Printing method, device and equipment for polypropylene workpiece and storage medium

Abstract

The invention relates to the technical field of 3D printing, and discloses a printing method, device and equipment for a polypropylene workpiece and a storage medium. The method comprises the steps of obtaining initial printing parameters, performing noise reduction and feature extraction to obtain a preprocessed data set, establishing a mapping relation between a physical space and a virtual space through spatial feature vector extraction, and generating an association database of printing environment parameters and material features; performing parameter mapping by adopting a multi-layer neural network, and performing optimization in combination with the crystallization dynamic characteristics of the material to obtain a parameter control strategy; carrying out interlayer distance and path planning on the parameter control strategy to generate a layered printing track; based on the heterogeneous crystallization characteristic of the material, flow and crystallization coupling analysis is conducted on the layered printing track, and a filling printing scheme is obtained through area density calculation; and the filling printing scheme is converted into an equipment instruction, printing is executed, and a target workpiece is finally obtained through real-time monitoring and correction. The printing quality and precision of the polypropylene workpiece are improved.

Description

Technical Field

[0001] The present invention relates to the field of 3D printing technology, and in particular to a printing method, device, equipment and storage medium for polypropylene workpieces. Background Art

[0002] In the field of polymer material molding and processing, polypropylene is widely used in the manufacture of craft products due to its excellent mechanical properties, chemical stability and cost-effectiveness. As an emerging manufacturing method, 3D printing technology can quickly and efficiently manufacture polypropylene workpieces with complex shapes. However, due to the semi-crystalline characteristics and anisotropy of polypropylene materials, problems such as insufficient interlayer bonding and warping deformation are prone to occur during the printing process, which directly affects the performance and quality of the workpiece. Therefore, developing a method to accurately control the printing process is crucial to ensure the manufacturing quality of polypropylene workpieces.

[0003] Nowadays, researchers try to improve the printing quality of polypropylene workpieces by adjusting printing parameters, optimizing filling paths or improving printing environments. However, these methods still face challenges in controlling the crystallization behavior of materials, optimizing interlayer bonding strength and compensating for deformation. In addition, these printing methods often ignore some unique characteristics of polypropylene materials, such as heterogeneous crystallization characteristics, melt rheological behavior, and thermomechanical coupling effects, which may have a significant impact on the molding quality and dimensional accuracy of the workpiece. That is, the existing polypropylene workpiece printing methods ignore the coupling relationship between material properties and process parameters, resulting in poor quality and accuracy of the final printed workpiece. Summary of the invention

[0004] The main purpose of the present invention is to solve the problem that the existing polypropylene workpiece printing method ignores the coupling relationship between material properties and process parameters, resulting in poor quality and accuracy of the final printed workpiece.

[0005] A first aspect of the present invention provides a printing method for a polypropylene workpiece, the method for printing a polypropylene workpiece comprising: obtaining a plurality of initial printing parameter data corresponding to a target polypropylene material and preset printing environment parameters, and performing noise reduction and extraction of spatiotemporal correlation features on the initial printing parameter data to obtain a preprocessed data set; extracting a spatial feature vector from the preprocessed data set to obtain a space mapping matrix corresponding to a physical space and a virtual space, and performing multi-dimensional correlation calculation on the space mapping matrix to generate a printing correlation database of the printing environment parameters and preset polypropylene material features; performing parameter mapping of a multi-layer neural network on the printing correlation database based on preset process and material parameters to obtain a parameter optimization direction matrix, and performing parameter iteration on the parameter optimization direction matrix based on the crystallization kinetics characteristics corresponding to the target polypropylene material. The method comprises the steps of: performing generation optimization and error compensation calculation to obtain a parameter control strategy; performing interlayer distance and path planning calculation of the target polypropylene material corresponding to the bonding feature on the parameter control strategy to obtain initial layered trajectory data, and performing interlayer bonding strength calculation on the initial layered trajectory data based on the material shrinkage deformation feature corresponding to the target polypropylene material to obtain a layered printing trajectory; performing flow and crystallization coupling distribution calculation on the layered printing trajectory based on the heterogeneous crystallization feature corresponding to the target polypropylene material to obtain material state distribution data, and performing regional density calculation of crystallinity on the material state distribution data to generate an initial filling printing plan; performing numerical control conversion and printing execution output on the initial filling printing plan to obtain workpiece printing state data, and performing workpiece deviation and correction calculation on the workpiece printing state data to obtain a target polypropylene workpiece.

[0006] Optionally, in a first implementation method of the first aspect of the present invention, the denoising and spatiotemporal correlation feature extraction of the initial printing parameter data to obtain a preprocessed data set includes: performing wavelet transform decomposition on the initial printing parameter data to obtain multi-scale parameter feature data, and filtering and threshold denoising on the multi-scale feature data to obtain effective parameter feature data; performing timestamp extraction and calibration on the effective parameter feature data to obtain a reference time series, and performing multi-source data interpolation and resampling calculations on the reference time series to obtain a multi-source data time series relationship; performing statistical feature and distribution calculations on the multi-source data time series relationship to obtain data evaluation indicators, and based on the data evaluation indicators, clustering and threshold screening the effective parameter feature data to obtain a spatiotemporal correlated preprocessed data set.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the extraction of spatial feature vectors from the preprocessed data set to obtain a spatial mapping matrix corresponding to the physical space and the virtual space, and multi-dimensional correlation calculation of the spatial mapping matrix to generate a printing association database of the printing environment parameters and preset polypropylene material characteristics include: decomposing the polypropylene crystallization parameter data in the preprocessed data set in the time and space dimensions to obtain a crystallization feature vector, and calculating and cross-comparing the correlation coefficient between the crystallization feature vector and the material crystallinity corresponding to the target polypropylene material to obtain a parameter and crystallinity influence matrix; and calculating the parameter and crystallinity influence matrix. The matrix is ​​structured modeled and meshed discretized to obtain a space mapping matrix corresponding to the physical space and the virtual space, and the space mapping matrix is ​​subjected to feature transformation and value conversion to obtain virtual space state data; based on the measured crystallization parameter data in the preprocessing data set, the virtual space state data residual is calculated and the data is standardized to obtain a state correction parameter, and based on the historical printing data in the preprocessing data set, the state correction parameter is subjected to state trend and prediction calculation to obtain parameter evolution data; the parameter evolution data is subjected to feature clustering and association rule mining to generate a printing association database of the printing environment parameters and preset polypropylene material characteristics.

[0008] Optionally, in a third implementation of the first aspect of the present invention, based on the preset process and material parameters, performing multi-layer neural network parameter mapping on the printing association database to obtain a parameter optimization direction matrix, and based on the crystallization kinetics characteristics corresponding to the target polypropylene material, performing parameter iterative optimization and error compensation calculation on the parameter optimization direction matrix to obtain a parameter control strategy, including: based on the preset process and material parameters, performing principal component analysis and weight calculation on the rheological parameter data in the printing association database to obtain a rheological parameter correlation matrix, and performing multi-layer parameter mapping and gradient calculation on the rheological parameter correlation matrix to obtain a parameter optimization direction matrix; based on the crystallization kinetics characteristics corresponding to the target polypropylene material, performing quantitative index extraction on the parameter optimization direction matrix to obtain material sensitivity parameters, and performing compensation calculation of crystallization and flow coupling on the material sensitivity parameters to obtain process compensation parameters; performing melt stability evaluation and process verification on the process compensation parameters to obtain a verification parameter group, and performing process rule extraction and printing control conversion on the verification parameter group to obtain a parameter control strategy.

[0009] Optionally, in a fourth implementation manner of the first aspect of the present invention, the parameter control strategy performs interlayer distance and path planning calculations of the target polypropylene material corresponding to the combination characteristics to obtain initial layered trajectory data, and based on the material shrinkage and deformation characteristics corresponding to the target polypropylene material, performs interlayer bonding strength calculations on the initial layered trajectory data to obtain a layered printing trajectory, including: performing contour boundary extraction and curvature calculations on the three-dimensional model data corresponding to the workpiece to be printed to obtain a geometric feature point cloud, and performing force calculations on the geometric feature distribution map for the combination characteristics corresponding to the target polypropylene material to obtain a molten stress distribution field; performing layer selection and slice position calculations on the molten stress distribution field to obtain initial layered trajectory data, and based on the material shrinkage and deformation characteristics corresponding to the target polypropylene material, performing deformation compensation and stress balance on the initial layered trajectory data to obtain deformation compensation parameters; performing interlayer bonding strength calculations on the deformation compensation parameters to obtain interlayer bonding parameters, and based on the melt flow characteristics corresponding to the target polypropylene material, performing path continuity planning and turning angle calculations on the interlayer bonding parameters to obtain a trajectory planning scheme; performing motion parameter conversion and trajectory interpolation on the trajectory planning scheme to generate a layered printing trajectory.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, based on the heterogeneous crystallization characteristics corresponding to the target polypropylene material, the distribution calculation of flow and crystallization coupling is performed on the layered printing trajectory to obtain material state distribution data, and the regional density calculation of crystallinity is performed on the material state distribution data to generate an initial filling printing scheme, including: based on a preset printing temperature range, the viscosity and shear rate response measurements of the polypropylene melt corresponding to the target polypropylene material at multiple temperatures are performed to obtain temperature-dependent flow characteristic data, and based on the heterogeneous crystallization characteristics corresponding to the target polypropylene material, the temperature-dependent flow characteristic data is calculated. The phase change point calibration and crystallization rate calculation of the corresponding material cooling are performed to obtain the flow and crystallization coupling parameters; based on the layered printing trajectory, the thermal state distribution calculation of the flow and crystallization coupling parameters is performed to obtain material state distribution data, and the local area density calculation and distribution of the material state distribution data are performed to obtain the regional density distribution scheme; the strength prediction and stiffness evaluation of the regional density distribution scheme are performed to obtain the filling structure parameters, and the molten state flow path adjustment of the filling structure parameters is performed to obtain the filling sequence scheme; the printing motion parameter configuration and printing path conversion are performed on the filling sequence scheme to generate an initial filling printing scheme.

[0011] Optionally, in a sixth implementation manner of the first aspect of the present invention, the workpiece printing state data is subjected to workpiece deviation and correction calculation to obtain a target polypropylene workpiece, including: performing time series feature extraction and multi-source feature fusion of crystallinity on the workpiece printing state data to obtain printed workpiece state data, and based on the crystallization and rheological characteristics corresponding to the target polypropylene material, performing deviation calculation and numerical quantification on the printed workpiece state data to obtain workpiece correction parameters; performing parameter conversion and feedback compensation for workpiece printing on the workpiece correction parameters to obtain equipment control instructions, and based on the equipment control instructions, adjusting the initial filling printing scheme to obtain a target polypropylene workpiece that meets preset molding accuracy and interlayer bonding indicators.

[0012] A second aspect of the present invention provides a printing device for a polypropylene workpiece, the printing device for a polypropylene workpiece comprising: A preprocessing module is used to obtain a plurality of initial printing parameter data corresponding to the target polypropylene material and the preset printing environment parameters, and to perform noise reduction and extraction of spatiotemporal correlation features on the initial printing parameter data to obtain a preprocessing data set; a spatial mapping module is used to extract spatial feature vectors from the preprocessing data set to obtain a spatial mapping matrix corresponding to the physical space and the virtual space, and to perform multi-dimensional correlation calculation on the spatial mapping matrix to generate a printing correlation database of the printing environment parameters and the preset polypropylene material characteristics; a parameter mapping module is used to perform parameter mapping of a multi-layer neural network on the printing correlation database based on preset process and material parameters to obtain a parameter optimization direction matrix, and to perform parameter iterative optimization and error compensation calculation on the parameter optimization direction matrix based on the crystallization kinetics characteristics corresponding to the target polypropylene material to obtain a parameter control strategy; a path A planning module is used to perform interlayer distance and path planning calculations of the target polypropylene material corresponding to the bonding characteristics of the parameter control strategy to obtain initial layered trajectory data, and based on the material shrinkage deformation characteristics corresponding to the target polypropylene material, perform interlayer bonding strength calculations on the initial layered trajectory data to obtain a layered printing trajectory; a density calculation module is used to perform flow and crystallization coupling distribution calculations on the layered printing trajectory based on the heterogeneous crystallization characteristics corresponding to the target polypropylene material to obtain material state distribution data, and perform regional density calculations of crystallinity on the material state distribution data to generate an initial filling printing plan; a printing correction module is used to perform numerical control conversion and printing execution output on the initial filling printing plan to obtain workpiece printing state data, and perform workpiece deviation and correction calculations on the workpiece printing state data to obtain a target polypropylene workpiece.

[0013] A third aspect of the present invention provides a printing device for polypropylene workpieces, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the printing device for polypropylene workpieces performs the various steps of the above-mentioned printing method for polypropylene workpieces.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the various steps of the above-mentioned printing method for a polypropylene workpiece.

[0015] The above-mentioned printing method, device, equipment and storage medium for polypropylene workpieces. In the embodiment of the present invention, a plurality of initial printing parameter data corresponding to the target polypropylene material and the preset printing environment parameters are obtained, and the initial printing parameter data are subjected to noise reduction and extraction of spatiotemporal correlation features to obtain a preprocessed data set; the spatial feature vector is extracted from the preprocessed data set to obtain a space mapping matrix corresponding to the physical space and the virtual space, and the space mapping matrix is ​​subjected to multi-dimensional correlation calculation to generate a printing correlation database of the printing environment parameters and the preset polypropylene material characteristics; based on the preset process and material parameters, the printing correlation database is subjected to parameter mapping of a multi-layer neural network to obtain a parameter optimization direction matrix, and based on the crystallization kinetics characteristics corresponding to the target polypropylene material, the parameter optimization direction matrix is ​​subjected to parameter iteration optimization and error compensation calculation to obtain a parameter optimization direction matrix. The method adopts a numerical control strategy; the interlayer distance and path planning calculation of the target polypropylene material corresponding to the bonding characteristics of the parameter control strategy are performed to obtain the initial layered trajectory data, and the interlayer bonding strength calculation is performed on the initial layered trajectory data based on the material shrinkage deformation characteristics corresponding to the target polypropylene material to obtain the layered printing trajectory; based on the heterogeneous crystallization characteristics corresponding to the target polypropylene material, the distribution calculation of flow and crystallization coupling is performed on the layered printing trajectory to obtain the material state distribution data, and the regional density calculation of crystallinity is performed on the material state distribution data to generate the initial filling printing plan; the initial filling printing plan is numerically controlled and printed and executed to output, the workpiece printing state data is obtained, and the workpiece deviation and correction calculation is performed on the workpiece printing state data to obtain the target polypropylene workpiece. Compared with the prior art, the basic process characteristics are obtained by multi-dimensional acquisition and feature extraction of polypropylene material characteristics, and then these characteristics are spatially mapped and associated analyzed to obtain the process-material mapping relationship; then the control strategy is obtained and the printing path is planned through multi-layer parameter optimization and dynamic feature analysis; finally, the printing execution and quality correction are performed based on the material characteristics to output the target workpiece. Through hierarchical data processing and feature analysis, the problem of precise control of polypropylene workpiece printing was solved, especially in terms of crystallinity control, interlayer bonding and dimensional accuracy. The heterogeneous characteristics of the material and process characteristics were fully considered, and the printing quality was effectively improved. In addition, multi-level feature mapping and parameter optimization strategies were adopted to realize the correlation analysis between process parameters and enhance the controllability of the printing process. In addition, through crystallization-flow coupling and quality correction, the material state and workpiece performance were accurately controlled, thereby achieving high-quality and precise printing of polypropylene workpieces as a whole.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of a first embodiment of a printing method for a polypropylene workpiece according to an embodiment of the present invention; Figure 2 A schematic diagram of an embodiment of a printing device for a polypropylene workpiece in an embodiment of the present invention; Figure 3 The figure is a schematic diagram of an embodiment of a printing device for a polypropylene workpiece in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0021] To facilitate understanding of this embodiment, the specific process of the embodiment of the present invention is described below. Figure 1 , a first embodiment of a printing method for a polypropylene workpiece in an embodiment of the present invention includes: 101. Acquire a plurality of initial printing parameter data corresponding to a target polypropylene material and preset printing environment parameters, and perform noise reduction and spatiotemporal correlation feature extraction on the initial printing parameter data to obtain a preprocessed data set; The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0022] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0023] In this embodiment, the printing environment parameters here refer to the displacement data, spatial coordinate data and various environmental data of the printing platform; the initial printing parameter data is decomposed by wavelet transform to obtain multi-scale parameter feature data, and the multi-scale feature data is filtered and threshold denoised to obtain effective parameter feature data; the effective parameter feature data is timestamp extracted and calibrated to obtain a reference time series, and the reference time series is interpolated and resampled by multi-source data to obtain the time series relationship of the multi-source data; the time series relationship of the multi-source data is statistically characterized and distributed to obtain data evaluation indicators, and based on the data evaluation indicators, the effective parameter feature data is clustered and threshold screened to obtain a preprocessed data set with spatiotemporal association.

[0024] In practical applications, various parameter data of the target polypropylene material are collected through a sensor network, including temperature field data, pressure data, displacement data, etc. (such as collecting temperature field data of the printing space, and obtaining the temperature gradient data of the workpiece surface through sub-micron thermal field distribution measurement and processing; performing full spectrum scanning of the printing process, and obtaining material melting and solidification state data and measured crystallization parameter data and other material data related to the target polypropylene material through spectral analysis and processing; collecting mechanical parameters between the nozzle and the workpiece, and obtaining stress data of the material extrusion process through three-dimensional force analysis and processing; monitoring the displacement of the printing platform, and obtaining workpiece deformation displacement data through six-degree-of-freedom coordinate transformation, etc.), and obtaining environmental parameters such as temperature, humidity, and airflow of the printing environment at the same time. These data together constitute the initial printing parameter data set; then the collected initial printing parameter data is decomposed by wavelet transform, and by selecting appropriate wavelet basis functions, the signal is decomposed into approximate components and detail components of different scales, thereby obtaining multi-scale parameter feature data containing different frequency characteristics, and these multi-scale feature data are filtered by a low-pass filter, and combined with an adaptive threshold method to remove noise, wherein the selection of the threshold is based on the local variance characteristics of the signal. In this way, more accurate effective parameter feature data can be obtained; then the data is processed in the time dimension. First, the timestamp information of each data point is extracted, and the time deviation between different sensors is eliminated by comparing and calibrating with the standard clock source, and a unified benchmark time series is established. Based on this benchmark time series, the cubic spline interpolation method is used to interpolate multi-source data with different sampling frequencies, and linear resampling is used to unify the sampling frequency of the data. In this way, data from different sources are aligned to the same time axis to form a multi-source data time series relationship with a strict correspondence; then feature extraction and statistical analysis are performed on the formed multi-source data time series relationship, and statistical features such as mean, variance, skewness, and kurtosis are calculated. At the same time, the probability distribution characteristics of the data are analyzed. Through these calculations, a series of data evaluation indicators are obtained. Based on these evaluation indicators, the K-means clustering algorithm is used to perform cluster analysis on the effective parameter feature data, and data points with similar features are classified. Threshold conditions based on data quality are set to eliminate data points that do not meet the requirements. Then, by analyzing the time and space correlation of the retained data, a correlation model between parameters is established, and finally a high-quality preprocessed data set with time and space correlation characteristics is obtained.

[0025] 102. Extracting spatial feature vectors from the preprocessed data set to obtain a spatial mapping matrix corresponding to the physical space and the virtual space, and performing multi-dimensional correlation calculation on the spatial mapping matrix to generate a printing correlation database of printing environment parameters and preset polypropylene material characteristics; In this embodiment, the polypropylene material characteristics here refer to the semi-crystalline characteristics and anisotropy of the polypropylene material and other material characteristics; the polypropylene crystallization parameter data in the preprocessing data set is decomposed in time and space dimensions to obtain a crystallization feature vector, and the correlation coefficient between the crystallization feature vector and the material crystallinity corresponding to the target polypropylene material is calculated and cross-compared to obtain a parameter and crystallinity influence matrix; the parameter and crystallinity influence matrix is ​​structured modeled and meshed discretized to obtain a space mapping matrix corresponding to the physical space and the virtual space, and the space mapping matrix is ​​feature transformed and numerically converted to obtain virtual space state data; based on the measured crystallization parameter data in the preprocessing data set, the virtual space state data residual is calculated and data is standardized to obtain a state correction parameter, and based on the historical printing data in the preprocessing data set, the state correction parameter is calculated for state trend and prediction to obtain parameter evolution data; feature clustering and association rule mining are performed on the parameter evolution data to generate a printing association database of the printing environment parameters and preset polypropylene material characteristics.

[0026] In practical applications, the principal component analysis method is used to decompose the polypropylene crystallization parameter data (referring to the key characteristic parameters describing the crystallization process of polypropylene materials) in the preprocessed data set in the time and space dimensions, and the crystallization characteristic vectors containing key characteristics such as crystallization temperature, crystallization rate, and spherulite size are extracted. These crystallization characteristic vectors are compared with the standard crystallinity data of the target polypropylene material, and the Pearson correlation coefficient is used to calculate the degree of correlation between each parameter and the crystallinity. At the same time, the reliability of the correlation is verified by the cross-validation method, and finally a parameter and crystallinity influence matrix reflecting the influence of process parameters on the crystallization behavior of the material is constructed; then the finite element method is used to perform structured modeling on the matrix, and the continuous physical space is discretized into a finite number of grid units, each unit containing physical quantity information such as temperature, pressure, and speed. By establishing a mapping relationship between the physical space (referring to the real environment in the actual printing process (temperature, pressure, displacement, etc.)) and the virtual space (the computational model space established by the digital twin technology), a spatial mapping matrix describing the correspondence between the two spaces is formed, and the matrix is ​​subjected to feature transformation and numerical processing to convert the physical quantity into the virtual space. State variables are obtained to obtain virtual space state data that characterize the state of the material in the virtual space; then the actual crystallization parameter measurement data collected in the preprocessing data set is used as a reference to calculate the residual between the virtual state and the actual state, and the data of different physical quantities are made comparable through standardization processing, thereby obtaining parameters for state correction, and analyzing the historical printing records stored in the preprocessing data set to extract the law of material state evolution over time, and using the time series prediction method to predict the change trend of the state correction parameters to generate parameter evolution data containing state evolution information; then the density clustering algorithm is used to cluster the parameter combinations with similar evolution characteristics, and the potential correlation between the parameters is discovered through the association rule mining algorithm, thereby establishing a database containing printing environment parameters, material characteristic parameters and the correlation between them. This database not only stores static parameter information, but also contains the dynamic characteristics of parameter changes over time, realizes accurate mapping from physical space to virtual space, and constructs a complete database that can reflect the relationship between polypropylene material properties and printing process parameters, laying a data foundation for achieving high-quality polypropylene workpiece printing.

[0027] 103. Based on the preset process and material parameters, the multi-layer neural network parameter mapping is performed on the printing association database to obtain the parameter optimization direction matrix, and based on the crystallization kinetic characteristics corresponding to the target polypropylene material, the parameter optimization direction matrix is ​​subjected to parameter iterative optimization and error compensation calculation to obtain the parameter control strategy; In this embodiment, based on the preset process and material parameters, the rheological parameter data in the printing association database are subjected to principal component analysis and weight calculation to obtain a rheological parameter correlation matrix, and multi-layer parameter mapping and gradient calculation are performed on the rheological parameter correlation matrix to obtain a parameter optimization direction matrix; based on the crystallization kinetics characteristics corresponding to the target polypropylene material, quantitative index extraction is performed on the parameter optimization direction matrix to obtain material sensitivity parameters, and compensation calculation of crystallization and flow coupling is performed on the material sensitivity parameters to obtain process compensation parameters; melt stability evaluation and process verification are performed on the process compensation parameters to obtain a verification parameter group, and process rule extraction and printing control conversion are performed on the verification parameter group to obtain a parameter control strategy.

[0028] In practical applications, based on pre-set process parameters (such as printing temperature, speed, pressure, etc.) and material parameters (such as molecular weight, crystallinity, etc.), principal component analysis is performed on the rheological parameter data in the printing association database to extract the main characteristic components that have the most significant impact on the printing process. The weight coefficient of each parameter is calculated by the hierarchical analysis method, thereby constructing a rheological parameter correlation matrix that reflects the degree of correlation between the rheological parameters. The correlation matrix is ​​mapped by a multi-layer neural network to establish a nonlinear mapping relationship between the parameters, and the gradient direction of parameter optimization is calculated at the same time, and finally the parameter optimization direction matrix guiding the parameter optimization direction is obtained; then based on the crystallization kinetics characteristics of the target polypropylene material, including crystallization temperature, crystallization rate and crystallinity, the parameters in the parameter optimization direction matrix are quantitatively analyzed to provide a reference for the optimization of the parameters. The quantitative index reflecting the degree of response of the material to the change of process parameters is taken out to form the material sensitivity parameter; then, considering the coupling effect of the crystallization and flow behavior of the polypropylene material during the printing process, the material sensitivity parameter is compensated and calculated, and the influence of the temperature field and the shear field on the material flow and crystallization behavior is comprehensively considered to obtain the process compensation parameters that can balance the material fluidity and crystallization behavior (for example, assuming that the rheological data of a polypropylene material at 190°C shows that when the shear rate increases from 1s^-1 to 100s^-1, the viscosity decreases from 3000Pa·s to 300Pa·s. By calculation, it is obtained that: zero shear viscosity η0=3200Pa·s, critical shear stress τ*=2.5×10^4Pa, power law index n=0.35, and thus the crystallization kinetic parameters calculated by combining the crystallization kinetic equation ( , n=2.8) (where K is the crystallization rate constant, in units of s^-n, indicating the speed of material crystallization. The larger the K value, the faster the crystallization speed. s is the unit of time (seconds))) to predict the material behavior under given printing conditions and generate corresponding process compensation parameters; then these parameters are evaluated for melt stability, and the flow stability of the melt and the uniformity of crystallization behavior under different printing conditions are analyzed. At the same time, the actual effect of the parameters is evaluated through experimental verification methods, thereby obtaining a verified parameter combination, and then the verification parameter group is analyzed to extract process rules with general guiding significance, and these rules are converted into specific printing control instructions to form a complete parameter control strategy. It not only ensures the accuracy of the optimization direction of the printing parameters, but also realizes the precise control of material properties, providing a reliable parameter control solution for the printing of high-quality polypropylene workpieces.

[0029] 104. Calculate the interlayer distance and path planning of the target polypropylene material corresponding to the bonding characteristics of the parameter control strategy to obtain initial layered trajectory data, and calculate the interlayer bonding strength of the initial layered trajectory data based on the material shrinkage deformation characteristics corresponding to the target polypropylene material to obtain a layered printing trajectory; In this embodiment, contour boundary extraction and curvature calculation are performed on the three-dimensional model data corresponding to the workpiece to be printed to obtain a geometric feature point cloud, and force calculation is performed on the geometric feature distribution map according to the bonding characteristics of the target polypropylene material to obtain a molten stress distribution field; layer selection and slice position calculation are performed on the molten stress distribution field to obtain initial layered trajectory data, and based on the material shrinkage deformation characteristics corresponding to the target polypropylene material, deformation compensation and stress balance are performed on the initial layered trajectory data to obtain deformation compensation parameters; interlayer bonding strength calculation is performed on the deformation compensation parameters to obtain interlayer bonding parameters, and based on the melt flow characteristics corresponding to the target polypropylene material, path continuity planning and angle calculation are performed on the interlayer bonding parameters to obtain a trajectory planning scheme; motion parameter conversion and trajectory interpolation are performed on the trajectory planning scheme to generate a layered printing trajectory.

[0030] In practical applications, the contour features of the three-dimensional model data of the workpiece to be printed are extracted through a boundary tracking algorithm, and the curvature value of each point on the surface is calculated at the same time, so as to obtain point cloud data describing the geometric features of the workpiece. The combined characteristic parameters of the target polypropylene material are introduced into the analysis process, and the mechanical properties of the material in the molten state are considered. Finite element analysis is performed on the geometric feature point cloud to calculate the stress distribution of the workpiece during the printing process, such as using the viscoelastic constitutive equation to calculate the local stress distribution: ; in: is the stress tensor, is the shear viscosity, is the shear rate, is the deformation rate deviator tensor, is the bulk viscosity, is the elastic modulus, is a memory function (for example, when the material is printed at 200°C at a speed of 50 mm / s, the local stress distribution can be calculated by the viscoelastic constitutive equation), thereby generating field data characterizing the molten state stress distribution; based on the obtained molten state stress distribution field, an adaptive stratification algorithm is used to select the optimal stratification slicing position, and the interlayer stress balance equation is: ; in, is the computational domain, It's the border. is the displacement field, is the density, It is body force (for example, for a 20mm high cylinder, it may be divided into 100 layers, each layer is 0.2mm) according to the stress distribution, so that a smaller layer thickness is selected in the area with greater stress to improve accuracy, and a larger layer thickness is selected in the area with less stress to improve efficiency. The initial layered trajectory data is generated, and the shrinkage deformation characteristics of the polypropylene material are taken into account, and the initial layered trajectory is compensated and calculated. The shrinkage behavior of the material is predicted by establishing a thermal-mechanical coupling model, and on this basis, the position and thickness of each layer are adjusted to achieve a balanced distribution of stress and obtain deformation compensation parameters; then the deformation compensation parameters are used to calculate the bonding strength between adjacent layers, and the influence of factors such as temperature and pressure on the interlayer bonding quality is considered to generate parameters describing the interlayer bonding state, and combined with the melt flow characteristics of the polypropylene material, the printing path is optimized to ensure continuous extrusion and uniform deposition of the material. At the same time, special treatment is performed at the corners of the path, and the printing quality is guaranteed by adjusting the speed and temperature parameters. Finally, a complete trajectory planning scheme is formed, and then the trajectory planning scheme is converted into motion parameters executable by the printing device, and a continuous motion trajectory is generated through the trajectory interpolation algorithm to form the final layered printing trajectory. Thus, the conversion from the 3D model to the actual printing trajectory is realized, while the influence of material properties on the printing process is fully considered, providing accurate trajectory guidance for achieving high-quality printing of polypropylene workpieces.

[0031] 105. Based on the heterogeneous crystallization characteristics corresponding to the target polypropylene material, the flow and crystallization coupling distribution calculation is performed on the layered printing trajectory to obtain the material state distribution data, and the regional density of the crystallinity is calculated on the material state distribution data to generate the initial filling printing plan; In this embodiment, based on a preset printing temperature range, the viscosity and shear rate response measurements of the polypropylene melt corresponding to the target polypropylene material are performed at multiple temperatures to obtain temperature-related flow characteristic data, and based on the heterogeneous crystallization characteristics corresponding to the target polypropylene material, the temperature-related flow characteristic data is calibrated for the phase change point of the corresponding material cooling and the crystallization rate is calculated to obtain flow and crystallization coupling parameters; based on the layered printing trajectory, the flow and crystallization coupling parameters are calculated for thermal state distribution to obtain material state distribution data, and the local area density calculation and distribution are performed on the material state distribution data to obtain a regional density distribution scheme; the regional density distribution scheme is subjected to strength prediction and stiffness evaluation to obtain filling structure parameters, and the filling structure parameters are subjected to molten state flow path adjustment to obtain a filling sequence scheme; the filling sequence scheme is subjected to printing motion parameter configuration and printing path conversion to generate an initial filling printing scheme.

[0032] In practical applications, within the preset printing temperature range (usually 180-250°C), a capillary rheometer is used to perform rheological tests on the target polypropylene material under different temperature conditions. By changing the shear rate and measuring the corresponding viscosity change, the flow characteristic data of the material under different temperature and shear conditions are obtained; and considering the heterogeneous crystallization characteristics of the polypropylene material, a differential scanning calorimeter is used to analyze the cooling process of the target polypropylene material, that is, by calibrating the crystallization starting point and end point of the material, the crystallization rate under different cooling conditions is calculated, and these data are combined with the flow characteristics to obtain parameters describing the coupling relationship between the material flow and crystallization behavior; then, based on the layered printing trajectory generated in the previous order, the flow and crystallization coupling parameters are further analyzed, that is, by calculating the temperature field distribution during the printing process through the finite element method, the material is predicted to be in different positions. The thermal history of the material is set, so as to obtain the distribution data characterizing the material state. According to the material state distribution data, combined with the changes in crystallinity and fluidity, the optimal filling density of different areas of the workpiece is calculated to form a regional density distribution scheme that adapts to local performance requirements. In this process, special attention is paid to the density control of the stress concentration area; the regional density distribution scheme is then used to predict the strength and stiffness characteristics of the structure under different filling densities through the finite element method, and a parameter set describing the filling structure characteristics is generated. Based on these parameters, the flow path of the molten material is optimized to ensure that the material can be evenly distributed and maintain good interlayer bonding during the filling process, thereby obtaining a detailed filling sequence scheme; the filling sequence scheme is then converted into specific printing parameters, including nozzle movement speed, extrusion rate, etc., and the corresponding motion control instructions are generated to form a complete initial filling printing scheme. Precise control of the internal structure of the polypropylene workpiece is achieved.

[0033] 106. Perform CNC conversion and print execution output on the initial filling printing plan to obtain workpiece printing status data, and perform workpiece deviation and correction calculation on the workpiece printing status data to obtain a target polypropylene workpiece.

[0034] In this embodiment, the workpiece printing state data is subjected to crystallinity time series feature extraction and multi-source feature fusion to obtain printed workpiece state data, and based on the crystallization and rheological characteristics corresponding to the target polypropylene material, the printed workpiece state data is subjected to deviation calculation and numerical quantification to obtain workpiece correction parameters; the workpiece correction parameters are subjected to parameter conversion and feedback compensation for workpiece printing to obtain equipment control instructions, and based on the equipment control instructions, the initial filling printing scheme is adjusted to obtain a target polypropylene workpiece that meets preset molding accuracy and interlayer bonding indicators.

[0035] In practical applications, the initial filling printing plan is converted into numerical control codes recognizable by the 3D printing equipment, including nozzle movement instructions, temperature control instructions, and material extrusion instructions, and the printing operation is performed; in the printing process, the printing status data of the workpiece is collected in real time through multiple sensors, including temperature distribution data collected by infrared sensors, pressure data collected by force sensors, deformation data collected by displacement sensors, and surface morphology data collected by optical sensors; the characteristics of the change of material crystallinity over time are extracted, and the multi-source sensor data are feature fused to form complete printed workpiece status data; based on the predetermined crystallization and rheological characteristic parameters of the target polypropylene material, the printed workpiece is The state data is deeply analyzed, that is, by calculating the deviation between the actual printing state and the expected state, including geometric size deviation, crystallinity deviation and interlayer bonding strength deviation, and by establishing a quantitative evaluation model, these deviations are converted into specific numerical indicators, and a parameter set for workpiece correction is generated, and then the workpiece correction parameters are processed and converted into control instructions executable by the printing device, including temperature adjustment instructions, speed compensation instructions and material flow correction instructions, etc. While executing these control instructions, the correction effect is monitored in real time through a closed-loop feedback mechanism, and the initial filling printing plan is dynamically adjusted according to the feedback results, and the printing parameters are continuously optimized until the workpiece quality meets the preset requirements. Throughout the process, by continuously monitoring the molding accuracy and interlayer bonding strength of the workpiece, it is ensured that the final printed polypropylene workpiece meets the design indicators, and through this real-time monitoring and dynamic adjustment method, various deviations in the printing process can be effectively compensated, and finally a high-quality target polypropylene workpiece can be obtained.

[0036] In the embodiment of the present invention, the basic process characteristics are obtained by multi-dimensional collection and feature extraction of polypropylene material characteristics, and then these characteristics are spatially mapped and correlated to obtain the process-material mapping relationship; then, through multi-layer parameter optimization and dynamic feature analysis, the control strategy is obtained and the printing path is planned; finally, the printing execution and quality correction are performed based on the material characteristics to output the target workpiece. Through hierarchical data processing and feature analysis, the problem of precise control of polypropylene workpiece printing is solved, especially in terms of crystallinity control, interlayer bonding and dimensional accuracy, the heterogeneous characteristics and process characteristics of the material are fully considered, and the printing quality is effectively improved; and the multi-level feature mapping and parameter optimization strategy are adopted, which not only realizes the correlation analysis between process parameters, but also enhances the controllability of the printing process; in addition, through crystallization-flow coupling and quality correction, the material state and workpiece performance are accurately controlled, thereby achieving high-quality and precise printing of polypropylene workpieces as a whole.

[0037] The above describes the printing method for a polypropylene workpiece in an embodiment of the present invention. The following describes the printing device for a polypropylene workpiece in an embodiment of the present invention. Figure 2 , an embodiment of a printing device for a polypropylene workpiece in an embodiment of the present invention comprises: The preprocessing module 201 is used to obtain a plurality of initial printing parameter data corresponding to the target polypropylene material and the preset printing environment parameters, and to perform noise reduction and extraction of spatiotemporal correlation features on the initial printing parameter data to obtain a preprocessing data set; The space mapping module 202 is used to extract the space feature vector of the pre-processed data set to obtain the space mapping matrix corresponding to the physical space and the virtual space, and perform multi-dimensional correlation calculation on the space mapping matrix to generate a printing correlation database of the printing environment parameters and the preset polypropylene material characteristics; The parameter mapping module 203 is used to perform parameter mapping of a multi-layer neural network on the printing association database based on preset process and material parameters to obtain a parameter optimization direction matrix, and perform parameter iterative optimization and error compensation calculation on the parameter optimization direction matrix based on the crystallization kinetic characteristics corresponding to the target polypropylene material to obtain a parameter control strategy; The path planning module 204 is used to perform interlayer distance and path planning calculations on the parameter control strategy according to the bonding characteristics of the target polypropylene material, obtain initial layered trajectory data, and perform interlayer bonding strength calculations on the initial layered trajectory data based on the material shrinkage deformation characteristics corresponding to the target polypropylene material, to obtain a layered printing trajectory; The density calculation module 205 is used to perform flow and crystallization coupling distribution calculation on the layered printing trajectory based on the heterogeneous crystallization characteristics corresponding to the target polypropylene material to obtain material state distribution data, and perform regional density calculation of crystallinity on the material state distribution data to generate an initial filling printing plan; The printing correction module 206 is used to perform numerical control conversion and printing execution output on the initial filling printing scheme to obtain workpiece printing state data, and perform workpiece deviation and correction calculation on the workpiece printing state data to obtain a target polypropylene workpiece.

[0038] In the embodiment of the present invention, the basic process characteristics are obtained by multi-dimensional collection and feature extraction of polypropylene material characteristics, and then these characteristics are spatially mapped and correlated to obtain the process-material mapping relationship; then, through multi-layer parameter optimization and dynamic feature analysis, the control strategy is obtained and the printing path is planned; finally, the printing execution and quality correction are performed based on the material characteristics to output the target workpiece. Through hierarchical data processing and feature analysis, the problem of precise control of polypropylene workpiece printing is solved, especially in terms of crystallinity control, interlayer bonding and dimensional accuracy, the heterogeneous characteristics and process characteristics of the material are fully considered, and the printing quality is effectively improved; and the multi-level feature mapping and parameter optimization strategy are adopted, which not only realizes the correlation analysis between process parameters, but also enhances the controllability of the printing process; in addition, through crystallization-flow coupling and quality correction, the material state and workpiece performance are accurately controlled, thereby achieving high-quality and precise printing of polypropylene workpieces as a whole.

[0039] above Figure 2 The printing device for polypropylene workpieces in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The printing device for polypropylene workpieces in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0040] Figure 31 is a schematic diagram of a structure of a printing device for polypropylene workpieces provided by an embodiment of the present invention. The printing device 300 for polypropylene workpieces may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage devices) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 may be temporary storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the printing device 300 for polypropylene workpieces. Furthermore, the processor 310 may be configured to communicate with the storage medium 330 to execute a series of instruction operations in the storage medium 330 on the printing device 300 for polypropylene workpieces.

[0041] The printing device 300 for polypropylene workpieces may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 3 The structure of the printing device for polypropylene workpieces shown does not constitute a limitation on the printing device for polypropylene workpieces, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0042] The present invention also provides a printing device for polypropylene workpieces, wherein the computer device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes each step of the printing method for polypropylene workpieces in the above-mentioned embodiments.

[0043] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the various steps of the printing method for polypropylene workpieces.

[0044] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0045] If the integrated unit is implemented in the form of 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 is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0046] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0047] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A printing method for a polypropylene workpiece, characterized in that: The method for printing a polypropylene workpiece comprises: Acquire a plurality of initial printing parameter data corresponding to the target polypropylene material and the preset printing environment parameters, and perform noise reduction and spatiotemporal correlation feature extraction on the initial printing parameter data to obtain a preprocessed data set; Extracting spatial feature vectors from the preprocessed data set to obtain a spatial mapping matrix corresponding to the physical space and the virtual space, and performing multi-dimensional correlation calculation on the spatial mapping matrix to generate a printing correlation database of the printing environment parameters and preset polypropylene material characteristics; Based on the preset process and material parameters, the printing association database is subjected to parameter mapping of a multi-layer neural network to obtain a parameter optimization direction matrix, and based on the crystallization kinetics characteristics corresponding to the target polypropylene material, the parameter optimization direction matrix is ​​subjected to parameter iterative optimization and error compensation calculation to obtain a parameter control strategy; The parameter control strategy is used to calculate the interlayer distance and path planning of the target polypropylene material corresponding to the bonding characteristics to obtain initial layered trajectory data, and based on the material shrinkage deformation characteristics corresponding to the target polypropylene material, the interlayer bonding strength of the initial layered trajectory data is calculated to obtain a layered printing trajectory; Based on the heterogeneous crystallization characteristics corresponding to the target polypropylene material, the flow and crystallization coupling distribution calculation is performed on the layered printing trajectory to obtain material state distribution data, and the regional density of crystallinity is calculated on the material state distribution data to generate an initial filling printing plan; The initial filling printing scheme is numerically converted and printed to execute output to obtain workpiece printing state data, and workpiece deviation and correction calculation is performed on the workpiece printing state data to obtain a target polypropylene workpiece.

2. The printing method for polypropylene workpiece according to claim 1, characterized in that: The denoising and spatiotemporal correlation feature extraction of the initial printing parameter data to obtain a preprocessed data set includes: Performing wavelet transform decomposition on the initial printing parameter data to obtain multi-scale parameter feature data, and performing filtering and threshold noise reduction on the multi-scale feature data to obtain effective parameter feature data; Extracting and calibrating the timestamp of the effective parameter characteristic data to obtain a reference time series, and performing interpolation and resampling calculations of multi-source data on the reference time series to obtain a time series relationship of the multi-source data; Statistical characteristics and distribution calculations are performed on the time series relationship of the multi-source data to obtain data evaluation indicators, and based on the data evaluation indicators, clustering and threshold screening are performed on the effective parameter feature data to obtain a pre-processed data set with spatiotemporal association.

3. The printing method for polypropylene workpiece according to claim 1, characterized in that: The extracting of the spatial feature vector of the preprocessed data set to obtain a spatial mapping matrix corresponding to the physical space and the virtual space, and performing multi-dimensional correlation calculation on the spatial mapping matrix to generate a printing correlation database of the printing environment parameters and the preset polypropylene material characteristics include: Decomposing the polypropylene crystallization parameter data in the preprocessed data set in terms of time and space dimensions to obtain a crystallization characteristic vector, and calculating and cross-comparing the correlation coefficient between the crystallization characteristic vector and the material crystallinity corresponding to the target polypropylene material to obtain a parameter and crystallinity influence matrix; Performing structured modeling and mesh discretization on the parameter and crystallinity influence matrix to obtain a space mapping matrix corresponding to the physical space and the virtual space, and performing feature transformation and value conversion on the space mapping matrix to obtain virtual space state data; Based on the measured crystallization parameter data in the preprocessing data set, residual calculation and data standardization are performed on the virtual space state data to obtain state correction parameters, and based on the historical printing data in the preprocessing data set, state trend and prediction calculation are performed on the state correction parameters to obtain parameter evolution data; Feature clustering and association rule mining are performed on the parameter evolution data to generate a printing association database of the printing environment parameters and preset polypropylene material features.

4. The printing method for polypropylene workpiece according to claim 1, characterized in that: Based on the preset process and material parameters, the printing association database is subjected to parameter mapping of a multi-layer neural network to obtain a parameter optimization direction matrix, and based on the crystallization kinetics characteristics corresponding to the target polypropylene material, the parameter optimization direction matrix is ​​subjected to parameter iterative optimization and error compensation calculation to obtain a parameter control strategy, including: Based on the preset process and material parameters, the rheological parameter data in the printing association database are subjected to principal component analysis and weight calculation to obtain a rheological parameter correlation matrix, and multi-layer parameter mapping and gradient calculation are performed on the rheological parameter correlation matrix to obtain a parameter optimization direction matrix; Based on the crystallization kinetics characteristics corresponding to the target polypropylene material, the parameter optimization direction matrix is ​​subjected to quantitative index extraction to obtain material sensitivity parameters, and the material sensitivity parameters are subjected to compensation calculation of crystallization and flow coupling to obtain process compensation parameters; The process compensation parameters are subjected to melt stability evaluation and process verification to obtain a verification parameter group, and the verification parameter group is subjected to process rule extraction and printing control conversion to obtain a parameter control strategy.

5. The printing method for polypropylene workpiece according to claim 1, characterized in that: The parameter control strategy is used to calculate the interlayer distance and path planning of the target polypropylene material corresponding to the bonding characteristics to obtain initial layered trajectory data, and based on the material shrinkage deformation characteristics corresponding to the target polypropylene material, the interlayer bonding strength of the initial layered trajectory data is calculated to obtain a layered printing trajectory, including: Performing contour boundary extraction and curvature calculation on the three-dimensional model data corresponding to the workpiece to be printed to obtain a geometric feature point cloud, and performing force calculation on the corresponding combination features of the target polypropylene material on the geometric feature distribution map to obtain a molten stress distribution field; The molten stress distribution field is subjected to layer selection and slice position calculation to obtain initial layering trajectory data, and based on the material shrinkage deformation characteristics corresponding to the target polypropylene material, deformation compensation and stress balance are performed on the initial layering trajectory data to obtain deformation compensation parameters; Performing interlayer bonding strength calculation on the deformation compensation parameters to obtain interlayer bonding parameters, and performing path continuity planning and turning angle calculation on the interlayer bonding parameters based on the melt flow characteristics corresponding to the target polypropylene material to obtain a trajectory planning scheme; The trajectory planning scheme is subjected to motion parameter conversion and trajectory interpolation to generate a layered printing trajectory.

6. The printing method for polypropylene workpiece according to claim 1, characterized in that: Based on the heterogeneous crystallization characteristics corresponding to the target polypropylene material, the distribution calculation of flow and crystallization coupling is performed on the layered printing trajectory to obtain material state distribution data, and the regional density calculation of crystallinity is performed on the material state distribution data to generate an initial filling printing plan, including: Based on a preset printing temperature range, the viscosity and shear rate response of the polypropylene melt corresponding to the target polypropylene material are measured at various temperatures to obtain temperature-dependent flow characteristic data, and based on the heterogeneous crystallization characteristics corresponding to the target polypropylene material, the phase change point calibration and crystallization rate calculation of the temperature-dependent flow characteristic data corresponding to the material cooling are performed to obtain the flow and crystallization coupling parameters; Based on the layered printing trajectory, thermal state distribution calculation is performed on the flow and crystallization coupling parameters to obtain material state distribution data, and local area density calculation and distribution are performed on the material state distribution data to obtain a regional density distribution scheme; Performing strength prediction and stiffness evaluation on the regional density distribution scheme to obtain filling structure parameters, and adjusting the molten flow path of the filling structure parameters to obtain a filling sequence scheme; The printing motion parameter configuration and printing path conversion are performed on the filling sequence scheme to generate an initial filling printing scheme.

7. The printing method for polypropylene workpiece according to claim 1, characterized in that: The step of performing workpiece deviation and correction calculation on the workpiece printing state data to obtain a target polypropylene workpiece includes: Performing crystallinity time series feature extraction and multi-source feature fusion on the workpiece printing state data to obtain the printed workpiece state data, and performing deviation calculation and numerical quantification on the printed workpiece state data based on the crystallization and rheological characteristics corresponding to the target polypropylene material to obtain the workpiece correction parameters; The workpiece correction parameters are converted into parameters and feedback compensation is performed on the workpiece printing to obtain a device control instruction, and based on the device control instruction, the initial filling printing scheme is adjusted to obtain a target polypropylene workpiece that meets preset molding accuracy and interlayer bonding indicators.

8. A printing device for polypropylene workpieces, characterized in that: The printing device for polypropylene workpieces comprises: A preprocessing module, used to obtain a plurality of initial printing parameter data corresponding to the target polypropylene material and the preset printing environment parameters, and to perform noise reduction and extraction of spatiotemporal correlation features on the initial printing parameter data to obtain a preprocessing data set; A spatial mapping module is used to extract spatial feature vectors from the preprocessed data set to obtain a spatial mapping matrix corresponding to the physical space and the virtual space, and to perform multi-dimensional correlation calculation on the spatial mapping matrix to generate a printing correlation database of the printing environment parameters and preset polypropylene material characteristics; A parameter mapping module, for performing parameter mapping of a multi-layer neural network on the printing association database based on preset process and material parameters to obtain a parameter optimization direction matrix, and performing parameter iterative optimization and error compensation calculation on the parameter optimization direction matrix based on the crystallization kinetic characteristics corresponding to the target polypropylene material to obtain a parameter control strategy; A path planning module, used to perform interlayer distance and path planning calculations on the parameter control strategy according to the bonding characteristics of the target polypropylene material, obtain initial layered trajectory data, and perform interlayer bonding strength calculations on the initial layered trajectory data based on the material shrinkage deformation characteristics corresponding to the target polypropylene material, to obtain a layered printing trajectory; A density calculation module, for performing flow and crystallization coupling distribution calculation on the layered printing trajectory based on the heterogeneous crystallization characteristics corresponding to the target polypropylene material, obtaining material state distribution data, performing regional density calculation of crystallinity on the material state distribution data, and generating an initial filling printing plan; The printing correction module is used to perform numerical control conversion and printing execution output on the initial filling printing scheme to obtain workpiece printing status data, and perform workpiece deviation and correction calculation on the workpiece printing status data to obtain a target polypropylene workpiece.

9. A printing device for polypropylene workpieces, characterized in that: The printing device for polypropylene workpieces comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the printing device for polypropylene workpieces to perform each step of the printing method for polypropylene workpieces according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the printing method for a polypropylene workpiece as described in any one of claims 1 to 7 are implemented.

Images