A rapid processing method for fine finishing the surface of a nylon 3D printed complex structural part

Abstract

This invention relates to the field of nylon 3D printing technology and discloses a rapid method for fine-smoothing the surface of complex nylon 3D printed structural parts. The method first acquires surface characteristic data of the nylon 3D printed structural parts, and based on this, generates a surface treatment point set and a surface treatment path direction; calculates a surface roughness stability factor, and constructs a surface smoothness change prediction network; then sets a surface defect perception mode and a smoothing mechanism, and performs surface smoothness control according to the smoothing mechanism, finally generating a surface treatment result containing surface treatment data packets and surface treatment signals. This method, through precise perception of surface characteristics, intelligent planning of processing paths, and dynamic control of the processing process, can specifically address issues such as layer textures and defects on the surface of complex structural parts, improving processing efficiency and surface quality consistency, and is applicable to the surface treatment of complex nylon 3D printed structural parts.

Description

Technical Field

[0001] This invention relates to the field of nylon 3D printing technology, specifically to a method for rapid surface finishing of complex nylon 3D printed structural parts. Background Technology

[0002] Nylon materials have been widely used in the field of 3D printing due to their excellent mechanical properties, chemical stability, and molding adaptability, especially suitable for manufacturing structural parts with complex internal structures and irregular contours. However, during the nylon 3D printing process, due to the layer-by-layer molding principle, the surface of the printed part often forms obvious layer textures and uneven microstructures, and may also be accompanied by defects such as bubbles, flash, and microcracks. These problems seriously affect the surface quality of the structural parts. Currently, surface treatment methods for nylon 3D printed structural parts mainly include mechanical grinding, chemical etching, and sandblasting. While mechanical grinding can reduce surface roughness to some extent, traditional grinding tools are difficult to reach structural parts with complex internal cavities, deep holes, or fine textures, easily leading to localized over-grinding or omissions, resulting in uneven surface quality. Chemical etching methods use specific solvents to dissolve surface protrusions, which can improve surface smoothness, but the concentration, temperature, and reaction time of the chemical reagents are difficult to control precisely during the process, easily causing problems such as uneven etching and decreased dimensional accuracy on the structural part surface. Furthermore, the use of chemical reagents may also cause environmental pollution. Sandblasting removes protrusions by impacting the surface with high-speed jets of sand particles. However, for complex structural parts, such as corners and gaps, the sand particles cannot work evenly, easily creating blind spots. Furthermore, over-blasting can damage the mechanical properties of the structural parts. In addition, existing methods largely rely on manual experience for parameter setting, lacking precise perception and dynamic control of surface characteristics, resulting in low processing efficiency and difficulty in meeting the consistent surface quality requirements of mass production. Therefore, developing a method for accurately and efficiently processing the surface characteristics of complex nylon 3D printed parts has become a pressing issue in this field. Summary of the Invention

[0003] The purpose of this invention is to provide a method for rapidly and finely smoothing the surface of complex nylon 3D printed structural parts, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides a method for rapid and fine surface finishing of complex nylon 3D printed structural parts, the method comprising: Obtain surface property data of nylon 3D printed structural parts; A surface treatment point set is generated based on the surface characteristic data; Generate the surface treatment path direction based on the surface treatment point set; Calculate the surface roughness stability factor and construct a surface smoothness change prediction network; Set a surface defect sensing mode; set a smoothing treatment mechanism; execute surface smoothing control according to the smoothing treatment mechanism; Generate surface treatment results, which include surface treatment data packets and surface treatment signals.

[0005] Preferably, generating a surface treatment point set based on the surface characteristic data includes: Extract the material properties and structural complexity from the surface characteristic data; The corresponding surface property curve is found in the surface property curve lookup table based on the material properties and structural complexity. Obtain the processing range of the processing device, and divide the processing range into several equal processing intervals; The number of test points corresponding to the processing interval is obtained by using the allocation coefficient of the processing interval. The number of surface treatment points is set equally among the test points within the corresponding processing interval; The surface characteristic curve lookup table is constructed by obtaining several nylon 3D printed structural parts samples with the same material properties and structural complexity, adjusting the processing parameters from low to high, obtaining the surface finish value of each sample, taking the average of the surface finish values ​​of each sample as the surface finish value under the corresponding processing parameters, fitting the surface finish values ​​under each processing parameter to obtain the surface characteristic curves corresponding to the material properties and structural complexity, and constructing the surface characteristic curve lookup table based on the material properties, structural complexity and surface characteristic curves.

[0006] Preferably, generating the surface treatment path direction based on the surface treatment point set includes: Mark the surface treatment point with the highest gloss value as the high gloss treatment point; Mark the surface treatment point with the lowest gloss value as a low gloss treatment point; Obtain the smoothness value 1 of the surface characteristic curve corresponding to the high-smoothness treatment point; Obtain the smoothness value 2 of the surface characteristic curve corresponding to the low smoothness treatment point; Obtain the smoothness value of the surface characteristic curve corresponding to the initial surface treatment point; Determine whether the difference between the first smoothness value and the third smoothness value is less than the difference between the second smoothness value and the third smoothness value; When the difference between the first gloss value and the third gloss value is less than the difference between the second gloss value and the third gloss value, the first surface treatment direction is defined as starting from the initial surface treatment point and ending at the high gloss treatment point, the second surface treatment direction is defined as starting from the high gloss treatment point and ending at the initial surface treatment point, and the third surface treatment direction is defined as starting from the initial surface treatment point and ending at the low gloss treatment point. When the difference between the first gloss value and the third gloss value is not less than the difference between the second gloss value and the third gloss value, the first surface treatment direction is defined as starting from the initial surface treatment point and ending at the low gloss treatment point, the second surface treatment direction is defined as starting from the low gloss treatment point and ending at the initial surface treatment point, and the third surface treatment direction is defined as starting from the initial surface treatment point and ending at the high gloss treatment point. The first surface treatment direction, the second surface treatment direction, and the third surface treatment direction are integrated into a surface treatment path direction.

[0007] Preferably, the initial surface treatment point is the surface treatment point whose smoothness value is closest to the smoothness calibration value in the surface characteristic curve corresponding to each surface treatment point.

[0008] Preferably, the calculation of the surface roughness stability factor includes: Obtain a historical set of surface finish data; The historical surface finish data set is cleaned using data analysis methods to obtain a historical surface finish data set. The variance of the surface smoothness data set from the cleaning history is calculated to obtain the surface roughness stability factor; The data analysis method includes sorting the historical surface finish data set, determining the upper and lower limits of the cluster, and statistically analyzing the historical surface finish data located between the upper and lower limits of the cluster.

[0009] Preferably, the construction of the surface smoothness change prediction network includes: Using the surface roughness stability factor as an index, a centralized search is performed in the surface treatment configuration space to determine the target processing step size; The surface smoothness change prediction network is constructed based on the target processing step size; The surface treatment configuration space is constructed by acquiring multiple sample surface roughness stability factors and corresponding multiple sample processing steps, wherein the coordinate axes of the three-dimensional space are surface roughness stability factors. The multiple sample surface roughness stability factors and the multiple sample processing steps are input into the three-dimensional space to obtain multiple sample space points. The multiple sample processing steps are used to identify the multiple sample space points to construct the surface treatment configuration space.

[0010] Preferably, the setting of the surface defect sensing mode includes: Real-time sensing of the surface condition of nylon 3D printed structural parts; Analyze the surface smoothness change trend of the surface smoothness change prediction network; By combining the surface condition and the trend of surface smoothness changes, potential surface defect factors are identified; Identify the risk level of the potential surface defect factors, and set the surface risk strain mode according to the risk level; The surface defect perception mode is set by combining the risk level and the surface risk strain mode.

[0011] Preferably, the smoothing treatment mechanism includes: Based on the surface defect perception mode, surface anomalies of nylon 3D printed structural parts are identified. Locate the abnormal region corresponding to the surface anomaly; Analyze the defect types in the abnormal regions; A surface distress strain layer is set based on the aforementioned defect type; The surface smoothness change prediction network identifies the adjacent smoothness trajectory of the abnormal region. The escape path for the abnormal area is set based on the smooth trajectory of the adjacent area; The smoothing mechanism is set by combining the surface distress strain layer and the escape path.

[0012] Preferably, the step of performing surface smoothness control according to the smoothing treatment mechanism includes: Obtain the surface treatment data corresponding to the current surface treatment point; Extract the time variation curves of each processing item in the surface treatment data; Determine whether the time variation curves of all projects are stable; When all project time variation curves stabilize, a positive smoothing adjustment signal is generated; When the time change curves of all projects are unstable and the cumulative time is greater than the time threshold, the direction of the surface treatment path is determined and a reverse gloss adjustment signal is generated. When the time variation curves of all projects are unstable and the cumulative time does not exceed the time threshold, a stop adjustment signal is generated. Surface finish control is performed based on the positive finish adjustment signal, the reverse finish adjustment signal, or the stop adjustment signal.

[0013] Preferably, the generation of the surface treatment result includes: Obtain the difference in processing values ​​for the same surface treatment points of the same processing project in the first surface treatment direction and the second surface treatment direction; Determine whether the difference in the processing values ​​of each surface treatment point is less than the difference threshold. When the difference in the processing values ​​of each surface treatment point is less than the difference threshold and the difference in the processing values ​​of the initial surface treatment points in the first surface treatment direction and the second surface treatment direction is less than the difference threshold, the processing values ​​of each surface treatment point in the first surface treatment direction are taken as the final processing values, and the processing values ​​of each surface treatment point in the third surface treatment direction are also set as the final processing values. The final processing values ​​of each surface treatment point are fitted into the project surface characteristic curve. The project surface characteristic curves of each processing project of multiple nylon 3D printed structural parts are packaged to generate the surface treatment data package, and a processing completion signal is generated. When the difference in the processing values ​​of each surface treatment point is not less than the difference threshold, the processing values ​​of each surface treatment point in the first surface treatment direction are taken as the final processing values. The final processing values ​​of each surface treatment point in the first surface treatment direction are fitted into a partial surface characteristic curve. The partial surface characteristic curves of each processing item of multiple nylon 3D printed structural parts are packaged to generate the surface treatment data package, and a retest signal in the third surface treatment direction is generated.

[0014] Compared with the prior art, the beneficial effects of the present invention are: By acquiring surface characteristic data, we can accurately grasp the microstructure and defect distribution of structural components, providing comprehensive information support for subsequent processing. The surface treatment point set generated based on the surface characteristic data ensures that the processing is targeted and avoids blind operation, making the processing more effective. The generation of surface treatment path directions enables the planning of optimal processing trajectories based on the actual surface conditions, reducing ineffective operations, improving processing efficiency, and ensuring the uniformity of processing in each area, avoiding local over- or under-processing caused by unreasonable paths. Calculating the surface roughness stability factor and constructing a surface smoothness change prediction network allows for the prediction of surface state changes during processing, facilitating timely adjustments to processing strategies and making it easier to achieve the desired surface quality. The surface defect detection mode enables real-time monitoring of new or incompletely treated defects during processing, ensuring comprehensiveness and reducing missed detections. The smoothing mechanism provides standardized operating procedures, avoiding uncertainties arising from reliance on human experience and making the process more stable and reliable. By implementing surface finish control based on the surface finishing mechanism, dynamic adjustments to processing parameters can be achieved to adapt to differences in surface characteristics across different areas, ensuring consistent processing results across all parts of complex structural components. The generated surface finishing data packages and signals not only facilitate traceability and analysis of the processing process but also provide a reference for the processing of similar structural components in the future, contributing to the formation of standardized processing procedures and improving overall processing quality. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating the working principle of the rapid surface finishing method for complex nylon 3D printed structural parts described in this invention. Figure 2 A flowchart for generating a set of surface treatment points; Figure 3 This is a flowchart for calculating the surface roughness stability factor; Figure 4 A flowchart for setting up a surface defect perception mode. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 This invention provides a method for rapid surface finishing of complex nylon 3D printed structural parts, the specific implementation steps of which are as follows: Obtain surface property data of the target nylon 3D printed structural part. Based on the obtained surface property data, generate a set of surface treatment points containing information on multiple key locations.

[0018] Based on this set of surface treatment points, the optimal surface treatment path direction is calculated and determined.

[0019] A surface roughness stability factor characterizing the degree of surface quality fluctuation is calculated, and a surface smoothness change prediction network is constructed based on this factor to predict the trend of surface smoothness changes. A surface defect sensing mode is set up in the method for real-time monitoring and identification of surface anomalies.

[0020] Based on the foregoing information, a specific surface finishing mechanism is established. According to this mechanism, precise surface finishing control operations are performed on the structural components.

[0021] Generate a surface treatment result, which includes a surface treatment data package that integrates key processing data and surface treatment signals that characterize the processing status or instructions.

[0022] Example 1: See Figure 2 Obtaining surface property data for nylon 3D printed structural parts is the initial step. This data comes from the measurement and analysis of the target structural part and contains multi-dimensional information reflecting its physical state. From the acquired surface property data, two core parameters are extracted: material properties and structural complexity. Material properties encompass the inherent characteristics of the material, such as its specific type, density, melt flow index, and molecular weight distribution. Structural complexity quantifies the geometric complexity of the component, involving factors such as the distribution of feature dimensions, the range of surface curvature variation, the proportion of overhanging structures, and the topological morphology of internal cavities. These parameters collectively define the basic behavioral characteristics of the structural part's surface.

[0023] Using extracted material property parameters and structural complexity parameters, a matching query is performed in a pre-built surface characteristic curve lookup table. This lookup table stores surface characteristic curves corresponding to different combinations of material properties and structural complexity. The construction process of the surface characteristic curve lookup table follows rigorous steps. First, a set of nylon 3D printed structural parts with the same material property parameters and the same structural complexity parameters are selected as a sample set. The number of samples must meet the statistical significance requirement. For each selected sample, a set of key processing parameters are selected, such as laser power, scanning speed, and defocusing amount in laser polishing, or solution concentration, temperature, and immersion time in chemical polishing. Starting from the lowest value of its adjustable range, the selected processing parameters are gradually adjusted to the highest value according to the preset increment step. At each specific processing parameter setting point, the surface finish of the current sample is measured using standardized measurement equipment and methods, and the specific measurement values ​​are recorded, such as surface roughness Ra value, Rz value, or other recognized surface finish indicators. After completing the measurement of a sample at all processing parameter points, the same parameter adjustment and measurement process is repeated for the next sample. After all samples have been measured, for each processing parameter point, the arithmetic mean of the surface finish values ​​measured for all samples at that point is calculated. This average represents the typical surface finish level achievable at that processing parameter point under a specific combination of material properties and structural complexity. Each processing parameter point and its corresponding average surface finish value are treated as a data point. All data points corresponding to all processing parameter points are aggregated, and a curve fitting algorithm is used to generate a smooth and continuous curve. This curve represents the surface characteristic curve corresponding to the specific combination of material property parameters and structural complexity parameters. This curve visually demonstrates the variation of surface finish with processing parameters. By systematically changing the combination of material property parameters and structural complexity parameters, the above process of sample selection, parameter adjustment, measurement, averaging, and fitting is repeated to generate a series of surface characteristic curves covering different parameter combinations. Finally, these surface characteristic curves indexed by material property parameters and structural complexity parameters are integrated to form a surface characteristic curve lookup table. This table allows for quick retrieval of the corresponding surface characteristic curve by inputting specific material property parameters and structural complexity parameters.

[0024] Obtain the specific processing capability parameters of the equipment used to perform surface finishing, and define its effective processing range. This processing range defines the parameter interval boundaries within which the equipment can operate stably, such as the minimum and maximum laser power, the minimum and maximum scanning speed, etc. Divide this continuous processing range evenly into a predetermined number of processing intervals. The division is based on the total width of the processing range and the preset number of intervals, with each interval having an equal width. For example, if the laser power processing range is 50W to 200W, and the preset division is 10 intervals, then each interval has a width of 15W. Each processing interval is assigned a specific allocation coefficient based on its relative position within the overall processing range, its importance weight, or other preset rules. This allocation coefficient is a numerical value used to quantify the proportion of that interval in the overall processing strategy. The allocation coefficient may be set based on experience or a specific algorithm; for example, intervals located in the middle of the processing range may be assigned a higher coefficient, while intervals closer to the ends may be assigned a lower coefficient; or the coefficient may be dynamically allocated based on the slope trend of the surface characteristic curve. Based on the allocation coefficient assigned to each processing interval, the number of test points to be allocated to that interval is determined through a preset mapping relationship or calculation formula. The number of test points is usually proportional to the allocation coefficient; that is, intervals with higher allocation coefficients require more test points to obtain more detailed data. After determining the number of test points for each processing interval, the corresponding number of surface treatment points are evenly distributed within that interval. Even distribution means that these points are evenly spaced along the processing parameter dimension. For example, for a laser power processing interval with a width of 15W, if 5 test points are allocated, these 5 points will be set at the start point, 1 / 4 point, midpoint, 3 / 4 point, and end point (or equivalent division points) of the interval, respectively. Each surface treatment point represents the specific location or parameter setting for surface finish measurement or processing operations on the structural component surface under that processing parameter value. All surface treatment points set within all processing intervals are sorted according to their corresponding processing parameter values, collectively forming the final set of surface treatment points. This point set fully covers the entire effective processing range of the device, and the distribution density of the points reflects the differences in importance of different processing parameter areas.

[0025] Example 2: After generating the surface treatment point set, the process proceeds to the stage of determining the surface treatment path direction. This process begins with a systematic analysis of the generated surface treatment point set. Each point in the set is associated with a specific surface characteristic curve, which reflects the variation of surface finish with the processing progress under the corresponding processing parameters. All surface treatment points in the set are traversed, and for each point, the corresponding surface finish value under the currently selected processing parameters is obtained based on its associated surface characteristic curve. By comparing these surface finish values ​​for all points, the point with the highest value is identified. This point with the highest surface finish value is explicitly marked as the high-gloss treatment point. Similarly, by comparison, the point with the lowest surface finish value in the set is identified and marked as the low-gloss treatment point. These two points represent the positions of the best and worst expected surface finish states under the current processing parameter settings, respectively.

[0026] Next, a key reference point needs to be determined: the initial surface treatment point. This point is not chosen arbitrarily but based on a preset surface finish calibration value. This calibration value typically represents the desired surface finish level for the final treatment result, or serves as a process control benchmark. Iterating through all points in the surface treatment point set, for each point, the surface finish value derived from its associated surface characteristic curve under the current treatment parameters is calculated. This value is then compared to the preset surface finish calibration value, and the absolute difference between the two is calculated. The initial surface treatment point is selected as the one with the smallest absolute difference between its surface finish value and the calibration value. In other words, this point, under the current parameters, exhibits a surface finish state that most closely resembles the desired target or benchmark state.

[0027] Subsequently, key data is acquired. The surface finish value calculated using the surface characteristic curve associated with the high-gloss treatment point under the current processing parameters is obtained; this value is defined as Gloss Value 1. Similarly, the surface finish value calculated using the surface characteristic curve associated with the low-gloss treatment point under the current processing parameters is obtained; this value is defined as Gloss Value 2. Simultaneously, the surface finish value calculated using the surface characteristic curve associated with the determined initial surface treatment point under the current processing parameters is obtained; this value is defined as Gloss Value 3. These three values ​​represent the instantaneous surface finish status of the high-gloss point, low-gloss point, and initial point, respectively.

[0028] Based on the obtained smoothness values ​​1, 2, and 3, perform a logical judgment operation. Calculate the absolute difference between smoothness value 1 and smoothness value 3, i.e., |smoothness value 1 - smoothness value 3|. Simultaneously, calculate the absolute difference between smoothness value 2 and smoothness value 3, i.e., |smoothness value 2 - smoothness value 3|. Compare the magnitudes of these two differences.

[0029] If the calculation results show that |smoothness value 1 - smoothness value 3| < |smoothness value 2 - smoothness value 3|, meaning the smoothness state of the initial point is closer to the state of the high smoothness point than the state of the low smoothness point, then the three processing path directions are defined according to the following rules: The first path direction: the starting point is the initial surface treatment point, and the ending point is the high-gloss finish point. This direction indicates that the processing starts from the position closest to the target / reference state and moves towards the expected optimal finish position. This direction is named the first surface treatment direction.

[0030] The second path direction: the starting point is the high-gloss treatment point, and the ending point is the initial surface treatment point. This direction is opposite to the first direction, indicating a return from the optimal gloss state position to the initial position. This direction is named the second surface treatment direction.

[0031] The third path direction: the starting point is the initial surface treatment point, and the ending point is the low-gloss treatment point. This direction indicates movement from the initial position to the position where the worst-case surface finish is expected. This direction is named the third surface treatment direction.

[0032] If the calculation results show that |smoothness value 1 - smoothness value 3| >= |smoothness value 2 - smoothness value 3|, meaning the smoothness state of the initial point is closer to the state of the lower smoothness point or is comparable to the distance between the two, then three processing path directions are defined according to another set of rules: The first path direction: the starting point is the initial surface treatment point, and the ending point is the low-gloss treatment point. This direction indicates that the processing starts from the position closest to the target / reference state (which is closer to the worst point) and moves towards the expected worst-gloss state position. This direction is also named the first surface treatment direction.

[0033] The second path direction starts at the lowest surface finish point and ends at the initial surface finish point. This direction is opposite to the first direction, indicating a return from the worst surface finish position to the initial position. This direction is named the second surface finish direction.

[0034] The third path direction: the starting point is the initial surface treatment point, and the ending point is the high-gloss finish point. This direction indicates the movement from the initial position to the position where the expected optimal surface finish is desired. This direction is named the third surface treatment direction.

[0035] Regardless of the judgment result used to define the path direction, the defined first, second, and third surface treatment directions must ultimately be integrated. Integration is not a simple juxtaposition, but rather the formation of an ordered sequence or strategy framework that guides the movement and processing operations of the processing equipment on the surface of the nylon 3D printed structure. This integrated product is the final surface treatment path direction. This path direction clarifies the possible exploration routes of the processing: including advancement from the initial point to an extreme point (high or low finish point) (first direction), backtracking from that extreme point back to the initial point (second direction), and exploration from the initial point to another extreme point (third direction). This set of path directions provides guidance for the spatial movement and processing sequence of subsequent finishing mechanisms, aiming to systematically explore the surface response of different areas and optimize the overall processing effect.

[0036] Example 3: See Figure 3 In the process of generating the surface treatment path direction, the determination of the initial surface treatment point is based on specific criteria. The selection of this point depends on a preset surface finish calibration value, which represents the desired surface finish level or serves as a benchmark reference value during the processing. All points in the surface treatment point set are traversed, and for each point, the surface finish value calculated from its associated surface characteristic curve under the current processing parameters is obtained. The absolute difference between this surface finish value and the preset surface finish calibration value is calculated. The initial surface treatment point is defined as the point with the smallest absolute difference, meaning its current surface finish is closest to the preset target or benchmark.

[0037] The calculation of the surface roughness stability factor relies on a historical surface finish dataset. This dataset contains a series of surface finish measurements recorded in the past when processing similar nylon 3D printed structural parts, organized chronologically or by processing batch. Before calculating the variance, the historical surface finish dataset must be cleaned to remove outliers or noise. The cleaning process employs specific data analysis methods. First, all data points in the historical surface finish dataset are sorted in ascending order of their numerical values. The distribution characteristics of the sorted data are analyzed to identify the region with the highest concentration of data points. The upper limit boundary of this dense region is determined, called the cluster upper bound (CQB). The lower bound of the numerical value of this dense region is called the lower bound of the clustering (). The upper and lower bounds of clustering can be determined by observing the cumulative frequency of the data or by using statistical methods (such as those based on quartiles). The screening process retains all values ​​that meet the criteria. Historical surface smoothness data points of the conditions These selected data points constitute a dataset of surface finish data from the cleaning history. ,in It represents the number of data points remaining after cleaning.

[0038] Based on historical surface finish data Calculate the surface roughness stability factor This factor is calculated by the set. The sample variance is obtained, and its calculation formula is:

[0039] in: This represents the calculated surface roughness stability factor; This represents a collection of historical surface finish data after cleaning. The total number of data points included; The index variable represents the summation operation, iterating from 1 to... All data points; This represents a collection of historical surface finish data after cleaning. The first in Each data point represents a specific surface finish measurement value. This represents a collection of historical surface finish data after cleaning. The arithmetic mean of all data points in the dataset, i.e. ; Represents the summation symbol, for index From 1 to Summing all terms; : Represents the difference between each data point and the average. The square of the deviation.

[0040] The calculation process is as follows: First, calculate the cleaned dataset. average Then, for each data point in the set... Calculate its value and average value. The difference And square this difference Next, all data points Add the values ​​together. Finally, divide the sum by... The final surface roughness stability factor is obtained. . The value quantifies the dispersion or fluctuation range of historical surface finish data after cleaning. The larger the value, the greater the fluctuation in historical surface finish across the cleaned dataset, indicating poorer stability; conversely, a smaller value indicates lower stability. The smaller the value, the more tightly the historical data clusters around the average, indicating better stability. This factor... The output is used as a key input parameter for constructing a surface finish change prediction network, and is used to characterize the consistency level of surface finish during historical processing.

[0041] Example 4: See Figure 4 The operation of constructing a surface finish variation prediction network uses the calculated surface roughness stability factor as the core input parameter. This factor characterizes the fluctuation characteristics of surface finish in historical processing data. The construction process relies on a pre-established three-dimensional mathematical model, called the surface treatment configuration space. The construction process of this space is as follows: A large amount of sample surface roughness stability factor data generated during the processing of different nylon 3D printed structural parts is collected. Simultaneously, the actual sample processing step size data used during processing is recorded. The processing step size refers to the magnitude of adjustment of key processing parameters each time during processing, such as the specific watt value for each increase or decrease in laser power, the millimeter-per-second value for each change in scanning speed, or the percentage value for each adjustment of chemical polishing slurry concentration. A three-dimensional Cartesian coordinate system is established, with one axis specifically used to represent the surface roughness stability factor. Each collected data pair, i.e., a sample surface roughness stability factor value and its corresponding sample processing step size value, is input as a data point into this three-dimensional coordinate system. The position of this data point in the coordinate system is determined by its sample surface roughness stability factor value. Each data point is identified or labeled using its corresponding sample processing step size value. By aggregating all input data points and their corresponding processing step size identifiers, a complete surface treatment configuration space is constructed. This space is essentially a mapping library that associates different surface roughness stability factor levels with historically used processing step sizes.

[0042] In the constructed surface treatment configuration space, the surface roughness stability factor value calculated for the current structural part is used as the search basis. A concentrated search is conducted in the vicinity of the spatial location corresponding to this factor value. The search objective is to find spatial points in the vicinity of this region that are marked with an identifier (i.e., processing step size). By evaluating the processing step sizes identified by these neighboring spatial points, combined with preset optimization criteria (e.g., selecting the step size corresponding to the best historical processing result, or selecting the step size with the highest frequency of occurrence), an optimal processing step size value is determined. This selected processing step size is called the target processing step size. Based on the determined target processing step size, a surface finish change prediction network is constructed. This network is a mathematical model or algorithmic framework whose function is to simulate and predict the possible trajectory and trend of surface finish changes in the nylon 3D printed structural part when the processing parameters are adjusted using this target processing step size under the current surface roughness stability factor conditions. The prediction network can establish the correlation between surface finish changes and parameter adjustment step sizes based on historical data patterns, physical models, or statistical learning methods.

[0043] Setting up a surface defect perception mode involves a series of progressive steps. The first step is to use a sensor system integrated into the finishing equipment to perceive the surface state of the nylon 3D printed structure in real time during the finishing process. These sensors may include non-contact optical sensors (for monitoring surface reflectivity, color changes, and texture features), infrared sensors (for monitoring surface temperature distribution), or contact tactile sensors (for sensing minute surface deformations or resistance). The sensor data, after processing, provides a real-time information stream about the current physical state of the surface. The second step is to analyze the prediction results output by the aforementioned surface finish change prediction network. This result provides a trend line of expected changes in surface finish at the target processing step size; for example, whether the finish is expected to continue improving, stabilize, or potentially deteriorate. The third step is to comprehensively compare and analyze the real-time perceived surface state information with the finish change trend information provided by the prediction network. Through this comprehensive analysis, potential factors that may lead to surface defects are identified. These potential factors may include abnormally high temperatures in localized areas (potentially causing material ablation or deformation), significantly uneven material removal rates (leading to surface irregularities), detected signs of stress concentration (potentially inducing cracks), or areas where sensor readings deviate significantly from predicted trends. The fourth step is to conduct a risk assessment for each identified potential surface defect factor, determining its potential impact on the final surface quality and categorizing it into different preset risk levels. The risk level classification may be based on factors such as the severity of the defect factor, its diffusion rate, and its repairability. The fifth step is to pre-set corresponding surface risk response methods for different risk levels. For example, for low-risk levels, the response might simply be recording and marking, followed by continued observation; for medium-risk levels, the response might include reducing the processing intensity in that area (e.g., decreasing laser power or slowing down the scanning speed) or switching the processing mode (e.g., changing from continuous scanning to point scanning); for high-risk levels, the response might require immediately suspending processing in that area and conducting a detailed diagnosis. Finally, combining the identified risk level determination results with the preset surface risk response methods for that level, a specific surface defect perception mode is set. This model defines a set of specific strain response instructions that the system should automatically trigger when a potential defect factor of a specific risk level is detected during real-time processing, forming an active monitoring and response mechanism for surface quality issues.

[0044] Example 5: The process of setting up the surface finishing mechanism begins with a response to the output information of the surface defect sensing mode. This mode continuously monitors the processing, and when it detects an anomaly on the surface of the nylon 3D printed structural part, it triggers the mechanism setting process. A specific nylon 3D printed helical gear component is used as an example. During the laser polishing process, the surface defect sensing mode identifies an abnormal area on a certain tooth surface of the gear through real-time optical imaging and thermal analysis. This area displays abnormal reflection spots and localized high temperature phenomena, and is marked as a surface anomaly.

[0045] Upon identifying the anomaly, the primary task is to accurately locate the abnormal area. Using a high-precision vision positioning system mounted on the processing equipment, combined with the 3D model data of the structural component, the specific location and spatial extent of the abnormal area on the gear surface are determined. The system establishes a local coordinate system with the gear center as the origin, recording the boundary coordinates of the abnormal area. Assuming the abnormal area is located in the middle of the tooth surface and is approximately elliptical in shape, the system records its center point coordinates (e.g., X=15.2mm, Y=8.7mm, Z=0.0mm in the gear coordinate system), with a major axis length of approximately 2.5mm, a minor axis length of approximately 1.8mm, and a coverage area of ​​approximately 3.5mm². Table 1 summarizes the location information of this abnormal area.

[0046] Table 1: Parameters for locating abnormal areas are as follows:

[0047] After precise location, the defect type of the abnormal area is analyzed. The system retrieves detailed sensor data for the area (high-resolution image, thermal distribution map, historical processing parameter records) and compares it with a preset defect feature database. The analysis shows that there is a slight melting and accumulation of material in the area (resulting in high-reflectivity spots), accompanied by temperature anomalies caused by excessive local energy absorption. Based on feature comparison, the defect type is determined to be "localized thermal accumulation of material".

[0048] Based on the identified defect type "localized thermal buildup of materials," a corresponding surface strain layer is established. This strain layer includes specific treatment strategies and parameter adjustment rules for this type of defect. Energy control strategy: Immediately reduce the laser energy density to 60% of the standard value. Specific operations include: reducing the laser power setting, increasing the spot size to disperse energy, or reducing the scanning speed to extend the cooling time (to avoid further overheating).

[0049] Cooling enhancement strategy: Activate the auxiliary cooling system, increase the gas cooling flow rate in the area, or introduce a pulse cooling mode.

[0050] Path adjustment strategy: Avoid repeated scanning or high overlap processing in this area.

[0051] Priority setting: Mark this area as a high-risk area and adjust its processing priority to the highest level to prevent the defect from escalating.

[0052] Next, a surface finish change prediction network was used to analyze the finish trajectory of the normal area (side area) surrounding the abnormal area. The system extracted historical finish data sequences and corresponding processing parameter sequences of the side area (e.g., a ring-shaped zone extending 1 mm outward from the boundary of the abnormal area) during the processing. The prediction network analyzed these data and fitted a trajectory curve of the side area finish changing with processing parameters (such as laser power and scanning speed). The analysis revealed that in the side area, when using medium to low power and a medium scanning speed, the finish showed a stable upward trend, and no signs of heat accumulation were observed.

[0053] Based on the analysis results of the smooth trajectory in the adjacent area, an escape path is planned for the abnormal region. The core idea of ​​the escape path is to learn from the successful processing parameter combinations in the adjacent area and guide the smoothness of the abnormal region back to a normal state in a gradual and controllable manner. Starting point setting: The current processing status of the abnormal region (high power causing backlog) is used as the starting point of the path.

[0054] Target setting: A parameter combination that stably improves surface finish and eliminates defects in the side area (medium to low power). medium scan speed () as the endpoint of the path.

[0055] Path planning: Employ a small-step, multi-step parameter adjustment strategy. Step 1: Significantly reduce the laser power to well below the target power (e.g., of ), scan speed increased to a higher value (e.g. of The purpose is to rapidly cool and stop the buildup from worsening. The second step: After confirming temperature stability, gradually increase the power in small increments (each increment...). of At the same time, slightly reduce the scanning speed (reduce by each time). of ), to target parameter combination ( , The process involves approximating the target area. After each adjustment, the response (temperature, morphology changes) of the area is monitored in real time using a surface defect sensing mode. Only after confirming a normal response (no further deterioration or signs of improvement) is the next adjustment performed. If a negative response is detected, the parameters are reverted to the previous step or a more conservative adjustment is made.

[0056] This approach combines a surface strain layer designed to address "localized material thermal buildup" defects (including strategies such as energy control, enhanced cooling, and path adjustment) with a progressive escape path planned for this abnormal region (including parameter adjustment steps and a monitoring and feedback mechanism). This combination is reflected in the specific instruction set of the surface finishing mechanism: when the system identifies and locates such a defect, it automatically applies the corresponding strain layer strategy (e.g., immediately reducing power and increasing cooling) and simultaneously initiates a customized escape path program for that region (executing a preset sequence of parameter adjustment steps and making fine adjustments or interruptions based on real-time monitoring feedback). This mechanism ensures targeted and adaptive handling capabilities for specific surface defects.

[0057] Example 6: The process of controlling surface finish according to a set finish treatment mechanism involves monitoring the real-time processing status and making dynamic decisions. The operation of a complex nylon 3D-printed heat sink component during laser polishing is illustrated as an example. This component has a dense fin structure, posing a challenge to surface finish treatment. The system is currently processing a specific surface treatment point on the fins.

[0058] Obtain real-time surface treatment data corresponding to the current surface treatment point. This data comes from the sensor and control system logs of the processing equipment, including: Processing parameters: Current laser power (e.g., 80W), scanning speed (e.g., 1200mm / s), defocusing amount (e.g., +0.5mm), polishing number (e.g., second scan).

[0059] Environmental parameters: Assist gas pressure (e.g., 0.3 MPa), coolant flow rate (e.g., 5 mL / min).

[0060] Monitoring data: real-time measured surface temperature (e.g., 155°C), and current surface finish Ra value (e.g., 3.2 μm) read by an online roughness meter.

[0061] Timestamp: The moment when data was recorded.

[0062] From the acquired real-time surface treatment data, curves showing the changes of each individual treatment item over time are extracted; these are known as item-time variation curves. These curves reflect the dynamic behavior of each key parameter within a recent time window. For example: Laser power time variation curve: The laser power value sampled once per second over the past 30 seconds is recorded (e.g., 78W, 79W, 80W, 80W, 80W, 81W, 80W,...).

[0063] Scanning speed over time curve: Records the scanning speed values ​​sampled once per second over the past 30 seconds (e.g., 1190mm / s, 1205mm / s, 1200mm / s, 1200mm / s, 1198mm / s, 1202mm / s,...).

[0064] Surface temperature over time curve: Records the surface temperature values ​​sampled once per second over the past 30 seconds (e.g., 148°C, 152°C, 155°C, 157°C, 156°C, 155°C, 154°C,...).

[0065] Real-time Ra value time variation curve: Records online roughness measurement values ​​(e.g., 3.5μm, 3.4μm, 3.3μm, 3.2μm,...) sampled every 5 seconds over the past 30 seconds.

[0066] Determine whether the project time variation curves for all processed items are in a stable state. The determination of a stable state is based on preset rules, typically examining whether the fluctuation range of the curve within the most recent evaluation period (e.g., 10 seconds) is less than the allowable fluctuation threshold for that item. For example: Laser power permissible fluctuation threshold: ±1W. Check if the difference between the maximum and minimum values ​​in the curve over the past 10 seconds is ≤2W (i.e., within the ±1W range), and if there are no continuous large jumps.

[0067] Scan speed fluctuation threshold: ±10 mm / s. Check if the difference between the maximum and minimum values ​​of its curve over the past 10 seconds is ≤20 mm / s.

[0068] Surface temperature fluctuation threshold: ±5°C. Check if the difference between the maximum and minimum values ​​over the past 10 seconds is ≤10°C.

[0069] The allowable fluctuation threshold for the real-time Ra value is ±0.1 μm. Check whether the difference between the maximum and minimum values ​​of its curve over the past 10 seconds (or the corresponding sampling period) is ≤0.2 μm.

[0070] Assuming that at the current evaluation moment, the fluctuations of all four curves over the past 10 seconds are less than twice their corresponding allowable fluctuation thresholds (i.e., the stability condition is met), and there are no abnormal trends (such as a continuous unidirectional rise or fall), the system determines that all project time-varying curves are stable. In this case, the system generates a positive surface finish adjustment signal. This signal instructs the control system to continue the processing according to the current processing trend or a preset optimization strategy. Possible actions include: slightly increasing the laser power to further improve the surface finish (e.g., increasing by 2W), or moving to the next adjacent processing point in the current surface treatment path direction to continue processing.

[0071] Now consider another scenario. Suppose that during processing, the surface temperature-time variation curve becomes unstable. For example, in the past 20 seconds, the temperature values ​​recorded are: 150°C, 153°C, 160°C, 165°C, 168°C, 170°C, 172°C, 169°C, 171°C,... Its fluctuation range (172°C - 150°C = 22°C) far exceeds the allowable threshold (10°C) and shows a clear upward trend. Simultaneously, the real-time Ra value curve may show stagnation or even a slight increase (e.g., 3.0μm, 3.0μm, 3.1μm, 3.1μm, 3.2μm), also deviating from the expected downward trend. The system determines that there is instability in the project's time variation curve (here, both the temperature and Ra value curves are unstable). The system begins to accumulate the duration of this unstable state. Assume the preset time threshold is 15 seconds. When the accumulated time reaches 16 seconds (exceeding the 15-second threshold), the system needs to further determine the direction of the currently followed surface processing path. Assume the current path direction is the first surface treatment direction (e.g., advancing from the initial point towards the high finish point). In this case, the system generates a reverse finish adjustment signal. This signal instructs the control system to take a reverse operation. Possible actions include: reversing along the current path direction to the previous processing point; or, if technically feasible, immediately reversing the direction of the processing parameter adjustment at the current point (e.g., reducing the increasing laser power or slowing down the accelerating scan speed). The aim is to attempt to eliminate or mitigate factors causing instability.

[0072] Consider the third scenario. The system also detects instability in the project's time-varying curves (e.g., brief spikes in the temperature curve: 155°C, 158°C, 162°C, 159°C, 156°C), but the cumulative instability time is only 8 seconds, not exceeding the preset time threshold (e.g., 15 seconds). In this case, the system generates a stop adjustment signal. This signal instructs the control system to pause active adjustment of the parameters at the current processing point, maintaining the current parameter settings. The system will continue to closely monitor the time-varying curves of each project, observing whether the unstable state recovers spontaneously after a brief fluctuation or continues to deteriorate until the cumulative time exceeds the threshold. During the stop adjustment period, processing may still be in progress (e.g., the laser beam is still scanning), but the parameters are locked at their current values.

[0073] The system executes specific surface finish control operations based on real-time generated signals (forward finish adjustment signal, reverse finish adjustment signal, or stop adjustment signal). For example: Upon receiving a positive surface adjustment signal: the control system increases the laser power from 80W to 82W, or instructs the robotic arm to move to the coordinates of the next processing point on the path.

[0074] Upon receiving a reverse laser adjustment signal: the control system will reduce the laser power from the current unstable 82W back to the previous stable point of 80W, or instruct the robotic arm to move in the reverse direction along the path back to the previous processing point.

[0075] Upon receiving a stop adjustment signal: the control system locks the laser power at the current value of 80W and continues scanning at the current point. At the same time, the monitoring system continuously collects data until the curve returns to stability (triggers a positive signal) or the cumulative timeout (triggers a reverse signal).

[0076] Example 7: Generating the surface treatment result is the final output stage of the entire method, involving the comprehensive evaluation and integration of data acquired during the processing. This process is performed separately for each individual processing item (such as laser power, scanning speed, polishing time, etc.). For a selected processing item, the final processing values ​​for each surface treatment point in the first surface treatment direction are obtained. These values ​​represent the parameter values ​​or surface finish results determined after actual processing or optimization in that path direction. Similarly, the final processing values ​​corresponding to the same surface treatment point position in the second surface treatment direction are obtained. The data in the second direction provides the results when processing the same position on the reverse path.

[0077] Calculate the absolute difference between the values ​​in the first and second surface treatment directions at the same surface treatment point for the same processing project. For example, for a specific surface treatment point, if the optimal laser power recorded in the first direction is 85W and the value recorded in the second direction is 83W, then the difference is 2W. Repeat this calculation for all surface treatment points to obtain a sequence of processing value differences for each point.

[0078] It is determined whether the difference in processing values ​​at each surface treatment point is less than a preset difference threshold. This difference threshold is set according to the nature of the processing project and the processing accuracy requirements. For example, the difference threshold for laser power may be set to 3W, and the difference threshold for scanning speed may be set to 20mm / s. Additionally, it is determined whether the difference in processing values ​​at the initial surface treatment point (i.e., the point closest to the surface finish calibration value) in the first and second surface treatment directions is also less than this difference threshold.

[0079] If the difference in processing values ​​at all surface treatment points is less than the difference threshold, and the difference at the initial surface treatment point is also less than the difference threshold, then the processing values ​​of each surface treatment point in the first surface treatment direction are adopted as the final valid processing values ​​for that point. Simultaneously, the processing values ​​of each surface treatment point in the third surface treatment direction are also set as the final valid processing values. This means that the data in the first and third directions are considered reliable and consistent. For this processing project, the final valid processing values ​​of each surface treatment point (including all points in the first and third directions) are used as data points. These data points contain processing parameter values ​​(X-axis) and the corresponding surface finish result or optimal setting value (Y-axis). Using curve fitting algorithms (such as polynomial fitting, spline interpolation, etc.), these data points are fitted into a smooth and continuous surface characteristic curve for the project. This curve describes the variation of surface finish or optimal parameters with processing position or parameters under this processing project. After fitting the surface characteristic curves for all processing projects (such as laser power, scanning speed, polishing time, etc.), the processing results of multiple nylon 3D printed structural parts (usually referring to multiple components from the same batch or with the same design specifications) are aggregated. The surface characteristic curve data (including curve equations, data points, fitting parameters, etc.) corresponding to all treatment items for each component are packaged into a structured surface treatment data package. This data package can be used for archiving, quality traceability, or to guide the treatment of subsequent identical structural components. Simultaneously, a processing completion signal is generated, indicating that the surface finishing process for this batch or group of structural components has been successfully executed and verified.

[0080] If the difference in processing values ​​at at least one surface treatment point is not less than a preset difference threshold (i.e., exceeds the allowable range), then only the processing values ​​of each surface treatment point in the first surface treatment direction are adopted as the final valid processing values. Data from the second direction is discarded due to inconsistency. These adopted final valid processing values ​​(from only the first direction) are used as data points and fitted into a partial surface characteristic curve. It is called "partial" because this curve is based only on data from a single direction (the first direction) and does not include or validate data from the third direction. Similarly, partial surface characteristic curve data from various processing projects of multiple nylon 3D printed structural parts are collected and packaged into a surface treatment data package. This data package contains the best processing information based on available data but indicates limitations on the data source. Simultaneously, a retest signal for the third surface treatment direction is generated. This signal indicates that the data in the third surface treatment direction needs to be remeasured or reprocessed in the current processing results. Possible reasons include the non-adoption of data from the third direction (which is only adopted in the first case) or inconsistencies in the second direction data suggesting potential problems requiring revalidation of the third direction. This signal triggers the system to re-execute the processing flow in the third surface treatment direction to obtain new, reliable data for subsequent analysis or to supplement the data package.

[0081] The final output surface treatment results, whether they are data packets containing complete project surface characteristic curves and processing completion signals, or data packets containing partial curves and retest signals, provide a record and feedback of the processing process and results, serving quality control and process optimization.

Claims

1. A method for rapid surface finishing of complex nylon 3D printed structural parts, characterized in that, The method includes: Obtain surface property data of nylon 3D printed structural parts; A surface treatment point set is generated based on the surface characteristic data; Generate the surface treatment path direction based on the surface treatment point set; Calculate the surface roughness stability factor and construct a surface smoothness change prediction network; Set the surface defect detection mode; Set up a smoothing process mechanism; Surface smoothness control is performed according to the aforementioned smoothing treatment mechanism; Generate surface treatment results, which include surface treatment data packets and surface treatment signals; The generation of the surface treatment path direction based on the surface treatment point set includes: Mark the surface treatment point with the highest gloss value as the high gloss treatment point; Mark the surface treatment point with the lowest gloss value as a low gloss treatment point; Obtain the smoothness value 1 of the surface characteristic curve corresponding to the high-smoothness treatment point; Obtain the smoothness value 2 of the surface characteristic curve corresponding to the low smoothness treatment point; Obtain the smoothness value of the surface characteristic curve corresponding to the initial surface treatment point; Determine whether the difference between the first smoothness value and the third smoothness value is less than the difference between the second smoothness value and the third smoothness value; When the difference between the first gloss value and the third gloss value is less than the difference between the second gloss value and the third gloss value, the first surface treatment direction is defined as starting from the initial surface treatment point and ending at the high gloss treatment point, the second surface treatment direction is defined as starting from the high gloss treatment point and ending at the initial surface treatment point, and the third surface treatment direction is defined as starting from the initial surface treatment point and ending at the low gloss treatment point. When the difference between the first gloss value and the third gloss value is not less than the difference between the second gloss value and the third gloss value, the first surface treatment direction is defined as starting from the initial surface treatment point and ending at the low gloss treatment point, the second surface treatment direction is defined as starting from the low gloss treatment point and ending at the initial surface treatment point, and the third surface treatment direction is defined as starting from the initial surface treatment point and ending at the high gloss treatment point. The first surface treatment direction, the second surface treatment direction, and the third surface treatment direction are integrated into a surface treatment path direction.

2. The method for rapid surface finishing of complex nylon 3D printed structural parts according to claim 1, characterized in that, The generation of a surface treatment point set based on the surface characteristic data includes: Extract the material properties and structural complexity from the surface characteristic data; The corresponding surface property curve is found in the surface property curve lookup table based on the material properties and structural complexity. Obtain the processing range of the processing device, and divide the processing range into several equal processing intervals; The number of test points corresponding to the processing interval is obtained by using the allocation coefficient of the processing interval. The number of surface treatment points is set equally among the test points within the corresponding processing interval; The surface characteristic curve lookup table is constructed by obtaining several nylon 3D printed structural parts samples with the same material properties and structural complexity, adjusting the processing parameters from low to high, obtaining the surface finish value of each sample, taking the average of the surface finish values ​​of each sample as the surface finish value under the corresponding processing parameters, fitting the surface finish values ​​under each processing parameter to obtain the surface characteristic curves corresponding to the material properties and structural complexity, and constructing the surface characteristic curve lookup table based on the material properties, structural complexity and surface characteristic curves.

3. The method for rapid surface finishing of complex nylon 3D printed structural parts according to claim 2, characterized in that, The initial surface treatment point is the surface treatment point whose smoothness value is closest to the smoothness calibration value in the surface characteristic curve corresponding to each surface treatment point.

4. The method for rapid surface finishing of complex nylon 3D printed structural parts according to claim 3, characterized in that, The calculation of the surface roughness stability factor includes: Obtain a historical set of surface finish data; The historical surface finish data set is cleaned using data analysis methods to obtain a historical surface finish data set. The variance of the surface smoothness data set from the cleaning history is calculated to obtain the surface roughness stability factor; The data analysis method includes sorting the historical surface finish data set, determining the upper and lower limits of the cluster, and statistically analyzing the historical surface finish data located between the upper and lower limits of the cluster.

5. A method for rapid surface finishing of complex nylon 3D printed structural parts according to claim 4, characterized in that, The construction of the surface smoothness change prediction network includes: Using the surface roughness stability factor as an index, a centralized search is performed in the surface treatment configuration space to determine the target processing step size; The surface smoothness change prediction network is constructed based on the target processing step size; The surface treatment configuration space is constructed by acquiring multiple sample surface roughness stability factors and corresponding multiple sample processing steps, wherein the coordinate axes of the three-dimensional space are surface roughness stability factors. The multiple sample surface roughness stability factors and the multiple sample processing steps are input into the three-dimensional space to obtain multiple sample space points. The multiple sample processing steps are used to identify the multiple sample space points to construct the surface treatment configuration space.

6. The method for rapid surface finishing of complex nylon 3D printed structural parts according to claim 5, characterized in that, The setting of the surface defect sensing mode includes: Real-time sensing of the surface condition of nylon 3D printed structural parts; Analyze the surface smoothness change trend of the surface smoothness change prediction network; By combining the surface condition and the trend of surface smoothness changes, potential surface defect factors are identified; Identify the risk level of the potential surface defect factors, and set the surface risk strain mode according to the risk level; The surface defect perception mode is set by combining the risk level and the surface risk strain mode.

7. A method for rapid surface finishing of complex nylon 3D printed structural parts according to claim 6, characterized in that, The aforementioned smoothing treatment mechanism includes: Based on the surface defect perception mode, surface anomalies of nylon 3D printed structural parts are identified. Locate the abnormal region corresponding to the surface anomaly; Analyze the defect types in the abnormal regions; A surface distress strain layer is set based on the aforementioned defect type; The surface smoothness change prediction network identifies the adjacent smoothness trajectory of the abnormal region. The escape path for the abnormal area is set based on the smooth trajectory of the adjacent area; The smoothing mechanism is set by combining the surface distress strain layer and the escape path.

8. A method for rapid surface finishing of complex nylon 3D printed structural parts according to claim 7, characterized in that, The step of performing surface smoothness control according to the smoothing treatment mechanism includes: Obtain the surface treatment data corresponding to the current surface treatment point; Extract the time variation curves of each processing item in the surface treatment data; Determine whether the time variation curves of all projects are stable; When all project time variation curves stabilize, a positive smoothing adjustment signal is generated; When the time change curves of all projects are unstable and the cumulative time is greater than the time threshold, the direction of the surface treatment path is determined and a reverse gloss adjustment signal is generated. When the time variation curves of all projects are unstable and the cumulative time does not exceed the time threshold, a stop adjustment signal is generated. Surface finish control is performed based on the positive finish adjustment signal, the reverse finish adjustment signal, or the stop adjustment signal.

9. A method for rapid surface finishing of complex nylon 3D printed structural parts according to claim 8, characterized in that, The generated surface treatment result includes: Obtain the difference in processing values ​​for the same surface treatment points of the same processing project in the first surface treatment direction and the second surface treatment direction; Determine whether the difference in the processing values ​​of each surface treatment point is less than the difference threshold. When the difference in the processing values ​​of each surface treatment point is less than the difference threshold and the difference in the processing values ​​of the initial surface treatment points in the first surface treatment direction and the second surface treatment direction is less than the difference threshold, the processing values ​​of each surface treatment point in the first surface treatment direction are taken as the final processing values, and the processing values ​​of each surface treatment point in the third surface treatment direction are also set as the final processing values. The final processing values ​​of each surface treatment point are fitted into the project surface characteristic curve. The project surface characteristic curves of each processing project of multiple nylon 3D printed structural parts are packaged to generate the surface treatment data package, and a processing completion signal is generated. When the difference in the processing values ​​of each surface treatment point is not less than the difference threshold, the processing values ​​of each surface treatment point in the first surface treatment direction are taken as the final processing values. The final processing values ​​of each surface treatment point in the first surface treatment direction are fitted into a partial surface characteristic curve. The partial surface characteristic curves of each processing item of multiple nylon 3D printed structural parts are packaged to generate the surface treatment data package, and a retest signal in the third surface treatment direction is generated.

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