A hybrid common edge analysis method and system for nesting and typesetting based on laser cutting

Abstract

The present invention relates to the field of part layout technology, and in particular to a nesting layout mixed common edge analysis method and system based on laser cutting. The method comprises the following steps: obtaining the initial layout part structure data, and performing part common edge relationship analysis to obtain part common edge relationship data; optimizing the common edge path according to the initial layout part structure data to obtain optimized part cutting path data; performing laser cutting simulation based on the optimized part cutting path data to obtain laser cutting simulation data; optimizing the layout heat balance according to the laser cutting simulation data to obtain optimized part layout data; evaluating the cutting waste rate of the optimized part layout data to obtain layout cutting waste rate data, and optimizing the optimized part layout data for the minimum cutting waste rate based on the layout cutting waste rate data to obtain the optimal part layout data. The present invention improves cutting efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of parts layout, and in particular to a nesting layout hybrid common edge analysis method and system based on laser cutting. Background Art

[0002] As the demand for high precision and high efficiency in the manufacturing industry, especially the metal processing industry, continues to increase, laser cutting technology, as an advanced material processing method, has been widely used in the cutting of metal sheets. Laser cutting has become one of the core technologies in modern manufacturing due to its high precision, complex graphics cutting capabilities and low processing costs. In the actual production process, especially in the cutting and layout process of metal sheets, how to efficiently perform nesting and layout, reduce material waste and reduce production costs is an important issue that needs to be solved in the current application of laser cutting technology. Nesting and layout, or material layout, refers to the reasonable layout of the cutting path of parts on a plate of a given size, so that the use efficiency of materials in the cutting process is maximized and the generation of waste is reduced. Traditional nesting and layout methods are mostly for layout of rectangular or regular-shaped parts. For irregular-shaped parts, accurate layout data is often unable to be provided, resulting in material waste during the cutting process. At the same time, the traditional method is also very limited in the processing of common edges, and cannot effectively identify and utilize the common edge relationship between different parts, thereby increasing the number of edges cut and reducing cutting efficiency. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a hybrid common edge analysis method and system for nesting and typesetting based on laser cutting to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a hybrid common edge analysis method for nesting layout based on laser cutting includes the following steps:

[0005] Step S1: obtaining the preliminary layout part structure data, and performing part geometric morphology analysis on the preliminary layout part structure data, thereby obtaining a part geometric digital model set; performing part common edge relationship analysis based on the part geometric digital model set, thereby obtaining part common edge relationship data;

[0006] Step S2: planning the part cutting path according to the initial layout part structure data, thereby obtaining the part cutting path data, and optimizing the part cutting path data based on the part common edge relationship data, thereby obtaining the optimized part cutting path data;

[0007] Step S3: performing linear layout planning based on the optimized part cutting path data, thereby obtaining part layout data; obtaining a laser cutting parameter set, and performing laser cutting simulation on the part layout data according to the laser cutting parameter set, thereby obtaining laser cutting simulation data;

[0008] Step S4: Visualizing the thermal distribution of plate cutting according to the laser cutting simulation data, thereby obtaining the thermal distribution data of plate cutting, and optimizing the layout heat balance of parts layout data according to the thermal distribution data of plate cutting, thereby obtaining the optimized parts layout data;

[0009] Step S5: Evaluate the cutting waste rate of the optimized part layout data to obtain layout cutting waste rate data, and optimize the optimized part layout data for the minimum cutting waste rate based on the layout cutting waste rate data to obtain the optimal part layout data.

[0010] The present invention can deeply understand the size and shape characteristics of the parts by obtaining the initial layout part structure data and performing geometric morphological analysis, thereby providing more accurate basic data for subsequent layout. The part geometry digital model set at this stage provides an accurate theoretical basis for subsequent path planning and common edge relationship analysis, ensuring the efficiency of layout work and the rationality of cutting paths. Next, the common edge relationship analysis of the parts can identify the adjacent faces or edges between the parts, which can effectively reduce the repeated paths in the cutting process, improve the cutting efficiency, and reduce unnecessary edge waste, thereby reducing the loss of materials and the time cost of cutting. Optimizing the cutting path is a crucial link. Through the common edge path optimization, not only can the repeated actions in the cutting process be reduced, but also the movement efficiency of the laser cutting head can be improved, unnecessary movements can be reduced, and the entire cutting process can be made smoother and faster. In addition, linear layout planning based on the optimized path can ensure that the parts are arranged more compactly on the plate, thereby maximizing the use of materials and reducing the generation of waste. At the same time, laser cutting simulation can detect potential problems in advance and make adjustments by simulating the cutting process, avoiding the situation of uneven cutting or inconsistent precision during actual cutting. By visualizing the thermal distribution of cutting, it is possible to intuitively identify the uneven heating of the sheet during the cutting process, and then optimize the layout of the parts to make the heat distribution more balanced, thereby reducing the deformation of the sheet or other quality problems caused by thermal stress. This thermal optimization can not only improve the cutting quality, but also improve the cutting accuracy and reduce the subsequent finishing work. In addition, the evaluation of the layout waste rate can effectively predict the material waste under different layout methods. Optimization based on the waste rate data can not only reduce production costs, but also reduce resource waste at the environmental level. Through the optimization of the minimum waste rate, the most ideal part layout plan can be finally determined, which can minimize material waste and maximize material utilization while ensuring cutting accuracy and efficiency. In summary, this series of steps not only improves the efficiency and accuracy of the cutting process, but also significantly reduces production costs and material waste through precise geometric analysis, common edge optimization, path planning, thermal distribution analysis and waste rate optimization.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: obtaining the preliminary layout part structure data, and meshing the part outline of the preliminary layout part structure data, thereby obtaining the preliminary layout part structure mesh data;

[0013] Step S12: integrating the grid structure features according to the initial layout part structure grid data, thereby obtaining the part grid structure feature data;

[0014] Step S13: performing part morphological feature analysis based on the part mesh structure feature data, thereby obtaining part geometric feature data;

[0015] Step S14: constructing a three-dimensional geometric model of the part according to the geometric feature data of the part, thereby obtaining a set of geometric digital models of the part;

[0016] Step S15: performing part common edge relationship analysis based on the part geometric digital model set, thereby obtaining part common edge relationship data.

[0017] The present invention can accurately depict the outline and structural features of the parts by obtaining the initial layout part structure data and gridding it. This process provides a basis for subsequent analysis and modeling, making the transformation process from preliminary design to final product more efficient and accurate. The gridded data lays a solid foundation for the subsequent integration of grid structure features, which can help engineers more clearly identify the various structural modules of the parts and their interrelationships, and provide strong support for optimizing design solutions and discovering potential problems. By performing morphological analysis on the mesh structure features of the parts, the geometric features of the parts can be further extracted, providing a scientific basis for the functional analysis, stress analysis and performance evaluation in the design process. This step not only improves the understanding of the part morphology, but also provides necessary data support for subsequent refined design. Based on the geometric features of the parts, constructing a three-dimensional geometric model is not only a key step in design verification, but also provides a necessary digital foundation for manufacturing technologies such as CNC machining and 3D printing, thereby ensuring the high precision and feasibility of the parts in actual manufacturing. By analyzing the common edge relationship of the parts, the geometric connection and interaction between the parts can be further clarified, providing strong guidance for overall assembly, functional integration and subsequent design optimization. This series of steps helps to improve the accuracy of part design, manufacturing efficiency and the compatibility of the overall system, ultimately promoting the smooth implementation of product design from concept to production, and effectively reducing the trial and error costs and resource waste in the design process.

[0018] Optionally, step S13 is specifically:

[0019] Step S131: extracting the part skeleton structure based on the part mesh structure feature data, thereby obtaining the part skeleton structure data;

[0020] Step S132: performing morphological corrosion and expansion processing on the part mesh structure feature data, thereby obtaining part mesh morphological processing structure data, and performing part morphological feature integration according to the part mesh morphological processing structure data, thereby obtaining mesh part morphological feature data;

[0021] Step S133: performing part morphological symmetry measurement on the part skeleton structure data according to the mesh part morphological feature data, thereby obtaining morphological symmetry data;

[0022] Step S134: performing mesh morphological feature similarity calculation based on the mesh part morphological feature data, thereby obtaining mesh morphological feature similarity data, and performing mesh connection relationship integration based on the mesh morphological feature similarity data, thereby obtaining part mesh connection data;

[0023] Step S135: performing symmetrical shape connection correction on the part mesh connection data according to the shape symmetry data, thereby obtaining part shape connection data;

[0024] Step S136: Integrate the part morphology geometric features based on the morphology symmetry data and the part morphology connection data to obtain part geometric feature data.

[0025] The present invention can help engineers deeply understand the core morphology of parts by extracting the skeleton structure data of parts, and ensure that their morphology can be accurately controlled in the subsequent design and processing process. This provides a basis for the subsequent morphological integration and optimization of geometric features of parts. The mesh structure of parts is processed by morphological corrosion and expansion processing, which can not only improve the morphological quality of the mesh, but also eliminate irregular parts in the mesh, and improve its adaptability in actual processing. By integrating the morphological features of the mesh of parts, the overall stability and structural strength of the mesh can be improved without losing accuracy. Measuring the morphological symmetry of the skeleton structure based on the morphological feature data of the mesh parts not only helps to reveal the symmetry defects that may exist in the design of parts, but also provides guidance for subsequent structural optimization, thereby reducing the complexity of design and the difficulty of processing, reducing material waste, and improving the functional consistency and performance of parts. By calculating the similarity of the mesh morphological features and integrating the mesh connection relationship, the adaptability of parts in diversified designs is further improved. The calculation of similarity can not only provide a basis for the repetitive design of parts, but also optimize different structures, make the assembly and connection of parts more accurate, and further improve the accuracy and stability of parts in practical applications. Symmetrical morphological connection correction can eliminate errors caused by imperfect part morphological symmetry and ensure the assembly quality and performance of parts in production. Through this correction step, the structural accuracy of parts can be significantly improved, and assembly problems caused by morphological deviations can be avoided, thereby improving production efficiency and reducing rework and scrap rates. By integrating the morphological symmetry data and part connection data, the geometric feature data of the parts can be integrated, which can realize multi-dimensional analysis and optimization of parts and provide a more accurate basis for subsequent design iterations and production processes. This integration process can ensure that the design and manufacturing of parts are more consistent and predictable, thereby further improving the overall production quality and efficiency and reducing production costs.

[0026] Optionally, step S15 is specifically:

[0027] Step S151: extracting part boundary features based on the part geometric digital model set, thereby obtaining part boundary feature data;

[0028] Step S152: performing edge endpoint coordinate matching according to the part boundary feature data, thereby obtaining edge endpoint matching data;

[0029] Step S153: determining the common edge relationship of parts based on the edge endpoint matching data, thereby obtaining the common edge determination data of parts;

[0030] Step S154: constructing an adjacency matrix for the part common edge determination data, thereby obtaining a part adjacency matrix;

[0031] Step S155: identifying the part common edge relationship of the part geometric digital model set according to the part adjacency matrix, thereby obtaining the part common edge relationship data.

[0032] The present invention can accurately capture the geometric shape information of the part by extracting the boundary features of the part, and provide basic data for the subsequent edge endpoint matching. This step can ensure the integrity and accuracy of the boundary features, and lay a solid foundation for the smooth progress of the entire processing process. Next, by matching the edge endpoint coordinates, the corresponding problem of each endpoint on the part boundary is effectively solved, the continuity and consistency of the data are ensured, and the determination of the common edge relationship is made more accurate and reliable. In this process, the accurate matching of the part endpoints reduces errors, improves the efficiency and quality of geometric modeling, and provides a clear and accurate basis for the determination of the common edge relationship. In the determination process of the common edge relationship of the parts, the physical contact and relationship between the parts can be effectively identified and described, which helps to improve the assembly accuracy and processing efficiency of the parts, especially in the design and manufacturing process of complex parts, which can greatly reduce potential design errors and manufacturing costs. By constructing an adjacency matrix, not only can the spatial relationship between the parts be systematically represented, but also the mutual dependence between the parts can be clearly displayed, thereby providing strong support for further automated design and analysis. By identifying the common edge relationship of the part geometry digital model set, not only the geometric connection between parts is enhanced, but also the efficiency of digital modeling and simulation analysis is optimized, which helps to accelerate the product design, assembly and optimization iteration process. In general, these steps effectively improve the processing accuracy and efficiency of part geometry models, promote the progress of intelligent manufacturing and digital design, and provide strong technical support for modern mechanical design, manufacturing and engineering analysis.

[0033] Optionally, step S2 specifically includes:

[0034] Step S21: planning the part cutting path according to the initial layout part structure data, thereby obtaining the part cutting path data;

[0035] Step S22: marking the part co-edge structure of the preliminary layout part structure data according to the part co-edge relationship data, thereby obtaining the preliminary layout part co-edge structure data;

[0036] Step S23: integrating the part cutting path data with the common edge structure cutting path according to the initial layout part common edge structure data, thereby obtaining the common edge cutting path data;

[0037] Step S24: performing layout common edge spatial distribution integration on the initial layout part common edge structure data, thereby obtaining layout common edge spatial distribution data;

[0038] Step S25: performing repeated cutting path weight reduction optimization on the common edge cutting path data based on the typesetting common edge spatial distribution data, thereby obtaining the reduced weight cutting path data;

[0039] Step S26: performing cutting path coupling on the weight reduction cutting path data and the part cutting path data, so as to obtain optimized part cutting path data.

[0040] The present invention can ensure that the cutting path of each part meets the predetermined design requirements by planning the cutting path of the part structure, and minimize the invalid movement during the cutting process, thereby improving the working efficiency of the cutting machine. By marking the common edge relationship of the parts, unnecessary gaps can be avoided during the typesetting process, making the layout of different parts more compact, thereby reducing material waste and optimizing the production rhythm. By integrating the cutting path using the common edge structure data, it is possible to ensure the optimization of the cutting path in the common edge part, reduce repeated cutting, improve the overall processing efficiency, and reduce the wear and energy consumption of the equipment through reasonable path arrangement. By integrating the spatial distribution of the common edge of the parts, the space utilization rate of the parts during typesetting can be further improved, and the use of the cutting plate can be maximized, thereby reducing material waste. At the same time, by optimizing the weight reduction of the repeated cutting path, unnecessary repeated cutting and downtime can be reduced, the continuity and fluency of the cutting process are improved, and the cutting operation is more efficient and accurate. This optimization can also reduce manual intervention, reduce the probability of error and the complexity of operation, and save a lot of time and resources in the production process. By coupling the cutting paths, the relationship between the part cutting path and the common edge path can be comprehensively considered, and the cutting order and path arrangement of the parts can be further optimized, thereby improving the coordination and cutting accuracy of the entire production process, ensuring the high efficiency of production and the accuracy of the finished product, and ultimately achieving a win-win effect of production efficiency and cost control.

[0041] Optionally, step S3 specifically includes:

[0042] Step S31: extracting part layout features from the initial layout part structure data, thereby obtaining initial part layout data;

[0043] Step S32: Calculating the cutting path deviation based on the optimized part cutting path data and the part cutting path data, thereby obtaining the part cutting path deviation data;

[0044] Step S33: performing layout linear adjustment on the initial part layout data according to the part cutting path deviation data, thereby obtaining the part layout data;

[0045] Step S34: obtaining a laser cutting parameter set, and setting a laser cutting simulation boundary condition according to the laser cutting parameter set, thereby obtaining a laser cutting simulation boundary condition;

[0046] Step S35: performing laser cutting simulation on the part layout data according to the laser cutting parameter set and using the laser cutting simulation boundary conditions, thereby obtaining laser cutting simulation data.

[0047] The present invention performs feature extraction on the initial layout part structure data, can accurately obtain the initial part layout data, provides clear basic information for subsequent processing steps, and reduces the difficulty of later adjustment and optimization. In the subsequent cutting path deviation calculation process, combined with the optimized cutting path data, the deviation between the actual cutting path and the theoretical path can be accurately calculated. This process helps to identify and correct the errors in the cutting process, ensure the accuracy of the cutting path, and thus improve the dimensional accuracy and cutting quality of the finished product. By performing layout linear adjustment on the initial part layout data, the mutual interference between parts and the space waste during cutting can be effectively reduced, making the layout more reasonable, improving the utilization rate of materials, and preparing for the subsequent laser cutting process. Obtaining the laser cutting parameter set and setting the simulation boundary conditions can accurately simulate various boundary conditions that may be encountered in the cutting process according to specific cutting processes and material properties, thereby providing more accurate simulation data for the laser cutting process and reducing the risks in actual operation. Through laser cutting simulation, all the details of the cutting process can be simulated before actual cutting, and possible problems can be predicted and adjusted to ensure the improvement of cutting accuracy and efficiency, while also avoiding the waste of resources caused by operating errors or improper parameters. The entire process effectively improves the automation level of part cutting and reduces the need for manual intervention and adjustment, thereby saving production costs and improving product quality consistency.

[0048] Optionally, step S4 is specifically:

[0049] Step S41: extracting thermal distribution features of laser cutting simulation data to obtain simulated thermal distribution data;

[0050] Step S42: Visualizing the thermal distribution of the plate according to the simulated thermal distribution data, thereby obtaining the thermal distribution data of the plate cutting;

[0051] Step S43: identifying the hot spots of plate cutting based on the plate cutting thermal distribution data, thereby obtaining the hot spots of plate cutting data, and calculating the local heat load of the plate based on the hot spots of plate cutting data, thereby obtaining the local heat load data of the plate;

[0052] Step S44: clustering the thermal imbalance regions of the plate according to the local heat load data of the plate, thereby obtaining the thermal imbalance region data of the plate;

[0053] Step S45: Based on the data of the plate heat imbalance area, the part layout data is optimized for the local heat load balance of the plate layout, so as to obtain the optimized part layout data.

[0054] The present invention can accurately obtain the temperature distribution during the cutting process by extracting the thermal distribution characteristics of the laser cutting simulation data, laying the foundation for subsequent thermal analysis. Through the visualization of the thermal distribution data, the temperature change of the plate during the cutting process can be intuitively displayed, helping the operator to better understand the thermal stress distribution that may exist during the cutting process, thereby providing data support for optimizing the cutting path and process. Further identification of the hot spots of plate cutting helps to accurately identify the high-temperature areas that may exist on the plate, and prevent problems such as material deformation or poor cutting caused by excessive local temperature. Calculation of local heat load based on hot spot data can not only provide a basis for the temperature control strategy during the cutting process, but also help design more reasonable process parameters to ensure the maximization of cutting quality and efficiency. Through further analysis of the local heat load data, the thermal imbalance areas on the plate can be effectively identified. These thermal imbalance areas are often caused by unreasonable cutting paths or improper layout. Optimizing these areas can significantly reduce thermal stress and deformation, thereby improving cutting accuracy and material yield. By optimizing the thermal load balance of the parts layout, more efficient use of the plate can be achieved, the generation of thermal imbalance areas can be reduced, and a more uniform temperature distribution during the cutting process can be ensured. This optimization process can not only improve the overall cutting performance, but also extend the service life of the equipment, improve the stability of the production line and reduce production costs.

[0055] Optionally, step S5 specifically includes:

[0056] Step S51: dividing the optimized part layout data into layout areas, thereby obtaining part layout area data;

[0057] Step S52: evaluating the regional cutting waste rate according to the part layout area data, thereby obtaining layout cutting waste rate data;

[0058] Step S53: optimizing the plate layout waste rate of the optimized part layout data according to the layout cutting waste rate data, thereby obtaining waste rate optimized layout data;

[0059] Step S54: performing minimum scrap rate layout selection on the scrap rate optimized layout data, thereby obtaining the optimal part layout data.

[0060] The present invention can clearly determine the distribution area of ​​each part on the plate by dividing the part layout area, thereby providing clear guidance for the subsequent cutting process. This area division can help reduce unnecessary waste generation and improve the overall layout efficiency. The regional cutting waste rate evaluation helps to accurately measure the actual benefits of the current layout scheme. Through the prediction and analysis of the waste rate, the optimization space can be identified in time, providing data support for the subsequent waste rate reduction. This evaluation process is crucial for identifying potential resource waste and providing improvement plans, which helps to optimize the layout plan in the design stage. After obtaining the layout cutting waste rate data, the layout data is further optimized for waste rate, which can maximize the utilization of plate resources based on the existing data and reduce unnecessary waste. This optimization process can not only improve the utilization rate of the plate, but also further reduce the production cost and improve the economic benefits of the entire production process. At the same time, the layout data after the waste rate optimization performs the minimum waste rate layout selection, and through multiple screening and comparison, it is finally ensured that the selected layout plan has the minimum waste rate, thereby achieving the best layout effect. This final selection process provides the best solution for the production process, reduces unnecessary waste of resources, brings significant economic benefits to the enterprise, and improves the overall quality and competitiveness of the products.

[0061] Optionally, step S52 is specifically:

[0062] Step S521: Calculating the regional parts scrap rate for the parts layout regional data, thereby obtaining regional parts scrap rate data;

[0063] Step S522: classifying the parts scrap rate according to the regional parts scrap rate data, thereby obtaining regional high scrap rate parts data and regional low scrap rate parts data;

[0064] Step S523: Calculating the regional parts scrap rate ratio for the regional high scrap rate parts data and the regional low scrap rate parts data, thereby obtaining the regional parts scrap rate ratio data;

[0065] Step S524: classifying the part layout area data according to the regional part scrap rate ratio data, thereby obtaining high scrap rate area data and low scrap rate area data;

[0066] Step S525: Calculate the average typesetting waste rate based on the high waste rate area data and the low waste rate area data, so as to obtain the typesetting cutting waste rate data.

[0067] The present invention calculates the scrap rate of the parts layout area data, which can help production personnel more clearly understand the scrap situation of each part in the layout area, and provide basic data for subsequent waste management and optimization. By classifying parts with high scrap rates and low scrap rates, the scrap production characteristics of different parts can be further distinguished, helping to identify which parts need special attention to reduce unnecessary waste. At the same time, by calculating the scrap rate ratio of parts, the scrap rate differences in each area can be analyzed more accurately, so as to make targeted improvements, find areas with higher scrap rates, and take corresponding measures to optimize the layout or cutting scheme of the area, and further reduce waste losses. In addition, dividing the area into high scrap rate areas and low scrap rate areas helps to clarify the specific location with high scrap rates, facilitates the concentration of resources for targeted optimization or technical adjustments, and ensures the improvement of overall production efficiency. Ultimately, by calculating the average scrap rate of the layout area, the level of cutting scrap in the overall layout process can be quantified, providing data support for further optimization of the production process. At the same time, it provides a practical solution for reducing scrap rate and improving material utilization, thereby achieving the dual goals of improving production efficiency and reducing costs.

[0068] Optionally, the present invention further provides a nesting layout hybrid common edge analysis system based on laser cutting for executing the nesting layout hybrid common edge analysis method based on laser cutting as described above, and the nesting layout hybrid common edge analysis system based on laser cutting comprises:

[0069] The morphological analysis module is used to obtain the preliminary layout part structure data, and perform part geometric morphological analysis on the preliminary layout part structure data, so as to obtain the part geometric digital model set; based on the part geometric digital model set, perform part common edge relationship analysis, so as to obtain part common edge relationship data;

[0070] The common edge path optimization module is used to plan the part cutting path according to the initial layout part structure data, so as to obtain the part cutting path data, and optimize the part cutting path data based on the part common edge relationship data, so as to obtain the optimized part cutting path data;

[0071] The laser cutting simulation module is used to perform linear layout planning based on the optimized part cutting path data, thereby obtaining part layout data; obtain a laser cutting parameter set, and perform laser cutting simulation on the part layout data according to the laser cutting parameter set, thereby obtaining laser cutting simulation data;

[0072] The heat balance optimization module is used to visualize the thermal distribution of plate cutting according to the laser cutting simulation data, so as to obtain the thermal distribution data of plate cutting, and optimize the layout heat balance of parts layout data according to the thermal distribution data of plate cutting, so as to obtain the optimized part layout data;

[0073] The scrap rate optimization module is used to evaluate the cutting scrap rate of the optimized part layout data, so as to obtain the layout cutting scrap rate data, and optimize the optimized part layout data to the minimum cutting scrap rate based on the layout cutting scrap rate data, so as to obtain the optimal part layout data.

[0074] The hybrid common edge analysis system for nesting and typesetting based on laser cutting of the present invention can realize any one of the hybrid common edge analysis methods for nesting and typesetting based on laser cutting of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the hybrid common edge analysis method for nesting and typesetting based on laser cutting. The internal modules of the system cooperate with each other to improve the cutting efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0076] Figure 1 It is a schematic diagram of the steps of the hybrid common edge analysis method for nesting and typesetting based on laser cutting of the present invention;

[0077] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0078] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0079] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0080] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0081] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0082] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0083] To achieve this, please refer to Figures 1 to 3 The present invention provides a hybrid common edge analysis method for nesting layout based on laser cutting, the method comprising the following steps:

[0084] Step S1: obtaining the preliminary layout part structure data, and performing part geometric morphology analysis on the preliminary layout part structure data, thereby obtaining a part geometric digital model set; performing part common edge relationship analysis based on the part geometric digital model set, thereby obtaining part common edge relationship data;

[0085] In this embodiment, the preliminary design data of the parts is obtained through the parts processing plant, including the layout of the panels, the size, shape and outer contour information of the parts. Next, the geometric features of the parts are analyzed in detail using the geometric morphological analysis method to identify the key geometric forms such as the boundaries, angles, and arcs of the parts. For example, morphological expansion and corrosion operations are used to analyze the convex and concave forms of the parts and extract the geometric digital model set of the parts. Based on these geometric data, a common edge relationship analysis is performed to determine which parts have edges that touch or share boundaries. For example, an edge of two parts can share a cutting path, thereby reducing the duplication of cutting paths and improving material utilization. The analysis results will generate a "common edge relationship data table" showing which parts' cutting paths can be shared or close.

[0086] Step S2: planning the part cutting path according to the initial layout part structure data, thereby obtaining the part cutting path data, and optimizing the part cutting path data based on the part common edge relationship data, thereby obtaining the optimized part cutting path data;

[0087] In this embodiment, based on the preliminary part structure data, a tool path planning algorithm (such as the optimized secant method or the graphic secant method) is used to calculate the cutting path of each part. Based on the geometric data of the part, the cutting trajectory is planned. Then, the path optimization is performed based on the obtained common edge relationship data. For example, if two parts share an edge, the cutting path can be planned as a continuous path to reduce the intersection and repeated cutting of the path, thereby optimizing the cutting time and reducing energy consumption. The optimization adjusts the order and direction of the cutting path through the algorithm to ensure that the path minimizes unnecessary movement and improves efficiency.

[0088] Step S3: performing linear layout planning based on the optimized part cutting path data, thereby obtaining part layout data; obtaining a laser cutting parameter set, and performing laser cutting simulation on the part layout data according to the laser cutting parameter set, thereby obtaining laser cutting simulation data;

[0089] In this embodiment, the optimized cutting path data is used to perform linear layout planning of parts. This process designs the layout of parts on the plate in a way that minimizes waste by using an optimization algorithm that maximizes part utilization. Specifically, a "rotation and flip" strategy is used to adjust the position of parts so that the gap between parts is minimized and material waste is reduced. Next, a cutting parameter set suitable for the laser cutting machine (such as laser power, cutting speed, focal length, etc.) is obtained from the parts processing plant. These parameters have a direct impact on the quality and efficiency of the cutting process. These parameters are used to simulate the layout data to simulate the heating, melting and cooling process of the material during the laser cutting process, thereby obtaining laser cutting simulation data to help evaluate the cutting effect and adjust the parameters.

[0090] Step S4: Visualizing the thermal distribution of plate cutting according to the laser cutting simulation data, thereby obtaining the thermal distribution data of plate cutting, and optimizing the layout heat balance of parts layout data according to the thermal distribution data of plate cutting, thereby obtaining the optimized parts layout data;

[0091] In this embodiment, a thermal distribution diagram of the plate surface during the cutting process is generated through laser cutting simulation data. Specifically, a thermodynamic simulation tool (such as ANSYS, COMSOL) is used to model the thermal deformation of the plate during laser cutting, and analyze the changes in heat distribution during the cutting process. For example, some areas may be deformed or thermally cracked due to excessive heat, thereby affecting the cutting quality. Through the visualization of the thermal distribution, you can intuitively see which areas have too much heat and which areas cool too quickly. Based on these data, the part layout is further optimized to make the heat distribution more uniform. By arranging parts in areas with lower or more uniform heat, deformation can be reduced and cutting accuracy can be improved.

[0092] Step S5: Evaluate the cutting waste rate of the optimized part layout data to obtain layout cutting waste rate data, and optimize the optimized part layout data for the minimum cutting waste rate based on the layout cutting waste rate data to obtain the optimal part layout data.

[0093] In this embodiment, based on the optimized part layout data, a scrap rate evaluation algorithm (such as linear programming, dynamic programming) is used to calculate the amount of scrap generated during the cutting process. This process simulates the execution of the cutting path, evaluates the material loss in each cutting area, and calculates the scrap rate. For example, if the gap between two parts is too large, it may lead to excess material waste. Based on the evaluation results, the layout of the parts is further adjusted to reduce unnecessary gaps. The optimization goal is to minimize the scrap rate, usually by adjusting the position and rotation angle of the parts to maximize the utilization of the material. After the optimal layout data is generated, the cost-effectiveness of the overall production process is further evaluated to ensure that both the production efficiency requirements are met and the material waste is minimized.

[0094] Optionally, step S1 specifically includes:

[0095] Step S11: obtaining the preliminary layout part structure data, and meshing the part outline of the preliminary layout part structure data, thereby obtaining the preliminary layout part structure mesh data;

[0096] In this embodiment, computer-aided design (CAD) software is used to model the preliminary layout of the parts, and the preliminary layout data of the parts are obtained, including the outline, size and key features of the parts. Taking automobile parts as an example, it is assumed that the preliminary layout model of an automobile part has been created in the CAD system of the parts processing plant, which includes the shape and internal hole positions of the part. On this basis, a meshing algorithm (such as Delaunay triangulation or quadrilateral mesh generation algorithm) is used to mesh the outline of the part, and the outline of the part is divided into multiple small grid units to obtain the preliminary layout part structure grid data. The boundaries, vertex coordinates and adjacent relationships of each grid unit are calculated and recorded, thereby providing a structured data basis for subsequent analysis and optimization.

[0097] Step S12: integrating the grid structure features according to the initial layout part structure grid data, thereby obtaining the part grid structure feature data;

[0098] In this embodiment, a mesh structure feature extraction algorithm is used to integrate the mesh data of the part to extract the geometric features of the mesh. For example, the geometric distribution of the mesh area can be identified by calculating the area, side length, angle and other information of each mesh unit and combining the topological relationship of adjacent mesh units. If the mesh of the part structure contains multiple areas (such as holes or grooves), it is necessary to further integrate the mesh information of these areas, divide the mesh into different sub-areas through clustering or feature analysis methods, and describe the geometric morphology of each sub-area. The integration result can provide more accurate mesh structure feature data, which is of great significance for subsequent part morphological analysis and three-dimensional modeling.

[0099] Step S13: performing part morphological feature analysis based on the part mesh structure feature data, thereby obtaining part geometric feature data;

[0100] In this embodiment, morphological feature analysis is performed based on grid structure feature data, for example, the main geometric features of the parts are identified by morphological filtering or edge detection methods. For complex part morphologies, mathematical morphology-based methods (such as expansion, corrosion, opening and closing operations, etc.) can be used to analyze the geometric morphology of the parts. In practical applications, assuming that the analyzed part is a mechanical transmission gear, morphological analysis will identify the gear's tooth shape, tooth pitch, outer contour, hole distribution and other features. Through this process, the geometric feature data of the part can be calculated, including the part's outer contour, size, hole distribution, contact area and other geometric features. These data provide a basis for building a three-dimensional model of the part and conducting subsequent analysis.

[0101] Step S14: constructing a three-dimensional geometric model of the part according to the geometric feature data of the part, thereby obtaining a set of geometric digital models of the part;

[0102] In this embodiment, a three-dimensional geometric model of the part is constructed by a three-dimensional modeling software (such as SolidWorks, CATIA or Autodesk Inventor) based on the acquired part geometric feature data. Taking aviation parts as an example, assuming that the geometric feature data of the part includes a complex hollow structure and multiple fine hole distributions, based on these geometric features, the three-dimensional model is created using the feature modeling methods (such as stretching, rotation, sweeping, lofting, etc.) in the modeling software. In this process, the model will be accurately constructed according to the size and geometric features of the part to ensure consistency with the actual part design. Ultimately, the generated three-dimensional geometric model can be converted into a digital format (such as a STEP or IGES file) to form a set of geometric digital models of the part, which will serve as the basis for subsequent manufacturing or simulation analysis.

[0103] Step S15: performing part common edge relationship analysis based on the part geometric digital model set, thereby obtaining part common edge relationship data.

[0104] In this embodiment, based on the geometric digital model set of the part, the co-edge relationship analysis of the part is performed. First, the geometric model of the part is detected by the algorithm to identify all the edges and faces in the model. Taking the complex automobile shell as an example, it is assumed that the geometric model of the part contains multiple joints, curved surfaces and holes. When performing the co-edge analysis, the algorithm will find the relationship between the shared edges of different faces, determine which faces are adjacent and connected by common edges. For example, if the intersection edge between two faces is analyzed, if the two faces share one or more edges, the co-edge relationship between them can be determined. Finally, the co-edge relationship of all faces and edges will be recorded as a data set, describing the contact and connection between the various components of the part. These co-edge relationship data are crucial in the subsequent assembly analysis, finite element analysis and manufacturing process, and can guide the processing technology design and assembly process optimization of the parts.

[0105] Optionally, step S13 is specifically:

[0106] Step S131: extracting the part skeleton structure based on the part mesh structure feature data, thereby obtaining the part skeleton structure data;

[0107] In this embodiment, the skeleton structure of the mesh is extracted by applying a three-dimensional mesh analysis algorithm to the feature data of the mesh structure of the part. First, the CAD model of the part is obtained and converted into mesh data. A refinement algorithm (such as triangulation or voxelization technology) is used to remove redundant faces and edges and retain the main supporting structure of the part. The algorithm can be used to cut layer by layer to extract the most representative skeleton line segments to ensure that the skeleton structure reflects the most core geometric features of the part.

[0108] Step S132: performing morphological corrosion and expansion processing on the part mesh structure feature data, thereby obtaining part mesh morphological processing structure data, and performing part morphological feature integration according to the part mesh morphological processing structure data, thereby obtaining mesh part morphological feature data;

[0109] In this embodiment, after obtaining the mesh structure feature data of the part, a morphological corrosion process is first performed, that is, the mesh structure is corroded by structural elements to remove possible noise and redundant parts. For example, on a part with a complex surface texture, a 3x3 rectangular corrosion kernel is used to perform multiple corrosions to smooth the surface of the part. Then, an expansion process is performed to restore the details and shape of the skeleton. The expansion operation makes the mesh features of the part clearer and repairs the local losses caused by corrosion. After the corrosion and expansion operations are performed on the part, the morphological processing structure data of the part mesh is obtained. These data will be used to further integrate the morphological features of the part. Then, the mesh morphological features (such as surfaces, boundaries, angles, etc.) of the part after the corrosion and expansion operations are integrated to obtain the mesh part morphological feature data.

[0110] Step S133: performing part morphological symmetry measurement on the part skeleton structure data according to the mesh part morphological feature data, thereby obtaining morphological symmetry data;

[0111] In this embodiment, based on the skeleton structure of the part, a symmetry detection algorithm (such as an algorithm based on maximum symmetry matching) is used to analyze the morphology of the mesh part. The morphological symmetry of the part is evaluated by calculating the symmetry measurement values ​​(such as Euclidean distance, geometric center alignment, etc.) on both sides of the part. Specifically, the mesh can be divided into two parts, left and right, and the transformation matrix can be used for comparison to obtain the symmetry data of the morphology, further promoting the morphological optimization. Symmetry detection algorithms can be used, such as calculation methods based on point symmetry or reflection symmetry. For example, if a part has an obvious axis of symmetry, such as a symmetrical wing design often encountered in aircraft components, its symmetry measurement value can be determined by calculating the relationship between the geometric center point of the part and the axis of symmetry. The value of the symmetry measurement is usually a numerical value, indicating the degree of symmetry of the part in a certain axis, 0 means no symmetry, and 1 means complete symmetry. This data can help with subsequent morphological adjustment and geometric correction.

[0112] Step S134: performing mesh morphological feature similarity calculation based on the mesh part morphological feature data, thereby obtaining mesh morphological feature similarity data, and performing mesh connection relationship integration based on the mesh morphological feature similarity data, thereby obtaining part mesh connection data;

[0113] In this embodiment, the morphological features of the mesh parts are similarly calculated, and a similarity evaluation method based on local descriptors (such as SIFT or SURF features) is used. First, the local geometric features of the part mesh are extracted, and the similarity data of the mesh is obtained by calculating the similarity measurement between the feature points. The connection relationship is integrated according to the mesh groups with high similarity to ensure that the parts with similar morphology are combined into a reasonable mesh structure. Based on the morphological feature data of the mesh parts, the similarity between different mesh morphologies is calculated using a morphological similarity measurement method. For example, the morphological similarity between different parts is evaluated by calculating the Euclidean distance, cosine similarity or Hausdorff distance between the geometric morphological features of the parts (such as the angles, distances, etc. of the mesh points, edges, and faces). Assuming that there are mesh data A and B of two mechanical parts, by calculating the similarity of their morphological features, a numerical value can be obtained to indicate the degree of morphological similarity between the two parts. Based on this similarity data, the mesh connection relationship can be further integrated, so that the mesh parts with similar morphology are connected to form a more accurate part mesh structure.

[0114] Step S135: performing symmetrical shape connection correction on the part mesh connection data according to the shape symmetry data, thereby obtaining part shape connection data;

[0115] In this embodiment, symmetrical morphological connection correction is performed by combining the obtained morphological symmetry data with the grid connection data. For example, in a part with symmetrical characteristics, such as the support frame of a car body, the connection relationship of the part grid needs to take symmetry into account to avoid misjudging the connection relationship in the symmetrical part. For example, in the symmetrical position of the car body frame, if the connection relationship between two grid areas is identified, but in fact the two grid areas should be processed separately due to symmetry, correction can be performed based on the symmetry data to remove redundant connection points or reconstruct the connection relationship. This process can avoid misjudgment caused by symmetrical characteristics by analyzing the relative position and connection of the grids on both sides of the symmetry axis.

[0116] Step S136: Integrate the part morphology geometric features based on the morphology symmetry data and the part morphology connection data to obtain part geometric feature data.

[0117] In this embodiment, the geometric features are integrated by combining the morphological symmetry data and connection data of the parts. First, the geometric features (such as angles, side lengths, curvatures, etc.) of different grid areas are uniformly processed, and the geometric features of each part are integrated through geometric transformation techniques (such as coordinate mapping, geometric deformation, etc.) to form the final part geometric data. This process can be optimized through multiple iterations to ensure that the integrated geometric feature data is accurate and precise, and suitable for subsequent manufacturing or analysis. It is also possible to merge multiple morphologically similar or symmetrical parts based on symmetry metrics and grid connection data to extract the geometric features of parts, such as surfaces, boundaries, angles, etc. For example, in the design of a mechanical part, the actual geometric size, appearance characteristics and internal structure of the part can be determined by integrating its symmetrical parts and connection data.

[0118] Optionally, step S15 is specifically:

[0119] Step S151: extracting part boundary features based on the part geometric digital model set, thereby obtaining part boundary feature data;

[0120] In this embodiment, the boundary features of the part are extracted from a geometric digital model set of the part (e.g., in STL or CAD format). First, the three-dimensional model of the part is converted into boundary surface data suitable for analysis using computer-aided design (CAD) software or special tools. Then, all boundary features on the surface of the part are identified through geometric algorithms (such as surface boundary extraction, edge detection, etc.). These boundary features include contour lines, surface boundaries, cutting lines, etc. on the surface of the part. Specifically, the intersection line can be extracted as a boundary feature by performing normal analysis on each face. For example, the intersection line between two faces is identified using the normal vector method and extracted as a boundary feature.

[0121] Step S152: performing edge endpoint coordinate matching according to the part boundary feature data, thereby obtaining edge endpoint matching data;

[0122] In this embodiment, the endpoint coordinates of the edge are matched based on the extracted part boundary feature data. Each edge can be represented by the three-dimensional coordinates of the endpoint. In order to match the endpoint coordinates, the extracted edge features are first converted into a point set form, and precision matching is performed based on their coordinate values ​​(X, Y, Z) at the time of initial typesetting. A spatial distance algorithm (such as Euclidean distance) is used to determine which endpoints are located in the same position, and then determine whether they belong to the same edge. For example, if the coordinate difference between endpoint A and endpoint B is less than a certain threshold, they are considered to be the same endpoint and are matched.

[0123] Step S153: determining the common edge relationship of parts based on the edge endpoint matching data, thereby obtaining the common edge determination data of parts;

[0124] In this embodiment, based on the edge endpoint matching data, the common edge relationship of the part edges is further determined. The common edge relationship refers to whether two edges intersect or share endpoints at the same position. When determining, by comparing whether the endpoint coordinates of the edges are the same, it is determined whether there is a common edge relationship. For example, if the endpoint A of edge 1 is the same as the endpoint A of edge 2, it means that the two edges are common. In order to improve the accuracy, a tolerance value can be set to take into account the numerical error when matching to ensure the accuracy of the common edge relationship.

[0125] Step S154: constructing an adjacency matrix for the part common edge determination data, thereby obtaining a part adjacency matrix;

[0126] In this embodiment, an adjacency matrix of a part is constructed based on the co-edge determination data of the part. An adjacency matrix is ​​a matrix structure that represents the relationship between the edges of a part, in which each row and each column represents an edge, and the value of the matrix represents whether two edges are adjacent. If two edges are in a co-edge relationship (i.e., share endpoints) in the geometric model of the part, the corresponding element value in the adjacency matrix is ​​1, otherwise it is 0. In specific implementation, the corresponding adjacency matrix is ​​generated by traversing the co-edge relationship data. For example, if edge 1 is co-edge with edge 2, the positions (1, 2) and (2, 1) in the adjacency matrix will be filled with 1.

[0127] Step S155: identifying the part common edge relationship of the part geometric digital model set according to the part adjacency matrix, thereby obtaining the part common edge relationship data.

[0128] In this embodiment, the common edge relationship of the geometric digital model set of the parts is identified based on the data of the adjacency matrix. Through the adjacency matrix, it is possible to identify which edges are connected to each other in space, and then determine the geometric shape and structure of the parts. This step is usually used to identify the assembly characteristics of parts or optimize the manufacturing process of parts. For example, by identifying the common edge relationship, it is possible to find out which parts can be spliced ​​or assembled through certain connection methods, thereby optimizing the design and manufacturing process. The adjacency matrix can be processed using a graphical algorithm to identify independent common edge groups or subgraphs, and further analyze the relationship between parts.

[0129] Optionally, step S2 specifically includes:

[0130] Step S21: planning the part cutting path according to the initial layout part structure data, thereby obtaining the part cutting path data;

[0131] In this embodiment, based on the initial layout part structure data, a cutting path planning algorithm (such as A* algorithm, Dijkstra algorithm or genetic algorithm) is used to generate the cutting path of the part. The algorithm will consider factors such as the geometric shape of the part, the cutting direction of the cutting machine and the tool path to ensure the effectiveness and optimization of the cutting path. It is necessary to discretize parts of complex shapes and ensure that the cutting path is executable in machine processing. For example, for a rectangular part, the cutting path can be set to start from the edge of the part and cut at a certain interval to avoid wasting materials. According to the initial layout part structure data, laser cutting is used for path planning. Specifically, a suitable cutting path model is first generated for each part, taking into account the maximum processing range of the cutting machine, the geometric shape of the cutting tool and the relative position between the parts. The shortest path algorithm (such as Dijkstra algorithm) can be used to optimize the cutting order and path of each part to avoid invalid movement. Finally, a cutting path data set is generated according to the boundary of each part, including the starting point, end point, path curve shape, cutting order and other information of each path. For example, suppose the initial layout parts are a batch of metal sheets with a size of 1000mm×500mm. By analyzing the shape of each part (such as rectangle, circle or complex polygon), a suitable cutting path can be generated. If the cutting path is laser cutting, the path will take into account the straight-line cutting ability of the laser beam and generate a cutting path at each outer contour and hole of the part. The cutting sequence can use a greedy algorithm to minimize the empty stroke of the cutting head and save time.

[0132] Step S22: marking the part co-edge structure of the preliminary layout part structure data according to the part co-edge relationship data, thereby obtaining the preliminary layout part co-edge structure data;

[0133] In this embodiment, the corresponding common edges of the parts in the preliminary layout part structure are marked according to the identified common edge relationship data of the parts, so as to obtain the common edge structure data of the preliminary layout parts.

[0134] Step S23: integrating the part cutting path data with the common edge structure cutting path according to the initial layout part common edge structure data, thereby obtaining the common edge cutting path data;

[0135] In this embodiment, based on the part structure data on which the common edge relationship has been marked, the cutting paths of the parts having the common edge relationship in the part cutting path data are extracted to obtain the common edge cutting path data.

[0136] Step S24: performing layout common edge spatial distribution integration on the initial layout part common edge structure data, thereby obtaining layout common edge spatial distribution data;

[0137] In this embodiment, by analyzing the layout of parts, the spatial distribution of the common edge relationship is further optimized to ensure the reasonable layout of parts during the layout process. First, the optimal position of each part in the layout process is determined by in-depth analysis of the geometric size, shape and common edge relationship of the parts. A layout optimization algorithm (such as a simulated annealing algorithm or a genetic algorithm) is used to ensure that the spatial utilization of the parts in the layout area is maximized while maintaining the continuity and stability of the common edge relationship. Finally, the layout common edge spatial distribution data is generated, which contains the position information of each part and the distribution of its common edge relationship.

[0138] Step S25: performing repeated cutting path weight reduction optimization on the common edge cutting path data based on the typesetting common edge spatial distribution data, thereby obtaining the reduced weight cutting path data;

[0139] In this embodiment, redundant cutting paths are reduced by utilizing the spatial distribution data of the layout common edges, thereby optimizing the cutting process. Specifically, repeated cutting paths are identified and merged according to the positions of the parts in the layout. In this process, a path merging algorithm is used to reduce the weight of repeated paths and eliminate redundant parts in the paths. By adjusting the starting and ending points of the path, repeated cutting actions are avoided, thereby reducing processing time. The optimization algorithm can use heuristic search methods, such as ant colony algorithms, to quickly find the optimal weight reduction solution. Assume that during the layout process, part A and part C are located on both sides of the workbench, and there are repeated parts between their cutting paths. Through weight reduction optimization, part of the cutting path of part A can be merged with the cutting path of part C to avoid repeated cutting and reduce repeated movements of the cutting head.

[0140] Step S26: performing cutting path coupling on the weight reduction cutting path data and the part cutting path data, so as to obtain optimized part cutting path data.

[0141] In this embodiment, the weight-reduced cutting path is integrated with the original part cutting path to finally obtain an optimized part cutting path. This process requires comparing the weight-reduced path with the initial path to ensure the rationality of the cutting order and path. Through coupling technology, the connectivity of the path and the working characteristics of the cutting tool are combined to generate the optimal cutting path. This process can use heuristic algorithms or optimization algorithms (such as A* algorithms) to achieve further integration of the path. For example, after the previous weight reduction steps, the cutting paths of parts A and B have been optimized and merged. At this point, the path coupling technology is used to combine the weight-reduced cutting path with the paths of other parts to ensure that the working order of the cutting head is the most reasonable. Finally, the output optimized part cutting path data contains the cutting paths of each part, and is arranged in the optimal order to ensure an efficient processing process.

[0142] Optionally, step S3 specifically includes:

[0143] Step S31: extracting part layout features from the initial layout part structure data, thereby obtaining initial part layout data;

[0144] In this embodiment, the arrangement order of the preliminary part layout is determined by extracting the arrangement information of the parts in the preliminary layout part structure data.

[0145] Step S32: Calculating the cutting path deviation based on the optimized part cutting path data and the part cutting path data, thereby obtaining the part cutting path deviation data;

[0146] In this embodiment, the cutting path for each part is generated by the control software of the laser cutting machine or the dedicated path optimization software. The cutting path is usually composed of the motion trajectory of the cutter head of the laser cutting machine and the required control parameters such as laser power and speed. Then, the optimized cutting path is compared with the cutting path obtained by the preliminary calculation, and the deviation between the two is calculated. Error calculation methods (such as maximum deviation, root mean square error, etc.) can be used to quantify the error of the path, and data processing and deviation calculation can be performed through error analysis tools such as MATLAB and Python's NumPy library. Deviation data includes cutting deviation values ​​of each point, as well as parameters such as path curvature. For example, for a circular part, the cutting path optimization algorithm will make slight adjustments to the original circular path to reduce the possibility of the laser beam deviating from the original path during cutting. By calculating the deviation of the path, it can be determined whether the cutting path will cause distortion or irregularity of the shape of the part.

[0147] Step S33: performing layout linear adjustment on the initial part layout data according to the part cutting path deviation data, thereby obtaining the part layout data;

[0148] In this embodiment, the initial part layout data is adjusted according to the obtained cutting path deviation data. Specifically, the layout of the parts can be linearly corrected based on the statistical analysis results of the deviation data (such as mean, standard deviation, etc.). The correction method may include fine-tuning, rotating, or adjusting the arrangement order of the part position. Common adjustment methods include applying a linear regression model or a least squares algorithm to minimize the deviation of the cutting path. Different adjustment strategies are required for parts of different shapes. For example, for parts with complex curved shapes, the curvature change of the path may need to be considered during adjustment. For example, for multiple rectangular parts, if there is a large deviation in the cutting path of some parts, the impact of the cutting deviation on the final part quality can be reduced by adjusting the position of these parts in the layout area or by slightly rotating the parts.

[0149] Step S34: obtaining a laser cutting parameter set, and setting a laser cutting simulation boundary condition according to the laser cutting parameter set, thereby obtaining a laser cutting simulation boundary condition;

[0150] In this embodiment, a set of key parameters in the laser cutting process is obtained from the parts processing manufacturer or relevant literature. Typical laser cutting parameters include laser power, cutting speed, pulse frequency, focal position, cutting gas type and pressure, etc. According to these parameters, the laser cutting process is simulated and modeled by numerical simulation software (such as ANSYS, COMSOL, Simulia, etc.). Boundary conditions need to be considered during simulation, such as the distribution of the temperature field of the cutting surface, the propagation path of the laser beam, the thermal conduction effect of the material, etc. Appropriate simulation boundary conditions, such as heat flow boundaries, radiation boundaries, etc., are set to simulate the actual cutting environment. For example, when simulating the laser cutting of metal materials, the laser power can be set to 2 kW, the cutting speed can be set to 2 m / min, and the focal position can be adjusted according to the thermal conduction characteristics of the material to simulate the cutting process of the material and set the corresponding thermal boundary conditions.

[0151] Step S35: performing laser cutting simulation on the part layout data according to the laser cutting parameter set and using the laser cutting simulation boundary conditions, thereby obtaining laser cutting simulation data.

[0152] In this embodiment, according to the set laser cutting parameter set and boundary conditions, the part layout data is subjected to laser cutting simulation. The laser cutting process is numerically simulated using simulation software to obtain relevant data such as deformation of parts during the cutting process, heat-affected zone, and cutting quality. The simulation results may include information such as temperature field distribution, stress distribution, and cutting surface quality (such as roughness and burrs). In specific implementation, a suitable solver and meshing method can be selected to perform detailed calculations to ensure the accuracy of the simulation results. For example, in the simulation of a metal sheet cutting process, the thermal expansion of the sheet during the cutting process, the cooling rate, and the heat-affected zone (HAZ) of the edge can be obtained through simulation, and then the influence of different laser parameters on the cutting effect can be deduced to ensure the final part cutting accuracy and quality.

[0153] Optionally, step S4 is specifically:

[0154] Step S41: extracting thermal distribution features of laser cutting simulation data to obtain simulated thermal distribution data;

[0155] In this embodiment, laser simulation software (such as ANSYS, COMSOL, etc.) is used to perform thermal analysis on the sheet to simulate the irradiation process of the laser beam and its heat conduction on the metal sheet. The temperature distribution data at each time step is obtained through simulation analysis, and key thermal characteristics are extracted, such as local temperature peak, temperature gradient, and heat diffusion rate. Furthermore, data processing technology is used to filter and smooth the simulation data to remove noise, ensuring that the extracted thermal distribution characteristics reflect the actual cutting process.

[0156] Step S42: Visualizing the thermal distribution of the plate according to the simulated thermal distribution data, thereby obtaining the thermal distribution data of the plate cutting;

[0157] In this embodiment, the thermal distribution of the plate is three-dimensionally modeled and visualized using thermodynamic visualization tools (such as MATLAB, ParaView, etc.) based on the thermal distribution data. In this process, the temperature data is mapped to the surface of the plate to form a heat map. The color depth of the heat map represents different temperature ranges, highlighting the area with the highest temperature, and intuitively presenting the degree of heat received by each part during the laser cutting process. This visualization result can help analyze which areas have a large heat load, thereby providing data support for subsequent heat load calculations and hot spot identification.

[0158] Step S43: identifying the hot spots of plate cutting based on the plate cutting thermal distribution data, thereby obtaining the hot spots of plate cutting data, and calculating the local heat load of the plate based on the hot spots of plate cutting data, thereby obtaining the local heat load data of the plate;

[0159] In this embodiment, based on the obtained thermal distribution map, a hot spot identification algorithm, such as an algorithm based on a temperature threshold or an algorithm based on image processing, is used to identify the hot spot areas during the sheet cutting process. These hot spots are usually areas where the temperature rises sharply, which may cause sheet deformation or unstable cutting quality. Clustering algorithms (such as K-means, DBSCAN, etc.) are used to classify the hot spot areas to obtain clear hot spot locations and ranges. On this basis, the local heat load of each hot spot area is calculated, for example, the total heat absorption of the area is calculated by the integral method, and the local heat load data of the sheet is obtained by combining physical properties such as thermal conductivity and thermal diffusion rate.

[0160] Step S44: clustering the thermal imbalance regions of the plate according to the local heat load data of the plate, thereby obtaining the thermal imbalance region data of the plate;

[0161] In this embodiment, after obtaining the local heat load data of each area of ​​the plate, a clustering algorithm (such as K-means or hierarchical clustering) is used to perform a spatial distribution analysis of the heat load, and the areas with similar heat loads are clustered together to form heat load imbalance areas. For example, if the heat load of a certain area is too high and the surrounding areas are relatively low, these areas will be classified as heat imbalance areas. Through this step, the overheated areas or unevenly cooled areas that may exist on the plate are identified, providing a basis for subsequent optimization.

[0162] Step S45: Based on the data of the plate heat imbalance area, the part layout data is optimized for the local heat load balance of the plate layout, so as to obtain the optimized part layout data.

[0163] In this embodiment, the layout of parts is adjusted according to the obtained data of the heat imbalance area through an optimization algorithm (such as a genetic algorithm, a simulated annealing algorithm, etc.) to achieve a balanced distribution of the heat load. The specific method is to adjust the layout of the parts on the plate according to the contribution of each part to the heat load, so that the originally concentrated heat load area can be evacuated to avoid overheating or excessive temperature differences in certain areas of the plate. For example, large-area parts are adjusted to areas with lower heat loads, while small parts or parts that need to be cooled quickly are arranged in areas with higher heat loads. Through this process, a more balanced temperature distribution is ensured during the cutting process, and the cutting quality and the service life of the plate are improved. The optimized layout data will be input into the cutting machine as the final cutting path to guide the actual operation.

[0164] Optionally, step S5 specifically includes:

[0165] Step S51: dividing the optimized part layout data into layout areas, thereby obtaining part layout area data;

[0166] In this embodiment, the area division is performed according to the size, shape and arrangement of the parts based on the optimized part layout data. The plate can be divided into multiple available areas using methods such as rectangular area cutting, square area cutting or regular polygonal division. For example, a linear regression algorithm or a minimum circumscribed rectangle algorithm is used to perform preliminary area division, determine the size and shape of the area where each part is located, and then generate an area division map. Then, for different areas, a regional adjustment algorithm can be applied to ensure that there is no overlap or waste of space between different parts.

[0167] Step S52: evaluating the regional cutting waste rate according to the part layout area data, thereby obtaining layout cutting waste rate data;

[0168] In this embodiment, the scrap rate of each area is evaluated. The scrap rate refers to the ratio of the area of ​​the remaining unused part to the area of ​​the entire plate during the cutting process. In this step, the Monte Carlo method or the dynamic programming algorithm can be used to simulate the waste generation during the cutting process. Assuming that there are multiple layout areas, the parts in each area may generate different cutting wastes according to different layout methods. The scrap rate of each area is calculated by analyzing the cutting shape of each area, the gap between adjacent parts and the density of the layout. For example, if two parts are arranged in a certain area, its cutting scrap rate is 10%, while another area may generate a scrap rate of 15% due to the distribution of part sizes.

[0169] Step S53: optimizing the plate layout waste rate of the optimized part layout data according to the layout cutting waste rate data, thereby obtaining waste rate optimized layout data;

[0170] In this embodiment, the key to optimizing the layout scrap rate is how to reduce the scrap rate of the entire sheet by adjusting the position of the parts, the arrangement direction, or selecting different cutting strategies. In this step, a particle swarm optimization (PSO) algorithm or a simulated annealing algorithm can be introduced to achieve layout optimization of the sheet. Assuming that in a certain layout area, the spacing between the parts is too large, resulting in a high scrap rate, the relative position of the parts can be adjusted through the particle swarm optimization algorithm to minimize the gap between adjacent parts, ultimately achieving the purpose of optimizing the scrap rate. For example, parts with more regular shapes are placed in the corners of the area, while parts with irregular shapes can be adjusted to a suitable rotation angle to maximize the use of the remaining space.

[0171] Step S54: performing minimum scrap rate layout selection on the scrap rate optimized layout data, thereby obtaining the optimal part layout data.

[0172] In this embodiment, a greedy algorithm or a genetic algorithm is used to select an optimal solution from multiple waste rate optimization layout schemes. Assuming that multiple optimized layout schemes have been generated, each scheme corresponds to a waste rate value. In this process, the algorithm will evaluate the waste rate of each layout scheme and select the scheme with the smallest waste rate as the final optimal solution. For example, if the waste rate of one scheme is 7% and the waste rate of another scheme is 9%, the scheme with a waste rate of 7% is selected as the optimal solution. In order to ensure the effectiveness of the optimal selection, multi-objective optimization can be introduced to consider not only the waste rate, but also factors such as time consumption and material cost during the cutting process.

[0173] Optionally, step S52 is specifically:

[0174] Step S521: Calculating the regional parts scrap rate for the parts layout regional data, thereby obtaining regional parts scrap rate data;

[0175] In this embodiment, when calculating the scrap rate of the part layout area data, it is first necessary to obtain the size, shape and relative position of all parts in the layout area to the layout boundary. Assume that an area contains 10 parts, which are different shapes such as circles, rectangles, triangles, etc., and their distances and positions from the layout edge are different. By calculating the difference between the theoretical occupied area of ​​each part (i.e., the part area) and the area required for the actual layout, the scrap rate of each part can be obtained. Specifically, the calculation formula for the scrap rate is: scrap rate = (total area of ​​layout area − total area of ​​parts) / total area of ​​layout area; for example, for a rectangular area, the area is 1000 square millimeters, and the total area of ​​the parts after layout is 700 square millimeters, then the scrap rate of the area is 30%.

[0176] Step S522: classifying the parts scrap rate according to the regional parts scrap rate data, thereby obtaining regional high scrap rate parts data and regional low scrap rate parts data;

[0177] In this embodiment, based on the scrap rate data obtained, the parts are classified according to the scrap rate. For example, suppose that in a typesetting area, there are several parts with scrap rates of 10%, 20%, 50%, 60%, and 5%, respectively. According to the preset classification threshold (for example, 30% is the limit), the parts with scrap rates higher than 30% are classified as "high scrap rate parts", and the parts with scrap rates lower than 30% are classified as "low scrap rate parts". At this time, the data of high scrap rate parts are parts with scrap rates of 50% and 60%, and the data of low scrap rate parts are parts with scrap rates of 10%, 20%, and 5%.

[0178] Step S523: Calculating the regional parts scrap rate ratio for the regional high scrap rate parts data and the regional low scrap rate parts data, thereby obtaining the regional parts scrap rate ratio data;

[0179] In this embodiment, for the classified "high scrap rate parts" and "low scrap rate parts", their scrap rate ratio needs to be calculated. The ratio is calculated by first finding the average scrap rates of the high scrap rate parts and the low scrap rate parts, and then calculating the ratio of the two average scrap rates.

[0180] Step S524: classifying the part layout area data according to the regional part scrap rate ratio data, thereby obtaining high scrap rate area data and low scrap rate area data;

[0181] In this embodiment, different layout areas can be classified according to the calculated scrap rate ratio data. Specifically, the layout area is divided into "high scrap rate area" and "low scrap rate area" according to the size of the ratio. For example, if the scrap rate ratio of a certain area exceeds 2, the area is considered to be a "high scrap rate area", otherwise it is a "low scrap rate area". Suppose that in multiple layout areas, some areas have a scrap rate ratio of 3.67, and other areas have a ratio of 1.5. According to the preset threshold 2, the area with a scrap rate ratio higher than 2 (such as an area with a ratio of 3.67) will be classified as a "high scrap rate area", and the rest will be "low scrap rate areas".

[0182] Step S525: Calculate the average typesetting waste rate based on the high waste rate area data and the low waste rate area data, so as to obtain the typesetting cutting waste rate data.

[0183] In this embodiment, after the layout area is classified, the average scrap rate needs to be calculated for each category (high scrap rate area and low scrap rate area) in order to obtain the cutting scrap rate of the entire layout. The specific implementation method is to first calculate the total scrap rate of parts in each area, and then average it by area category. For example, in a high scrap rate area, the scrap rates of the parts contained are 50%, 60%, and 70% respectively, then the average scrap rate of the area is: the average scrap rate of the high scrap rate area = (50% + 60% + 70%) / 3 = 60%; for the low scrap rate area, the scrap rates of the parts are 10%, 20%, and 25% respectively, and the average scrap rate of the area is: the average scrap rate of the low scrap rate area = (10% + 20% + 25%) / 3 ≈ 18.33%. Finally, the average scrap rates of the high scrap rate area and the low scrap rate area are weighted averaged (according to the proportion of each area in the total layout area) to obtain the cutting scrap rate of the entire layout.

[0184] Optionally, the present invention further provides a nesting layout hybrid common edge analysis system based on laser cutting for executing the nesting layout hybrid common edge analysis method based on laser cutting as described above, and the nesting layout hybrid common edge analysis system based on laser cutting comprises:

[0185] The morphological analysis module is used to obtain the preliminary layout part structure data, and perform part geometric morphological analysis on the preliminary layout part structure data, so as to obtain the part geometric digital model set; based on the part geometric digital model set, perform part common edge relationship analysis, so as to obtain part common edge relationship data;

[0186] The common edge path optimization module is used to plan the part cutting path according to the initial layout part structure data, so as to obtain the part cutting path data, and optimize the part cutting path data based on the part common edge relationship data, so as to obtain the optimized part cutting path data;

[0187] The laser cutting simulation module is used to perform linear layout planning based on the optimized part cutting path data, thereby obtaining part layout data; obtain a laser cutting parameter set, and perform laser cutting simulation on the part layout data according to the laser cutting parameter set, thereby obtaining laser cutting simulation data;

[0188] The heat balance optimization module is used to visualize the thermal distribution of plate cutting according to the laser cutting simulation data, so as to obtain the thermal distribution data of plate cutting, and optimize the layout heat balance of parts layout data according to the thermal distribution data of plate cutting, so as to obtain the optimized part layout data;

[0189] The scrap rate optimization module is used to evaluate the cutting scrap rate of the optimized part layout data, so as to obtain the layout cutting scrap rate data, and optimize the optimized part layout data to the minimum cutting scrap rate based on the layout cutting scrap rate data, so as to obtain the optimal part layout data.

[0190] The hybrid common edge analysis system for nesting and typesetting based on laser cutting of the present invention can realize any one of the hybrid common edge analysis methods for nesting and typesetting based on laser cutting of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the hybrid common edge analysis method for nesting and typesetting based on laser cutting. The internal modules of the system cooperate with each other to improve the cutting efficiency and accuracy.

[0191] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0192] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A hybrid common edge analysis method for nesting layout based on laser cutting, characterized in that: The following steps are involved: Step S1: obtaining the preliminary layout part structure data, and performing part geometric morphology analysis on the preliminary layout part structure data, thereby obtaining a part geometric digital model set; performing part common edge relationship analysis based on the part geometric digital model set, thereby obtaining part common edge relationship data; Wherein step S1 comprises the following steps: Step S11: obtaining the preliminary layout part structure data, and meshing the part outline of the preliminary layout part structure data, thereby obtaining the preliminary layout part structure mesh data; Step S12: integrating the grid structure features according to the initial layout part structure grid data, thereby obtaining the part grid structure feature data; Step S13: morphological feature analysis of the part is performed based on the mesh structure feature data of the part, so as to obtain geometric feature data of the part; wherein step S13 includes the following steps: Step S131: extracting the part skeleton structure based on the part mesh structure feature data, thereby obtaining the part skeleton structure data; Step S132: performing morphological corrosion and expansion processing on the part mesh structure feature data, thereby obtaining part mesh morphological processing structure data, and performing part morphological feature integration according to the part mesh morphological processing structure data, thereby obtaining mesh part morphological feature data; Step S133: performing part morphological symmetry measurement on the part skeleton structure data according to the mesh part morphological feature data, thereby obtaining morphological symmetry data; Step S134: performing mesh morphological feature similarity calculation based on the mesh part morphological feature data, thereby obtaining mesh morphological feature similarity data, and performing mesh connection relationship integration based on the mesh morphological feature similarity data, thereby obtaining part mesh connection data; Step S135: performing symmetrical shape connection correction on the part mesh connection data according to the shape symmetry data, thereby obtaining part shape connection data; Step S136: integrating part morphology geometric features based on the morphology symmetry data and part morphology connection data, thereby obtaining part geometric feature data; Step S14: constructing a three-dimensional geometric model of the part according to the geometric feature data of the part, thereby obtaining a set of geometric digital models of the part; Step S15: performing part common edge relationship analysis based on the part geometric digital model set, thereby obtaining part common edge relationship data; Step S2: planning the part cutting path according to the initial layout part structure data, thereby obtaining the part cutting path data, and optimizing the part cutting path data based on the part common edge relationship data, thereby obtaining the optimized part cutting path data; Step S3: performing linear layout planning based on the optimized part cutting path data, thereby obtaining part layout data; obtaining a laser cutting parameter set, and performing laser cutting simulation on the part layout data according to the laser cutting parameter set, thereby obtaining laser cutting simulation data; Step S4: Visualizing the thermal distribution of plate cutting according to the laser cutting simulation data, thereby obtaining the thermal distribution data of plate cutting, and optimizing the layout heat balance of parts layout data according to the thermal distribution data of plate cutting, thereby obtaining the optimized parts layout data; Step S5: Evaluate the cutting waste rate of the optimized part layout data to obtain layout cutting waste rate data, and optimize the optimized part layout data for the minimum cutting waste rate based on the layout cutting waste rate data to obtain the optimal part layout data.

2. The hybrid common edge analysis method for nesting and typesetting based on laser cutting according to claim 1 is characterized in that: Step S15 is specifically as follows: Step S151: extracting part boundary features based on the part geometric digital model set, thereby obtaining part boundary feature data; Step S152: performing edge endpoint coordinate matching according to the part boundary feature data, thereby obtaining edge endpoint matching data; Step S153: determining the common edge relationship of parts based on the edge endpoint matching data, thereby obtaining the common edge determination data of parts; Step S154: constructing an adjacency matrix for the part common edge determination data, thereby obtaining a part adjacency matrix; Step S155: identifying the part common edge relationship of the part geometric digital model set according to the part adjacency matrix, thereby obtaining the part common edge relationship data.

3. The hybrid common edge analysis method for nesting and typesetting based on laser cutting according to claim 1 is characterized in that: Step S2 is specifically as follows: Step S21: planning the part cutting path according to the initial layout part structure data, thereby obtaining the part cutting path data; Step S22: marking the part co-edge structure of the preliminary layout part structure data according to the part co-edge relationship data, thereby obtaining the preliminary layout part co-edge structure data; Step S23: integrating the part cutting path data with the common edge structure cutting path according to the initial layout part common edge structure data, thereby obtaining the common edge cutting path data; Step S24: performing layout common edge spatial distribution integration on the initial layout part common edge structure data, thereby obtaining layout common edge spatial distribution data; Step S25: performing repeated cutting path weight reduction optimization on the common edge cutting path data based on the typesetting common edge spatial distribution data, thereby obtaining the reduced weight cutting path data; Step S26: performing cutting path coupling on the weight reduction cutting path data and the part cutting path data, so as to obtain optimized part cutting path data.

4. The hybrid common edge analysis method for nesting and typesetting based on laser cutting according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S31: extracting part layout features from the initial layout part structure data, thereby obtaining initial part layout data; Step S32: Calculating the cutting path deviation based on the optimized part cutting path data and the part cutting path data, thereby obtaining the part cutting path deviation data; Step S33: performing layout linear adjustment on the initial part layout data according to the part cutting path deviation data, thereby obtaining the part layout data; Step S34: obtaining a laser cutting parameter set, and setting a laser cutting simulation boundary condition according to the laser cutting parameter set, thereby obtaining a laser cutting simulation boundary condition; Step S35: performing laser cutting simulation on the part layout data according to the laser cutting parameter set and using the laser cutting simulation boundary conditions, thereby obtaining laser cutting simulation data.

5. The hybrid common edge analysis method for nesting and typesetting based on laser cutting according to claim 1 is characterized in that: Step S4 is specifically as follows: Step S41: extracting thermal distribution features of laser cutting simulation data to obtain simulated thermal distribution data; Step S42: Visualizing the thermal distribution of the plate according to the simulated thermal distribution data, thereby obtaining the thermal distribution data of the plate cutting; Step S43: identifying the hot spots of plate cutting based on the plate cutting thermal distribution data, thereby obtaining the hot spots of plate cutting data, and calculating the local heat load of the plate based on the hot spots of plate cutting data, thereby obtaining the local heat load data of the plate; Step S44: clustering the thermal imbalance regions of the plate according to the local heat load data of the plate, thereby obtaining the thermal imbalance region data of the plate; Step S45: Based on the data of the plate heat imbalance area, the part layout data is optimized for the local heat load balance of the plate layout, so as to obtain the optimized part layout data.

6. The hybrid common edge analysis method for nesting and typesetting based on laser cutting according to claim 1 is characterized in that: Step S5 is specifically as follows: Step S51: dividing the optimized part layout data into layout areas, thereby obtaining part layout area data; Step S52: evaluating the regional cutting waste rate according to the part layout area data, thereby obtaining layout cutting waste rate data; Step S53: optimizing the plate layout waste rate of the optimized part layout data according to the layout cutting waste rate data, thereby obtaining waste rate optimized layout data; Step S54: performing minimum scrap rate layout selection on the scrap rate optimized layout data, thereby obtaining the optimal part layout data.

7. The hybrid common edge analysis method for nesting and typesetting based on laser cutting according to claim 6 is characterized in that: Step S52 is specifically as follows: Step S521: Calculating the regional parts scrap rate for the parts layout regional data, thereby obtaining regional parts scrap rate data; Step S522: classifying the parts scrap rate according to the regional parts scrap rate data, thereby obtaining regional high scrap rate parts data and regional low scrap rate parts data; Step S523: Calculating the regional parts scrap rate ratio for the regional high scrap rate parts data and the regional low scrap rate parts data, thereby obtaining the regional parts scrap rate ratio data; Step S524: classifying the part layout area data according to the regional part scrap rate ratio data, thereby obtaining high scrap rate area data and low scrap rate area data; Step S525: Calculate the average typesetting waste rate based on the high waste rate area data and the low waste rate area data, so as to obtain the typesetting cutting waste rate data.

8. A hybrid common edge analysis system for nesting and typesetting based on laser cutting, characterized in that: Used to execute the nesting layout mixed common edge analysis method based on laser cutting as claimed in claim 1, the nesting layout mixed common edge analysis system based on laser cutting comprises: The morphological analysis module is used to obtain the preliminary layout part structure data, and perform part geometric morphological analysis on the preliminary layout part structure data, so as to obtain the part geometric digital model set; based on the part geometric digital model set, perform part common edge relationship analysis, so as to obtain part common edge relationship data; The common edge path optimization module is used to plan the part cutting path according to the initial layout part structure data, so as to obtain the part cutting path data, and optimize the part cutting path data based on the part common edge relationship data, so as to obtain the optimized part cutting path data; The laser cutting simulation module is used to perform linear layout planning based on the optimized part cutting path data, thereby obtaining part layout data; obtain a laser cutting parameter set, and perform laser cutting simulation on the part layout data according to the laser cutting parameter set, thereby obtaining laser cutting simulation data; The heat balance optimization module is used to visualize the thermal distribution of plate cutting according to the laser cutting simulation data, so as to obtain the thermal distribution data of plate cutting, and optimize the layout heat balance of parts layout data according to the thermal distribution data of plate cutting, so as to obtain the optimized part layout data; The scrap rate optimization module is used to evaluate the cutting scrap rate of the optimized part layout data, so as to obtain the layout cutting scrap rate data, and optimize the optimized part layout data to the minimum cutting scrap rate based on the layout cutting scrap rate data, so as to obtain the optimal part layout data.

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