μm. At the same time, the polishing pressure should be set in a low range, such as 0.2MPa to 0.4MPa, to prevent the generation of local surface microcracks due to excessive mechanical stress. In the chemical polishing stage, a low-concentration (0.1%-0.3%) fluorinated surfactant solution is used to control the reaction activity, avoid excessive corrosion of the ordered chain segments in the crystalline region, and maintain the integrity of the surface structure. For plasma polishing, low-power (100W to 150W) argon plasma is selected for short-term action to fine-tune surface micro-defects and promote the activation of molecular chain end groups, thereby improving the local interfacial bonding ability. For amorphous regions, due to their disordered molecular chain arrangement, low density, and soft surface, their mechanical properties are relatively weak. Therefore, in the mechanical polishing stage, larger diamond particles are appropriately used, with a particle size set between 0.7 μm and 1.0 μm, and the polishing pressure is increased to 0.5 MPa to 0.7 MPa to increase removal efficiency while ensuring surface smoothness. In the chemical polishing stage, a high-concentration (0.3%-0.5%) fluorinated surfactant solution is used to enhance the chemical interaction with amorphous chain segments and improve surface activation. Plasma polishing requires increasing the power to 200W to 300W and extending the processing time to effectively remove potential surface defects in amorphous regions and promote surface reconstruction and molecular chain orientation. By differentiating the parameters for mechanical polishing, chemical polishing, and plasma polishing, optimal surface modification effects can be achieved for the microstructural characteristics of different regions, resulting in a set of process parameter data covering each region. The regional process parameter data is then input into the NSGA-III multi-objective genetic algorithm for comprehensive optimization to simultaneously meet the synergistic improvement of three major performance indicators: interfacial bonding strength, surface roughness, and molecular chain orientation. As a new generation of multi-objective evolutionary algorithm, the NSGA-III algorithm adopts a multi-objective optimization mechanism based on dominance relations and reference points, which can effectively handle the solution set distribution problem in high-dimensional complex objective spaces.In practice, each region's process parameters are encoded as an individual's gene sequence. Real-number encoding is used to standardize process variables such as particle size, pressure, polishing time, polishing slurry concentration, and plasma power, constructing an initial population with a size of 200 individuals, a maximum generation count of 500, a crossover probability of 0.9, and a mutation probability of 0.1 to ensure population diversity and facilitate global exploration of the search space. For each individual, multi-objective performance is evaluated based on a defined fitness function. Interface bonding strength fitness is calculated using molecular dynamics simulations combined with experiments to estimate the magnitude of aromatic π-π interactions; surface roughness fitness is calculated using the Ra value through simulation of the three-dimensional profile of the polished surface; and molecular chain orientation is quantitatively evaluated using molecular orientation simulations combined with actual Raman spectral intensity ratios. By performing non-dominated sorting of the three fitness objectives and performing density estimation and selection based on preset reference points, high-quality individuals located at the Pareto front and with uniform distribution are preferentially retained. Simultaneously, simulated binary crossover and polynomial mutation operations are used for gene recombination and fine-tuning, continuously evolving and optimizing the population. The Pareto optimal solution set, obtained after multiple generations of evolution, contains a series of process parameter combinations that achieve the best trade-off between maximizing interfacial bonding strength, minimizing surface roughness, and optimizing molecular chain orientation.

[0096] In one specific embodiment, the process of inputting the regional process parameter data into the NSGA-III multi-objective genetic algorithm to calculate the Pareto optimal solution for interface bonding strength, surface roughness, and molecular chain orientation, and obtaining the polishing process parameter combination, can specifically include the following steps:

[0097] The initial population data is obtained by encoding individual populations and generating the initial population based on regional process parameter data and the NSGA-III multi-objective genetic algorithm.

[0098] Based on the initial population data, a three-objective fitness function was calculated for interfacial bonding strength, surface roughness, and molecular chain orientation to obtain numerical data of population fitness.

[0099] The population fitness data is input into the genetic algorithm evolver to perform selection, crossover, and mutation evolutionary operations to obtain the evolved population data.

[0100] Pareto front screening, based on dominance relationship judgment and reference point distance calculation using evolutionary population data, yields the combination of polishing process parameters.

[0101] Specifically, the regional process parameter data were structured. For different crystalline and amorphous regions of PEEK material, seven key process parameters were extracted: mechanical polishing particle size, grinding pressure, grinding speed, chemical polishing solution concentration, immersion time, plasma polishing power, and action time. These parameters were then normalized to the [0,1] interval to avoid the influence of different dimensions on subsequent genetic operations. Based on the standardized regional process parameters, each individual was encoded using real-number encoding, with a combination of process parameters corresponding to a chromosome in the population, forming a gene sequence containing multidimensional continuous variables. A certain number of initial population individuals were uniformly generated in the parameter space using Latin hypercube sampling or uniform random sampling, with a population size of 200 to ensure diversity and wide distribution in the initial stage, obtaining initial population data covering a broad solution space. Fitness evaluation was performed on each individual. The fitness function was designed around three key objectives: interfacial bonding strength, surface roughness, and molecular chain orientation. For interfacial bonding strength, the magnitude of aromatic π-π interaction forces is estimated based on molecular dynamics simulations or existing experimental models, and quantified using indicators such as interfacial bonding energy density or fracture toughness. Surface roughness is predicted by fitting experimental or simulation models of the relationship between polishing process parameters and surface Ra values; a smaller Ra value indicates higher fitness. Molecular chain orientation relies on simulations of molecular chain orientation or is quantified based on analysis of temperature and shear force fields during polishing, combined with the orientation intensity ratio of Raman polarization spectra; a higher orientation indicates more ordered molecular chain arrangement, and the fitness score increases accordingly. After standardizing the three sets of performance index values ​​corresponding to each individual, a three-objective fitness function is output, which is then aggregated to obtain the fitness numerical data matrix of the entire initial population, serving as the evaluation basis for the genetic algorithm's evolution. The above population fitness numerical data is input into the evolver module of the NSGA-III multi-objective genetic algorithm to initiate the genetic evolution process. First, a selection operation is performed. Based on the principle of non-dominated sorting, the population is hierarchically divided, prioritizing Pareto-dominant individuals. Simultaneously, crowding distance or the distribution density of reference points is considered to ensure that the selected individuals are evenly distributed in the solution space, avoiding premature convergence to local optima. Next, a crossover operation is performed using the simulated binary crossover (SBX) method to recombine the selected parent individuals. This involves a controlled random exchange of genes between two parents in the parameter space, generating new offspring individuals. The crossover probability is typically set to 0.9 to ensure genetic diversity. Finally, a multinomial mutation operation is used, randomly selecting a certain proportion of gene loci in the offspring gene vector for minor perturbations. The mutation probability is usually set to 0.1 to prevent the population from falling into local optima traps.Through continuous operations of selection, crossover, and mutation, new evolutionary population data is obtained. Individuals in this new population not only inherit superior genes from their parents but also introduce new genetic diversity, thus continuously approaching the global optimum. After several generations of population evolution, dominance relationship judgment and reference point distance calculation are performed based on the latest evolutionary population data to screen out the optimal solution set for the Pareto front. The population is non-dominated and sorted, with all individuals divided into different levels according to dominance relationships. Individuals in the first level of non-dominated sets form the initial Pareto front. To further improve the diversity and uniformity of the solution set, a reference point method is used for distance calculation. Based on a pre-defined set of reference points evenly distributed in the target space, the distance from each individual to the nearest reference point is calculated, prioritizing the retention of individuals that are close to the reference points and have uniform coverage, avoiding excessive clustering of the solution set in a certain local area of ​​the target space. This method, combining dominance relationship judgment and reference point distance calculation, effectively selects the Pareto optimal solution set that achieves the best balance between maximizing interface bonding strength, minimizing surface roughness, and optimizing molecular chain orientation, ultimately forming a series of polishing process parameter combinations.

[0102] In one specific embodiment, the process of performing step 300 may specifically include the following steps:

[0103] Based on the combination of polishing process parameters, process allocation is performed on mechanical polishing parameters, chemical polishing parameters, and plasma polishing parameters to obtain synergistic parameter allocation data;

[0104] Based on the synergistic parameter allocation data, the glass transition temperature range of PEEK material is controlled to obtain temperature control parameter data;

[0105] Temperature control parameter data is input into a multi-process synergistic composite polishing equipment to perform synergistic polishing of PEEK material through mechanical polishing, chemical polishing, and plasma polishing, thereby obtaining multi-process synergistic polishing data;

[0106] Gradient interface strengthening of PEEK material by rearranging aromatic molecular chains based on multi-process synergistic polishing data yields interface-strengthened polished semi-finished product.

[0107] Specifically, process allocation is based on the combination of polishing process parameters. Core parameters in the polishing parameter combination, such as mechanical polishing particle size, grinding pressure, rotation speed, chemical polishing slurry concentration, immersion time, plasma polishing power, and treatment duration, are categorized and organized according to their respective process requirements. The process parameters for mechanical polishing, chemical polishing, and plasma polishing are assigned to corresponding modules, forming a system-wide collaborative parameter allocation data. In this process, mechanical polishing parameters are used to determine the removal rate and surface morphology control during grinding. The particle size is set between 0.25μm and 1.0μm to adjust the polishing precision, the pressure is set between 0.2MPa and 0.8MPa to adjust the mechanical removal depth, and the rotation speed directly affects the shear force and heat distribution on the material surface, set between 100rpm and 500rpm. Chemical polishing parameters are mainly controlled by adjusting the concentration of the fluorinated surfactant solution and the treatment time to control the activation of molecular chain end groups and the reduction of surface free energy. 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 depending on the crystallinity region. The plasma polishing parameters are set by adjusting the argon plasma power between 100W and 300W and the interaction time between 60 and 300 seconds to achieve microscopic defect repair and molecular chain rearrangement in different areas. After generating the synergistic parameter allocation data, the glass transition temperature range is controlled based on the allocation data to ensure that the molecular chains have an appropriate thermal activation state during the polishing process. Since the glass transition temperature (Tg) of PEEK material is approximately 143°C, the molecular chains exhibit optimal local mobility in the temperature range close to or slightly above Tg. Therefore, it is necessary to control the substrate surface temperature within the range of Tg ± 10°C, i.e., between 133°C and 153°C, during the polishing process. By calculating the total heat input during the process based on the synergistic parameter allocation data, and comprehensively considering the heat generated by mechanical friction, the heat released by chemical reaction, and the energy of plasma bombardment, combined with the heat flux density and the thermal diffusivity of the substrate, the total energy input required for the target temperature range is derived. The process parameters are then dynamically adjusted according to different process stages to form temperature control parameter data. The temperature control parameter data and the synergistic parameter allocation data are then input into the multi-process synergistic composite polishing equipment. The equipment integrates mechanical polishing, chemical polishing, and plasma polishing units, possessing independent controllable process modules and adaptive process scheduling capabilities. In operation, the mechanical polishing module is activated to perform preliminary smoothing under controlled particle size, pressure, and speed parameters, removing macroscopic processing marks and initially adjusting surface roughness. It then switches to the chemical polishing module, using a pre-concentrated fluorinated surfactant solution to wet the surface, activating the end groups of the molecular chains and promoting surface energy reduction and improved molecular chain flexibility. Finally, it switches to the plasma polishing module, where argon plasma bombardment and heating induce a moderate local reconstruction effect in the surface micro-regions, further repairing microscopic defects and promoting molecular chain rearrangement.The entire process dynamically adjusts the process time and energy input of each module through a process scheduler, achieving seamless connection and precise control of each polishing stage. This generates continuous and stable multi-process collaborative polishing data, recording changes in key parameters such as temperature curves, energy input, and surface stress state at each stage. After multi-process collaborative polishing is completed, based on the accumulated polishing and temperature control data, the aromatic molecular chains on the PEEK material surface are rearranged to achieve the goal of gradient interface strengthening. Due to the sufficient local fluidity of the molecular chains in the near-Tg temperature range, coupled with the coupling effect of mechanical shear force and plasma energy, the aromatic molecular chains can undergo ordered arrangement under external force, gradually forming a gradient structure with decreasing molecular chain orientation from the surface layer to the subsurface layer. By controlling the surface stress release rate and heat input gradient, the surface brittleness problem caused by excessive chain segment accumulation is effectively avoided, while simultaneously improving the spatial order of the molecular chains and the interfacial bonding strength. Through this series of multi-process collaborative composite polishing and dynamic temperature control methods, a continuous gradient interface structure from the high-orientation surface layer to the low-orientation interior is formed on the PEEK material surface.

[0108] In one specific embodiment, the process of performing step 400 may specifically include the following steps:

[0109] Real-time monitoring of π-π interaction forces was performed on interface-strengthened polished semi-finished products using atomic force microscopy, and π-π interaction force monitoring data was obtained.

[0110] Based on the monitoring data of π-π interaction force and the numerical value of target π-π interaction force, the deviation is calculated to obtain the π-π interaction force deviation calculation data;

[0111] PID feedback control is applied to the calculated deviation data of π-π interaction force to obtain dynamic adjustment data of process parameters;

[0112] Based on the dynamic adjustment data of process parameters, the grinding pressure, rotation speed, and polishing fluid flow rate are dynamically adjusted in real time to obtain the interface-strengthened polished finished product.

[0113] Specifically, an atomic force microscopy (AFM) system with high sensitivity and high spatial resolution was constructed, and a suitable operating mode was selected based on the surface characteristics of PEEK materials. Force-distance curve mode or force modulation imaging mode was employed, with the probe's elastic constant set between 0.01 N / m and 0.1 N / m to ensure sufficient responsiveness to changes in intermolecular forces at the nano-Newton level. A dot matrix scan was performed on the surface of the interface-reinforced polished semi-finished product, applying a small positive indentation force at each scan point and recording the mechanical response data generated during the contact and detachment of the probe and the material surface, thereby acquiring the π-π interaction forces between aromatic molecular chains in real time. During the scanning process, the AFM controlled the probe to periodically contact and stretch different micro-regions on the surface. Combined with the changing characteristics of the force-distance curve, the π-π interaction force at each location could be quantified, with the detection range controlled between 0.1 nN and 10 nN. By summarizing and statistically analyzing data from multiple micro-regions, a spatial distribution map of π-π interaction forces is generated. Simultaneously, the average π-π interaction force of the entire surface is extracted, yielding π-π interaction force monitoring data with time-series characteristics. This monitoring data reflects the ordered arrangement of molecular chains on the surface of the polished semi-finished product and reveals the spatial uniformity of interfacial bonding performance. Real-time π-π interaction force monitoring data is compared with a preset target π-π interaction force value, set within the range of 2.5 ± 0.5 nN. This ensures both the ordered arrangement of molecular chains and avoids increased brittleness due to excessive compaction of the surface structure. By performing difference analysis between each real-time monitoring value and the target value, the deviation between the current surface state and the target interfacial performance is calculated in real time. Specifically, the difference between the real-time π-π interaction force value and the target value is calculated, retaining the sign to distinguish the direction of deviation, yielding instantaneous deviation data. Simultaneously, by introducing time-series integration and differentiation operations, the cumulative amount and instantaneous rate of change of historical deviations are calculated, thus forming π-π interaction force deviation calculation data. PID feedback control is applied to the calculated π-π interaction force deviation data to adjust the process parameters in real time during polishing. The PID controller uses the π-π interaction force deviation as the input signal and adjusts it according to three control components: proportional (Kp), integral (Ki), and derivative (Kd). The proportional term forms a fast response by directly multiplying it by the deviation; the integral term eliminates steady-state error by accumulating the historical deviation; and the derivative term predicts future trends based on the deviation change rate to prevent overshoot or oscillation. By weighted summing the results of these three parts, the comprehensive control output, i.e., 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 between 0.3 and 0.5, and the derivative coefficient Kd between 0.05 and 0.1 to ensure that the system has both good response speed and strong stability and disturbance rejection capability.Based on the dynamic adjustment data of process parameters output by the PID controller, the key parameters of the polishing equipment are dynamically adjusted in real time. Grinding pressure, as the main control quantity in the mechanical polishing process, directly affects the compaction degree of surface micro-regions and the local arrangement behavior of molecular chains. When the π-π interaction force is lower than the target value, the grinding pressure is appropriately increased to enhance surface shear force and heat input, promoting molecular chain rearrangement; conversely, when the force is too high, the grinding pressure is reduced to prevent excessive molecular chain accumulation. Rotation speed affects the surface shear stress distribution and local heating rate. The rotation speed is increased or decreased in a timely manner according to the deviation, with the adjustment range set between 100 rpm and 500 rpm. Polishing fluid flow rate control adjusts the stability of the surface reaction environment and the shear stress transfer efficiency, with the flow rate set between 10 mL / min and 50 mL / min. Increasing the flow rate can improve the polishing medium refresh rate and reduce local stress concentration, while decreasing it enhances the local shear effect. Through real-time dynamic adjustment of these three key process parameters—grinding pressure, rotation speed, and polishing fluid flow rate—the polishing process can adaptively optimize according to the real-time changes in the surface molecular chain arrangement state, dynamically approaching the preset interface strengthening target. As the PID feedback system continues to operate, the deviation of the surface π-π interaction force gradually converges and eventually stabilizes within the target range, ensuring that the interface molecular chains have a highly ordered arrangement, the surface bonding energy reaches the optimal level, and the surface roughness after polishing is also controlled within the ideal range, ultimately resulting in an interface-strengthened polished finished product.

[0114] In one specific embodiment, the process of performing PID feedback control on the π-π interaction force deviation calculation data to obtain dynamic adjustment data of process parameters can specifically include the following steps:

[0115] Based on the π-π interaction force deviation calculation data and the proportional coefficient, the proportional term product is calculated to obtain the proportional control component data;

[0116] Based on the π-π interaction force deviation calculation data, the historical cumulative deviation value and integral coefficient are integrated to obtain the integral control component data;

[0117] Based on the deviation calculation data of π-π interaction force and the differential coefficient, the differential term of the deviation change rate is calculated to obtain the differential control component data;

[0118] The dynamic adjustment data of process parameters are obtained by weighted summation of PID control inputs based on proportional control component data, integral control component data, and derivative control component data.

[0119] Specifically, the π-π interaction force monitoring data is compared in real time with the set target π-π interaction force value to calculate the instantaneous deviation data at each moment. This deviation data is a signed continuous variable; 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 is calculated by multiplying the π-π interaction force deviation data with the proportional coefficient. The control concept of the proportional term (P term) is to generate a rapid response by instantly amplifying the deviation. The formula for calculating 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 generate a large adjustment when the deviation is large, prompting the system to quickly approach the target state, while the adjustment decreases when the deviation is small, maintaining the sensitivity of the system response. To prevent the proportional coefficient from being too large and causing system oscillation, or too small and causing sluggish response, Kp is set between 0.8 and 1.2, and specifically adjusted according to the dynamic characteristics of the polishing process. After the proportional control component calculation is completed, the integral term is accumulated for the π-π interaction force deviation calculation data. The control idea of ​​the integral term (I term) is to eliminate long-term steady-state errors by gradually correcting the system output through the accumulation of deviation values ​​over time, so that the system can accurately converge to the set target after a certain period of time. The calculation formula for the integral control component is I=Ki×∫e(t)dt, which is performed by discrete integration in actual calculation, that is, accumulating historical deviations and then multiplying by the integral coefficient Ki. To avoid integral saturation, i.e., excessive accumulation of integrals leading to over-adjustment of the system, Ki is set between 0.3 and 0.5. The integral control component data is obtained by summing the historical deviation data. The introduction of the integral term ensures that even if the system has a small steady-state deviation under proportional control, it can continuously approach the zero deviation state over time, ensuring that the π-π interaction force of the interface-strengthened polished product remains stable within the target range in the long term. After calculating the integral control component, the derivative term is calculated. The control concept of the derivative term (D term) is to predict the trend of deviation changes, thereby adjusting the system output in advance to prevent overshoot or oscillation caused by excessively rapid deviation changes. The formula for calculating the derivative control component is D = Kd × de(t) / dt. In actual calculations, discrete differentiation is used, that is, the difference between the current deviation value and the deviation value at the previous moment is divided by the time interval Δt to approximate the rate of deviation change, and then multiplied by the derivative coefficient Kd to obtain the derivative control component data. To ensure that the derivative term can effectively suppress unstable factors in the system response process, Kd is set between 0.05 and 0.1. The introduction of the derivative control component makes the system sensitive to the accelerating trend of deviation changes. When an accelerated rate of deviation change is detected, it can be suppressed in advance, reducing system oscillation and improving the dynamic stability of the system.The dynamic adjustment data of process parameters are obtained by weighted summation of PID control inputs based on proportional control component data, integral control component data, and derivative control component data.

[0120] In one specific embodiment, the PEEK material composite polishing method based on multi-process synergy further includes the following steps:

[0121] Molecular chain orientation gradient detection was performed on the finished product with interface strengthening and polishing to obtain molecular chain orientation detection data;

[0122] Based on molecular chain orientation detection data, the surface roughness and interfacial bonding strength of the interface-strengthened polished finished product are detected to obtain surface quality index detection data.

[0123] Based on the molecular chain orientation degree detection data and surface quality index detection data, a comprehensive performance analysis was conducted to obtain comprehensive performance analysis data.

[0124] Based on comprehensive performance analysis data, interface failure prediction and quality assessment level determination are performed to obtain polishing quality verification results.

[0125] Specifically, for the polished finished surface, high-precision Raman spectroscopy polarization analysis technology is used to detect the molecular chain orientation gradient. This utilizes the polarization characteristics of the incident light, combined with the Raman active vibration peak of the aromatic molecular chains of PEEK material at 1490 cm⁻¹. -1The response changes on the (benzene ring skeleton vibration) are analyzed by measuring and comparing the Raman scattering intensity at different orientation angles, and calculating the polarization intensity ratio (I∥ / I⊥), where I∥ is the scattering intensity under parallel polarized light and I⊥ is the scattering intensity under perpendicular polarized light. By taking continuous measurement points at different depths and combining micro-area ablation or ion beam tomography techniques, the variation curves of molecular chain orientation at different depths from the surface to the interior are obtained, forming molecular chain orientation gradient data to describe the continuous change law of surface order from high orientation to low orientation. Based on the molecular chain orientation detection data, the surface roughness and interfacial bonding strength of the polished product are detected. Surface roughness detection uses atomic force microscopy for three-dimensional morphological scanning to obtain the nanoscale undulation features of the surface. By statistically analyzing the Ra value (arithmetic mean roughness) and Rq value (root mean square roughness), a surface roughness distribution map is formed. Simultaneously, the interfacial bonding strength detection employs nanoindentation technology combined with micro-area tensile peeling experiments. By applying controlled loading forces to the surface layer, the energy density or fracture toughness required for interlayer dissociation is measured, thereby deriving the actual strength of aromatic π-π interactions. Two sets of data reflect the performance of the interface-strengthened polished product in terms of macroscopic surface morphology and planar bonding energy, respectively. By normalizing and standardizing the detection data, surface quality index detection data covering the entire surface of the finished product are obtained. The molecular chain orientation degree detection data and surface quality index detection data are fused to complete a comprehensive performance analysis. A multivariate regression analysis model or a prediction model based on machine learning methods (such as Support Vector Regression (SVR) or Random Forest Regression) is established to quantitatively analyze the correlation between molecular chain orientation degree gradient, surface roughness, and interfacial bonding strength. The orientation degree gradient directly affects the uniformity of interfacial bonding strength and surface energy distribution, while surface roughness is related to the concentration of local defects. Through model training and data regression, a quantitative coupling relationship between molecular chain orientation degree, surface roughness, and bonding strength is constructed, further mapping the detection data to an overall comprehensive performance score, forming comprehensive performance analysis data. Based on comprehensive performance analysis data, interface failure prediction and quality assessment levels are determined. Interface failure prediction uses comprehensive performance scores and historical failure sample data, employing logistic regression analysis or deep learning models (such as neural networks) to predict failure probabilities. By learning the correspondence between different performance combinations and failure modes, potential hidden defects and failure risks at the interface can be identified in advance. If there are significant fluctuations in the molecular chain orientation gradient, abnormal increases in surface roughness, or local interface bonding strength falling below the safety threshold, the prediction model will give a high failure probability, thus triggering an early warning.The quality assessment grade is determined based on clear grading standards set according to comprehensive performance indicators. For example, based on the uniformity of molecular chain orientation, the distribution range of surface roughness, and the mean and fluctuation of interfacial bonding strength, the finished products are divided into four grades: excellent, good, medium, and inferior. The excellent grade corresponds to high interfacial bonding strength, low surface roughness, and good continuity of molecular chain orientation gradient. The medium and inferior grades gradually show a trend of performance decline and increased failure risk. Combining the failure prediction results with the quality grade determination standards, polishing quality verification results are formed.

[0126] The above describes the PEEK material composite polishing method based on multi-process synergy in the embodiments of the present invention. The following describes the PEEK material composite polishing system based on multi-process synergy in the embodiments of the present invention. Please refer to [link / reference]. Figure 2 One embodiment of the PEEK material composite polishing system based on multi-process synergy in this invention includes:

[0127] Surface detection module 11 is used to perform surface detection on PEEK material to obtain crystallinity gradient distribution information;

[0128] The process parameter calculation module 12 is used to calculate the process parameters for the crystalline region and the amorphous region respectively based on the crystallinity gradient distribution information, so as to obtain the polishing process parameter combination.

[0129] The interface strengthening polishing module 13 is used to input the polishing process parameters into the multi-process collaborative composite polishing equipment to perform gradient interface strengthening polishing on PEEK material and obtain interface strengthening polishing semi-finished product.

[0130] The parameter dynamic adjustment module 14 is used to dynamically adjust the process parameters of the interface-strengthened polishing semi-finished product to obtain the interface-strengthened polishing finished product.

[0131] Through the synergistic collaboration of the aforementioned components, a three-dimensional gradient interface distribution model for PEEK materials was established by detecting the π-π packing state using Raman spectroscopy and analyzing the electron binding energy using X-ray photoelectron spectroscopy. This model accurately describes the spatial distribution evolution of aromatic molecular chains. The NSGA-III multi-objective genetic algorithm was employed to simultaneously optimize three objectives: interface bonding strength, surface roughness, and molecular chain orientation. By solving the Pareto optimal solution set, the technical challenge of over-optimization of one objective leading to a decline in the performance of other objectives, as seen in traditional methods, was effectively addressed. Based on a dual molecular recognition technology combining differential scanning calorimetry and infrared spectroscopy, precise segmentation of crystalline and amorphous regions of PEEK materials and calculation of differentiated process parameters were achieved, resolving the technical problem that traditional uniform processing methods cannot adapt to the complex structural characteristics of PEEK materials. Through the synergistic effect of mechanical polishing, chemical polishing, and plasma polishing, combined with precise control of the glass transition temperature range, the ordered rearrangement of aromatic molecular chains and gradient interface strengthening of PEEK materials were realized, overcoming the technical limitations of low efficiency in traditional sequential multi-process treatments. Atomic force microscopy was used to monitor changes in π-π interaction forces in real time. Combined with a PID feedback control algorithm, dynamic optimization and adjustment of process parameters during polishing were achieved, ensuring the stability and consistency of submolecular level interface precision control. Through molecular chain orientation gradient detection and comprehensive analysis of surface quality indicators, a multi-objective performance evaluation and interface failure prediction mechanism was established, providing systematic technical support for the polishing quality control of PEEK materials. By precisely controlling the density and crystallinity gradient distribution of aromatic functional groups, significant improvements in the surface properties of PEEK materials were achieved.

[0132] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0133] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0134] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions 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 invention.

Claims

1. A composite polishing method for PEEK materials based on multi-process synergy, characterized in that, include: Surface testing of PEEK material yields crystallinity gradient distribution information. Specifically, this includes: performing Raman spectroscopy on the PEEK material surface to obtain vibrational peak intensity data; calculating the inter-ring distance between benzene rings on the PEEK material surface based on the vibrational peak intensity data to obtain interaction region distribution data; inputting the interaction region distribution data into X-ray photoelectron spectroscopy to detect the surface electron binding energy of the PEEK material to obtain carbon atom density distribution data; calculating the molecular chain orientation gradient of the PEEK material based on the carbon atom density distribution data to obtain interface feature data; and performing differential scanning calorimetry and infrared spectroscopy on the PEEK material based on the interface feature data to obtain crystallinity gradient distribution information. Based on the crystallinity gradient distribution information, process parameters are calculated for the crystalline and amorphous regions to obtain a combination of polishing process parameters. Specifically, this includes: dividing the PEEK material into regions based on the crystallinity gradient distribution information to obtain distribution data of the crystalline and amorphous regions; performing differential calculations of mechanical polishing parameters, chemical polishing parameters, and plasma polishing parameters for the crystalline and amorphous regions based on the distribution data to obtain regional process parameter data; and inputting the regional process parameter data into the NSGA-III multi-objective genetic algorithm to calculate the Pareto optimal solution for interface bonding strength, surface roughness, and molecular chain orientation to obtain a combination of polishing process parameters. The polishing process parameters are input into a multi-process synergistic composite polishing device to perform gradient interface strengthening polishing on the PEEK material, obtaining an interface-strengthened polished semi-finished product. Specifically, this includes: allocating mechanical polishing parameters, chemical polishing parameters, and plasma polishing parameters based on the polishing process parameter combination to obtain synergistic parameter allocation data; controlling the glass transition temperature range of the PEEK material according to the synergistic parameter allocation data to obtain temperature control parameter data; inputting the temperature control parameter data into the multi-process synergistic composite polishing device to perform synergistic polishing of the PEEK material using mechanical polishing, chemical polishing, and plasma polishing to obtain multi-process synergistic polishing data; and performing gradient interface strengthening on the PEEK material based on the multi-process synergistic polishing data to rearrange the aromatic molecular chains, obtaining an interface-strengthened polished semi-finished product. The interface-reinforced polishing semi-finished product is subjected to dynamic adjustment of process parameters to obtain the interface-reinforced polishing finished product. Specifically, this includes: real-time monitoring of the π-π interaction force of the interface-reinforced polishing semi-finished product using an atomic force microscope to obtain π-π interaction force monitoring data; calculating the deviation based on the π-π interaction force monitoring data and the target π-π interaction force value to obtain π-π interaction force deviation calculation data; applying PID feedback control to the π-π interaction force deviation calculation data to obtain dynamic adjustment data for process parameters; and dynamically adjusting the grinding pressure, rotation speed, and polishing fluid flow rate in real time based on the dynamic adjustment data for process parameters to obtain the interface-reinforced polishing finished product.

2. The PEEK material composite polishing method based on multi-process synergy according to claim 1, characterized in that, The differential scanning calorimetry and infrared spectroscopy detection of the PEEK material based on the interface feature data are used to obtain crystallinity gradient distribution information, including: The interface feature data is input into a differential scanning calorimeter to detect the glass transition temperature and melting temperature of the PEEK material, thereby obtaining thermodynamic parameter data. Based on the thermodynamic parameter data, the crystallinity of the PEEK material was calculated to obtain crystallinity distribution data. The PEEK material was subjected to infrared spectral scanning detection based on the crystallinity distribution data to obtain functional group density distribution data; Based on the crystallinity distribution data and the functional group density distribution data, the differential gradient between crystalline and amorphous regions is calculated to obtain crystallinity gradient distribution information.

3. The PEEK material composite polishing method based on multi-process synergy according to claim 1, characterized in that, The process parameter data of the region is input into the NSGA-III multi-objective genetic algorithm to calculate the Pareto optimal solution for interface bonding strength, surface roughness, and molecular chain orientation, thereby obtaining a combination of polishing process parameters, including: Based on the regional process parameter data and the NSGA-III multi-objective genetic algorithm, individual population encoding and initial population generation are performed to obtain initial population data; Based on the initial population data, a three-objective fitness function was calculated for interfacial bonding strength, surface roughness, and molecular chain orientation to obtain population fitness numerical data. The population fitness data is input into the genetic algorithm evolver to perform selection, crossover, and mutation evolution operations to obtain the evolved population data. Based on the evolutionary population data, Pareto front screening is performed to determine dominance relationships and calculate reference point distances, resulting in a combination of polishing process parameters.

4. The PEEK material composite polishing method based on multi-process synergy according to claim 1, characterized in that, The process of applying PID feedback control to the calculated π-π interaction force deviation data to obtain dynamic adjustment data for process parameters includes: Based on the π-π interaction force deviation calculation data and the proportional coefficient, the proportional term product is calculated to obtain the proportional control component data; Based on the π-π interaction force deviation calculation data, the historical cumulative deviation value and integral coefficient are integrated to calculate the integral control component data. Based on the π-π interaction force deviation calculation data and differential coefficients, the differential term of the deviation change rate is calculated to obtain the differential control component data; The dynamic adjustment data of process parameters is obtained by weighted summation of the proportional control component data, the integral control component data, and the derivative control component data to synthesize the PID control quantity.

5. The PEEK material composite polishing method based on multi-process synergy according to claim 1, characterized in that, The PEEK material composite polishing method based on multi-process synergy also includes: The molecular chain orientation gradient detection was performed on the interface-strengthened polished finished product to obtain molecular chain orientation detection data. Based on the molecular chain orientation detection data, the surface roughness and interfacial bonding strength of the interface-strengthened polished finished product are detected to obtain surface quality index detection data. Based on the molecular chain orientation detection data and the surface quality index detection data, a comprehensive performance analysis is performed to obtain comprehensive performance analysis data. Based on the comprehensive performance analysis data, interface failure prediction and quality assessment level determination are performed to obtain polishing quality verification results.

6. A PEEK material composite polishing system based on multi-process synergy, characterized in that, A method for performing the multi-process synergistic PEEK material composite polishing method as described in any one of claims 1-5, comprising: The surface inspection module is used to inspect the surface of PEEK materials and obtain information on the crystallinity gradient distribution. The process parameter calculation module is used to calculate the process parameters for the crystalline region and the amorphous region respectively based on the crystallinity gradient distribution information, so as to obtain the polishing process parameter combination. The interface strengthening polishing module is used to input the polishing process parameters into a multi-process collaborative composite polishing equipment to perform gradient interface strengthening polishing on the PEEK material to obtain an interface strengthening polished semi-finished product. The parameter dynamic adjustment module is used to dynamically adjust the process parameters of the interface-strengthened polishing semi-finished product to obtain the interface-strengthened polishing finished product.

Images