CN117464999A

CN117464999A - G-code parallel generation method of 3D printing model

Info

Publication number: CN117464999A
Application number: CN202311442924.2A
Authority: CN
Inventors: 江泽星; 吴杰华; 邱海平; 陈丙云
Original assignee: Shenzhen Kings 3d Printing Equipment Technology Co ltd
Current assignee: Shenzhen Kings 3d Printing Equipment Technology Co ltd
Priority date: 2023-11-01
Filing date: 2023-11-01
Publication date: 2024-01-30
Anticipated expiration: 2043-11-01
Also published as: CN117464999B

Abstract

The invention relates to a 3D printing processing technical field, in particular to a G-code parallel generation method of a 3D printing model. The method comprises the following steps: carrying out structure analysis and segmentation processing on the 3D printing model to obtain a printing structure segmentation sub-model; performing hierarchical segmentation processing and regional segmentation processing on the printing structure segmentation submodel to obtain a printing submodel hierarchical channel and a submodel hierarchical channel segmentation region; g-code parallel generation processing is carried out on the sub-model hierarchical channels and sub-model hierarchical channels in the sub-regions, and hierarchical parallel G-code data and regional parallel G-code data are obtained; performing distributed speed evaluation on the regional parallel G-code data, and performing coordinated optimization on the hierarchical parallel G-code data to obtain G-code parallel optimized data; and executing corresponding 3D printing jobs according to the G-code parallel optimization data. The invention can improve the speed and efficiency of 3D printing.

Description

G-code parallel generation method of 3D printing model

Technical Field

The invention relates to a 3D printing processing technical field, in particular to a G-code parallel generation method of a 3D printing model.

Background

3D printing techniques have been widely used in a variety of fields including manufacturing, medical and artistic. Existing methods of generating G-codes for 3D printing models are typically serial, i.e. G-codes are generated layer by layer and sent to the 3D printer, which results in long waiting times when large, complex models are made. In addition, the serial generation method also limits the performance exertion of parallel hardware such as a multi-head 3D printer.

Disclosure of Invention

Based on this, the present invention needs to provide a method for generating G-codes of a 3D printing model in parallel, so as to solve at least one of the above technical problems.

In order to achieve the above purpose, a method for generating G-codes of a 3D printing model in parallel includes the following steps:

step S1: carrying out structure analysis and segmentation processing on the 3D printing model to obtain a printing structure segmentation sub-model; performing printing path planning processing on the printing structure segmentation sub-model to obtain sub-model path planning information data;

step S2: performing hierarchical segmentation processing on the printing structure segmentation sub-model to obtain a printing sub-model hierarchical channel; g-code layering parallel generation processing is carried out on the printing structure segmentation sub-model in the printing sub-model hierarchical channel based on the sub-model path planning information data so as to obtain hierarchical parallel G-code data;

Step S3: performing region segmentation processing on the printed sub-model hierarchical channels to obtain sub-model hierarchical channel sub-regions; g-code region parallel generation processing is carried out on the printing structure segmentation submodel in the submodel hierarchical channel region based on the submodel path planning information data so as to obtain region parallel G-code data;

step S4: performing distributed speed evaluation on the regional parallel G-code data to obtain a G-code generation speed influence factor; performing coordinated optimization on the hierarchical parallel G-code data according to the G-code generation speed influence factor to obtain G-code parallel optimization data; and executing corresponding 3D printing jobs according to the G-code parallel optimization data.

According to the invention, the 3D printing model is subjected to structure analysis and segmentation processing, so that the complex 3D printing model can be analyzed and segmented into smaller printing structure segmentation sub-models, and the segmentation process is beneficial to reducing the complexity of each sub-model, so that the sub-model is easier to process. And then, carrying out printing path planning processing on the segmented sub-models to obtain sub-model path planning information data, wherein the planned path information can provide basic data guarantee for subsequent processing steps, so that the path rationality and accuracy in the printing process are ensured. On the whole, the efficiency and the accuracy of processing the 3D printing model can be improved, and basic data are laid for the subsequent steps. Secondly, through carrying out hierarchical segmentation processing to the printing structure segmentation sub-model, the printing structure segmentation sub-model can be effectively divided into different hierarchical channels, and this helps management and control printing process to adapt to the structure of different complexity, can also improve the controllability and the adjustability of printing, ensures that manufacturing process is more accurate. Meanwhile, G-code layering parallel generation processing is carried out on the printing structure segmentation submodel in the printing submodel hierarchical channel by using the submodel path planning information data, so that the parallelism and efficiency of the 3D printing model in the printing process can be improved. By dividing the model into hierarchical channels and realizing parallel generation in each channel, the method can better utilize computing resources and accelerate the generation process of the G-code, and the parallel generation mode greatly reduces the overall generation time of the G-code and improves the printing speed and efficiency. Then, by performing region segmentation processing for each print submodel hierarchical channel, the overall print job is facilitated to be divided into smaller job unit regions, thereby improving manageability of the print job, and such segmentation process enables regions of different requirements and complexity to be processed independently, facilitating fine control of the print process for each region to meet specific manufacturing requirements. And the sub-model path planning information data is used for carrying out G-code region parallel generation processing on the printing structure segmentation sub-model in the sub-model hierarchical channel region, so that the parallelism and the processing efficiency of the 3D printing model in the printing process can be further improved. By subdividing each hierarchical channel into smaller regions and generating G-codes in parallel in the smaller regions, the advantages of parallel hardware can be more fully exerted, the G-code generation time is remarkably shortened, and the overall printing speed is increased. Finally, the influence factor of the G-code generation speed is obtained by carrying out distributed speed evaluation on the regional parallel G-code data. This factor reflects the extent to which each region affects the G-code generation speed. In addition, the hierarchical parallel G-code data are coordinated and optimized according to the factors, and the hierarchical parallel G-code data can be accurately optimized according to the influence factors of the areas, so that the overall printing efficiency is improved. By executing the corresponding 3D print job by using the optimized data, the print job can be executed in a high-efficiency and accurate manner, so that the final 3D print job can be completed at optimal speed and quality, and reliable guarantee is provided for the manufacturing process. Through the distributed speed evaluation and optimization process, the printing paths and parameters in the hierarchical channels can be flexibly adjusted according to the characteristics of different areas, so that the printing speed is improved to the greatest extent, the printing quality is ensured, and the intelligent optimization process greatly improves the efficiency and success rate of 3D printing, so that the method is suitable for large and complex 3D printing models.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:

FIG. 1 is a schematic flow chart of steps of a G-code parallel generation method of a 3D printing model of the present invention;

FIG. 2 is a detailed step flow chart of step S1 in FIG. 1;

fig. 3 is a detailed step flow chart of step S13 in fig. 2.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

To achieve the above object, referring to fig. 1 to 3, the present invention provides a method for generating G-codes of a 3D print model in parallel, the method comprising the steps of:

In the embodiment of the present invention, please refer to fig. 1, which is a schematic flow chart of steps of a method for generating G-codes of a 3D printing model in parallel, in this example, the steps of the method for generating G-codes of the 3D printing model include:

according to the embodiment of the invention, the 3D printing model is firstly imported into the 3D printing pretreatment equipment, then the 3D printing pretreatment equipment is used for analyzing the 3D printing model, the 3D printing model is converted into a processable data format, corresponding model structure data comprising geometric information, interlayer information, supporting structure and the like are extracted from the analyzed data, then the 3D printing model is subjected to structure segmentation according to the analyzed and extracted structure analysis data, so that connectivity and structure accuracy of the segmented sub-model are ensured, and the printing structure segmentation sub-model is obtained. And finally, determining the printing path of each printing structure segmentation sub-model by carrying out path planning processing on the printing structure segmentation sub-model, wherein the printing path comprises information data such as a moving path of a printing head, an interlayer path and the like, and finally obtaining sub-model path planning information data.

the embodiment of the invention firstly detects the printing structure segmentation sub-model to determine the structure complexity of the printing structure segmentation sub-model, and comprises the steps of detecting the internal structure, the cavity, the supporting structure and the like of the printing structure segmentation sub-model, evaluating the complexity of the printing structure segmentation sub-model by using a complex detection algorithm, dividing the printing structure segmentation sub-model into different complex levels according to the calculated complexity, dividing the corresponding printing structure segmentation sub-model into different level channels by using the calculated complex levels, and ensuring that each level channel contains the corresponding complete printing structure segmentation sub-model, thereby obtaining the printing structure segmentation sub-model level channel. And distributing corresponding threads for the printing structure segmentation sub-model in the printing sub-model hierarchical channel by using a multithreading technology, ensuring that each thread is responsible for generating G-codes of one printing structure segmentation sub-model, processing the G-code generation tasks in each hierarchy by the distributed threads in parallel, generating a path optimization scheme for the printing structure segmentation sub-model in each printing sub-model hierarchical channel by using sub-model path planning information data, and optimizing the G-code generation path of the G-code generation task according to the generated path optimization scheme so as to improve printing quality and efficiency, and finally obtaining the hierarchy parallel G-code data.

according to the embodiment of the invention, firstly, the structure and the characteristics of the printing structure segmentation sub-model in the printing sub-model hierarchical channel are subjected to demand analysis to determine which areas in the printing sub-model hierarchical channel are required to be subjected to printing tasks, a demand level is allocated to each area according to the importance of the printing quality and the printing efficiency, and then, the corresponding printing sub-model hierarchical channel is segmented into different areas by using the demand level obtained through analysis, so that each segmented area can meet the corresponding demand level, and the sub-model hierarchical channel segmented area is obtained. And distributing corresponding threads for the printing structure segmentation sub-model in the sub-model hierarchical channel region by using a multithreading technology, ensuring that each thread is responsible for generating the G-code of one printing structure segmentation sub-model, processing the G-code generation tasks in the sub-region in parallel by the distributed threads, generating a path optimization scheme for the printing structure segmentation sub-model in each sub-model hierarchical channel region by using sub-model path planning information data, and optimizing the G-code generation path of the G-code generation tasks according to the generated path optimization scheme so as to improve printing quality and efficiency, and finally obtaining regional parallel G-code data.

The method comprises the steps of firstly setting a distributed speed evaluation environment for regional parallel G-code data, comprising configuration and connection of computing nodes in a sub-model hierarchical channel region so as to ensure a computing process capable of processing the generation speed of the G-code data in parallel, secondly, computing the regional parallel G-code data by using the distributed speed evaluation nodes, independently computing the regional parallel G-code data by each computing node, and evaluating and analyzing the G-code influence factors of each region according to a computing result so as to obtain the G-code generation speed influence factors. Then, G-code generation speed of the hierarchical parallel G-code data is detected and analyzed through G-code generation speed influence factor feedback obtained through regional parallel G-code data evaluation and analysis, so that influence degrees of which regions in the hierarchical channels are influenced by the G-code generation process are determined through detection and analysis, influence priorities are distributed to each hierarchical parallel G-code data according to the detected influence degrees, priority coordination optimization is conducted on the corresponding hierarchical parallel G-code data according to the distributed influence priorities, and printing operation of high priority regions in the hierarchical channels is completed before low priority regions, potential influences are reduced to the greatest extent, and therefore G-code parallel optimized data are obtained. Finally, by inputting the G-codes in the G-code parallel optimization data into the 3D printing model in parallel, the 3D printing model performs corresponding 3D printing jobs layer by layer in an efficient and accurate manner.

Preferably, step S1 comprises the steps of:

step S11: importing the 3D printing model into 3D printing preprocessing equipment, and performing model analysis processing on the 3D printing model through the 3D printing preprocessing equipment to obtain printing model structure analysis data;

step S12: carrying out structure segmentation recognition analysis on the 3D printing model according to the printing model structure analysis data to obtain model structure segmentation points;

step S13: performing topology segmentation processing on the 3D printing model according to the model structure segmentation points to obtain a printing structure segmentation sub-model;

step S14: performing printing feature learning analysis on the printing structure segmentation sub-model to obtain sub-model printing experience feature data;

step S15: and performing self-adaptive path planning processing on the printing structure segmentation sub-model according to the sub-model printing experience characteristic data to obtain sub-model path planning information data.

As an embodiment of the present invention, referring to fig. 2, a detailed step flow chart of step S1 in fig. 1 is shown, in which step S1 includes the following steps:

According to the embodiment of the invention, the 3D printing model is firstly imported into the 3D printing pretreatment equipment, then the 3D printing pretreatment equipment is used for analyzing the 3D printing model, the 3D printing model is converted into a processable data format, corresponding model structure data including geometric information, interlayer information, supporting structure and the like are extracted from the analyzed data, and finally the printing model structure analysis data are obtained.

according to the embodiment of the invention, the 3D printing model is subjected to recognition analysis of the structure division points according to the model structure information in the printing model structure analysis data so as to analyze the structure change points in the 3D printing model, and finally the model structure division points are obtained.

according to the embodiment of the invention, the 3D printing model is segmented by using a proper topological segmentation method according to the model structure segmentation points obtained through analysis, so that the connectivity and the structure accuracy of the segmented sub-model are ensured, and the printing structure segmentation sub-model is finally obtained.

according to the embodiment of the invention, the printing characteristic learning processing is carried out on the segmented printing structure segmentation sub-model by using a machine learning algorithm, so that characteristic information related to printing characteristics, such as shape, size, material, wall thickness, hole distribution, supporting structure and the like, is extracted from each printing structure segmentation sub-model, and the sub-model printing experience characteristic data is finally obtained.

According to the embodiment of the invention, the path planning processing is carried out on the printing structure segmentation sub-model by using the sub-model printing experience characteristic data obtained through analysis, so that the printing path of each printing structure segmentation sub-model is determined, the information data such as the moving path of a printing head, the interlayer path and the like are included, and the sub-model path planning information data is finally obtained.

According to the invention, the 3D printing model is firstly imported into the 3D printing pretreatment equipment, meanwhile, the 3D printing pretreatment equipment is used for analyzing the 3D printing model, so that the original three-dimensional model can be converted into a data format which can be understood by a computer, specifically, complex geometric information, size, material information and structural relation of the 3D printing model can be analyzed into digital data, basic data is provided for subsequent processing steps, and accurate input is provided for the next structural division point identification processing process. Secondly, by using the structural analysis data of the printing model obtained by analysis to perform structural segmentation recognition analysis on the 3D printing model, structural segmentation points in the model can be recognized according to the structural analysis data, the structural segmentation points can decompose the whole model into smaller and relatively independent parts, the recognition process of the segmentation points is favorable for better managing the complex model, so that the complex model is easier to process and manufacture, and the determination of the structural segmentation points of each model is also favorable for improving the overall understandability, thereby providing a basis for subsequent path planning. Then, the 3D printing model is subjected to topology segmentation processing by using the model structure segmentation points obtained through recognition, so that the whole 3D printing model can be further decomposed into smaller and relatively independent parts, each part can be printed or processed independently, the topology segmentation process is beneficial to improving the efficiency of the subsequent processing process and reducing the risk of errors, and particularly in the case of complex structures. Next, by performing a print feature learning analysis on the print structure segmentation sub-model, print characteristics of each sub-model, such as shape, size, material and support structure, can be analyzed, and path planning of a subsequent processing process is affected according to the print characteristics obtained by the analysis, and the sub-model print experience feature data includes print time, material requirements, support structure design and the like, so that better understanding of the print requirements and characteristics of each part is facilitated, and important guidance is provided for the subsequent processing steps. Finally, by performing adaptive path planning processing on the printing structure segmentation sub-model by using the learned sub-model printing experience characteristic data, the optimal printing path and strategy can be selected according to the characteristics and requirements of each sub-model, which is helpful to improve the printing efficiency and quality and ensure that each part is manufactured in an optimal manner. The obtained sub-model path planning information data can provide detailed guidance for the G-code generation process of each part in the subsequent process, and ensure the smooth progress of the whole G-code generation process, thereby improving the 3D printing efficiency, reducing errors and improving the manufacturing quality, and is particularly suitable for 3D printing tasks with large or complex structures.

Preferably, step S13 comprises the steps of:

step S131: performing structure detection analysis on the 3D printing model to obtain 3D printing model structure element data;

step S132: performing topology construction processing on the 3D printing model structural element data to obtain a 3D printing structure topology model;

step S133: carrying out segmentation measurement detection on the model structure segmentation points to obtain the optimal model structure segmentation points;

step S134: and performing model segmentation processing on the 3D printing structure topological model based on the optimal segmentation points of the model structure to obtain a printing structure segmentation sub-model.

As an embodiment of the present invention, referring to fig. 3, a detailed step flow chart of step S13 in fig. 2 is shown, in which step S13 includes the following steps:

according to the embodiment of the invention, the structure of the 3D printing model is detected and identified by using a structure detection technology, so that the structural element data in the 3D printing model, including the geometric shape, the supporting structure, the holes, the wall thickness, the wall angle and other elements of the 3D printing model, are identified and extracted, and the structural element data of the 3D printing model are finally obtained.

according to the embodiment of the invention, the structural topological relation among the extracted 3D printing model structural element data is established according to the analysis result, and then modeling processing is carried out on the established structural topological relation by using a topological construction algorithm so as to ensure that the structural topological relation can be accurately represented, and finally the 3D printing structural topological model is obtained.

according to the embodiment of the invention, the measurement and detection are carried out on each model structure division point by using a division point measurement and calculation method, so that which model structure division points are most suitable for model division is determined, and finally the optimal model structure division point is obtained.

According to the embodiment of the invention, the 3D printing structure topological model is segmented based on the detection result of the optimal segmentation point of the model structure, so that the 3D printing structure topological model is segmented into proper sub-models, each sub-model can be independently processed and has independent printing path planning, and finally the printing structure segmentation sub-model is obtained.

According to the invention, firstly, structural element data in the 3D printing model can be identified and extracted by carrying out structural detection analysis on the 3D printing model, and the structural element data comprise key characteristics such as various geometric shapes, supporting structures, holes, wall thicknesses, wall angles and the like, so that the complexity and the relation among various parts of the 3D printing model can be better understood, and basic data are provided for the subsequent processing process. Secondly, by performing topology construction processing on the 3D printing model structural element data, the identified relation between the structural element data can be established to form a topology model, the model describes the connection, dependence and relative position between the 3D printing model structural element data, the overall structure of the 3D printing model can be better understood, and key information is provided for subsequent segmentation and path planning. Then, by performing segmentation metric detection on the model structure segmentation points, it is helpful to determine which model structure segmentation points are most effective for segmentation of the 3D printing model, so as to obtain the optimal segmentation points. The selection of the optimal division point is based on various factors such as geometric complexity, material requirement, manufacturing efficiency and the like, so that the selection of the division point is optimal, and the improvement of the manufacturing efficiency and the reduction of the resource consumption are facilitated. Finally, by performing segmentation processing on the 3D printed structural topology model at the position of the optimal segmentation point of the model structure, the 3D printed structural topology model can be accurately segmented into a plurality of relatively independent sub-models, which contributes to improving flexibility and efficiency of model segmentation, because each sub-model can be processed independently and has independent printing path planning, the model segmentation process helps to avoid erroneous segmentation, and can better meet specific requirements of different parts of the model, such as supporting structures, printing paths and the like, and also helps to improve controllability and quality of 3D printing manufacturing.

Preferably, step S15 comprises the steps of:

step S151: performing printing path influence analysis on the printing structure segmentation sub-model according to the sub-model printing experience characteristic data to obtain printing path experience influence data;

according to the embodiment of the invention, the empirical characteristic data related to the printing process is collected, wherein the empirical characteristic data comprise characteristic information such as printing speed, temperature, layer height and the like, then the collected empirical characteristic data are utilized to analyze the path influence of the printing structure segmentation sub-model so as to analyze and determine the influence of the printing path on printing quality and efficiency, and finally the printing path empirical influence data are obtained.

Step S152: performing path planning processing on the printing structure segmentation sub-model according to the printing path experience influence data to obtain sub-model initial path planning data;

according to the embodiment of the invention, the printing path experience influence data is obtained through analysis, a proper path planning algorithm is applied to the printing structure segmentation sub-model according to the analysis result and considering factors such as printing speed, shape complexity and the like, so that the printing path can be ensured to improve printing quality and efficiency to the greatest extent, and the sub-model initial path planning data is finally obtained.

Step S153: performing model region calculation on the printing structure segmentation sub-model by using a region filling density calculation formula to obtain a sub-model region filling density value;

according to the embodiment of the invention, a proper region filling density calculation formula is formed by combining the region filling density range parameter, the region filling density coordinate parameter, the region filling density amplitude parameter, the sine oscillation adjustment parameter, the cosine oscillation adjustment parameter, the region filling density amplitude adjustment coefficient, the tolerance parameter and the related parameters, so that the filling density of each sub-model region is determined, and finally the sub-model region filling density value is obtained. In addition, the area filling density calculation formula can also use any filling density detection algorithm in the field to replace the process of model area calculation, and is not limited to the area filling density calculation formula.

Step S154: judging a sub-model region filling density value according to a preset region filling density threshold, and when the sub-model region filling density value is larger than or equal to the preset region filling density threshold, performing high-density filling processing on a printing structure segmentation sub-model corresponding to the sub-model region filling density value to obtain high-density filling data of a model region; when the region filling density value of the sub-model is smaller than a preset region filling density threshold value, performing low-density filling processing on the printing structure segmentation sub-model corresponding to the region filling density value of the sub-model to obtain low-density filling data of the model region;

According to the embodiment of the invention, the calculated sub-model region filling density value is compared and judged according to the preset region filling density threshold value, if the sub-model region filling density value is larger than or equal to the preset region filling density threshold value, the fact that the filling density degree of the sub-model region divided by the printing structure corresponding to the sub-model region filling density value is higher is indicated, and then the high-density filling processing is carried out on the sub-model region divided by the printing structure corresponding to the sub-model region filling density value, so that the high-density filling data of the model region is obtained. However, if the filling density value of the sub-model region is smaller than the preset region filling density threshold, which indicates that the filling density degree of the sub-model region segmented by the printing structure corresponding to the filling density value of the sub-model region is lower, the low-density filling processing is performed on the sub-model region segmented by the printing structure corresponding to the filling density value of the sub-model region, so that the low-density filling data of the model region is obtained.

Step S155: carrying out region merging processing on the high-density filling data of the model region and the low-density filling data of the model region to obtain model density filling comprehensive data;

according to the embodiment of the invention, the printing structure segmentation sub-model areas corresponding to the high-density filling data of the model area and the low-density filling data of the model area are analyzed, then the filling data of the printing structure segmentation sub-model areas obtained through analysis are combined, so that seamless connection between the printing structure segmentation sub-model areas corresponding to the filling data is ensured, and finally the model density filling comprehensive data is obtained.

Step S156: and carrying out path planning adjustment processing on the sub-model initial path planning data according to the model density filling comprehensive data to obtain sub-model path planning information data.

According to the embodiment of the invention, the model density filling comprehensive data is aligned with the sub-model initial path planning data, and the corresponding path planning is adjusted according to the filling density result in the model density filling comprehensive data, so that the printing path is further optimized, and the sub-model path planning information data is finally obtained.

According to the invention, the printing path influence analysis is carried out on the printing structure segmentation sub-model by using the sub-model printing experience characteristic data, so that the characteristics of the sub-model, such as thin wall, suspension structure, complex geometric shape and the like, can be understood, and the path planning can be conveniently carried out. By analysis, empirical data can be obtained that has an impact on the print path, which helps to better account for problems that may occur during printing, such as support structures, inter-layer intersections, etc., in subsequent steps. Meanwhile, path planning processing is carried out on the printing structure segmentation sub-model by using the printing path experience influence data obtained through analysis so as to determine initial path planning data of the sub-model. The purpose of this step is to select the best path based on the empirical impact data of the print path to minimize problems and instabilities that may occur during the printing process. Print path experience impact data can be used to guide the selection of paths to ensure the efficiency and quality of printing. Thus by taking into account the nature and complexity of the sub-models, as well as path impact analysis, an optimal path can be formulated to ensure success of 3D print manufacturing. Secondly, by performing model region calculation on the printing structure segmentation sub-model by using a suitable region filling density calculation formula, the filling density of each sub-model, namely the distribution condition of printing materials in different regions, can be determined, and the density values can play a role in the subsequent steps so as to better manage the use of the materials and improve the printing quality. By this step, the distribution of the material can be precisely controlled, ensuring the quality and strength of the printing. And then judging the filling density value of the sub-model area according to a preset area filling density threshold value, and if the filling density value of the sub-model area is larger than or equal to the preset threshold value, performing high-density filling treatment on the sub-model area so as to increase the compactness of the printing material. If the density value is less than the threshold, a low density fill process is performed to reduce printing material usage, which helps balance the speed and resource consumption of 3D printing manufacturing, ensuring that the required quality and strength is achieved in different areas. And then, carrying out region merging processing on the high-density filling data and the low-density filling data of the model region, so as to help to integrate the filling information of each sub-model, and better manage the distribution and quality of materials in the whole printing process. By this step, it is ensured that the filling of the individual regions is taken into account comprehensively to meet the overall manufacturing requirements. Finally, path planning adjustment processing is carried out on the sub-model initial path planning data based on the model density filling comprehensive data, and the purpose is to optimize path planning according to the model density filling comprehensive data so as to adapt to different filling density areas and ensure printing quality and efficiency. By adjusting the path planning, the method can better adapt to the requirements of different areas in the printing process, and improves the manufacturing controllability and the printing quality. The combination of the series of steps is helpful for optimizing the 3D printing process, improving the manufacturing efficiency, reducing the resource waste and simultaneously ensuring the quality and the performance of the final printed product according to the characteristics of the submodel, the area filling density and the path planning experience.

Preferably, the area filling density calculation formula in step S153 is specifically:

wherein D is a submodel region filling density value, R is a region filling density range parameter of the printing structure segmentation submodel, u is a region filling density abscissa parameter of the printing structure segmentation submodel, v is a region filling density ordinate parameter of the printing structure segmentation submodel, θ is a region filling density amplitude parameter of the printing structure segmentation submodel, a is a sine oscillation adjustment parameter of the region filling density amplitude, b is a cosine oscillation adjustment parameter of the region filling density amplitude, c is a region filling density amplitude adjustment coefficient of the printing structure segmentation submodel, delta ₁ Filling the region with tolerance parameters of the density abscissa, delta ₂ Tolerance parameters are the ordinate of the region filling density, and epsilon is the correction value of the region filling density value of the submodel.

The invention constructs a regional filling density calculation formula for carrying out model regional calculation on the printing structure segmentation sub-model, and the regional filling density calculation formula calculates the local filling density in the sub-model by considering different filling density parameters and density amplitude oscillation adjustment parameters. In practice, this density value may be used to determine how to fill the different regions of the submodel to meet print quality and efficiency requirements. It is important to decide whether to perform high density filling or low density filling for the subsequent process by comparing the filling density value of the sub-model with a preset area filling density threshold, which helps to optimize the printing process, ensures a more efficient use of the material, while maintaining the required quality standard. The formula fully considers the region filling density value D of the sub-model, the region filling density range parameter R of the printing structure segmentation sub-model, the region filling density abscissa parameter u of the printing structure segmentation sub-model, the region filling density ordinate parameter v of the printing structure segmentation sub-model, the region filling density amplitude parameter theta of the printing structure segmentation sub-model, the sine oscillation adjustment parameter a of the region filling density amplitude, the cosine oscillation adjustment parameter b of the region filling density amplitude, the region filling density amplitude adjustment coefficient c of the printing structure segmentation sub-model and the tolerance parameter delta of the region filling density abscissa ₁ Tolerance parameter delta of region filling density ordinate ₂ The correction value epsilon of the sub-model area filling density value forms a functional relation according to the correlation relation between the sub-model area filling density value D and the parameters:

the formula can realize the model region calculation process of the printing structure segmentation sub-model, and meanwhile, the introduction of the correction value epsilon of the sub-model region filling density value can be adjusted according to the error condition in the calculation process, so that the accuracy and the applicability of the region filling density calculation formula are improved.

Preferably, step S2 comprises the steps of:

step S21: performing structural complexity detection analysis on the printed structural segmentation sub-model to obtain a sub-model structural complexity level;

the embodiment of the invention firstly detects the printing structure segmentation sub-model to determine the structure complexity of the printing structure segmentation sub-model, and comprises the steps of detecting the internal structure, the cavity, the supporting structure and the like of the printing structure segmentation sub-model, then evaluating the complexity of the printing structure segmentation sub-model by using a complex detection algorithm, dividing the printing structure segmentation sub-model into different complex levels according to the calculated complexity, and finally obtaining the complex levels of the sub-model structure.

Step S22: performing hierarchical division processing on the printing structure segmentation submodel according to the submodel structure complex hierarchy to obtain a printing submodel hierarchy channel;

according to the embodiment of the invention, the corresponding printing structure segmentation sub-model is segmented by using the calculated sub-model structure complex hierarchy, the corresponding printing structure segmentation sub-model is divided into different hierarchy channels, each hierarchy channel is ensured to contain a corresponding complete printing structure segmentation sub-model, and finally the printing sub-model hierarchy channel is obtained.

Step S23: carrying out channel connection smoothing treatment on each printing sub-model hierarchical channel to obtain hierarchical connection smoothing channels;

according to the embodiment of the invention, the smooth processing is carried out on the hierarchical channels of each printing submodel by using smoothing, filtering and other technologies, so that the connection among the hierarchical channels of each printing submodel is ensured not to generate discontinuous or abrupt change, and the hierarchical connection smooth channel is finally obtained.

Step S24: extracting path planning information data corresponding to each hierarchical connection smooth channel from the sub-model path planning information data to obtain hierarchical channel path information data;

according to the embodiment of the invention, the path planning information data corresponding to each path is extracted from the sub-model path planning information data according to the hierarchical connection smooth channel obtained by dividing, and the hierarchical channel path information data is finally obtained.

Step S25: carrying out channel association processing on the hierarchical connection smooth channel to obtain hierarchical channel association information data;

according to the embodiment of the invention, the association detection algorithm is used for carrying out association analysis on each hierarchical connection smooth channel so as to analyze the association relation between each hierarchical connection smooth channel and ensure that the hierarchical connection smooth channels can work cooperatively, and finally hierarchical channel association information data is obtained.

Step S26: g-code generating task division processing is carried out on the printing structure segmentation sub-model in the hierarchical connection smooth channel according to the hierarchical channel associated information data, so that a hierarchical G-code generating task is obtained;

according to the embodiment of the invention, the G-code generating task is reasonably divided and distributed into the printing structure segmentation sub-model in each hierarchical connection smooth channel according to the associated information data among the hierarchical channels, and the G-code data which are required to be generated in which hierarchy is determined, so that the hierarchical G-code generating task is finally obtained.

Step S27: performing G-code parallel generation processing on the printing structure segmentation sub model in the hierarchical connection smooth channel by utilizing a multithreading technology based on the hierarchical G-code generation task to obtain hierarchical G-code parallel generation data;

according to the embodiment of the invention, through analyzing the corresponding configuration of the hierarchical G-code generating task, then, through using a multithreading technology, corresponding threads are distributed to the printing structure segmentation sub-model in the hierarchical connection smooth channel corresponding to the hierarchical G-code generating task, each thread is ensured to be responsible for generating the G-code of one printing structure segmentation sub-model, the distributed threads are used for processing the G-code generating task in each hierarchical level in parallel, and finally, the hierarchical G-code parallel generating data is obtained.

Step S28: and carrying out parallel path optimization processing on the hierarchy G-code parallel generation data according to the hierarchy channel path information data so as to obtain hierarchy parallel G-code data.

According to the embodiment of the invention, the path optimization scheme is generated for the printing structure segmentation sub-model in each hierarchical connection smooth channel by using the extracted hierarchical channel path information data, and the G-code generation path of the hierarchical G-code parallel generation data is optimized according to the generated path optimization scheme, so that the printing quality and efficiency are improved, and the hierarchical parallel G-code data is finally obtained.

According to the invention, firstly, the printed structure segmentation sub-model is subjected to structure complexity detection analysis, so that the complexity of the printed structure segmentation sub-model can be recognized and evaluated. By detecting the structural complexity level of the submodel, important information can be provided for subsequent steps to better adapt to the printing requirements of different structures, which is helpful for improving the printing accuracy and efficiency and reducing potential manufacturing problems. Meanwhile, the printing structure segmentation sub-model is subjected to hierarchical segmentation processing according to the analyzed sub-model structure complex hierarchy, and the printing structure segmentation sub-model can be effectively divided into different hierarchical channels, so that the printing process can be managed and controlled to adapt to structures with different complexity. The layering segmentation processing process can improve the controllability and adjustability of printing and ensure more accurate manufacturing process. The channel connection smoothing treatment is carried out on each segmented printing sub-model hierarchical channel, so that discontinuity and mutation among different hierarchical channels are eliminated, the hierarchical connection smoothing channel is obtained, the mutation problem during printing is reduced, and the printing quality is improved, particularly in transition areas among different complexity levels. This processing step also helps to reduce the need for support structures and reduce manufacturing costs. Secondly, path planning information data of each hierarchical channel is extracted from the sub-model path planning information, so that each hierarchical channel has clear path planning, which is beneficial to the subsequent processing process, and ensures that the printing path of each hierarchical channel is optimized to meet the requirements of structural complexity and quality, and the controllability and adjustability of manufacturing are improved. Subsequently, by performing channel association processing on the hierarchical connection smooth channels, the hierarchical connection smooth channels are facilitated to be connected together to form an integrated printing structure, which is beneficial to ensuring continuity and coordination among the hierarchical channels, reducing problems and instability in the manufacturing process, and improving printing speed and efficiency. Then, by performing the G-code generation task division processing on the print structure division sub-model within the hierarchical connection smooth channel using the hierarchical channel association information data, the print structure division sub-model can be divided into different task units to achieve parallel generation of G-codes, which improves the efficiency of printing, allows a plurality of tasks to be executed simultaneously, and shortens the manufacturing time. And then, the hierarchical G-code generation task obtained through division carries out G-code parallel generation processing on the printing structure segmentation sub-model in the hierarchical connection smooth channel by utilizing a multithreading technology, so that the G-codes can be generated simultaneously by different printing structure segmentation sub-models, the manufacturing speed is improved, the production time is reduced, and meanwhile, the G-code generation of each task unit is ensured to be efficient. Finally, the parallel path optimization processing is carried out on the parallel generation data of the hierarchical G-code by using the hierarchical channel path information data, so that the generated G-code path is further optimized, the printing quality is improved, the resource waste is reduced, the final printing result is high in quality, and the printing cost is reduced to the greatest extent. This step improves the controllability and adjustability of printing, and adapts to different manufacturing requirements.

Preferably, step S23 comprises the steps of:

step S231: performing channel connection detection on each printing submodel hierarchical channel to obtain hierarchical channel connection points;

according to the embodiment of the invention, the hierarchical channels of each printing sub-model are detected by using the channel connection detection method, so that the connection point between the hierarchical channels of each printing sub-model is determined, and finally the hierarchical channel connection point is obtained.

Step S232: performing connection fitting calculation on the hierarchical channel connection points by using a connection fitting measurement calculation formula to obtain a connection point fitting degree value;

according to the embodiment of the invention, a proper connection fitting measurement calculation formula is formed by combining the space coordinate parameter, the space coordinate range parameter, the connection distance function, the connection distance weighting parameter, the connection fitting distance function, the space coordinate fitting mean value, the space coordinate fitting standard deviation, the space coordinate fitting coefficient, the connection fitting distance weighting parameter, the connection fitting association function, the connection fitting association weighting parameter, the integral time variable, the time adjustment parameter, the time attenuation parameter, the time weighting parameter and the related parameter of the hierarchical channel connection point, so that the connection fitting calculation is carried out on the hierarchical channel connection point to calculate the fitting degree of the connection point, and finally the connection point fitting degree value is obtained. In addition, the connection fitting metric calculation formula can also use any connection fitting detection algorithm in the field to replace the connection fitting calculation process, and is not limited to the connection fitting metric calculation formula.

Step S233: comparing and judging the connection points of the hierarchical channels according to the fitting degree value of the connection points so as to obtain the connection high-frequency points of the hierarchical channels;

in the embodiment of the invention, the fitting degree value of the connection point is compared and judged by presetting a proper fitting degree threshold of the connection point, if the fitting degree value of the connection point is larger than or equal to the preset fitting degree threshold of the connection point, the fitting degree of the connection point of the hierarchical channel corresponding to the fitting degree value of the connection point is higher, the connection point of the hierarchical channel corresponding to the fitting degree value of the connection point is marked as a high-frequency point of the hierarchical channel connection, otherwise, the connection point of the hierarchical channel corresponding to the fitting degree value of the connection point is marked as an invalid point of the hierarchical channel connection and is removed.

Step S234: cross connection transition processing is carried out on the high-frequency points connected with the hierarchical channels, so that hierarchical connection transition channels are obtained;

according to the embodiment of the invention, the transition area is designed and cross-connected in the surrounding area marked as the hierarchical channel connection high-frequency points, so that each hierarchical channel connection high-frequency point can be better connected, and the hierarchical connection transition channel is finally obtained.

Step S235: and carrying out connection smoothing treatment on the hierarchical connection transition channel to obtain a hierarchical connection smoothing channel.

The embodiment of the invention carries out smoothing treatment on the generated hierarchical connection transition channel by using a channel smoothing algorithm or a smoothing filtering technology so as to ensure that the transition process does not cause unnecessary discontinuous or abrupt change, and finally the hierarchical connection smooth channel is obtained.

The invention firstly carries out channel connection detection on the hierarchical channels of each printing sub-model to determine the connection points among the hierarchical channels of each printing sub-model, which is beneficial to ensuring smooth transition among different hierarchies of a printing structure, thereby avoiding discontinuity or fracture in the printing process. The detection of the connection point helps to accurately control the print path and improve the manufacturing quality. And secondly, performing connection fitting calculation on the hierarchical channel connection points by using a proper connection fitting measurement calculation formula so as to quantify the fitting degree of the connection points, namely the quality of the connection between different hierarchical channels, thus being beneficial to measuring the accuracy of the connection, allowing the quality of the printing structure to be evaluated and making corresponding adjustment so as to improve the printing accuracy. Then, by comparing the fitting degree values of the connection points, it is possible to determine which connection points are high frequency points, i.e. points with higher connection quality, which helps to identify the main connection points, to ensure that the critical parts of the printed structure have high quality connections, thereby improving the reliability of 3D printing manufacture. Next, through carrying out cross-connect transition to the hierarchical passageway and connecting the high frequency point, can link up the high frequency point between the tie point effectively, form hierarchical and connect the transition passageway, this is favorable to smooth transition structure between the different hierarchical passageways, avoids appearing abrupt change or fracture, still helps improving the sturdiness and the integrality of printing the structure. Finally, by carrying out connection smoothing treatment on the hierarchical connection transition channel, the quality of the connection transition channel is further improved, seamless and smooth transition is ensured, so that the surface quality of a printing structure is improved, the requirement for subsequent treatment is reduced, the manufacturing efficiency and accuracy are improved, and the printing structure meets the quality standard.

Preferably, the connection fitting metric calculation formula in step S232 is specifically:

wherein L is the fitting degree value of the connection points, x is the space abscissa parameter of the connection points of the hierarchical channel, x ₁ Is the lower limit of the space abscissa range of the hierarchical channel connection point, x ₂ For the upper limit of the spatial abscissa range of the hierarchical channel connection point, y is the spatial ordinate parameter of the hierarchical channel connection point, y1 is the lower limit of the spatial ordinate range of the hierarchical channel connection point, y ₂ For the upper limit of the spatial ordinate range of the hierarchical channel connection point, f (x, y) is the connection distance function of the hierarchical channel connection point, ρ ₁ For the connection distance weighting parameters, g (x, y) is a connection fitting distance function of the hierarchical channel connection points, ρ ₂ For the connection fitting distance weighting parameter, exp is an exponential function, μ _x Fitting a mean, σ, to the spatial abscissa of the hierarchical channel junction _x Fitting standard deviation, mu, to the spatial abscissa of the hierarchical channel junction _y Fitting a mean, σ, to the spatial ordinate of the hierarchical channel junction _y Fitting standard deviation for spatial ordinate of the hierarchical channel connection points, phi being the spatial coordinate fitting coefficient of the hierarchical channel connection points, h (x, y) being the connection fitting correlation function of the hierarchical channel connection points, ρ ₃ For the connection fitting, the weighting parameters are associated, T is the integral time variable calculated by the connection fitting, T is the time range parameter calculated by the connection fitting, and ζ ₁ Time adjustment parameters ζ calculated for connection fitting ₂ Calculated time decay parameters for connection fitting ρ ₄ And eta is a correction value of the fitting degree value of the connecting point and is a time weighting parameter of the time integral term.

The invention constructs a connection fitting metric calculation formula for connecting hierarchical channelsThe points are subjected to a connection fitting calculation, and the connection fitting measurement calculation formula evaluates the fitting degree of the hierarchical channel connection points by comprehensively considering the distance, fitting degree and relevance among the hierarchical channel connection points and integration in the time dimension, which can be used for determining the quality of the hierarchical channel connection points so as to select the hierarchical channel connection points with high quality for processing in the subsequent steps, and determines which hierarchical channel connection points are considered to be high-frequency points, namely, have better connectivity by using the hierarchical channel connection point fitting degree value. Transition processing and smoothing processing can also be performed based on the detected high frequency points to further improve the quality and continuity of the hierarchical channel connection. The formula fully considers the fitting degree value L of the connection points, the space abscissa parameter x of the hierarchical channel connection points and the lower limit x of the space abscissa range of the hierarchical channel connection points ₁ Upper limit x of the spatial abscissa range of the hierarchical channel connection point ₂ The space ordinate parameter y of the hierarchical channel connection point and the space ordinate range lower limit y of the hierarchical channel connection point ₁ Upper limit y of the spatial ordinate range of the hierarchical channel connection point ₂ Connection distance function f (x, y) of hierarchical channel connection points, connection distance weighting parameter ρ ₁ Connection fitting distance function g (x, y) of hierarchical channel connection points, connection fitting distance weighting parameter ρ ₂ Exponential function exp, spatial abscissa fit mean μ of hierarchical channel junction _x Spatial abscissa fitting standard deviation sigma of hierarchical channel connection points _x Spatial ordinate fitting mean mu of hierarchical channel connection points _y Spatial ordinate fitting standard deviation sigma of hierarchical channel connection points _y Spatial coordinate fitting coefficient phi of hierarchical channel connection points, connection fitting correlation function h (x, y) of hierarchical channel connection points, connection fitting correlation weighting parameter rho ₃ Connecting the integral time variable T of fitting calculation, the time range parameter T of fitting calculation and the time adjustment parameter xi of fitting calculation ₁ Connecting the time decay parameters xi calculated by fitting ₂ Time weighting parameter ρ of time integral term ₄ Correction value eta of the fitting degree value of the connection point, wherein the space abscissa of the connection point through the hierarchical channel The parameter x and the spatial ordinate parameter y of the hierarchical tunnel junction form a connection distance function f (x, y) relation of the hierarchical tunnel junctionThe space abscissa parameter x of the connection point of the hierarchical channel and the space ordinate parameters y and exp of the connection point of the hierarchical channel are exponential functions, mu _x Fitting a mean, σ, to the spatial abscissa of the hierarchical channel junction _x Fitting standard deviation, mu, to the spatial abscissa of the hierarchical channel junction _y Fitting a mean, σ, to the spatial ordinate of the hierarchical channel junction _y The spatial ordinate fitting standard deviation of the hierarchical channel connection points and the spatial coordinate fitting coefficient phi of the hierarchical channel connection points form a connection fitting distance function g (x, y) relation of the hierarchical channel connection pointsThe connection fitting association function h (x, y) relation of the hierarchical channel connection point is also formed by the space abscissa parameter x of the hierarchical channel connection point and the space ordinate parameter y of the hierarchical channel connection point>The correlation between the fitting degree value L of the connection point and the parameters forms a functional relation:

the formula can realize the connection fitting calculation process of the hierarchical channel connection points, and meanwhile, the introduction of the correction value eta of the connection point fitting degree value can be adjusted according to the error condition in the calculation process, so that the accuracy and the applicability of the connection fitting measurement calculation formula are improved.

Preferably, step S3 comprises the steps of:

step S31: performing regional printing demand analysis on the printing sub-model hierarchical channels to obtain hierarchical channel regional demand levels;

according to the embodiment of the invention, firstly, the structure and the characteristics of the printing structure segmentation sub-model in the printing sub-model hierarchical channel are subjected to demand analysis to determine which areas in the printing sub-model hierarchical channel are required to be subjected to printing tasks, and a demand level is allocated to each area according to the importance of the printing sub-model hierarchical channel on the printing quality and the printing efficiency, so that the demand level of the hierarchical channel area is finally obtained.

Step S32: carrying out regional segmentation treatment on the printed sub-model hierarchical channels according to the hierarchical channel region demand level to obtain sub-model hierarchical channel regional areas;

according to the embodiment of the invention, the corresponding printing sub-model hierarchical channel is divided into different areas by using the hierarchical channel area demand level obtained through analysis, so that each divided area can meet the corresponding demand level, and the sub-model hierarchical channel divided area is finally obtained.

Step S33: extracting path planning information data corresponding to each sub-model hierarchical channel subarea from the sub-model path planning information data to obtain subarea path information data;

According to the embodiment of the invention, the corresponding path planning information data are extracted from the sub-model path planning information data according to the sub-model hierarchical channel subareas obtained through division, and the subarea path information data are finally obtained.

Step S34: dividing the printing structure in the sub-model layer channel region into G-code generating tasks to obtain a divided region G-code generating task;

according to the embodiment of the invention, the G-code generating task is reasonably divided and distributed into the printing structure segmentation sub-model in each sub-model layer channel region, and the region which needs to generate the G-code data is determined, so that the regional G-code generating task is finally obtained.

Step S35: g-code parallel generation processing is carried out on the printing structure segmentation submodel in the hierarchical channel region of the submodel by utilizing a multithreading technology based on the regional G-code generation task, so that regional G-code parallel generation data are obtained;

according to the embodiment of the invention, the corresponding configuration of the regional G-code generating task is analyzed, then the corresponding threads are distributed to the printing structure segmentation submodel in the hierarchical channel region of the submodel corresponding to the regional G-code generating task by using a multithreading technology, each thread is ensured to be responsible for generating the G-code of one printing structure segmentation submodel, the regional G-code parallel generating task is processed through the distributed threads, and finally the regional G-code parallel generating data is obtained.

Step S36: and carrying out parallel path optimization processing on the regional G-code parallel generation data according to the regional path information data so as to obtain regional parallel G-code data.

According to the embodiment of the invention, the path optimization scheme is generated for the printing structure segmentation sub-model in each sub-model level channel region by using the extracted regional path information data, and the G-code generation path of the regional G-code parallel generation data is optimized according to the generated path optimization scheme, so that the printing quality and efficiency are improved, and finally the regional parallel G-code data is obtained.

The invention is helpful to determine the print demand level of different areas in the print sub-model hierarchical channel by firstly carrying out area print demand analysis on the print sub-model hierarchical channel, which enables identification of which areas need special attention and optimization to meet different print demands. By analyzing the demand level, the resource allocation can be optimized, the printing efficiency can be improved, and the printing quality of different areas can be ensured to meet the requirements. Meanwhile, the hierarchical channel of the printing sub-model is subjected to regional segmentation processing by using the hierarchical channel region demand level, so that the whole printing task is divided into smaller task unit regions, the manageability of the printing task is improved, the segmentation process enables regions with different demands and complexity to be independently processed, and the printing process of each region is controlled finely, so that specific manufacturing requirements are met. Secondly, path planning information data corresponding to the sub-model hierarchical channel subareas are extracted from the sub-model path planning information data, so that path planning information is built for each area, the printing path of each area is ensured to be optimal, the accuracy of path planning is improved, printing errors and waste are reduced, and meanwhile, the printing speed and quality are improved. Then, the division processing of the G-code generating task is carried out on the printing structure segmentation sub-model in the sub-model hierarchical channel region, so that the complex printing structure is divided into smaller task units, the efficiency of task management is improved, and each task unit can be properly allocated to meet the printing requirement. And then, the G-code parallel generation processing is carried out on the printing structure segmentation submodel in the submodel hierarchical channel region by using the multithreading technology through the partitioned regional G-code generation task, so that the parallel processing efficiency of the printing task can be improved, the overall printing time is reduced, and meanwhile, the simultaneous generation of the printing structures in different regions is ensured, so that the production efficiency is improved. Finally, the parallel path optimization processing is carried out on the split area G-code parallel generation data by using the split area path information data, so that the efficiency and quality of the G-code generation tasks of different areas can be further improved, path redundancy can be minimized, printing time and resource consumption can be reduced, and meanwhile, the printing quality can be ensured to reach the optimal level. The parallel path optimization improves the printing efficiency and the reliability, and is beneficial to realizing high-quality printing results.

Preferably, step S4 comprises the steps of:

step S41: the method comprises the steps that distributed speed evaluation nodes are introduced into regional parallel G-code data, and generation speed distribution calculation is conducted on the regional parallel G-code data through the distributed speed evaluation nodes to obtain regional parallel G-code generation speed;

the embodiment of the invention firstly sets a distributed speed evaluation environment for the regional parallel G-code data, comprises the configuration and connection of computing nodes in the regional areas of the sub-model hierarchical channel so as to ensure the computing process of the generating speed of the G-code data which can be processed in parallel, then, calculates the regional parallel G-code data by using the distributed speed evaluation nodes, and independently calculates the regional parallel G-code data respectively distributed by each computing node to finally obtain the generating speed of the regional parallel G-code.

Step S42: performing influence evaluation analysis on the regional parallel G-code generation speed to obtain a G-code generation speed influence factor;

according to the embodiment of the invention, the calculated regional parallel G-code generation speeds are evaluated and analyzed by using an influence evaluation algorithm, so that the generation speed difference of different regions and other influence factors related to printing quality and efficiency are analyzed and recorded, then the G-code influence factor of each region is calculated according to the result of evaluation and analysis, and finally the G-code generation speed influence factor is obtained.

Step S43: performing potential influence detection on the hierarchical parallel G-code data by utilizing the G-code generation speed influence factor to obtain hierarchical G-code influence priority;

according to the embodiment of the invention, the G-code generation speed of the hierarchical parallel G-code data is detected and analyzed by using G-code generation speed influence factor feedback obtained according to regional parallel G-code data evaluation and analysis, so that the influence degree of which regions in the hierarchical channel affect the G-code generation process is detected and analyzed, an influence priority is distributed to each hierarchical parallel G-code data according to the detected influence degree, and finally the hierarchical G-code influence priority is obtained.

Step S44: performing priority coordination optimization on the hierarchical parallel G-code data according to the hierarchical G-code influence priority to obtain G-code parallel optimization data;

according to the embodiment of the invention, the corresponding hierarchical parallel G-code data is subjected to priority coordination optimization according to the detected hierarchical G-code influence priority, and the G-code data is reordered, adjusted or reorganized to ensure that the printing operation of a high priority area in a hierarchical channel is finished before a low priority area, so that the potential influence is reduced to the greatest extent, and the G-code parallel optimization data is finally obtained.

Step S45: and executing corresponding 3D printing jobs according to the G-code parallel optimization data.

According to the embodiment of the invention, the G-codes in the G-code parallel optimization data are input into the 3D printing model in parallel, and the 3D printing model performs corresponding 3D printing operation layer by layer in an efficient and accurate mode.

According to the invention, the distributed speed evaluation nodes are introduced into the regional parallel G-code data, and meanwhile, the distributed speed evaluation nodes are used for generating speed distribution calculation on the regional parallel G-code data, so that the parallel processing capacity of a plurality of calculation nodes can be fully utilized, the generation speed of the regional parallel G-code data is improved, the required G-code data can be generated more quickly in the preparation stage before printing, the whole printing preparation time is shortened, and the production efficiency is improved. And secondly, by carrying out influence evaluation analysis on the generation speed of the region parallel G-codes, the influence degree of each region on the whole printing speed can be accurately evaluated, so that the region with larger influence on the printing speed can be identified, and an important reference is provided for the subsequent optimization step. Through accurate positioning of the influence factors, the method can be optimized in a targeted manner, and the overall printing efficiency is improved. Then, potential influence detection is performed on the hierarchical parallel G-code data by using the G-code generation speed influence factors, so that factors possibly influencing the printing speed can be accurately identified in the hierarchical structure, which is helpful for determining which parts of optimization can substantially improve the printing speed, and the optimization process is more accurate and efficient. And then, carrying out priority coordination optimization on the hierarchical parallel G-code data according to the detected hierarchical G-code influence priority, so that the G-code data of each hierarchy can be orderly processed, and the optimization sequence is determined according to the influence degree of the G-code data on the printing speed, thereby ensuring that the optimization process can be optimally balanced in terms of overall efficiency and effect, and improving the overall performance of the printing speed and quality. And finally, executing the corresponding 3D printing job according to the G-code parallel optimization data, and executing the printing job in a high-efficiency and accurate mode according to the previous optimization result, so that the final 3D printing job can be completed at the optimal speed and quality, and reliable guarantee is provided for the manufacturing process. Overall, this step ensures the smooth implementation of all the previous optimization work, so that the whole manufacturing process is smoothly carried out, and the production efficiency and the quality level are improved.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The G-code parallel generation method of the 3D printing model is characterized by comprising the following steps of:

2. The G-code parallel generation method of a 3D print model according to claim 1, wherein the step S1 comprises the steps of:

3. The G-code parallel generation method of a 3D print model according to claim 2, wherein the step S13 comprises the steps of:

4. The G-code parallel generation method of 3D printing model according to claim 2, wherein step S15 comprises the steps of:

5. The G-code parallel generation method of 3D print model according to claim 4, wherein the area filling density calculation formula in step S153 is specifically:

6. The G-code parallel generation method of a 3D print model according to claim 1, wherein the step S2 comprises the steps of:

7. The G-code parallel generation method of 3D printing model according to claim 6, wherein step S23 comprises the steps of:

8. The G-code parallel generation method of 3D print model according to claim 7, wherein the connection fitting metric calculation formula in step S232 is specifically:

wherein L is the fitting degree value of the connection points, x is the space abscissa parameter of the connection points of the hierarchical channel, x ₁ Is the lower limit of the space abscissa range of the hierarchical channel connection point, x ₂ Is the upper limit of the space abscissa range of the hierarchical channel connection point, y is the space ordinate parameter of the hierarchical channel connection point, y ₁ Is the lower limit of the space ordinate range of the hierarchical channel connection point, y ₂ For the upper limit of the spatial ordinate range of the hierarchical channel connection point, f (x, y) is the connection distance function of the hierarchical channel connection point, ρ ₁ For the connection distance weighting parameters, g (x, y) is a connection fitting distance function of the hierarchical channel connection points, ρ ₂ For the connection fitting distance weighting parameter, exp is an exponential function, μ _x Fitting a mean, σ, to the spatial abscissa of the hierarchical channel junction _x Fitting standard deviation, mu, to the spatial abscissa of the hierarchical channel junction _y Fitting a mean, σ, to the spatial ordinate of the hierarchical channel junction _y Fitting standard deviation for spatial ordinate of the hierarchical channel connection points, phi being the spatial coordinate fitting coefficient of the hierarchical channel connection points, h (x, y) being the connection fitting correlation function of the hierarchical channel connection points, ρ ₃ For the connection fitting, the weighting parameters are associated, T is the integral time variable calculated by the connection fitting, T is the time range parameter calculated by the connection fitting, and ζ ₁ Time adjustment parameters ζ calculated for connection fitting ₂ Calculated time decay parameters for connection fitting ρ ₄ And eta is a correction value of the fitting degree value of the connecting point and is a time weighting parameter of the time integral term.

9. The G-code parallel generation method of a 3D print model according to claim 1, wherein the step S3 comprises the steps of:

10. The G-code parallel generation method of a 3D print model according to claim 1, wherein the step S4 comprises the steps of: