CN118492413B - Path planning method and system based on gradient material 3D printing intelligent slice - Google Patents
Path planning method and system based on gradient material 3D printing intelligent slice Download PDFInfo
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Abstract
The invention discloses a path planning method and a system based on gradient material 3D printing intelligent slice, which belong to the field of 3D printing and specifically comprise the following steps: predicting the characteristics of a metal gradient functional material through a material performance prediction model, optimizing the model by establishing a three-dimensional model, slicing by adopting a deep learning and intelligent slicing algorithm, adaptively adjusting the slicing thickness and sequence, planning a printing head path through a genetic algorithm, adjusting printing parameters by combining an adaptive path planning method, verifying slicing and path by using 3D printing simulation software, optimizing potential problems, ensuring smooth printing by a real-time monitoring system in the printing process, finally, performing post-processing and performance test on a printed product, collecting user feedback to optimize a printing method and parameters, establishing digital file system record whole-course data, and improving printing efficiency and quality.
Description
Technical Field
The invention belongs to the field of 3D printing, and particularly relates to a path planning method and a path planning system based on gradient material 3D printing intelligent slices.
Background
With the rapid development of 3D printing technology, the application of the 3D printing technology in manufacturing industry is not limited to simple construction of single materials, but advances to complex structures and multifunctional materials, and the 3D printing technology of gradient materials is used as a leading-edge branch of the field, so that continuous changes of material composition, structure and performance are allowed to be realized in a single construction process, and composite material parts with specific functional gradients are created, and the parts have huge application potential in the fields of aerospace, biomedicine, electronics, energy sources and high-performance machinery, so that the structural performance can be optimized, the weight is reduced, and the durability is improved.
However, gradient material 3D printing faces unique challenges, especially the intellectualization of slice and path planning. Conventional 3D printing slicing techniques are generally based on uniform materials and single performance, where the slicing algorithm simply breaks the three-dimensional model into a series of two-dimensional plies and then plans a path for layer-by-layer printing, but for gradient materials, this simple approach is not satisfactory. Firstly, the composition and structure of the gradient material change continuously in space, the slicing process is required to consider geometric information, and accurately map the change of material properties, so that each printed layer is ensured to conform to the designed gradient distribution, and secondly, different materials or different stages of the same material need different printing parameters.
Currently, research is beginning to explore advanced algorithms and software to address these challenges, such as predicting thermal stress distribution in the printing process by finite element analysis, determining optimal material layout in combination with topology optimization, or developing intelligent path planning algorithms to dynamically adjust printing parameters, such as laser power and scanning speed, according to material characteristics to adapt to the material characteristics of different areas. However, existing solutions tend to focus on specific application scenarios, lack versatility and flexibility, and are difficult to apply widely to diverse gradient material systems and complex structural designs.
The Chinese patent with publication number CN116638767A discloses a method for intelligent 3D printing path planning, which comprises the following steps: slicing the three-dimensional model of the object to be printed by using slicing software to obtain two-dimensional slice surfaces, and extracting two-dimensional slice surface contour information; partitioning the two-dimensional slice surface by using a concave polygon rapid convex decomposition algorithm based on vertex visibility to obtain a plurality of convex polygon sub-partitions, then finding out the point closest to the printing head when the printing head starts to move, wherein the point has the largest single-side distance from the longest axis of the sub-partition, and calculating a printing path based on a tabu search algorithm; presetting a printing precision value by using 3D simulation software, changing a printing speed by adjusting the caliber of a 3D printing nozzle, obtaining the volume of a preset object to be printed, substituting the volume into a printing speed formula, calculating the required printing time, and detecting whether the printing work is within a precision allowable range according to the 3D simulation software; after the planning of the printing path is completed, outputting path parameters, and performing 3D printing based on the path.
The above prior art has the following problems: 1) Lack of optimization for specific material properties; 2) The printing path is calculated by using a concave polygon rapid convex decomposition algorithm based on vertex visibility and a tabu search algorithm, so that the accuracy is low; 3) Lack of simulation verification and automatic optimization, and do not mention real-time monitoring and emergency treatment measures; 4) The post-treatment steps and performance assessment are inadequate.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a path planning method and a path planning system based on gradient material 3D printing intelligent slicing, which are used for predicting the characteristics of metal gradient functional materials through a material performance prediction model, optimizing the model and slicing through a three-dimensional model by adopting deep learning and intelligent slicing algorithm, adaptively adjusting the thickness and sequence of the slicing, planning a printing head path through a genetic algorithm, adjusting printing parameters by combining with the adaptive path planning method, verifying the slicing and the path by using 3D printing simulation software, optimizing potential problems, ensuring that printing is smoothly carried out by a real-time monitoring system in the printing process, carrying out post-processing and performance test on a printed product, collecting user feedback to optimize the printing method and parameters, establishing digital archives system record whole-course data, and improving the printing efficiency and quality.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A path planning method based on gradient material 3D printing intelligent slice comprises the following steps:
Step S1: preparing printing equipment and metal gradient functional materials, checking and preprocessing, introducing a material performance prediction model, and predicting physical and chemical characteristics of the metal gradient functional materials by inputting metal gradient functional material information;
Step S2: according to the material performance prediction result, a three-dimensional model of the metal gradient functional material is established, a deep learning algorithm is adopted to automatically optimize the three-dimensional model, an intelligent slicing strategy is utilized to slice the three-dimensional model to obtain a 3D model thin layer, finite element analysis is carried out on the 3D model thin layer, a self-adaptive slicing strategy is introduced to adjust the slicing thickness and sequence, and a genetic algorithm is utilized to plan the moving path of the printing head;
Step S3: adopting a self-adaptive path planning method, combining material performance, adjusting printing parameters and a printing path, verifying slicing and path planning by using 3D printing simulation software, simulating a printing process, and optimizing slicing and path planning parameters according to simulation results if warpage and collapse problems exist;
step S4: leading the optimized slice and path planning data into 3D printing equipment, printing by control equipment according to preset parameters and paths, leading a real-time monitoring system, performing whole-process monitoring on the printing process, automatically starting emergency measures by the monitoring system if a fault problem occurs in the printing process, and notifying operators to perform timely treatment;
the specific steps of the step S2 include:
S201: collecting performance prediction results of the metal gradient functional material, determining performance targets and constraint conditions which are required to be achieved by the three-dimensional model, performing conceptual design according to the performance prediction results and the performance targets of the material by using a three-dimensional modeling tool to obtain the shape, the size and the characteristics of the three-dimensional model, adding detailed data of the three-dimensional model on the basis of the conceptual design, and considering performance characteristic parameters of the three-dimensional model to obtain a preliminary three-dimensional model;
S202: using a simulation function in a three-dimensional modeling tool to simulate and verify the preliminary three-dimensional model, checking whether the preliminary three-dimensional model meets performance requirements and constraint conditions, if not, training a deep learning model by using a cyclic neural network algorithm, inputting a simulation result of the preliminary three-dimensional model into the trained deep learning model, outputting optimized three-dimensional model parameters, and storing the optimized three-dimensional model;
The specific steps of the step S2 further include:
S203: setting the slice thickness as h, the slice direction as transverse, and the minimum and maximum spacing between slice planes as AndLoading the optimized three-dimensional model, and slicing the optimized three-dimensional model along a set slicing direction and slicing thickness by using an improved contour-based slicing algorithm to obtain n 3D model thin layers;
S204: dividing each 3D model thin layer into finite elements to obtain k units, dividing m nodes in each unit, defining physical quantity on each node, distributing material attribute for each unit, applying boundary condition and load, and building unit rigidity matrix according to unit type, material attribute and boundary condition The stiffness of k units is matrixIntegration into an overall stiffness matrix according to the node numbers in the unitsAnd integrating the loads on the k units into an overall load vector；
S205: according toAndEstablishing finite element equationsCombining the coefficients and constant terms of the finite element equation set into a matrix to obtain an augmented matrixBy row transformation pairsPerforming upper triangular matrix-unit upper triangular matrix transformation to obtainFrom the unit up triangular matrixStarting from the last line, gradually and back-substitution solving node displacement vector；
S206: solving the result according to the finite element equationExtracting performance parameters of maximum stress, average stress and temperature distribution on corresponding nodes, evaluating the thermal conductivity performance of the 3D model by using the performance parameters, intelligently adjusting the slice thickness of each 3D model thin layer according to the performance analysis result, optimizing the slice sequence, and performing iterative optimization on S205-S206 to obtain n 3D model thin layers after the optimization sequence;
S207: setting an evaluation standard and iteration times, generating a printing head moving path according to the 3D model thin layer after the optimization sequence, taking the generated moving path data as an initial population, generating a new printing head moving path through selection, intersection and mutation operations of a genetic algorithm according to the set evaluation standard, and performing iteration repetition until the preset iteration times are reached, so as to obtain a final moving path;
The specific steps of the modified contour-based slicing algorithm in S203 include:
S2031: defining a slice starting position, reading optimized three-dimensional model data, and determining the position of a first slice plane according to the slice direction and the starting position;
s2032: traversing each triangular grid in the three-dimensional model, preprocessing the model grids by using a KD tree, judging whether the triangular grids are in the search range of the current slicing plane or not for each grid, discarding if not, checking whether the triangular grids are intersected with the current slicing plane or not if not, discarding if not, introducing a cross product symbol detection strategy to calculate the position of an intersection point, introducing an intersection point caching mechanism, and directly searching data stored by the intersection point caching mechanism if repeated grids appear;
s2033: connecting all the intersection points of the grids intersecting with the current slice plane to form a slice contour, smoothing the slice contour, identifying the position of a fracture point and repairing if the contour fracture problem occurs, and deleting redundant parts or merging overlapped parts if the overlapping problem occurs to obtain a processed three-dimensional model data slice;
S2034: introducing a slice quality evaluation function, setting a slice contour quality standard, evaluating the processed three-dimensional model data slice by using the slice quality evaluation function, calculating a quality score of the slice, adjusting the position of a slice plane and carrying out slicing operation again if the quality of the current slice does not meet the slice contour quality standard, and storing the contour data of the current slice plane if the quality of the current slice does meet the slice contour quality standard;
s2035: and introducing a self-adaptive slicing strategy, updating the position of a slicing plane according to the slicing direction and the slicing thickness, and dynamically adjusting the slicing thickness according to the slicing quality evaluation result through the self-adaptive slicing strategy until the processing of all slices is completed, so as to obtain n dynamically adjusted 3D model thin layers.
Specifically, the performance target and the constraint condition in S202 are respectively expressed as:
，
Wherein F represents the actual stress of the three-dimensional model, Represents the maximum stress born by the three-dimensional model, E represents the elastic modulus of the three-dimensional model under the action of external force,Represents the minimum elastic modulus required by the three-dimensional model when the three-dimensional model is subjected to external force, m represents the mass of the three-dimensional model,Representing the target mass of the three-dimensional model when it is loaded, r representing the thermal properties of the three-dimensional model,Representing the coefficient of thermal expansion of the three-dimensional model, L representing the dimensions of the three-dimensional model,、Representing the minimum and maximum values of the dimensions of the three-dimensional model, respectively, w represents the manufacturing cost of the three-dimensional model,Representing the cost budget of the three-dimensional model,Representing the angle between any two line segments in the three-dimensional model,And the error of the allowed included angle between any two line segments in the three-dimensional model is represented.
Specifically, the thermal conductivity equation in S206 is:
Wherein, Indicating the location of the node within the cell,、、Representing node positionT represents temperature, q represents heat source density,Represents the density of the metal gradient functional material, c represents the specific heat capacity of the metal gradient functional material, t represents the current time,The effect of external fields on the thermal conduction of the metallic gradient functional material is shown.
Specifically, the specific content of the cross product symbol detection policy in S2032 includes:
Setting a slicing plane WhereinIs the normal vector of the plane surface,Representing the coordinates of any point of the slice plane, wherein H is a constant term; for each vertex of the triangle mesh, a vertex-to-plane distance formula is used to obtain the vertex-to-slice planeDistance of (2)I represents the number of vertices of the triangular mesh;
If it is Then the triangle mesh is not intersected with the slice plane;
If it is Then the triangle mesh is intersected with the slice plane;
selecting two end points of one edge of a triangle mesh AndIf (if)Then two end points are shown on two sides of the slicing plane, and the intersection point of the edge and the slicing plane is calculatedThe formula is:
wherein u represents the relative position of any point on the edge of the triangle mesh and the edge end point;
for each triangle intersecting the slice plane, the intersection point is stored in a caching mechanism.
A gradient material 3D printing intelligent slice-based path planning system comprising: the system comprises a material analysis module, an intelligent slicing module, a path planning module, a virtual simulation module, a printing control module and a post-processing module;
The material analysis module is used for collecting 3D model data, analyzing the distribution and the attribute of the metal gradient functional material, and adaptively adjusting slicing parameters, printing paths and printing parameters according to a real-time data set in the printing process;
The intelligent slicing module is used for slicing the 3D model according to the printing requirements and the characteristics of the metal gradient functional materials to generate slicing data for printing;
The path planning module is used for carrying out finite element analysis on the 3D model thin layer, generating a moving path printed by the 3D model thin layer, and carrying out iterative optimization on the moving path according to a genetic algorithm to obtain a final moving path;
The virtual simulation module is used for simulating the whole printing process through a virtual simulation technology before actual printing, predicting the occurrence of problems and taking emergency measures;
the printing control module is used for converting the generated final moving path data into a control instruction executable by the printer and controlling the printer to finish a printing task;
the post-processing module is used for carrying out post-processing on the printed metal gradient functional material, remotely monitoring the printing process, evaluating the influence of the printing process on the environment, and the sustainability and the recyclability of the material, and collecting feedback data for system optimization.
Specifically, the material analysis module includes: the system comprises a material database unit, a characteristic analysis unit, a data acquisition unit, a failure mode analysis unit and a multi-objective optimization unit;
the material database unit is used for storing data and performance parameters of the metal gradient functional material;
the characteristic analysis unit is used for simulating and analyzing the characteristics of the metal gradient functional material through the material performance prediction model according to the data in the database;
the data acquisition unit is used for monitoring the performance change data and the material data of the metal gradient functional material in the printing process in real time;
the failure mode analysis unit is used for analyzing whether the triangular mesh of the three-dimensional model is intersected with the slice plane or not through a KD tree and a search strategy, and judging whether the three-dimensional model data slice meets the slice contour quality standard or not through a slice quality evaluation function;
the multi-objective optimization unit is used for automatically adjusting and optimizing printing parameters through a deep learning algorithm according to the collected data.
Specifically, the intelligent slice module includes: the slice strategy unit, the slice quality evaluation unit and the slice efficiency optimization unit;
The slicing strategy unit is used for updating the position of a slicing plane according to the slicing direction and the slicing thickness, and dynamically adjusting the slicing thickness through a self-adaptive slicing strategy according to the slicing quality evaluation result;
The slice quality evaluation unit is used for evaluating the processed three-dimensional model data slices by using a slice quality evaluation function and evaluating the influence of different slice strategies on the printing quality;
The slice efficiency optimizing unit is used for optimizing and verifying parameters of slice and path planning according to the 3D printing simulation result by using an artificial intelligence algorithm.
Specifically, the path planning module includes: the device comprises a path generating unit, a path adjusting unit and a smoothing processing unit;
the path generation unit is used for generating a printed moving path according to slice data and metal gradient functional materials through deep learning and combining finite element analysis and a genetic algorithm;
the path adjusting unit is used for dynamically adjusting path planning parameters according to real-time data monitored in the printing process;
and the smoothing processing unit is used for smoothing the slice contour through a normal vector smoothing algorithm.
Compared with the prior art, the invention has the beneficial effects that:
1. The invention provides a path planning system based on gradient material 3D printing intelligent slicing, which is optimized and improved in architecture, operation steps and flow, and has the advantages of simple flow, low investment and operation cost and low production and working cost.
2. According to the path planning method based on the gradient material 3D printing intelligent slice, a material performance prediction model is introduced, physical and chemical characteristics of the material can be predicted according to the input metal gradient functional material information, so that the printing process can be optimized based on the actual performance of the material, and the printing accuracy and reliability are improved; the intelligent slicing strategy and finite element analysis are adopted, the slicing thickness and sequence are intelligently adjusted in each 3D model thin layer, and the accuracy and efficiency of the slicing process are ensured; the moving path of the printing head is planned by utilizing a genetic algorithm, so that the optimal printing path can be found, and the printing time and the material waste are reduced.
3. According to the path planning method based on the gradient material 3D printing intelligent slice, 3D printing simulation software is used for verifying the slice and path planning, so that the possible problems in the printing process can be simulated, and based on the problems existing in the simulation result, the slice and path planning parameters are automatically optimized according to the simulation result by using an artificial intelligent algorithm, so that the need of manual intervention is reduced, and the efficiency is improved; a real-time monitoring system is introduced to monitor the whole printing process, so that the stability and reliability of the printing process are ensured; and the printed metal gradient functional material is subjected to heat treatment and surface treatment, so that the quality and performance of the product are further improved.
Drawings
FIG. 1 is a schematic diagram of a path planning method based on gradient material 3D printing intelligent slices;
FIG. 2 is a workflow diagram of a path planning method based on gradient material 3D printing intelligent slices of the present invention;
FIG. 3 is a flow chart of a path planning method for moving a path planning based on a gradient material 3D printing intelligent slice;
FIG. 4 is a flow chart of a contour slicing algorithm of a path planning method based on gradient material 3D printing intelligent slicing;
Fig. 5 is a schematic diagram of a path planning system based on gradient material 3D printing intelligent slice according to the present invention.
Detailed Description
Example 1
Referring to fig. 1-4, an embodiment of the present invention is provided: a path planning method based on gradient material 3D printing intelligent slice comprises the following steps:
Step S1: preparing printing equipment and metal gradient functional materials, checking and preprocessing, introducing a material performance prediction model, and predicting physical and chemical characteristics of the metal gradient functional materials by inputting metal gradient functional material information;
The checking comprises the following steps: checking the integrity, the movement precision and the temperature control system of each part of the equipment; checking whether the quantity, quality, batch information and quality guarantee period of the metal gradient functional material meet the use requirements, and whether the material has impurities, pollution or damage; checking whether the ventilation system, the fireproof facility and the emergency stop device are operating normally.
The pretreatment comprises the following steps: 1) Material pretreatment, namely drying, deoiling and impurity removing the metal gradient functional material; 2) Preheating or calibrating equipment; 3) Environmental control, namely adjusting the temperature, the humidity and the cleanliness of a working environment to the conditions suitable for printing; 4) And setting software, namely setting printing parameters, slicing parameters and a supporting structure according to printing requirements.
The metal gradient functional material information comprises: the composition, macro/micro structure, manufacturing process and use environment of the metal gradient functional material.
The physical characteristics of the metal gradient functional material include: density, hardness, elastic modulus, coefficient of thermal expansion, thermal conductivity, electrical conductivity, magnetic permeability; the chemical characteristics include: chemical composition, corrosion resistance, chemical reactivity, thermal stability, oxidizing/reducing properties.
Step S2: according to the material performance prediction result, a three-dimensional model of the metal gradient functional material is established, a deep learning algorithm is adopted to automatically optimize the three-dimensional model, an intelligent slicing strategy is utilized to slice the three-dimensional model to obtain a 3D model thin layer, finite element analysis is carried out on the 3D model thin layer, a self-adaptive slicing strategy is introduced to adjust the slicing thickness and sequence, and a genetic algorithm is utilized to plan the moving path of the printing head;
common three-dimensional modeling tools include: autoCAD, solidWorks, maya, 3ds Max and Blender, ZBrush, the invention uses an AutoCAD three-dimensional modeling tool to modify the model by adjusting parameter values, thereby ensuring the correctness of the design under various conditions and reducing human errors.
Step S3: adopting a self-adaptive path planning method, combining material performance, adjusting printing parameters and a printing path, verifying slicing and path planning by using 3D printing simulation software, simulating a printing process, and optimizing slicing and path planning parameters according to simulation results if warpage and collapse problems exist;
the printing parameters include: 3D model thin layer height, printing speed, printing temperature, 3D model thin layer bonding strength, 3D model thin layer cooling degree and printing direction.
The self-adaptive path planning method comprises the following specific steps:
(1) Establishing a model of a 3D printing environment, wherein the model comprises the shape, the size and the boundary conditions of a printing platform and the performance parameters of materials;
(2) Setting initial printing parameters and path planning parameters according to the environment model and the material performance, wherein the path planning parameters comprise: printing a starting point, an ending point and a path type of a path;
(3) In the printing process, real-time monitoring and analysis are carried out, and printing parameters and path planning parameters are automatically adjusted according to real-time printing conditions and changes of material properties;
(4) And verifying the slice and the path planning by using 3D printing simulation software, and simulating a printing process.
Step S4: leading the optimized slice and path planning data into 3D printing equipment, printing by control equipment according to preset parameters and paths, leading a real-time monitoring system, performing whole-process monitoring on the printing process, automatically starting emergency measures by the monitoring system if a fault problem occurs in the printing process, and notifying operators to perform timely treatment;
Among them, the fault problems include: a printhead clogging and material supply interruption problem; the emergency measures comprise: suspending printing, alarming, diagnosing faults, automatically adjusting parameters, stopping in an emergency mode, starting an emergency plan, backing up data and switching standby equipment.
The specific implementation steps of importing data into the 3D printing device comprise:
(1) Ensuring that the optimized slice and path planning data are stored as a G-code file format recognizable by the equipment;
(2) Connecting the 3D printing device with a computer by using a USB wire and Wi-Fi;
(3) Opening slicing software compatible with the 3D printing device on a computer, and ensuring that the device is correctly connected and identified by the software;
(4) Selecting an 'import' option in slicing software, finding and selecting a file storing optimized slicing and path planning data, and clicking 'open' or 'import';
(5) Checking whether the imported data is correct or not in slicing software, including slicing layer number and path planning, and setting printing parameters according to equipment requirements and material performances;
(6) Clicking a 'start printing' option, sending the optimized slice and path planning data to the 3D printing equipment, and starting a printing process.
Step S5: and (3) performing post-processing on the printed metal gradient functional material, evaluating the performance index of the printer by adopting a performance test method, collecting feedback comments of a user on the performance index of the printer, adjusting and optimizing the printing method and parameters according to the feedback comments, and simultaneously, establishing a digital file system and recording design, printing and processing data of each step.
The post-treatment comprises the following steps: heat treatment and surface treatment; the performance indexes comprise: strength, corrosion resistance, wear resistance of the printer. Wherein the step of heat treatment comprises: cleaning and preparing-determining heat treatment parameters-heating-preserving-cooling, wherein the surface treatment comprises the following steps: cleaning and degreasing, roughening treatment, coating treatment, curing treatment, inspection and testing.
The specific steps of the performance test method for evaluating the performance index of the printer comprise:
(1) The method comprises the steps of determining the evaluated printer performance index, selecting a testing method and a testing tool according to a testing target, wherein the testing method selects a 3D printing testing model, and the testing tool selects a tensile testing machine;
(2) Making a test plan, including a test step, test conditions and test data records, executing a test according to the test plan, and recording the test data;
(3) And carrying out statistical analysis on the test data, and writing the test result and analysis into a test report.
The specific steps of the step S2 include:
S201: collecting performance prediction results of the metal gradient functional material, determining performance targets and constraint conditions which are required to be achieved by the three-dimensional model, performing conceptual design according to the performance prediction results and the performance targets of the material by using a three-dimensional modeling tool to obtain the shape, the size and the characteristics of the three-dimensional model, adding detailed data of the three-dimensional model on the basis of the conceptual design, and considering performance characteristic parameters of the three-dimensional model to obtain a preliminary three-dimensional model;
wherein the detail data includes: surface texture and detail of the three-dimensional model, and assembly and connection of the three-dimensional model; the performance characteristic parameters include: weight, strength, performance and lifetime of the three-dimensional model.
According to the invention, through concept design, the design direction is defined, the appearance and the shape of the metal gradient functional material are focused, the performance characteristic parameters of the product are considered, the design efficiency is improved, and meanwhile, potential problems and risks can be found out and solved in time through preliminary evaluation and analysis of the concept design stage.
S202: using a simulation function in a three-dimensional modeling tool to simulate and verify the preliminary three-dimensional model, checking whether the preliminary three-dimensional model meets performance requirements and constraint conditions, if not, training a deep learning model by using a cyclic neural network algorithm, inputting a simulation result of the preliminary three-dimensional model into the trained deep learning model, outputting optimized three-dimensional model parameters, and storing the optimized three-dimensional model;
further, the specific step of optimizing the three-dimensional model parameters by using the deep learning model comprises the following steps:
(1) Collecting historical three-dimensional model data, including shapes, sizes, characteristics and corresponding performance simulation results, and preprocessing;
(2) For the feature sequence of the three-dimensional model, determining the number of hidden layers, the number of neurons and an activation function by using an LSTM model, wherein the LSTM model is the prior art content in the field and is not an inventive scheme of the application and is not repeated here;
(3) Dividing the preprocessed three-dimensional model data into a training set, a verification set and a test set, training an LSTM model by using the training set data, updating model parameters by using a back propagation algorithm and a gradient descent optimizer, and after each training period, evaluating the performance of the model by using the verification set and saving the model weight with the optimal performance, wherein the back propagation algorithm is the prior art content in the field and is not an inventive scheme of the application, and is not repeated here;
(4) Evaluating the performance of the trained LSTM model by using a test set, and optimizing the LSTM model according to an evaluation result, wherein the method comprises the steps of adjusting a network structure and changing super parameters;
(5) Deploying the trained LSTM model into a three-dimensional modeling tool, and outputting optimized three-dimensional model parameters by the LSTM model when a simulation result of the preliminary three-dimensional model is input, wherein the formula is as follows:
Wherein, Representing the parameters of the three-dimensional model of the output,The activation function is represented as a function of the activation,The weight matrix is represented by a matrix of weights,The term of the bias is indicated,Represents the hidden layer state of the current moment of the LSTM model,Representing LSTM modelThe state of the hidden layer at the moment,Data representing the input LSTM model at the current time,Representing the hyperbolic tangent function,Representing the state of the cell at the current time;
(6) And applying the optimized parameters output by the LSTM model to the preliminary three-dimensional model to obtain an optimized three-dimensional model, and storing the optimized three-dimensional model.
S203: setting the slice thickness as h, the slice direction as transverse, and the minimum and maximum spacing between slice planes asAndLoading the optimized three-dimensional model, and slicing the optimized three-dimensional model along a set slicing direction and slicing thickness by using an improved contour-based slicing algorithm to obtain n 3D model thin layers;
S204: dividing each 3D model thin layer into finite elements to obtain k units, dividing m nodes in each unit, defining physical quantity on each node, distributing material attribute for each unit, applying boundary condition and load, and building unit rigidity matrix according to unit type, material attribute and boundary condition The stiffness of k units is matrixIntegration into an overall stiffness matrix according to the node numbers in the unitsAnd integrating the loads on the k units into an overall load vector；
Wherein the physical quantities include: displacement, stress, temperature on the current node; common material properties include: modulus of elasticity, poisson's ratio, shear modulus, density, yield strength, ultimate strength, fracture toughness.
The dispensing process for dispensing material properties for each cell includes: 1) Determining a material type; 2) Selecting material properties; 3) Dispensing material properties in a manner that includes: ① Direct allocation: directly selecting a unit and assigning material properties thereto; ② Using material areas: defining a material region in the model and assigning cells to the material region; ③ Using material mapping: if the model is imported from a CAD system, and material properties have been defined in the CAD system, then mapping these properties onto cells in the finite element model using a material mapping function; 4) And (5) verifying and checking.
Common boundary conditions include: displacement, velocity, acceleration, temperature and heat flux, symmetric and antisymmetric boundary conditions; the load is an external action applied on the model, and in the invention, the force, pressure and temperature gradient of the metal gradient functional material in the system are simulated, and common loads comprise: concentrated force, distributed force, surface pressure, volumetric force, and temperature load.
The specific steps for establishing the unit stiffness matrix comprise:
(1) Determining the type and parameters of the used units, and establishing a local coordinate system for each unit;
(2) Selecting interpolation function as potential shape function, calculating strain distribution in the unit according to displacement of unit node and potential shape function to form strain matrix The formula is:
Wherein, AndRepresenting the displacement of the nodes at both ends of the cell,Representing the strain of the metal gradient functional material;
(3) Calculating a material matrix according to the physical properties of the material and Hooke's law The formula is:
Wherein, Representing the stress of the metal gradient functional material;
(4) Obtaining a unit stiffness matrix according to the strain matrix and the material matrix The formula is:
Wherein, Representation ofIs a conjugate transpose of (2);
(5) And introducing constraint conditions to obtain a final unit stiffness matrix.
S205: according toAndEstablishing finite element equationsCombining the coefficients and constant terms of the finite element equation set into a matrix to obtain an augmented matrixBy row transformation pairsPerforming upper triangular matrix-unit upper triangular matrix transformation to obtainFrom the unit up triangular matrixStarting from the last line, gradually and back-substitution solving node displacement vector；
S206: solving the result according to the finite element equationExtracting performance parameters of maximum stress, average stress and temperature distribution on corresponding nodes, evaluating the thermal conductivity performance of the 3D model by using the performance parameters, intelligently adjusting the slice thickness of each 3D model thin layer according to the performance analysis result, optimizing the slice sequence, and performing iterative optimization on S205-S206 to obtain n 3D model thin layers after the optimization sequence;
S207: setting an evaluation standard and iteration times, generating a printing head moving path according to the 3D model thin layer after the optimization sequence, taking the generated moving path data as an initial population, generating a new printing head moving path through selection, intersection and variation operations of a genetic algorithm according to the set evaluation standard, and performing iteration repetition until the preset iteration times are reached to obtain a final moving path, wherein the selection, intersection and variation operations of the genetic algorithm are prior art contents in the field and are not the inventive scheme of the application, and are not repeated here.
The performance targets and constraints in S202 are expressed as:
，
Wherein F represents the actual stress of the three-dimensional model, Represents the maximum stress born by the three-dimensional model, E represents the elastic modulus of the three-dimensional model under the action of external force,Represents the minimum elastic modulus required by the three-dimensional model when the three-dimensional model is subjected to external force, m represents the mass of the three-dimensional model,Representing the target mass of the three-dimensional model when it is loaded, r representing the thermal properties of the three-dimensional model,Representing the coefficient of thermal expansion of the three-dimensional model, L representing the dimensions of the three-dimensional model,、Representing the minimum and maximum values of the dimensions of the three-dimensional model, respectively, w represents the manufacturing cost of the three-dimensional model,Representing the cost budget of the three-dimensional model,Representing the angle between any two line segments in the three-dimensional model,And the error of the allowed included angle between any two line segments in the three-dimensional model is represented.
Specifically, the thermal conductivity equation in S206 is:
Wherein, Indicating the location of the node within the cell,、、Representing node positionT represents temperature, q represents heat source density,Represents the density of the metal gradient functional material, c represents the specific heat capacity of the metal gradient functional material, t represents the current time,The effect of external fields on the thermal conduction of the metallic gradient functional material is shown.
The specific steps of the modified contour-based slicing algorithm in S203 include:
S2031: defining a slice starting position, reading optimized three-dimensional model data, and determining the position of a first slice plane according to the slice direction and the starting position;
S2032: traversing each triangular grid in the three-dimensional model, preprocessing the model grids by using a KD tree, judging whether the triangular grids are in the search range of the current slicing plane or not for each grid, discarding if not, checking whether the triangular grids are intersected with the current slicing plane or not if not, discarding if not, calculating the position of an intersection point by introducing a cross product symbol detection strategy if intersected, introducing an intersection point cache mechanism, and directly searching data stored by the intersection point cache mechanism if repeated grids appear, wherein the KD tree method is the prior art content in the field, and is not an inventive scheme of the application and is not repeated herein;
s2033: connecting all the intersection points of the grids intersecting with the current slice plane to form a slice contour, smoothing the slice contour, identifying the position of a fracture point and repairing if the contour fracture problem occurs, and deleting redundant parts or merging overlapped parts if the overlapping problem occurs to obtain a processed three-dimensional model data slice;
S2034: introducing a slice quality evaluation function, setting a slice contour quality standard, evaluating the processed three-dimensional model data slice by using the slice quality evaluation function, calculating a quality score of the slice, adjusting the position of a slice plane and carrying out slicing operation again if the quality of the current slice does not meet the slice contour quality standard, and storing the contour data of the current slice plane if the quality of the current slice does meet the slice contour quality standard;
s2035: and introducing a self-adaptive slicing strategy, updating the position of a slicing plane according to the slicing direction and the slicing thickness, and dynamically adjusting the slicing thickness according to the slicing quality evaluation result through the self-adaptive slicing strategy until the processing of all slices is completed, so as to obtain n dynamically adjusted 3D model thin layers.
Specific contents of the cross product symbol detection strategy in S2032 include:
Setting a slicing plane WhereinIs the normal vector of the plane surface,Representing the coordinates of any point of the slice plane, wherein H is a constant term; for each vertex of the triangle mesh, a vertex-to-plane distance formula is used to obtain the vertex-to-slice planeDistance of (2)I represents the number of vertices of the triangular mesh;
If it is Then the triangle mesh is not intersected with the slice plane;
If it is Then the triangle mesh is intersected with the slice plane;
selecting two end points of one edge of a triangle mesh AndIf (if)Then two end points are shown on two sides of the slicing plane, and the intersection point of the edge and the slicing plane is calculatedThe formula is:
wherein u represents the relative position of any point on the edge of the triangle mesh and the edge end point, and ，Respectively representing two end points on the sides of the triangle mesh;
for each triangle intersecting the slice plane, the intersection point is stored in a caching mechanism.
Example 2
Referring to fig. 5, another embodiment of the present invention is provided: a gradient material 3D printing intelligent slice-based path planning system comprising:
the system comprises a material analysis module, an intelligent slicing module, a path planning module, a virtual simulation module, a printing control module and a post-processing module;
the material analysis module is used for collecting 3D model data, analyzing the distribution and the attribute of the metal gradient functional material, and adaptively adjusting slice parameters, printing paths and printing parameters according to a real-time data set in the printing process;
The intelligent slicing module is used for slicing the 3D model according to the printing requirements and the characteristics of the metal gradient functional materials to generate slicing data for printing;
The path planning module is used for carrying out finite element analysis on the 3D model thin layer, generating a moving path printed by the 3D model thin layer, and carrying out iterative optimization on the moving path according to a genetic algorithm to obtain a final moving path;
The virtual simulation module is used for simulating the whole printing process through a virtual simulation technology before actual printing, predicting the occurrence of problems and taking emergency measures;
The printing control module is used for converting the generated final moving path data into a control instruction executable by the printer and controlling the printer to finish a printing task;
the post-processing module is used for carrying out post-processing on the printed metal gradient functional material, remotely monitoring the printing process, evaluating the influence of the printing process on the environment, and the sustainability and the recyclability of the material, and collecting feedback data for system optimization.
The material analysis module includes: the system comprises a material database unit, a characteristic analysis unit, a data acquisition unit, a failure mode analysis unit and a multi-objective optimization unit;
The material database unit is used for storing data and performance parameters of the metal gradient functional material;
the characteristic analysis unit is used for simulating and analyzing the characteristics of the metal gradient functional material through the material performance prediction model according to the data in the database;
The data acquisition unit is used for monitoring the performance change data and the material data of the metal gradient functional material in the printing process in real time;
The failure mode analysis unit is used for analyzing whether the triangular mesh of the three-dimensional model is intersected with the slice plane or not through the KD tree and the search strategy, and judging whether the three-dimensional model data slice meets the slice contour quality standard or not through the slice quality evaluation function;
And the multi-objective optimization unit is used for automatically adjusting and optimizing the printing parameters through a deep learning algorithm according to the collected data.
The intelligent slice module includes: the slice strategy unit, the slice quality evaluation unit and the slice efficiency optimization unit;
the slice strategy unit is used for updating the position of the slice plane according to the slice direction and the slice thickness, and dynamically adjusting the slice thickness through the self-adaptive slice strategy according to the slice quality evaluation result;
The slice quality evaluation unit is used for evaluating the processed three-dimensional model data slices by using a slice quality evaluation function and evaluating the influence of different slice strategies on the printing quality;
and the slice efficiency optimizing unit is used for optimizing and verifying parameters of slice and path planning according to the 3D printing simulation result by using an artificial intelligence algorithm.
The path planning module comprises: the device comprises a path generating unit, a path adjusting unit and a smoothing processing unit;
The path generation unit is used for generating a printed moving path according to slice data and the metal gradient functional material through deep learning and combining finite element analysis and a genetic algorithm;
The path adjusting unit is used for dynamically adjusting path planning parameters according to real-time data monitored in the printing process;
And the smoothing processing unit is used for smoothing the slice contour through a normal vector smoothing algorithm.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and variations, modifications, substitutions and alterations of the above-described embodiments may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the present invention as defined by the claims, which are all within the scope of the present invention.
Claims (8)
1. The path planning method based on the gradient material 3D printing intelligent slice is characterized by comprising the following steps of:
Step S1: preparing printing equipment and metal gradient functional materials, checking and preprocessing, introducing a material performance prediction model, and predicting physical and chemical characteristics of the metal gradient functional materials by inputting metal gradient functional material information;
Step S2: according to the material performance prediction result, a three-dimensional model of the metal gradient functional material is established, a deep learning algorithm is adopted to automatically optimize the three-dimensional model, an intelligent slicing strategy is utilized to slice the three-dimensional model to obtain a 3D model thin layer, finite element analysis is carried out on the 3D model thin layer, a self-adaptive slicing strategy is introduced to adjust the slicing thickness and sequence, and a genetic algorithm is utilized to plan the moving path of the printing head;
Step S3: adopting a self-adaptive path planning method, combining material performance, adjusting printing parameters and a printing path, verifying slicing and path planning by using 3D printing simulation software, simulating a printing process, and optimizing slicing and path planning parameters according to simulation results if warpage and collapse problems exist;
step S4: leading the optimized slice and path planning data into 3D printing equipment, printing by control equipment according to preset parameters and paths, leading a real-time monitoring system, performing whole-process monitoring on the printing process, automatically starting emergency measures by the monitoring system if a fault problem occurs in the printing process, and notifying operators to perform timely treatment;
the specific steps of the step S2 include:
S201: collecting performance prediction results of the metal gradient functional material, determining performance targets and constraint conditions which are required to be achieved by the three-dimensional model, performing conceptual design according to the performance prediction results and the performance targets of the material by using a three-dimensional modeling tool to obtain the shape, the size and the characteristics of the three-dimensional model, adding detailed data of the three-dimensional model on the basis of the conceptual design, and considering performance characteristic parameters of the three-dimensional model to obtain a preliminary three-dimensional model;
S202: using a simulation function in a three-dimensional modeling tool to simulate and verify the preliminary three-dimensional model, checking whether the preliminary three-dimensional model meets performance requirements and constraint conditions, if not, training a deep learning model by using a cyclic neural network algorithm, inputting a simulation result of the preliminary three-dimensional model into the trained deep learning model, outputting optimized three-dimensional model parameters, and storing the optimized three-dimensional model;
The specific steps of the step S2 further include:
S203: setting the slice thickness as h, the slice direction as transverse, and the minimum and maximum spacing between slice planes as AndLoading the optimized three-dimensional model, and slicing the optimized three-dimensional model along a set slicing direction and slicing thickness by using an improved contour-based slicing algorithm to obtain n 3D model thin layers;
S204: dividing each 3D model thin layer into finite elements to obtain k units, dividing m nodes in each unit, defining physical quantity on each node, distributing material attribute for each unit, applying boundary condition and load, and building unit rigidity matrix according to unit type, material attribute and boundary condition The stiffness of k units is matrixIntegration into an overall stiffness matrix according to the node numbers in the unitsAnd integrating the loads on the k units into an overall load vector；
S205: according toAndEstablishing finite element equationsCombining the coefficients and constant terms of the finite element equation set into a matrix to obtain an augmented matrixBy row transformation pairsPerforming upper triangular matrix-unit upper triangular matrix transformation to obtainFrom the unit up triangular matrixStarting from the last line, gradually and back-substitution solving node displacement vector；
S206: solving the result according to the finite element equationExtracting performance parameters of maximum stress, average stress and temperature distribution on corresponding nodes, evaluating the thermal conductivity performance of the 3D model by using the performance parameters, intelligently adjusting the slice thickness of each 3D model thin layer according to the performance analysis result, optimizing the slice sequence, and performing iterative optimization on S205-S206 to obtain n 3D model thin layers after the optimization sequence;
S207: setting an evaluation standard and iteration times, generating a printing head moving path according to the 3D model thin layer after the optimization sequence, taking the generated moving path data as an initial population, generating a new printing head moving path through selection, intersection and mutation operations of a genetic algorithm according to the set evaluation standard, and performing iteration repetition until the preset iteration times are reached, so as to obtain a final moving path;
The specific steps of the modified contour-based slicing algorithm in S203 include:
S2031: defining a slice starting position, reading optimized three-dimensional model data, and determining the position of a first slice plane according to the slice direction and the starting position;
s2032: traversing each triangular grid in the three-dimensional model, preprocessing the model grids by using a KD tree, judging whether the triangular grids are in the search range of the current slicing plane or not for each grid, discarding if not, checking whether the triangular grids are intersected with the current slicing plane or not if not, discarding if not, introducing a cross product symbol detection strategy to calculate the position of an intersection point, introducing an intersection point caching mechanism, and directly searching data stored by the intersection point caching mechanism if repeated grids appear;
s2033: connecting all the intersection points of the grids intersecting with the current slice plane to form a slice contour, smoothing the slice contour, identifying the position of a fracture point and repairing if the contour fracture problem occurs, and deleting redundant parts or merging overlapped parts if the overlapping problem occurs to obtain a processed three-dimensional model data slice;
S2034: introducing a slice quality evaluation function, setting a slice contour quality standard, evaluating the processed three-dimensional model data slice by using the slice quality evaluation function, calculating a quality score of the slice, adjusting the position of a slice plane and carrying out slicing operation again if the quality of the current slice does not meet the slice contour quality standard, and storing the contour data of the current slice plane if the quality of the current slice does meet the slice contour quality standard;
s2035: and introducing a self-adaptive slicing strategy, updating the position of a slicing plane according to the slicing direction and the slicing thickness, and dynamically adjusting the slicing thickness according to the slicing quality evaluation result through the self-adaptive slicing strategy until the processing of all slices is completed, so as to obtain n dynamically adjusted 3D model thin layers.
2. The path planning method based on gradient material 3D printing intelligent slice according to claim 1, wherein the performance goal and constraint condition in S202 are respectively expressed as:
，；
Wherein F represents the actual stress of the three-dimensional model, Represents the maximum stress born by the three-dimensional model, E represents the elastic modulus of the three-dimensional model under the action of external force,Represents the minimum elastic modulus required by the three-dimensional model when the three-dimensional model is subjected to external force, m represents the mass of the three-dimensional model,Representing the target mass of the three-dimensional model when it is loaded, r representing the thermal properties of the three-dimensional model,Representing the coefficient of thermal expansion of the three-dimensional model, L representing the dimensions of the three-dimensional model,、Representing the minimum and maximum values of the dimensions of the three-dimensional model, respectively, w represents the manufacturing cost of the three-dimensional model,Representing the cost budget of the three-dimensional model,Representing the angle between any two line segments in the three-dimensional model,And the error of the allowed included angle between any two line segments in the three-dimensional model is represented.
3. The method for planning a path based on gradient material 3D printing intelligent slice according to claim 2, wherein the thermal conductivity equation in S206 is:
；
Wherein, Indicating the location of the node within the cell,、、Representing node positionT represents temperature, q represents heat source density,Represents the density of the metal gradient functional material, c represents the specific heat capacity of the metal gradient functional material, t represents the current time,The effect of external fields on the thermal conduction of the metallic gradient functional material is shown.
4. A method for planning a path based on gradient material 3D printing intelligent slices as set forth in claim 3, wherein the specific content of the cross product symbol detection strategy in S2032 includes:
Setting a slicing plane WhereinIs the normal vector of the plane surface,Representing the coordinates of any point of the slice plane, wherein H is a constant term; for each vertex of the triangle mesh, a vertex-to-plane distance formula is used to obtain the vertex-to-slice planeDistance of (2)I represents the number of vertices of the triangular mesh;
If it is Then the triangle mesh is not intersected with the slice plane;
If it is Then the triangle mesh is intersected with the slice plane;
selecting two end points of one edge of a triangle mesh AndIf (if)Then two end points are shown on two sides of the slicing plane, and the intersection point of the edge and the slicing plane is calculatedThe formula is:
；
wherein u represents the relative position of any point on the edge of the triangle mesh and the edge end point;
for each triangle intersecting the slice plane, the intersection point is stored in a caching mechanism.
5. A gradient material 3D printing intelligent slice based path planning system implemented based on a gradient material 3D printing intelligent slice based path planning method according to any of claims 1-4, characterized in that the system comprises: the system comprises a material analysis module, an intelligent slicing module, a path planning module, a virtual simulation module, a printing control module and a post-processing module;
The material analysis module is used for collecting 3D model data, analyzing the distribution and the attribute of the metal gradient functional material, and adaptively adjusting slicing parameters, printing paths and printing parameters according to a real-time data set in the printing process;
The intelligent slicing module is used for slicing the 3D model according to the printing requirements and the characteristics of the metal gradient functional materials to generate slicing data for printing;
The path planning module is used for carrying out finite element analysis on the 3D model thin layer, generating a moving path printed by the 3D model thin layer, and carrying out iterative optimization on the moving path according to a genetic algorithm to obtain a final moving path;
The virtual simulation module is used for simulating the whole printing process through a virtual simulation technology before actual printing, predicting the occurrence of problems and taking emergency measures;
the printing control module is used for converting the generated final moving path data into a control instruction executable by the printer and controlling the printer to finish a printing task;
the post-processing module is used for carrying out post-processing on the printed metal gradient functional material, remotely monitoring the printing process, evaluating the influence of the printing process on the environment, and the sustainability and the recyclability of the material, and collecting feedback data for system optimization.
6. The gradient material 3D printing intelligent section-based path planning system of claim 5, wherein the material analysis module comprises: the system comprises a material database unit, a characteristic analysis unit, a data acquisition unit, a failure mode analysis unit and a multi-objective optimization unit;
the material database unit is used for storing data and performance parameters of the metal gradient functional material;
the characteristic analysis unit is used for simulating and analyzing the characteristics of the metal gradient functional material through the material performance prediction model according to the data in the database;
the data acquisition unit is used for monitoring the performance change data and the material data of the metal gradient functional material in the printing process in real time;
the failure mode analysis unit is used for analyzing whether the triangular mesh of the three-dimensional model is intersected with the slice plane or not through a KD tree and a search strategy, and judging whether the three-dimensional model data slice meets the slice contour quality standard or not through a slice quality evaluation function;
the multi-objective optimization unit is used for automatically adjusting and optimizing printing parameters through a deep learning algorithm according to the collected data.
7. The gradient material 3D printing intelligent slice-based path planning system of claim 6, wherein the intelligent slice module comprises: the slice strategy unit, the slice quality evaluation unit and the slice efficiency optimization unit;
The slicing strategy unit is used for updating the position of a slicing plane according to the slicing direction and the slicing thickness, and dynamically adjusting the slicing thickness through a self-adaptive slicing strategy according to the slicing quality evaluation result;
The slice quality evaluation unit is used for evaluating the processed three-dimensional model data slices by using a slice quality evaluation function and evaluating the influence of different slice strategies on the printing quality;
The slice efficiency optimizing unit is used for optimizing and verifying parameters of slice and path planning according to the 3D printing simulation result by using an artificial intelligence algorithm.
8. The gradient material 3D printing intelligent slice-based path planning system of claim 7, wherein the path planning module comprises: the device comprises a path generating unit, a path adjusting unit and a smoothing processing unit;
the path generation unit is used for generating a printed moving path according to slice data and metal gradient functional materials through deep learning and combining finite element analysis and a genetic algorithm;
the path adjusting unit is used for dynamically adjusting path planning parameters according to real-time data monitored in the printing process;
and the smoothing processing unit is used for smoothing the slice contour through a normal vector smoothing algorithm.
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