A method and system for mixed blanking layout and cutting path optimization of steel special-shaped components

Abstract

The application discloses a steel special-shaped component mixed blanking layout and cutting path optimization method and system, the method collects component contour and steel plate raw material information and standardizes processing; with the same material and thickness as the grouping basis, components suitable for different equipment are included in the same layout pool, and an improved adaptive genetic algorithm is used to globally optimize the plate utilization rate and the empty stroke loss as the joint target; in the layout iteration, the ant colony algorithm is used to evaluate the empty stroke and feedback to the fitness function, realizing the collaborative optimization of layout and path; a differentiated thermal deformation compensation model is established according to the contour curvature, the component contour is pre-compensated in the normal direction and the reserved spacing is dynamically adjusted; the cutting path code and process parameters are distributed to multiple devices for parallel execution, and when the device fails or the order changes, the task dynamic reallocation is automatically executed. The application effectively improves the comprehensive utilization rate of the plate, shortens the cutting empty stroke, guarantees the size accuracy of the special-shaped component, and enhances the disturbance resistance of the production line.

Description

Technical Field

[0001] This invention belongs to the field of steel irregular component processing technology, specifically relating to a method and system for optimizing mixed blanking and cutting paths of steel irregular components. Background Technology

[0002] In the steel structure manufacturing industry chain, the material cutting process is the primary link that determines material costs and production efficiency. Steel material costs typically account for a large proportion of the total cost of component production, and the raw material utilization rate and processing efficiency of the material cutting process directly determine the overall production cost and delivery cycle of the product. Currently, most steel structure workshops are equipped with various types of material cutting equipment, such as plasma flame cutting machines, planar laser cutting machines, and fiber laser tube cutting machines, to meet the processing needs of components with different thicknesses and precision requirements.

[0003] However, the existing material feeding production mode has the following technical defects:

[0004] First, each cutting machine typically designs its layout and assigns tasks independently. Steel components of the same material and thickness cannot share raw materials across different machines due to the different processing equipment used. This results in each machine generating a large amount of scrap material of varying specifications, which is difficult to reuse and leads to waste of steel plate raw materials. Furthermore, the significant differences in plate width and cutting process parameters between different machines exacerbate the problem of low raw material utilization efficiency through independent layout.

[0005] Secondly, the existing material preparation process generally adopts a step-by-step execution mode of "layout first, then path planning". The layout stage only considers the utilization rate of the material and does not take into account the idle travel loss of the cutting path for global optimization. The final layout plan often has problems such as excessively long empty cutting paths and unreasonable perforation point settings, which reduces the actual processing efficiency of the equipment. The decoupling of layout and path planning makes it impossible for the two stages to work together to find the best solution, making it difficult to simultaneously take into account both material utilization and processing efficiency.

[0006] Third, both plasma and laser cutting are thermal processing techniques. Localized high-temperature input during cutting can cause thermal deformation in steel components, especially for complex, irregularly shaped components where the amount of thermal shrinkage varies significantly at different curvature locations. Existing layout designs mostly only set fixed component spacing, failing to incorporate differentiated thermal deformation compensation mechanisms tailored to the contour characteristics of irregularly shaped components. This results in dimensional deviations exceeding tolerance requirements after cutting, necessitating additional rework or even outright scrapping, further increasing production costs and processing time.

[0007] Fourth, conventional nesting algorithms are not well-suited for steel irregular-shaped components with high contour complexity. Existing nesting algorithms are mostly designed for regular shapes (such as rectangles and circles), which makes it difficult to effectively handle the irregular geometric contours of irregular-shaped components. Furthermore, they cannot achieve high-density nesting while simultaneously meeting constraints such as cutting process feasibility, thermal deformation allowance spacing, and equipment processing area, making it difficult to balance raw material utilization and processing feasibility.

[0008] In summary, existing material cutting technologies have significant shortcomings in terms of multi-equipment collaboration, layout and path optimization, thermal deformation compensation, and adaptability to irregularly shaped components. There is an urgent need for a method and system for hybrid material cutting, layout, and cutting path optimization of irregularly shaped steel components that can coordinate multi-equipment resources, achieve global collaboration in layout and path, and adaptive thermal deformation compensation. Summary of the Invention

[0009] To address these issues, this invention provides a method and system for optimizing the mixed blanking and cutting path of steel irregular components. This method solves the problems of low raw material utilization caused by independent blanking by multiple devices in existing blanking technologies, high idle stroke ratio caused by step-by-step execution of blanking and path, insufficient component accuracy due to lack of differentiated thermal deformation compensation, and poor adaptability of conventional blanking algorithms to complex irregular components.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the mixed blanking and cutting path of steel irregular-shaped components, comprising the following steps:

[0011] Step S1: Collect the processing parameters of the cutting equipment in the workshop, the geometric contour and process requirements of the steel irregular component to be processed, and the basic information of the available steel plate raw materials. Perform noise reduction, simplification and parameterization on the geometric contour to establish a standardized digital dataset of raw materials and components.

[0012] Step S2: Based on the standardized raw material and component digital dataset, steel irregular components adapted to the cutting equipment are grouped into the same layout pool according to the same material and thickness. The joint optimization objectives are to maximize the comprehensive utilization rate of the plate and minimize the cutting idle stroke loss. In the layout optimization process, cutting process feasibility constraints, equipment processing area constraints and thermal deformation reserved spacing constraints are introduced. An improved adaptive genetic algorithm is used for global optimization to output a cross-equipment mixed layout scheme.

[0013] Step S3: In the nesting optimization process, a path planning algorithm is used to evaluate the cutting empty stroke of each candidate nesting scheme, and the evaluation result of the cutting empty stroke is fed back to the joint optimization objective in real time to realize the collaborative optimization of nesting and path; after the final nesting scheme is determined, cutting path planning is performed on the nesting result of a single board to generate interference-free cutting path code.

[0014] Step S4: Establish a thermal deformation prediction model based on the thermal input characteristics of the set cutting process. Combine the contour curvature distribution of the steel irregular component, the plate thickness and the cutting process parameters to calculate the thermal shrinkage compensation amount of the component contour. Perform normal pre-compensation correction on the component contour in the cross-equipment mixed nesting scheme output in Step S2. Dynamically adjust the reserved spacing of adjacent components according to the calculation results of the thermal shrinkage compensation amount to match the corresponding cutting power and cutting speed.

[0015] Step S5: Distribute the interference-free cutting path code generated in step S3 and the cutting power and cutting speed matched in step S4 to the CNC system of the designated cutting equipment to drive the cutting equipment to perform the material cutting operation in parallel; collect the operating status and processing progress of the cutting equipment in real time, and when equipment failure or order change occurs, re-include the remaining tasks to be processed in the sorting pool and perform dynamic task reallocation.

[0016] As a preferred scheme for the mixed blanking and cutting path optimization method of steel irregular components, step S1, the parameterization processing of the geometric contour includes: performing NURBS curve fitting on the contour, extracting the geometric centroid, minimum envelope rectangle and curvature value of the contour position of the component, and establishing a normalized feature vector space. The feature vector space is represented as:

[0017]

[0018] In the formula, Let be the normalized feature vector space of the j-th component; These are the geometric feature parameters extracted from the geometric contour after NURBS fitting.

[0019] As a preferred method for optimizing the mixed blanking and cutting path of steel irregular components, in step S2, the fitness function of the improved adaptive genetic algorithm is:

[0020]

[0021] In the formula, A comprehensive fitness score; The comprehensive utilization rate of the board material refers to the ratio of the actual area occupied by the components in the layout area to the total area of ​​the board material. To estimate the idle travel length factor, it is obtained by normalizing the approximate total idle travel length of all cutting head movement paths under the current layout; To constrain violations and penalties, a weighted sum is applied to cases of interference between components, exceeding the boundaries of the sheet metal, and insufficient allowance for thermal deformation. , , These are the dimensionless weighting coefficients for the comprehensive utilization rate of the board material, the estimated idle stroke length factor, and the constraint violation penalty term, respectively.

[0022] As a preferred scheme for the mixed blanking and cutting path optimization method for steel irregular components, in step S3, the ant colony algorithm is used to evaluate the cutting idle stroke of the candidate blanking scheme. In the ant colony algorithm, the state transition probability of each ant moving from the current node to the next node is:

[0023]

[0024] The pheromone update rules are as follows:

[0025]

[0026] In the formula, Let represent the probability that the k-th ant moves from node i to node j at time t; Let be the pheromone concentration on path ij at time t; , which is a heuristic factor, representing the quality of the distance from node i to j; As a factor of pheromone importance, The weights represent the importance of the heuristic factors, and both are dimensionless constants. The pheromone evaporation rate ranges from 0 to 1; s is the candidate node traversal variable; and allowed is the set of feasible nodes. The amount of pheromone added to path ij in this iteration; the shortest empty journey distance calculated by the ant colony algorithm is normalized and used as the estimated empty journey length factor. Input.

[0027] As a preferred scheme for the mixed blanking and cutting path optimization method of steel irregular components, in step S3, the cutting path planning adopts the A* algorithm for obstacle avoidance optimization. The planning order follows the priority principle of cutting the inner contour first and then the outer contour, and cutting the small-sized components first and then the large-sized components. The perforation point of the component is set at the corner of the straight section of the geometric contour.

[0028] As a preferred method for optimizing the mixed blanking and cutting path of steel irregular components, in step S4, the calculation formula for the heat shrinkage compensation amount is:

[0029]

[0030] In the formula, For the component profile in arc length The normal outward compensation amount at the location; This represents the curvature value of the contour at that location; The energy input for cutting the heat input line is the heat input per unit length; The thickness of the plate corresponding to the component; The thermal deformation response coefficient of the material is obtained by experimental calibration based on the linear expansion coefficient of the material under corresponding processes and constraint conditions. This is the curvature sensitivity coefficient, used to adjust the degree to which curvature amplifies the compensation amount; The curvature enhancement coefficient, which is in the dimension of length, is used to control the attenuation or amplification of the curvature effect as the arc length changes; the component contour is pre-compensated outward in the normal direction according to the thermal shrinkage compensation amount to generate the actual cutting contour.

[0031] As a preferred method for optimizing the mixed blanking and cutting path of steel irregular components, in step S4, the calculation formula for the dynamic reserved spacing between adjacent components is:

[0032]

[0033] In the formula, For two adjacent components and The actual reserved spacing between them; It is the basic process spacing, the minimum safe distance required to meet the cutting kerf and basic heat-affected zone; and Components and Arc length on corresponding contour Thermal shrinkage compensation at the location; This indicates taking the maximum value of the compensation amount on the component profile.

[0034] As a preferred scheme for the mixed blanking and cutting path optimization method for steel irregular components, in step S4, the matching relationship between the cutting speed and the cutting power is as follows:

[0035]

[0036] In the formula, This refers to the cutting speed; Energy absorption efficiency represents the proportion of input power actually used to heat and melt metal. Output power for the cutting equipment; The density of the material; The kerf width is the equivalent width of the effective heating area of ​​the nozzle. Specific heat capacity of the material; This refers to the melting point temperature of the material. The ambient temperature.

[0037] As a preferred scheme for the mixed blanking and cutting path optimization method for steel irregular components, in step S5, the dynamic task reassignment aims to minimize the maximum completion time of all remaining tasks to be processed. The formula for calculating the maximum completion time is:

[0038]

[0039] In the formula, The maximum completion time for all remaining pending tasks; Unfinished represents the set of pending tasks that have not yet been completed. The restart time of unprocessed task j is determined by the start time when the task is assigned to an available device in the rescheduling algorithm; The estimated processing time for this task is obtained by dividing the total length of the cutting path by the cutting speed of the corresponding equipment.

[0040] This invention also provides a system for optimizing the mixed cutting and layout of steel irregular-shaped components and the cutting path, used in the above-mentioned method for optimizing the mixed cutting and layout of steel irregular-shaped components and the cutting path, comprising:

[0041] The multi-source data acquisition and standardized preprocessing module is used to collect the processing parameters of the cutting equipment in the workshop, the geometric contours and process requirements of the steel irregular components to be processed, and the basic information of the available steel plate raw materials. The geometric contours are denoised, simplified and parameterized to establish a standardized digital dataset of raw materials and components.

[0042] The cross-equipment mixed layout global optimization module is used to group steel irregular components adapted to the set cutting equipment into the same layout pool based on the standardized raw material and component digital dataset, using the same material and thickness as the grouping criteria. The joint optimization objectives are to maximize the comprehensive utilization rate of the plate and minimize the cutting idle stroke loss. In the layout optimization process, cutting process feasibility constraints, equipment processing area constraints and thermal deformation reserved spacing constraints are introduced. An improved adaptive genetic algorithm is used for global optimization to output a cross-equipment mixed layout scheme.

[0043] The nesting-path collaborative optimization and cutting path generation module is used to evaluate the cutting empty stroke of each candidate nesting scheme by using a path planning algorithm during the nesting optimization process, and to feed back the evaluation result of the cutting empty stroke to the joint optimization objective in real time, so as to realize the collaborative optimization of nesting and path; after the final nesting scheme is determined, the cutting path planning is performed on the nesting result of a single board to generate interference-free cutting path code;

[0044] The cutting thermal deformation prediction and dynamic compensation module is used to establish a thermal deformation prediction model based on the thermal input characteristics of the set cutting process. Combining the contour curvature distribution of the steel irregular component, the plate thickness and the cutting process parameters, it calculates the thermal shrinkage compensation amount of the component contour, performs normal pre-compensation correction on the component contour in the output cross-equipment mixed nesting scheme, and dynamically adjusts the reserved spacing of adjacent components according to the calculation result of the thermal shrinkage compensation amount, matching the corresponding cutting power and cutting speed.

[0045] The multi-device task distribution and dynamic scheduling module is used to distribute the generated non-interference cutting path code and the matching cutting power and cutting speed to the CNC system of the designated cutting equipment, driving the cutting equipment to perform material cutting operations in parallel; it collects the operating status and processing progress of the cutting equipment in real time, and when equipment failure or order change occurs, it re-includes the remaining tasks to be processed in the sorting pool and performs dynamic task reallocation.

[0046] The present invention has the following advantages:

[0047] This invention breaks down the resource silos of each cutting device using raw materials independently by establishing a cross-equipment mixed sampling pool for components of the same material and thickness. It unifies and coordinates the collection of whole sheets and scrap materials, and combines the global optimization capability of the improved adaptive genetic algorithm to significantly improve the comprehensive utilization rate of steel plate raw materials, effectively reduce the amount of scrap materials generated, and save steel material procurement costs.

[0048] This invention incorporates a rapid evaluation mechanism for the empty travel of the cutting path during the nesting iteration process. The approximate shortest empty travel distance calculated by the ant colony algorithm is fed back to the nesting fitness function in real time, forming a closed-loop collaborative optimization of nesting and path planning. This avoids the defect of easily getting trapped in local optima under the traditional step-by-step execution mode, effectively shortens the invalid empty travel time of the cutting head, and improves the overall processing cycle and equipment utilization rate of multiple cutting devices operating in parallel.

[0049] This invention establishes a differentiated thermal deformation compensation model based on the curvature distribution of the component contour. Taking into account the characteristic that the greater the curvature, the more significant the thermal shrinkage, a curvature enhancement factor is used to achieve adaptive allocation of the compensation amount. Combined with the matching relationship between cutting power and speed, the heat input is controlled in reverse. The influence of thermal deformation is offset from two dimensions: contour pre-compensation and process parameter control. This significantly reduces the dimensional deviation of the component after cutting, reduces subsequent trimming processes and scrap losses, and ensures the delivery accuracy of complex irregular components.

[0050] This invention uses an improved adaptive genetic algorithm as the optimization engine for hybrid nesting and introduces an ant colony algorithm to quickly evaluate the idle travel loss of candidate nesting. The two work together to achieve high-density nesting under multiple conditions such as equipment processing area constraints, cutting process feasibility constraints, and thermal deformation reserved spacing constraints, which significantly improves the adaptability of this invention to the nesting of steel irregular components with complex contours and different shapes.

[0051] This invention collects the operating status and processing progress of each cutting device in real time. When unexpected situations such as equipment failure or order changes occur, it automatically performs rescheduling and reassignment of the remaining tasks. By dynamically updating the sorting pool and re-calling the optimization algorithm, it quickly generates an adjusted plan, avoiding the shutdown of the entire production line due to the downtime of a single device, and ensuring the continuity of the material cutting process and the resilience of the production line. Attached Figure Description

[0052] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0053] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0054] Figure 1 This is a schematic diagram of the method for optimizing the mixed blanking and cutting path of steel irregular components provided in an embodiment of the present invention;

[0055] Figure 2 This is a technical roadmap for the method of mixed blanking and cutting path optimization of steel irregular components provided in the embodiments of the present invention;

[0056] Figure 3 This is a comparative analysis chart of the comprehensive utilization rate of the board material provided in the embodiments of the present invention;

[0057] Figure 4 This is a comparative analysis diagram of the cutting empty stroke length provided in the embodiments of the present invention;

[0058] Figure 5 This is a comparative analysis diagram of the dimensional deviations of typical components after cutting, provided in an embodiment of the present invention.

[0059] Figure 6 This is a dynamic rescheduling response time decomposition diagram provided in an embodiment of the present invention;

[0060] Figure 7 This is a system architecture diagram of the mixed blanking and cutting path optimization system for steel irregular components provided in an embodiment of the present invention. Detailed Implementation

[0061] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Example 1

[0063] See Figure 1 and Figure 2 This invention provides a method for optimizing the mixed blanking and cutting path of steel irregular-shaped components, comprising the following steps:

[0064] Step S1: Collect the processing parameters of the cutting equipment in the workshop, the geometric contour and process requirements of the steel irregular component to be processed, and the basic information of the available steel plate raw materials. Perform noise reduction, simplification and parameterization processing on the geometric contour to establish a standardized digital dataset of raw materials and components.

[0065] Specifically, the cutting equipment in the workshop typically includes various types such as plasma cutters, laser cutters, and pipe cutters. Each piece of equipment has different processing areas, cutting speed ranges, cutting power, and nozzle parameters. These parameters directly affect the rationality of equipment matching during layout. The data of the components to be processed often comes from CAD design drawings. Drawings from different sources may contain noise points, redundant line segments, or tiny gaps. Directly using these for layout calculations can lead to collision detection failure or path planning failure. Therefore, it is necessary to perform noise reduction and simplification of the geometric contours. Parametric processing accurately describes the contours of irregular, non-standard components mathematically. This invention uses NURBS curve fitting technology to transform discrete coordinate points into continuous parametric curves. This technology can accurately describe arbitrarily complex shapes while maintaining the first and second order continuous differentiability of the curves, providing analytical geometric information for curvature calculation and path planning. The significance of establishing a standardized dataset lies in converting multi-source heterogeneous raw data (different CAD formats, different process templates, different equipment parameters) into computer-processable data in a unified format, avoiding inter-module compatibility issues caused by inconsistent data formats.

[0066] Step S2: Based on the standardized raw material and component digital dataset, steel irregular components adapted to the cutting equipment are grouped into the same layout pool according to the same material and thickness. The joint optimization objectives are to maximize the comprehensive utilization rate of the plate and minimize the cutting idle stroke loss. In the layout optimization process, cutting process feasibility constraints, equipment processing area constraints and thermal deformation reserved spacing constraints are introduced. An improved adaptive genetic algorithm is used for global optimization to output a cross-equipment mixed layout scheme.

[0067] Specifically, traditional nesting methods operate independently on a per-equipment basis. Components of the same material and thickness cannot be used on the same steel plate because they are assigned to different equipment, resulting in a large amount of scrap material of varying specifications generated by each equipment. This step groups components from different equipment into the same nesting pool based on the same material and thickness, breaking down the raw material barriers between equipment at the source. This allows components on a single steel plate to be allocated to both plasma cutting and laser cutting, achieving cross-equipment utilization. In the joint optimization objective, there is an inherent coupling relationship between maximizing the overall utilization rate of the plate and minimizing the cutting idle travel loss. Increasing the nesting density often leads to smaller gaps between components, which reduces the freedom of the cutting head's perforation position and may increase the idle travel distance in the cutting path. Therefore, an improved adaptive genetic algorithm is used instead of a single-objective optimization algorithm, which is essentially a multi-objective collaborative optimization method. Cutting process feasibility constraints are introduced. The nesting scheme must not only meet the geometric conditions of no interference mathematically, but also meet the feasibility requirements of the actual cutting process, such as the nozzle diameter limit for plasma cutting, the kerf width for laser cutting, and the pipe diameter range of the pipe cutting machine. A constraint on the processing area of ​​the equipment is introduced to ensure that the total size of the components on any sheet does not exceed the maximum cutting area of ​​the processing equipment corresponding to that sheet. A constraint on the allowance for thermal deformation is introduced to consider the impact of subsequent thermal deformation during the nesting stage, leaving a reasonable process allowance for compensation. Compared with the conventional genetic algorithm, the improved adaptive genetic algorithm improves by adaptively adjusting the crossover and mutation probability with population diversity and individual fitness. It can maintain a high global search capability in the early stage of the solution to quickly approach the optimal region, and reduce the mutation rate during the convergence stage in the later stage of the solution for fine adjustment. Thus, it converges to a high-quality nesting scheme faster and more stably under complex constraints.

[0068] Step S3: In the nesting optimization process, a path planning algorithm is used to evaluate the cutting empty stroke of each candidate nesting scheme, and the evaluation result of the cutting empty stroke is fed back to the joint optimization objective in real time to realize the collaborative optimization of nesting and path; after the final nesting scheme is determined, cutting path planning is performed on the nesting result of a single board to generate interference-free cutting path code.

[0069] Specifically, in conventional techniques, path planning only begins after layout is completed. This is a sequential process where layout only pursues area utilization, and the resulting layout may exhibit extremely long idle travel distances during the path planning stage, at which point it is too late to reverse the situation. This step embeds idle travel evaluation into the iteration of layout optimization. For all candidate layout schemes (potentially hundreds or thousands) generated in each generation of the genetic algorithm, the system quickly calls the path planning algorithm to calculate its approximate shortest idle travel length and feeds this length value back into the fitness function of S2. This ensures that in each iteration of genetic evolution, layout schemes with "high area utilization but long idle travel distances" are gradually eliminated with lower scores, while schemes with "slightly lower area utilization but extremely short idle travel distances" receive higher scores and are retained. This mechanism achieves closed-loop collaborative optimization of layout and path, avoiding local suboptimal optimizations in step-by-step optimization. After the final layout plan is determined, the path planning is switched from "rapid evaluation" to "fine planning" mode. The output non-interference cutting path code is the NC code directly sent to the equipment. It is necessary to ensure that the path sequence is reasonable (usually following the principle of inside to outside and small to large), the perforation point is selected appropriately (avoiding heat concentration in high curvature areas), and the path itself does not physically interfere with the already cut area to ensure that the actual cutting process is safe and smooth.

[0070] Step S4: Establish a thermal deformation prediction model based on the thermal input characteristics of the set cutting process. Combine the contour curvature distribution of the steel irregular component, the plate thickness and the cutting process parameters to calculate the thermal shrinkage compensation amount of the component contour. Perform normal pre-compensation correction on the component contour in the cross-equipment mixed nesting scheme output in step S2. Dynamically adjust the reserved spacing of adjacent components according to the calculation result of the thermal shrinkage compensation amount to match the corresponding cutting power and cutting speed.

[0071] Specifically, both plasma cutting and laser cutting are thermal processing technologies. During the cutting process, a high-temperature heat source moves along the component's contour, causing the local material to rapidly heat up to its melting point or even vaporization temperature. Simultaneously, a temperature gradient is generated in the surrounding base material due to heat conduction. When the component cools to room temperature after cutting, the shrinkage experienced by different parts is uneven. Areas with greater curvature, such as acute corners and rounded sections, shrink more significantly due to concentrated heat, while straight sections experience relatively uniform thermal shrinkage. Current technologies typically set the same foundation spacing for all components, ignoring this difference, leading to deviations between the actual dimensions of the cut component and the design dimensions, especially in high-curvature areas. The thermal deformation prediction model establishes a mapping relationship between the compensation amount and the contour curvature, heat input, and plate thickness, using a curvature enhancement factor to allocate a larger compensation amount to areas with high curvature. The pre-compensation correction of the component contour involves shifting it "outward" by a compensation amount during the design phase. The magnitude of the compensation amount is determined by the expected shrinkage during the cutting process, ensuring that after cutting and cooling shrinkage, the final dimensions of the component exactly match the design dimensions. The logic behind dynamically adjusting the spacing between adjacent components is that, as the compensated profile dimension increases, if the original basic spacing is still used, the gap between adjacent components will narrow, or even interfere. Therefore, based on the actual compensation amount, a corresponding increment is added to the basic spacing, ensuring both the process requirements of the cutting kerf and a safety margin for thermal deformation. Matching the cutting power and cutting speed controls the heat input from the source. By adjusting the cutting speed, the heat input linear energy per unit length is precisely controlled, ensuring a coordinated relationship between the thermal deformation compensation amount and the process parameters.

[0072] Step S5: Distribute the interference-free cutting path code generated in step S3 and the cutting power and cutting speed matched in step S4 to the CNC system of the designated cutting equipment to drive the cutting equipment to perform the material cutting operation in parallel; collect the operating status and processing progress of the cutting equipment in real time, and when equipment failure or order change occurs, re-include the remaining tasks to be processed in the sorting pool and perform dynamic task reallocation.

[0073] Specifically, distributing the optimized theoretical solution to the actual equipment requires converting the cutting path code into a specific format that each equipment's CNC system can recognize and execute, such as G-code. Different brands of cutting equipment CNC systems may have different extended instruction sets for G-code. Simultaneously, the cutting power, cutting speed, and other process parameters calculated by S4 are written into the corresponding process parameter table for each equipment. In this invention, multiple cutting devices operate in parallel; therefore, it is necessary to ensure a reasonable and balanced workload allocation for each device to avoid overloading one device while others remain idle. Real-time acquisition of equipment operating status is crucial for establishing closed-loop monitoring. Based on the collected data, the system determines the current operating status of each device (normal processing, standby, fault, stopped, etc.) and the number of completed components, forming a real-time production progress view. When equipment malfunctions, traditional production methods often lead to the entire production line halting while awaiting repair. However, the dynamic redistribution mechanism in this invention provides a rapid response. The system renumbers the unprocessed components on the faulty equipment and, along with other incomplete tasks from the original task list, re-includes them in the S2 nesting pool. It then re-invokes the hybrid nesting and path optimization algorithms to generate a new set of adjustment plans adapted to the currently available equipment and quickly distributes them. This dynamic scheduling capability gives the entire material handling process strong resilience and flexibility, enabling continuous production line operation even in unforeseen circumstances.

[0074] In one possible embodiment, step S1, parametric processing of the geometric contour includes: performing NURBS curve fitting on the contour, extracting the geometric centroid, minimum envelope rectangle, and curvature values ​​of the contour position, and establishing a normalized feature vector space. The feature vector space is represented as follows:

[0075]

[0076] In the formula, Let be the normalized feature vector space of the j-th component; These are the geometric feature parameters extracted from the geometric contour after NURBS fitting.

[0077] Specifically, NURBS (Non-Uniform Rational B-Spline) curves are a standard mathematical tool for describing complex free curves and surfaces in computer-aided geometric design. Their advantage lies in the ability to precisely control the curve shape through control points and weights, while maintaining the curve's continuous differentiability over the entire arc length. For irregularly shaped component profiles, their geometric centroid reflects the center position of the component's distribution on the plate surface. The minimum envelope rectangle, which is the smallest axis-aligned rectangle or arbitrary-direction rectangle that the component can just cover in the two-dimensional plane, is a fast screening criterion for determining whether components overlap in nesting collision detection. If the minimum envelope rectangles of two components do not intersect, their profiles definitely do not intersect, and no further fine-grained collision detection is needed. The curvature values ​​at each position of the profile are directly used for calculating the thermal deformation compensation in step S4. A normalized feature vector space is established. After unifying the dimensions of the above geometric feature parameters, a standardized mathematical vector is constructed. Each term in the feature vector represents a geometric feature dimension of the component, allowing components of different shapes and sizes to compare their geometric similarity in a unified feature space, providing a matching basis for crossover, mutation, and other operations in genetic algorithms.

[0078] In one possible embodiment, in step S2, the fitness function of the improved adaptive genetic algorithm is:

[0079]

[0080] In the formula, A comprehensive fitness score; The comprehensive utilization rate of the board material refers to the ratio of the actual area occupied by the components in the layout area to the total area of ​​the board material. To estimate the idle travel length factor, it is obtained by normalizing the approximate total idle travel length of all cutting head movement paths under the current layout; To constrain violations and penalties, a weighted sum is applied to cases of interference between components, exceeding the boundaries of the sheet metal, and insufficient allowance for thermal deformation. , , These are the dimensionless weighting coefficients for the comprehensive utilization rate of the board material, the estimated idle stroke length factor, and the constraint violation penalty term, respectively.

[0081] Specifically, the fitness function is the standard for evaluating the quality of individuals in a genetic algorithm; individuals with higher fitness are more likely to be selected for the next generation. The construction of this function embodies the multi-objective optimization idea of ​​this invention, with the positive term... Encouraging the generation of layouts with high board utilization is the only optimization objective of traditional layout algorithms, and negative terms... Layouts with excessively long empty paths are penalized, thereby prompting the algorithm to automatically avoid detours in the layout while seeking high utilization. Layouts with long empty paths often mean that the distance between components is far or the cutting order is unreasonable. As mandatory constraints, any scheme that violates interference constraints, plate boundary constraints, or thermal deformation allowance constraints will be severely penalized, causing these schemes to be rapidly eliminated in the genetic evolution process. Three weighting coefficients. , , The value can be adjusted according to the focus of the production site. If material costs are the main factor, increase the value. If processing efficiency is more critical, increase If extremely high precision is required, increase the [precision] level. The essential difference between this improved adaptive genetic algorithm and other algorithms lies in the fact that the entire fitness evaluation no longer focuses on the single dimension of area utilization, but instead advances the assessment of path efficiency to the nesting evolution stage to participate in individual evaluation, fundamentally changing the evolution direction of nesting, thereby achieving multi-objective synergy of "saving materials" and "saving time".

[0082] In one possible embodiment, in step S3, an ant colony algorithm is used to evaluate the candidate sorting scheme by cutting off empty runs. The state transition probability of each ant moving from the current node to the next node in the ant colony algorithm is:

[0083]

[0084] The pheromone update rules are as follows:

[0085]

[0086] In the formula, Let represent the probability that the k-th ant moves from node i to node j at time t; Let be the pheromone concentration on path ij at time t; , which is a heuristic factor, representing the quality of the distance from node i to j; As a factor of pheromone importance, The weights represent the importance of the heuristic factors, and both are dimensionless constants. The pheromone evaporation rate ranges from 0 to 1; s is the candidate node traversal variable; and allowed is the set of feasible nodes. The amount of pheromone added to path ij in this iteration; the shortest empty journey distance calculated by the ant colony algorithm is normalized and used as the estimated empty journey length factor. Input.

[0087] Specifically, the ant colony optimization algorithm is a heuristic optimization algorithm that simulates the foraging behavior of ants. Ants release pheromones along their paths while searching for food; paths with higher pheromone concentrations are more likely to be chosen by subsequent ants, thus forming a positive feedback mechanism that gradually approaches the optimal path. In this embodiment, nodes represent the perforation points of the outlines of each component to be cut in the layout, as well as the position where the cutting head returns to the starting point (or the starting point of the next component) after cutting. A path represents the empty route taken by the cutting head from the current perforation point to the next perforation point. The state transition probability formula... and The parameters determine the balance between the degree to which the algorithm relies on pheromones (historical experience) and the degree to which it relies on heuristic information (current distance) during the search process. When the size is large, ants tend to choose the better paths already taken by predecessors, which is beneficial for rapid convergence, but when... When the pheromone level is large, ants tend to choose paths that are shorter in the current distance but may not be globally optimal, which helps them escape local optima. The evaporation rate in the pheromone update rule... This ensures the algorithm doesn't infinitely amplify historical information, preventing erroneous information from early searches from continuously interfering with subsequent searches. The ant colony algorithm's advantage lies in its ability to comprehensively consider the search path results of multiple ants and continuously optimize the path through a pheromone positive feedback mechanism. It is particularly suitable for scenarios requiring the rapid search of suboptimal or optimal walking orders among multiple nodes.

[0088] In one possible embodiment, in step S3, the cutting path planning uses the A* algorithm for obstacle avoidance optimization. The planning order follows the priority principle of cutting the inner contour first and then the outer contour, and cutting small-sized components first and then large-sized components. The perforation point of the component is set at the corner of the straight segment of the geometric contour.

[0089] Specifically, the A* algorithm combines the global optimality of Dijkstra's algorithm with the heuristic strategy of best-first search, using an evaluation function. To guide the search direction, among which This represents the actual cumulative cost from the starting point to the current node (i.e., the actual path length already traversed). This represents the estimated cost from the current node to the target node (usually using Euclidean distance or Manhattan distance as a heuristic function), when... When the length of the remaining path is not greater than the actual remaining path length, the A* algorithm guarantees that the shortest path can be found. In planning the cutting order, the inner contour is cut first, followed by the outer contour. After the inner contour is cut, the material inside the component is separated. If the outer contour is cut first, the cutting process of the inner contour may cause the entire board to wobble due to reduced stiffness, affecting the cutting accuracy of the inner contour. Therefore, cutting the inner contour first and then the outer contour preserves the overall stiffness of the mother board as much as possible. Small-sized components are cut first, followed by large-sized components. Small-sized components can be quickly moved after cutting, providing more operating space for the subsequent cutting of large components and preventing large components from blocking the removal path of small components after cutting. The perforation points are set at the corners of the straight sections of the contour. The geometry of the straight sections is simple, and the deformation caused by heat input is concentrated in the corner areas with higher curvature. The thermal shock generated by the perforation has a much smaller impact on the accuracy of the straight sections than on the high-curvature areas.

[0090] In one possible embodiment, in step S4, the formula for calculating the heat shrinkage compensation amount is:

[0091]

[0092] In the formula, For the component profile in arc length The normal outward compensation amount at the location; This represents the curvature value of the contour at that location; The energy input for cutting the heat input line is the heat input per unit length; The thickness of the plate corresponding to the component; The thermal deformation response coefficient of the material is obtained by experimental calibration based on the linear expansion coefficient of the material under corresponding processes and constraint conditions. This is the curvature sensitivity coefficient, used to adjust the degree to which curvature amplifies the compensation amount; The curvature enhancement coefficient, which is in the dimension of length, is used to control the attenuation or amplification of the curvature effect as the arc length changes; the component contour is pre-compensated outward in the normal direction according to the thermal shrinkage compensation amount to generate the actual cutting contour.

[0093] Specifically, the essence of this formula is an engineering empirical model based on physical mechanisms. This specifies the baseline heat shrinkage. The higher the thermal input linear energy and the thinner the plate, the greater the heat shrinkage. This item represents the baseline amount of uniform heat shrinkage if the profile is a straight edge. (Item in brackets) It is the curvature enhancement factor, when the curvature When the value is zero (straight segment), the term in parentheses equals 1, and the compensation amount is the reference amount; as the curvature increases, the exponential term... The curvature increases rapidly, amplifying its effect through an exponential function, thus providing greater compensation to areas with high curvature. This model reflects an intuitive understanding from physics: the greater the curvature at a corner, the higher the heat concentration and the more significant the thermal contraction, thus requiring a larger pre-compensation. During actual cutting, the compensation is offset outward along the contour normal, resulting in a pre-compensated cutting trajectory. After cutting, the material's cooling and contraction precisely offsets the pre-compensation at each location, ensuring the final component's actual dimensions accurately match the original design dimensions. In the formula... The specific values ​​need to be determined in advance through process tests of the same material and thickness, measuring the dimensional deviations at different curvature positions before and after cutting, and then deriving the parameter fitting values ​​that satisfy the formula.

[0094] In one possible embodiment, in step S4, the formula for calculating the dynamic reserved spacing between adjacent components is:

[0095]

[0096] In the formula, For two adjacent components and The actual reserved spacing between them; It is the basic process spacing, the minimum safe distance required to meet the cutting kerf and basic heat-affected zone; and Components and Arc length on corresponding contour Thermal shrinkage compensation at the location; This indicates taking the maximum value of the compensation amount on the component profile.

[0097] Specifically, the actual distance between two adjacent components should not be less than the sum of the foundation process spacing and the maximum deformation of each component. (Foundation process spacing) The spacing between the bases is determined by the kerf width and the heat-affected zone width, and is usually a fixed value. For example, the kerf width for plasma cutting is approximately 3mm, and the heat-affected zone is approximately 1mm, so the minimum base spacing is 4mm. However, because the two components undergo thermal deformation in different directions during the cutting process (especially in areas near adjacent boundaries), if the layout is based solely on the base spacing, the overlapping deformation after cutting will cause the two components to squeeze and interfere with each other. In severe cases, the components may become unusable or stuck on the sheet metal and cannot be removed. The maximum values ​​of the two terms in the formula... and The maximum compensation value is taken at all positions on the contours of the two components. In other words, the system increases the spacing based on the point of most severe deformation on adjacent boundaries to ensure that the safety clearance requirement under the most stringent conditions is met, and there is no risk of interference at other positions. This strategy of taking the maximum value is called conservative design or worst-case design method in engineering. It leaves a margin based on the worst-case scenario to ensure safety in all situations.

[0098] In one possible embodiment, in step S4, the matching relationship between the cutting speed and the cutting power is as follows:

[0099]

[0100] In the formula, This refers to the cutting speed; Energy absorption efficiency represents the proportion of input power actually used to heat and melt metal. Output power for the cutting equipment; The density of the material; The kerf width is the equivalent width of the effective heating area of ​​the nozzle. Specific heat capacity of the material; This refers to the melting point temperature of the material. The ambient temperature.

[0101] Specifically, during the cutting process, the output power of the cutting equipment... A certain proportion (i.e., energy absorption efficiency) The actual amount of metal absorbed by the workpiece for heating and melting the material is [missing information], while the volume of metal cut per unit time is [missing information]. (Speed ​​× Plate Thickness × Knife Width) The energy required to heat this portion of metal from ambient temperature to its melting point is: Under steady-state cutting conditions, the input energy equals the energy required for melting, thus yielding this formula. In practical process control, this formula is used to determine the thermal input linear energy when the system needs to compensate for thermal deformation. As a control variable, it needs to remain stable. At this point, if the plate thickness... Changes, materials Or specific heat capacity The system needs to adjust the cutting power in the opposite direction if the power changes. or cutting speed To maintain The constant value. This matching relationship ensures that, in actual cutting and processing, even if different components have differences in thickness and material, the system can adjust through linkage. and Maintaining stable heat input per unit length ensures that the thermal deformation compensation model remains effective within a preset range.

[0102] In one possible embodiment, in step S5, the dynamic task reallocation aims to minimize the maximum completion time of all remaining tasks to be processed, and the formula for calculating the maximum completion time is:

[0103]

[0104] In the formula, The maximum completion time for all remaining pending tasks; Unfinished represents the set of pending tasks that have not yet been completed. The restart time of unprocessed task j is determined by the start time when the task is assigned to an available device in the rescheduling algorithm; The estimated processing time for this task is obtained by dividing the total length of the cutting path by the cutting speed of the corresponding equipment.

[0105] Specifically, in scheduling theory, the maximum completion time, or the total time taken for all tasks to complete from start to finish, is a metric for measuring the efficiency of a scheduling scheme. When a device fails, the remaining unfinished task set (Unfinished) includes tasks originally assigned to the failed device but not yet started, as well as tasks assigned to other available devices but potentially needing to be readjusted due to resource conflicts. Each task... From the rescheduling time onwards The time available for processing on the device assigned to the task depends on the device's current progress and the tasks queued ahead of it. The processing time is the result of dividing the cutting path length of the task itself by the cutting speed of the equipment. Solve for this. The minimization objective is essentially an integer programming problem. A greedy strategy is employed to sort the remaining tasks by processing time from shortest to longest, and then assign them sequentially to the equipment with the earliest completion time. This follows the greedy heuristic rule of "shortest processing time first, earliest completion equipment first," combined with a rolling window technique (processing tasks in batches instead of assigning all tasks to all equipment at once). This ensures solution quality while keeping computation time within seconds, enabling the system to respond to sudden failures in real time. This dynamic rescheduling mechanism enables the production process to self-heal. In the event of a sudden failure, the system does not need to wait for equipment repair but quickly diverts tasks to other available equipment, minimizing the impact on the overall production cycle and ensuring the continuity and resilience of the production line.

[0106] The embodiments of this invention verify the actual technical effects of the above-mentioned method through the following experimental examples. The core improvements of this invention lie in cross-equipment mixed layout, layout and path collaborative optimization, differentiated thermal deformation compensation, and dynamic rescheduling mechanism. In order to quantitatively evaluate the actual effects of these improvements, comparative experiments were conducted in a steel structure workshop environment to examine the comprehensive utilization rate of sheet metal, cutting idle stroke length, dimensional deviation of components after cutting, and dynamic rescheduling response time.

[0107] I. Experimental Objective

[0108] This test case aims to verify the actual technical effect of the method and system for mixed cutting and layout of steel irregular components proposed in this invention. The following indicators are examined: (1) comprehensive utilization rate of plate material; (2) cutting idle stroke length; (3) dimensional deviation of components after cutting (degree of thermal deformation); (4) continuous operation capability of production line under dynamic rescheduling.

[0109] II. Test Conditions

[0110] The experiment was conducted in a heavy steel structure workshop equipped with three cutting machines: a plasma flame cutting machine, a planar laser cutting machine, and a fiber laser tube cutting machine. These machines are designed to handle components of varying thicknesses and precision requirements. The plasma flame cutting machine is suitable for cutting medium-thick plates and large-sized components; the planar laser cutting machine is suitable for cutting thin plates and high-precision components; and the fiber laser tube cutting machine is suitable for cutting irregularly shaped tubular components. All three machines are CNC cutting equipment actually in use in the workshop, and their processing area, kinematic parameters, and cutting power meet industry mainstream standards.

[0111] The test plates were Q355B hot-rolled steel plates, 16mm thick, with dimensions of 3000mm×6000mm×16mm, totaling 5 sheets.

[0112] Area of ​​each sheet:

[0113] 3000 mm × 6000 mm = 18,000,000 mm 2 ;

[0114] Total area of ​​five boards used:

[0115] 18,000,000 mm 2 ×5 = 90,000,000 mm 2 .

[0116] The components to be processed are 16 irregularly shaped steel node components for a steel structure bridge project. The component outlines include rounded corners, acute angles, arc segments, and long straight edge segments, with the outline complexity ranging from simple to complex. The specific component numbers and main geometric features are shown in Table 1.

[0117] Table 1. Part Numbers and Main Geometric Features to be Processed

[0118]

[0119] The total area of ​​the 16 components is estimated as follows:

[0120] P1~P4 (4 pieces) take the average value of 2260000 mm 2 Approximately 9,040,000 mm 2 ;

[0121] P5~P10 (6 pieces) take the average value of 4,800,000 mm. 2 Approximately 28,800,000 mm 2 ;

[0122] P11~P14 (4 pieces) take the average value of 6,200,000 mm. 2 Approximately 24,800,000 mm 2 ;

[0123] P15~P16 (2 pieces) take the average value of 1,600,000 mm 2 Approximately 3,200,000 mm 2 ;

[0124] Total approximately 65,840,000 mm 2 .

[0125] The experiment was divided into a control group and the invention group. Both groups used the same component data, steel plate raw materials and equipment configuration, with the only difference being the optimization method.

[0126] III. Control Group Regimen (Traditional Method)

[0127] The control group used the traditional step-by-step feeding method:

[0128] (1) Independent layout of each equipment: After the components are allocated to the equipment, they are independently laid out using conventional layout software. The layout goal is only to maximize the utilization rate of the board area, without considering the empty travel of the cutting path, and without coordinating the surplus material across equipment.

[0129] (2) After the layout is completed, conventional path planning software is used to generate the cutting path in the default order of "inside first, outside first, large first, small first", without performing collaborative optimization of path and layout;

[0130] (3) The component spacing is set to a fixed value, 5mm for plasma cutting and 3mm for laser cutting, without any differential adjustment based on the component contour curvature;

[0131] (4) The cutting process parameters are set according to the equipment's factory experience values. The plasma cutting speed is 0.8 m / min, the laser cutting speed is 2.0 m / min, and the power is 80% of the equipment's rated power.

[0132] IV. Invention Scheme

[0133] The present invention group uses the method and system described in this invention, and the specific implementation parameters are as follows:

[0134] (1) After data standardization, the same material with 16mm diameter is grouped into the same sample pool. Plasma cutting machine and planar laser cutting machine are allowed to mix samples. P15~P16 are grouped into fiber laser tube cutting machine for separate sample arrangement.

[0135] (2) In the improved adaptive genetic algorithm, the fitness weight is set to , , The population size is 120, the maximum number of iterations is 200, the crossover probability is adaptively adjusted (initially 0.85, gradually reduced to 0.45 in the later stage), and the mutation probability is adaptively adjusted (initially 0.05, increased to 0.15 in the later stage to ensure local search capability).

[0136] (3) Path evaluation uses the ant colony algorithm with 20 ants and 30 iterations. , , ;

[0137] (4) In the thermal deformation compensation model, the material thermal deformation response coefficient is determined through preliminary calibration tests. Curvature sensitivity coefficient Curvature enhancement coefficient ;

[0138] (5) The cutting speed and power are dynamically adjusted according to the matching relationship. The heat input line energy Q of plasma cutting is controlled at 200±10 J / mm, and that of laser cutting is controlled at 120±8 J / mm.

[0139] V. Experimental Results and Analysis

[0140] (I) Comparison of the comprehensive utilization rate of wood panels

[0141] First, the comprehensive utilization rate of the board material in the two schemes was statistically compared, and the results are as follows: Figure 3 As shown in Table 2.

[0142] Table 2 Comparison of Comprehensive Utilization Rate of Board Materials

[0143]

[0144] from Figure 3As shown in Table 2, the overall utilization rate of the sheet metal in the control group was 73.20%, while that in the present invention group reached 82.50%, an increase of 9.30 percentage points. The reason for this is that in the control group, the plasma cutting machine and the planar laser cutting machine arranged their components independently. Only components assigned to that specific machine could be placed on the five 16mm sheet metals, leaving unused space on the remaining sheet metals due to size mismatch, resulting in scrap material on each machine. In contrast, the present invention group comprehensively arranged P1~P4 and P11~P14 on the five sheet metals, fully utilizing the remaining space on each sheet metal and achieving unified allocation of components across different machines, thus significantly improving material utilization.

[0145] (II) Comparison of cutting idle stroke length

[0146] After verifying the improved material utilization, the cutting idle stroke length of the two schemes was further examined, and the results are as follows: Figure 4 As shown in Table 3.

[0147] Table 3 Comparison of Cutting Idle Stroke Length

[0148]

[0149] Depend on Figure 4 As shown in Table 3, the total idle travel length of the five boards in the control group was 82.4m, while that in the present invention group was 67.7m, a reduction of 17.84%. The fundamental reason for this difference is that the control group's layout stage only aimed to maximize area utilization without considering cutting path efficiency. This resulted in some components having high utilization rates, but uneven spacing between components and unreasonable perforation point configurations, leading to excessively long idle travel distances for the cutting head between components. In the present invention group, the idle travel length evaluated by the ant colony algorithm was fed back to the fitness function in real time during the layout iteration process. This forced the algorithm to actively eliminate layout schemes with "high area utilization but long idle travel distances" during the evolution process. The final layout generated was more compact and orderly in terms of component distribution, with naturally aligned perforation points, thus effectively shortening the idle travel distance.

[0150] (III) Comparison of dimensional deviations caused by thermal deformation

[0151] To verify the actual effect of the thermal deformation compensation model, typical components P5, P8, and P13 (representing medium curvature, high curvature, and complex multi-curve contours, respectively) were selected for dimensional inspection after cutting. Eight measurement points were evenly selected along the contour of each component, and the absolute deviation between the actual dimension and the design dimension at each point was measured. The average and maximum deviation values ​​were recorded. The results are shown below. Figure 5 As shown in Table 4.

[0152] Table 4 Comparison of dimensional deviations after component cutting

[0153]

[0154] from Figure 5 As shown in Table 3, the control group used a fixed spacing and uniform process parameters, P13 (maximum curvature 0.055mm) -1 The maximum deviation of the component with the largest area reached 1.85mm, far exceeding the factory's allowable tolerance of ±1.0mm. However, the present invention adaptively allocates compensation amounts based on the curvature distribution of the component contour: approximately 0.6mm for P5 (low curvature), approximately 0.9mm for P8 (medium curvature), and approximately 1.3mm for P13 (high curvature). Simultaneously, the reserved spacing is dynamically adjusted according to the maximum compensation amount, and combined with the matching control of cutting speed and power, the maximum deviation of P13 is reduced to 0.80mm, and all components meet the ±1.0mm tolerance requirement. This result demonstrates that the differentiated thermal deformation compensation model can effectively ensure the cutting dimensional accuracy of irregularly shaped components.

[0155] (iv) Dynamic rescheduling response effect

[0156] After verifying the combined effects of layout, path, and thermal compensation, the system's dynamic response capability under sudden failure was examined. During the experiment, a plasma cutting machine was artificially simulated to stop suddenly when the second sheet was processed to 60% completion. The stoppage location included components P11 and P12 (the remaining portions were uncut). The dynamic rescheduling system of this invention performed the following operations and response times after detecting the fault signal: Figure 6 As shown in Table 5.

[0157] Table 5 Statistics on Dynamic Rescheduling Response Time

[0158]

[0159] Depend on Figure 6 As shown in Table 5, the dynamic rescheduling mechanism of this invention completes the entire process of fault detection, task reordering, path replanning, and code re-deployment within approximately 25 seconds, enabling the remaining tasks to be quickly diverted to the planar laser cutting machine and fiber laser tube cutting machine for continued execution. In contrast, in the traditional mode, plasma cutting machine failures often require waiting for repairs or manual task reassignment, typically taking several hours or more. The dynamic rescheduling mechanism of this invention significantly shortens downtime and minimizes the impact on the overall production line cycle time.

[0160] The following conclusions can be drawn from the above experimental results:

[0161] This invention improves the overall utilization rate of sheet metal by approximately 9.30 percentage points through cross-equipment hybrid nesting, significantly reducing steel material consumption and verifying the first beneficial effect. Through nesting and path optimization, this invention shortens the total cutting idle stroke length by approximately 17.84%, effectively improving equipment processing efficiency and verifying the second beneficial effect. Using a differentiated thermal deformation compensation model, this invention reduces the maximum dimensional deviation of complex irregular-shaped components from 1.85mm to 0.80mm, ensuring that component dimensional accuracy meets tolerance requirements. Through a dynamic rescheduling mechanism, this invention restores the production line within approximately 25 seconds in the event of a sudden equipment failure, ensuring the continuity of the material cutting process and the resilience of the production line.

[0162] Example 2

[0163] See Figure 7 Embodiment 2 of the present invention also provides a mixed cutting and layout system for steel irregular components, used in the above-described method for mixed cutting and layout of steel irregular components, comprising:

[0164] The multi-source data acquisition and standardization preprocessing module 100 is used to collect the processing parameters of the cutting equipment in the workshop, the geometric contour and process requirements of the steel irregular component to be processed, and the basic information of the available steel plate raw materials. The geometric contour is denoised, simplified and parameterized to establish a standardized digital dataset of raw materials and components.

[0165] The cross-equipment mixed layout global optimization module 200 is used to group steel irregular components adapted to the set cutting equipment into the same layout pool based on the standardized raw material and component digital dataset, using the same material and thickness as the grouping basis. The joint optimization objectives are to maximize the comprehensive utilization rate of the plate and minimize the cutting idle stroke loss. In the layout optimization process, cutting process feasibility constraints, equipment processing area constraints and thermal deformation reserved spacing constraints are introduced. An improved adaptive genetic algorithm is used for global optimization to output a cross-equipment mixed layout scheme.

[0166] The nesting-path collaborative optimization and cutting path generation module 300 is used to evaluate the cutting empty stroke of each candidate nesting scheme by using a path planning algorithm during the nesting optimization process, and to feed back the evaluation result of the cutting empty stroke to the joint optimization objective in real time, so as to realize the collaborative optimization of nesting and path; after the final nesting scheme is determined, cutting path planning is performed on the nesting result of a single board to generate interference-free cutting path code;

[0167] The cutting thermal deformation prediction and dynamic compensation module 400 is used to establish a thermal deformation prediction model based on the thermal input characteristics of the set cutting process. It combines the contour curvature distribution of the steel irregular component, the plate thickness and the cutting process parameters to calculate the thermal shrinkage compensation amount of the component contour. It performs normal pre-compensation correction on the component contour in the output cross-equipment mixed layout scheme and dynamically adjusts the reserved spacing of adjacent components according to the calculation result of the thermal shrinkage compensation amount to match the corresponding cutting power and cutting speed.

[0168] The multi-device task distribution and dynamic scheduling module 500 is used to distribute the generated non-interference cutting path code and the matching cutting power and cutting speed to the CNC system of the designated cutting equipment, driving the cutting equipment to perform material cutting operations in parallel; it collects the operating status and processing progress of the cutting equipment in real time, and when equipment failure or order change occurs, it re-includes the remaining tasks to be processed in the sorting pool and performs dynamic task reallocation.

[0169] It should be noted that the information interaction and execution process between the modules of the above system are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0170] Example 3

[0171] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium storing program code for a method of mixed blanking and layout and cutting path optimization of steel irregular components. The program code includes instructions for executing the method of mixed blanking and layout and cutting path optimization of steel irregular components as described in Embodiment 1 or any possible implementation thereof.

[0172] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0173] Example 4

[0174] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0175] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can call the program instructions to execute the method for optimizing the mixed blanking and cutting path of steel irregular components in Embodiment 1 or any possible implementation thereof.

[0176] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0177] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0178] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0179] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for optimizing the layout and cutting path of steel profiled member mixed blanking, characterized in that, Includes the following steps: Step S1: Collect the processing parameters of the cutting equipment in the workshop, the geometric contour and process requirements of the steel irregular component to be processed, and the basic information of the available steel plate raw materials. Perform noise reduction, simplification and parameterization on the geometric contour to establish a standardized digital dataset of raw materials and components. Step S2: Based on the standardized raw material and component digital dataset, steel irregular components adapted to the cutting equipment are grouped into the same layout pool according to the same material and thickness. The joint optimization objectives are to maximize the comprehensive utilization rate of the plate and minimize the cutting idle stroke loss. In the layout optimization process, cutting process feasibility constraints, equipment processing area constraints and thermal deformation reserved spacing constraints are introduced. An improved adaptive genetic algorithm is used for global optimization to output a cross-equipment mixed layout scheme. Step S3: In the nesting optimization process, a path planning algorithm is used to evaluate the cutting of empty strokes for each candidate nesting scheme, and the evaluation results of cutting empty strokes are fed back to the joint optimization objective in real time to achieve collaborative optimization of nesting and path. After the final layout plan is determined, the cutting path is planned for the layout results of a single sheet of material to generate non-interference cutting path code; Step S4: Establish a thermal deformation prediction model based on the thermal input characteristics of the set cutting process. Combine the contour curvature distribution of the steel irregular component, the plate thickness and the cutting process parameters to calculate the thermal shrinkage compensation amount of the component contour. Perform normal pre-compensation correction on the component contour in the cross-equipment mixed nesting scheme output in Step S2. Dynamically adjust the reserved spacing of adjacent components according to the calculation results of the thermal shrinkage compensation amount to match the corresponding cutting power and cutting speed. Step S5: Distribute the interference-free cutting path code generated in step S3 and the cutting power and cutting speed matched in step S4 to the CNC system of the designated cutting equipment to drive the cutting equipment to perform the material cutting operation in parallel; collect the operating status and processing progress of the cutting equipment in real time, and when equipment failure or order change occurs, re-include the remaining tasks to be processed in the sorting pool and perform dynamic task reallocation.

2. The method of claim 1, wherein the method further comprises: In step S1, the parameterization processing of the geometric contour includes: performing NURBS curve fitting on the contour, extracting the geometric centroid, minimum envelope rectangle, and curvature values ​​of the contour position, and establishing a normalized feature vector space. The feature vector space is represented as follows: ， In the formula, is the normalized feature vector space of the jth component; are the geometric feature parameters extracted from the NURBS fitting of the geometric profile, respectively.

3. The method of claim 2, wherein the method further comprises: determining a cutting path for each of the steel profiled members based on the determined layout of the steel profiled members. In step S2, the fitness function of the improved adaptive genetic algorithm is: ， wherein, is the overall fitness score; is the overall utilization rate of the plate, which refers to the ratio of the actual area occupied by the components in the layout area to the total area of the plate; is the estimated idle stroke length factor, which is obtained by normalizing the approximate total idle length of the movement paths of all cutting heads under the current layout; is the constraint violation penalty term, which includes the weighted sum of the cases of insufficient interference between components, exceeding the plate boundary, and insufficient heat deformation reserved spacing; , , are the dimensionless weight coefficients of the overall utilization rate of the plate, the estimated idle stroke length factor, and the constraint violation penalty term, respectively.

4. The method of claim 1, wherein, In step S3, the ant colony algorithm is used to evaluate the candidate sorting scheme by cutting off empty runs. In the ant colony algorithm, the state transition probability of each ant moving from the current node to the next node is: ， The pheromone update rules are as follows: ， In the formula, Let represent the probability that the k-th ant moves from node i to node j at time t; Let be the pheromone concentration on path ij at time t; , which is a heuristic factor, representing the quality of the distance from node i to j; As a factor of pheromone importance, The weights represent the importance of the heuristic factors, and both are dimensionless constants. The pheromone evaporation rate ranges from 0 to 1; s is the candidate node traversal variable; and allowed is the set of feasible nodes. The amount of pheromone added to path ij in this iteration; the shortest empty journey distance calculated by the ant colony algorithm is normalized and used as the estimated empty journey length factor. Input.

5. The method for optimizing the mixed blanking and cutting path of steel irregular components according to claim 1, characterized in that, In step S3, the cutting path planning uses the A* algorithm for obstacle avoidance optimization. The planning order follows the priority principle of cutting the inner contour first and then the outer contour, and cutting small-sized components first and then large-sized components. The perforation point of the component is set at the corner of the straight segment of the geometric contour.

6. The method for optimizing the mixed blanking and cutting path of steel irregular components according to claim 1, characterized in that, In step S4, the formula for calculating the heat shrinkage compensation amount is: ， In the formula, For the component profile in arc length The normal outward compensation amount at the location; This represents the curvature value of the contour at that location; The energy input for cutting the heat input line is the heat input per unit length; The thickness of the plate corresponding to the component; The thermal deformation response coefficient of the material is obtained by experimental calibration based on the linear expansion coefficient of the material under corresponding processes and constraint conditions. This is the curvature sensitivity coefficient, used to adjust the degree to which curvature amplifies the compensation amount; The curvature enhancement coefficient, which is in the dimension of length, is used to control the attenuation or amplification of the curvature effect as the arc length changes; the component contour is pre-compensated outward in the normal direction according to the thermal shrinkage compensation amount to generate the actual cutting contour.

7. The method for optimizing the mixed blanking and cutting path of steel irregular components according to claim 6, characterized in that, In step S4, the formula for calculating the dynamic reserved spacing between adjacent components is: ， In the formula, For two adjacent components and The actual reserved spacing between them; It is the basic process spacing, the minimum safe distance required to meet the cutting kerf and basic heat-affected zone; and Components and Arc length on corresponding contour Thermal shrinkage compensation at the location; This indicates taking the maximum value of the compensation amount on the component profile.

8. The method for optimizing the mixed blanking and cutting path of steel irregular components according to claim 6, characterized in that, In step S4, the matching relationship between the cutting speed and the cutting power is as follows: ， In the formula, This refers to the cutting speed; Energy absorption efficiency represents the proportion of input power actually used to heat and melt metal. Output power for the cutting equipment; The density of the material; The kerf width is the equivalent width of the effective heating area of ​​the nozzle. Specific heat capacity of the material; This refers to the melting point temperature of the material. The ambient temperature.

9. The method for optimizing the mixed blanking and cutting path of steel irregular components according to claim 1, characterized in that, In step S5, the dynamic task reallocation aims to minimize the maximum completion time of all remaining tasks to be processed. The formula for calculating the maximum completion time is: ， In the formula, The maximum completion time for all remaining pending tasks; Unfinished represents the set of pending tasks that have not yet been completed. The restart time of unprocessed task j is determined by the start time when the task is assigned to an available device in the rescheduling algorithm; The estimated processing time for this task is obtained by dividing the total length of the cutting path by the cutting speed of the corresponding equipment.

10. A system for optimizing the mixed cutting and layout of steel irregular-shaped components, used to execute the method for optimizing the mixed cutting and layout of steel irregular-shaped components as described in any one of claims 1 to 9, characterized in that, include: The multi-source data acquisition and standardized preprocessing module is used to collect the processing parameters of the cutting equipment in the workshop, the geometric contours and process requirements of the steel irregular components to be processed, and the basic information of the available steel plate raw materials. The geometric contours are denoised, simplified and parameterized to establish a standardized digital dataset of raw materials and components. The cross-equipment mixed layout global optimization module is used to group steel irregular components adapted to the set cutting equipment into the same layout pool based on the standardized raw material and component digital dataset, using the same material and thickness as the grouping criteria. The joint optimization objectives are to maximize the comprehensive utilization rate of the plate and minimize the cutting idle stroke loss. In the layout optimization process, cutting process feasibility constraints, equipment processing area constraints and thermal deformation reserved spacing constraints are introduced. An improved adaptive genetic algorithm is used for global optimization to output a cross-equipment mixed layout scheme. The nesting-path collaborative optimization and cutting path generation module is used to evaluate the cutting empty stroke of each candidate nesting scheme by using a path planning algorithm during the nesting optimization process, and to feed back the evaluation result of the cutting empty stroke to the joint optimization objective in real time, so as to realize the collaborative optimization of nesting and path; after the final nesting scheme is determined, the cutting path planning is performed on the nesting result of a single board to generate interference-free cutting path code; The cutting thermal deformation prediction and dynamic compensation module is used to establish a thermal deformation prediction model based on the thermal input characteristics of the set cutting process. Combining the contour curvature distribution of the steel irregular component, the plate thickness and the cutting process parameters, it calculates the thermal shrinkage compensation amount of the component contour, performs normal pre-compensation correction on the component contour in the output cross-equipment mixed nesting scheme, and dynamically adjusts the reserved spacing of adjacent components according to the calculation result of the thermal shrinkage compensation amount, matching the corresponding cutting power and cutting speed. The multi-device task distribution and dynamic scheduling module is used to distribute the generated non-interference cutting path code and the matching cutting power and cutting speed to the CNC system of the designated cutting equipment, driving the cutting equipment to perform material cutting operations in parallel; it collects the operating status and processing progress of the cutting equipment in real time, and when equipment failure or order change occurs, it re-includes the remaining tasks to be processed in the sorting pool and performs dynamic task reallocation.

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