A material utilization-based automatic tape-laying path planning method and system

Abstract

The present application belongs to the technical field of data processing, and particularly relates to a material utilization rate-based automatic tape laying path planning method and system, which comprises the following steps: a genetic algorithm is adopted, and a dynamic layout optimization operator is introduced in the iteration process, the operator identifies the idle area and flexible parts with optimization potential by calculating the geometric agility of the parts and the local optimizable degree of the plate, and fine-tunes the target parts to reduce the idle area. The present application changes the layout scheme from a static constraint to an evolutionary entity, and can improve the material utilization rate under the premise of ensuring that the processing time meets the constraint.

Description

Technical Field

[0001] This invention relates to the field of data processing technology. More specifically, this invention relates to an automatic tape laying path planning method and system based on material utilization. Background Technology

[0002] In aerospace, automotive manufacturing, and other fields, automated tape laying technology is a core process for manufacturing large, complex composite components. This process typically involves laying and cutting multiple parts of varying shapes from expensive prepreg raw materials. Therefore, efficiently planning the arrangement of parts on the sheet metal and the processing path of the tape laying equipment to maximize material utilization is a key aspect of reducing manufacturing costs and improving production efficiency.

[0003] To solve this complex combinatorial optimization problem, existing technologies often employ intelligent optimization algorithms such as genetic algorithms. However, these algorithms typically suffer from the following technical problems: First, traditional path planning methods often treat the layout of parts as a pre-defined, static, hard constraint. That is, a one-time layout is completed first, and subsequent path optimization is entirely based on this fixed layout, which severely limits the algorithm's optimization space. If the initial layout has even minor flaws or room for optimization, subsequent algorithms cannot adjust it, making it difficult to overcome the bottleneck of material utilization. Second, existing methods often focus solely on material utilization during optimization, neglecting the stringent requirements of processing time in industrial production. This may result in plans with extremely high material utilization but excessively long processing times, making them impractical for actual production.

[0004] Therefore, how to improve the algorithm to dynamically adjust the layout during the optimization process while taking into account multiple production constraints has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] To address the aforementioned technical problem of poor automatic tape laying path planning performance, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides an automatic tape laying path planning method based on material utilization, comprising:

[0007] Obtain the geometric information of all parts to be processed and the performance parameters of the tape laying equipment; generate an initial layout scheme containing the position information of each part based on a heuristic algorithm, and construct the initial population of the genetic algorithm by combining random processing order and equipment allocation; during the iteration of the genetic algorithm, execute a dynamic layout optimization operator to adjust the initial layout scheme to obtain an adjusted layout scheme. The dynamic layout optimization operator includes: calculating the geometric agility of each part, wherein the geometric agility is negatively correlated with the part area and positively correlated with the square of the part perimeter; dividing the sheet metal into multiple local regions; calculating the local optima of each local region, wherein the local optima is positively correlated with the vacant area of ​​the corresponding local region and the average geometric agility of its adjacent parts; taking the local region with the largest local optima as the target region, and taking the adjacent part with the largest geometric agility in the target region as the target part; moving the target part within a set search range to reduce the vacant area of ​​the target region; calculating the material utilization rate based on the adjusted layout scheme and obtaining the fitness value of each individual; operating the population based on the fitness value until the termination condition is met, and decoding the individual with the highest fitness value to obtain the final tape laying path.

[0008] This invention embeds the geometric information of the layout scheme as an evolvable entity into the iterative process of a genetic algorithm. Through a dynamic layout optimization operator, utilizing geometric agility and local optima indices, it intelligently identifies and fine-tunes the most easily adjustable parts and empty areas, achieving collaborative optimization of the layout and tape laying path. This dynamic adjustment mechanism can search for better solutions globally, thereby improving material utilization.

[0009] Preferably, the heuristic algorithm is a best-fit algorithm, including:

[0010] Sort all parts to be processed in descending order of area; for each sorted part, traverse all available blank areas on the board and place it in the position that minimizes the fragmentation of the remaining blank areas; repeat the placement process until all parts have been placed to obtain the initial layout scheme.

[0011] This invention generates an initial layout scheme by using the optimal adaptation method. The parts are sorted by area from largest to smallest and placed in positions that minimize the fragmentation of the remaining blank areas. This results in a high-quality initial layout, providing a better starting point for subsequent genetic algorithm optimization, and helping to accelerate algorithm convergence and find a globally optimal solution.

[0012] Preferably, the geometric agility satisfies the expression:

[0013] ;

[0014] In the formula, This represents the geometric agility of the i-th part; This represents the area of ​​the i-th part; Let represent the perimeter of the i-th part; It represents pi (π).

[0015] This invention comprehensively considers the area and shape complexity of the parts to obtain their geometric agility, providing a more accurate and calculable basis for dynamic layout optimization operators. This enables them to more efficiently select the most suitable parts for filling irregular gaps, thereby improving the accuracy and effectiveness of layout optimization.

[0016] Preferably, the degree of local optimizability satisfies the expression:

[0017] ;

[0018] In the formula, This represents the degree of local optimizability of the r-th local region; This represents the empty area of ​​the r-th local region; Indicates all and local regions The number of adjacent or partially overlapping parts; This represents the geometric agility of the j-th adjacent part in the r-th local region; This indicates a very small positive value.

[0019] This invention obtains the optimizability of a local area by weighting the vacant area within the region with the average geometric agility of adjacent parts. It can identify areas that not only have large gaps but are also surrounded by agile parts that are easy to move and adjust, thereby concentrating computing resources on the locations with the greatest optimization potential and improving the efficiency and success rate of dynamic layout adjustments.

[0020] Preferably, dividing the board into multiple local areas includes:

[0021] The board area is divided into grids, and each grid cell is treated as a local area.

[0022] Preferably, the grid division includes:

[0023] The side length of the grid cell is the minimum value of the short side length of the envelope rectangle of all parts to be processed.

[0024] Preferably, moving the target part within the set search range to reduce the vacant area of ​​the target region includes:

[0025] Centered on the current position of the target part, multiple candidate fine-tuning perturbation vectors are generated within a set search range. Each vector contains translation and rotation components. Each candidate fine-tuning perturbation vector is applied to the target part, and the reduction in the empty area of ​​the target region is calculated. The candidate fine-tuning perturbation vector that maximizes the reduction is selected as the final fine-tuning perturbation vector, and the target part is adjusted accordingly.

[0026] This invention generates multiple candidate fine-tuning vectors within a set local search range and selects the vector that minimizes the empty area to adjust the target part. This enables precise fine-tuning of the part's position with low computational cost, effectively filling local gaps while avoiding destructive impacts on the overall layout, making the dynamic optimization process more stable and efficient.

[0027] Preferably, the search range includes:

[0028] by The search range for circular translation with radius, and The search range is a sector-shaped rotation with an included angle.

[0029] Preferably, obtaining the fitness values ​​of each individual includes:

[0030] The material utilization rate and total processing time are calculated based on the adjusted layout scheme; a threshold for the total processing time is set; the material utilization rate is used as the fitness value of an individual; when the total processing time of an individual exceeds the threshold, its fitness value is corrected to the set minimum positive value.

[0031] Secondly, the present invention provides an automatic tape laying path planning system based on material utilization, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned automatic tape laying path planning method based on material utilization is implemented.

[0032] By adopting the above technical solution, a computer program is generated from the above-mentioned automatic tape laying path planning method based on material utilization rate, and stored in the memory so that it can be loaded and executed by the processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.

[0033] The beneficial effects of this invention are as follows: by integrating the part layout scheme as a dynamic gene into the iterative process of the genetic algorithm, and through dynamic layout optimization operators, using indicators such as geometric agility and local optima, the part layout is intelligently fine-tuned during optimization, achieving the co-evolution of layout and path. This invention, while ensuring that processing time meets actual production constraints, can perform global optimization, thereby improving the utilization rate of expensive composite materials. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating an automatic tape laying path planning method based on material utilization in this invention.

[0035] Figure 2 This is a schematic diagram illustrating the relationship between area, shape factor, and geometric agility. Detailed Implementation

[0036] This invention discloses an automatic tape laying path planning method based on material utilization, referring to... Figure 1 This includes steps S1-S4:

[0037] S1: Obtain the geometric information of the parts to be processed and the basic information of the tape laying equipment; generate an initial layout scheme based on a heuristic algorithm, and construct an initial population of genetic algorithms containing a hybrid coding structure.

[0038] It is important to note that in the path planning of automated composite material tape laying processes, the quality of the layout scheme directly determines the upper limit of the utilization rate of expensive prepreg tape materials. Traditional path planning methods typically treat layout as a pre-defined, static, hard constraint. Once the layout is determined, subsequent path optimization can only be performed at fixed part positions, causing the algorithm to only seek optimization within a local space and making it difficult to overcome the bottleneck of material utilization. Therefore, this invention adopts a hybrid coding structure that includes the layout scheme, the order within the scheme, equipment allocation, and the global processing order. The geometric information of the layout scheme is directly embedded into the chromosome of the genetic algorithm, transforming it from a static constraint into an evolvable entity. This design not only allows the algorithm to fine-tune the layout while searching for paths, but also provides a basic interface for data operations for the subsequent introduction of dynamic layout optimization operators, enabling the algorithm to adaptively adjust the layout based on the remaining space.

[0039] Specifically, the geometric information of the parts to be processed and the basic information of the tape laying equipment are obtained; an initial layout scheme is generated based on a heuristic algorithm, and an initial population of a genetic algorithm containing a hybrid coding structure is constructed, including:

[0040] Obtain the contour coordinates, area, and perimeter of all parts to be processed; obtain the specifications of available sheet metal and the quantity and performance parameters of the tape laying equipment.

[0041] The initial layout scheme is generated using the optimal adaptation method. Specifically, all parts to be processed are first sorted in descending order of area. Then, for each part, all available blank areas on the board are traversed, and the fragmentation degree of the remaining space after the part is placed is calculated. For example, the minimum side length of the remaining rectangle is taken as the fragmentation degree, and the position that minimizes the fragmentation degree of the remaining space is selected for placement. This process is repeated until all parts are placed to obtain the initial layout scheme.

[0042] Based on the initial layout scheme, an initial population for the genetic algorithm is randomly generated. It should be noted that each chromosome in the initial population copies the part position data from the initial layout scheme as its initial gene, and simultaneously generates its own processing order and equipment allocation genes randomly. In subsequent evolutionary processes, the layout data of each individual will evolve independently.

[0043] This completes the data acquisition and population initialization.

[0044] S2: During the iteration process of the genetic algorithm, a dynamic layout optimization operator is introduced; based on the geometric features of any part, the geometric agility of the part is calculated; the board area is divided into several local areas, and based on the geometric agility of the neighboring parts of any local area and the area of ​​the empty area of ​​the local area, the local optima degree of the local area is obtained; based on the local optima degree, the final fine-tuning perturbation vector is generated to adjust the layout scheme of the current individual, and the adjusted layout scheme is obtained.

[0045] It should be noted that when dynamically adjusting the layout, not all parts are suitable as fillers for gaps, and not all empty areas have optimization value. Blindly moving large or simple-shaped parts often fails to fill irregular, small gaps and easily causes new collisions. Furthermore, if an empty area is surrounded by rigid, large parts, its optimization feasibility is low. Therefore, this invention designs a dynamic layout optimization operator. First, it uses geometric agility to filter flexible parts, and then combines this with local optima potential to locate areas with optimization value, thereby achieving precise layout fine-tuning.

[0046] Specifically, calculating the geometric agility of any part based on its geometric features includes:

[0047] It should be noted that smaller parts occupy less space and are easier to fit into gaps created by other parts; at the same time, the more complex the shape of a part, the more possible it is to fit together when rotated, like assembling a kit. Figure 1 This allows the component to enter irregular gaps. Therefore, this invention constructs a geometric agility index by combining the geometric features of the part.

[0048] The geometric agility of any part satisfies the expression:

[0049] ;

[0050] In the formula, This represents the geometric agility of the i-th part; This represents the area of ​​the i-th part; Let represent the perimeter of the i-th part; It represents pi (π).

[0051] In the formula, This represents the size factor, and its value varies with... The decrease in area and the increase in area reflect that the larger the part area, the weaker the mobility and the weaker the geometric agility. The shape factor is represented by the ratio of the perimeter to the area of ​​a part. For a circle, this value is 1. As the complexity of the shape increases, such as in the form of a long strip or a star, the ratio of the perimeter to the area increases, and the value increases accordingly. The larger the value, the more the i-th part combines the characteristics of small size and complex shape, and the greater its likelihood of being used as a filler in layout optimization.

[0052] It should be noted that, as Figure 2 This diagram illustrates the relationship between area, shape factor, and geometric agility, showing the values ​​of area and shape factor for each part, as well as the resulting distribution of geometric agility values.

[0053] It should be noted that while geometric agility can identify the most suitable parts for adjustment, a global search of the entire sheet metal requires further identification of localized areas with gaps that are easily filled. Therefore, this invention combines spatial information and geometric agility to evaluate the optimization potential of various localized areas on the sheet metal.

[0054] Preferably, the sheet metal area is divided into several local regions. Based on the geometric agility of neighboring parts in any local region and the area of ​​the vacant area in the local region, the local optimizability of the local region is obtained, including:

[0055] The sheet material area is divided into grids, with each grid cell representing a local region. It should be noted that since the sheet material is a continuous space, directly evaluating empty areas on a continuous domain makes it difficult to define boundaries. Therefore, this invention employs rasterization to discretize the continuous space. Regarding the size of the grid cells, if the grid is too large, it cannot capture minute gap features; if the grid is too small, it will incur significant computational overhead. Therefore, for example, the side length of the grid cell is set to the shorter side of the envelope rectangle of the smallest part in the set of parts to be processed. For example, 10mm, to ensure the grid has sufficient resolution to identify available fill space.

[0056] It's important to note that after determining the local region, to identify the area most likely to be optimized, it's necessary not only to calculate the area of ​​the empty space within that region but also to analyze whether that empty space is functional enough to accommodate parts. Therefore, the optimization value of a local region is jointly determined by the empty area and the flexibility of neighboring parts. Only when a large empty area is surrounded by clusters of highly agile small parts is it most effective to fill that area by fine-tuning these neighbors. Thus, the local optima of any given local region can be calculated.

[0057] Obtain the area of ​​any local region that is not covered by any parts, and record it as the vacant area of ​​the local region.

[0058] All parts that have geometric overlap or boundary contact with any local region are identified and denoted as the adjacent parts of the local region.

[0059] The degree of local optimizability of any local region satisfies the expression:

[0060] ;

[0061] In the formula, This represents the degree of local optimizability of the r-th local region; This represents the empty area of ​​the r-th local region; Indicates all and local regions The number of adjacent or partially overlapping parts; This represents the geometric agility of the j-th adjacent part in the r-th local region; Indicates a very small positive value, used to avoid interfering with local areas. When the number of adjacent or partially overlapping parts is 0, the denominator is 0. For example, .

[0062] In the formula, This represents the average geometric agility of all adjacent parts in the r-th local region, reflecting the maneuverability of parts surrounding the r-th local region; This indicates that the vacant space is weighted and amplified through adjustability. The larger the value, the larger the vacant area of ​​the r-th local region, and the more movable the adjacent parts are, thus enabling layout optimization.

[0063] It should be noted that, furthermore, the local optimizability of the local area needs to be converted into specific optimization operations to calculate the degree to which the part should move and rotate, thereby reducing the gap.

[0064] Preferably, based on the degree of local optima, a final fine-tuning perturbation vector is generated to adjust the layout scheme of the current individual, including:

[0065] The local region with the greatest optimizability is taken as the target region.

[0066] The part with the highest geometric agility among the adjacent parts of the target area is selected as the target part.

[0067] It should be noted that, for specific movement commands, this invention sets a search range to enable the target part to move to the target empty area within the search range, provided that no collision occurs.

[0068] Centered on the current position of the target part, a local search is performed within a search range to generate several candidate fine-tuning perturbation vectors for the target part. The search range includes a translation search range and a rotation search range. For example, the translation search range is set to... Within a circular region of radius , the rotation search range is set to Within the fan-shaped region of the included angle. The candidate fine-tuning perturbation vector is, for example... , .

[0069] The final fine-tuning perturbation vector of the target part satisfies the expression:

[0070] ;

[0071] In the formula, This represents the final fine-tuning perturbation vector of the target part; Represents the optimal translation component of the target part; Represents the optimal rotational component of the target part; This represents the set of candidate fine-tuning perturbation vectors for the target part; This represents the reduction in vacant area within the target region after applying the final candidate fine-tuning perturbation vector to the target part; This represents the candidate fine-tuning perturbation vector that maximizes the reduction in vacant area within the target region among all candidate fine-tuning perturbation vectors.

[0072] This completes the dynamic optimization of the layout.

[0073] S3: Based on the adjusted layout scheme and processing path, calculate the material utilization rate and total processing time; set a processing time threshold as a feasibility constraint, and construct a fitness function with the material utilization rate as the core to obtain the fitness value of each individual in the population.

[0074] It should be noted that in industrial production environments, optimization objectives are often not equitable. For example, the production time of a shift is a fixed, hard boundary; any solution exceeding this time, regardless of material savings, is unfeasible. Therefore, this invention employs a constrained optimization model. First, it ensures that the processing efficiency of each solution meets the time constraint. Then, among all feasible solutions, it seeks the one with the highest material utilization rate. Transforming a multi-objective problem into a constrained single-objective problem makes the evaluation criteria more objective and the logic clearer.

[0075] Specifically, based on the adjusted layout scheme and processing path, material utilization rate and total processing time are calculated; a processing time threshold is set as a feasibility constraint, and a fitness function is constructed with material utilization rate as the core to obtain the fitness value of each individual in the population, including:

[0076] Based on the current layout scheme of each individual component, obtain the total area of ​​all parts and the minimum envelope rectangle area of ​​the board material, and calculate the material utilization rate.

[0077] Based on the current processing sequence and equipment allocation scheme of each individual, the tape laying process is simulated, the effective laying time and idle travel time of the tape laying head are statistically analyzed, and the total processing time is calculated.

[0078] Set a processing time threshold. It should be noted that the processing time threshold is determined based on the performance of the initial scheme. For example, using the total processing time of the initial layout scheme as a benchmark, the processing time threshold is set to 1.1 times the total processing time of the initial layout scheme.

[0079] The fitness value of any individual satisfies the expression:

[0080] ;

[0081] In the formula, Indicates the first The fitness value of each individual; Indicates the first Material utilization rate of each individual; Indicates the first Total processing time for each individual; Indicates the processing time threshold; Indicates a very small positive value, for example, .

[0082] In the formula, A feasibility threshold was established: when an individual's total processing time does not exceed the threshold, the individual is considered a feasible solution, and its fitness value is directly equal to its material utilization rate. In this case, the algorithm's driving force is to select the solution with the highest material utilization rate among all time-compliant solutions. When an individual's total processing time exceeds the threshold, the individual is considered an infeasible solution, and its fitness value is assigned a minimum positive value. This ensures that any processing scheme that exceeds the time limit, regardless of its material utilization rate, will be eliminated in the survival of the fittest stage of the genetic algorithm, thereby guiding the population to converge in the direction that satisfies the time constraint.

[0083] At this point, the fitness values ​​of each individual have been obtained.

[0084] S4: Based on the fitness values ​​of each individual, perform selection, crossover, and mutation operations, and update the population in conjunction with dynamic layout optimization operators; determine whether the iteration termination condition is met, and if so, decode the optimal individual and output the final paving path planning scheme.

[0085] It should be noted that the core of genetic algorithms lies in searching for the optimal solution through an evolutionary mechanism of survival of the fittest. This invention retains highly fit individuals through selection; crossover recombines gene segments from different superior individuals to produce even better offspring; and mutation introduces diversity into the population, preventing the algorithm from prematurely converging to a local optimum. Specifically, this invention uses a dynamic layout optimization operator as a mutation operation, working in conjunction with conventional random mutation to jointly drive the population towards the global optimum.

[0086] Specifically, based on the fitness values ​​of each individual, selection, crossover, and mutation operations are performed, and the population is updated using dynamic layout optimization operators. It is then determined whether the iteration termination condition is met; if so, the optimal individual is decoded, and the final paving path planning scheme is output, including:

[0087] The roulette wheel selection method is used to select parent individuals from the current population.

[0088] The selected parent individuals are subjected to multi-point crossover and regular mutation operations to generate the offspring population. For example, regular mutation includes applying Gaussian perturbation to the typesetting coordinate gene and randomly exchanging the processing order gene.

[0089] Select a subset of individuals from the offspring population with a preset probability, and perform a dynamic layout optimization operator to deeply optimize their layout scheme.

[0090] The offspring population generated after the above operations is merged with the parent population, and an elite retention strategy is adopted to select the individuals with the highest fitness to form a new generation population.

[0091] Repeat the above operations until the preset maximum number of iterations is reached, or the optimal fitness value of the population no longer shows a significant improvement within the preset number of iterations. For example, the maximum number of iterations is 500 generations, and the preset number of iterations is 50 generations.

[0092] The individual with the highest fitness value in the final population is selected as the optimal individual. Based on the chromosome data of the optimal individual, the final layout diagram, part processing sequence table, and equipment allocation instructions are decoded, and processing code that can be directly used for CNC equipment is generated accordingly.

[0093] At this point, the track planning was completed.

[0094] This invention also discloses an automatic tape laying path planning system based on material utilization, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an automatic tape laying path planning method based on material utilization according to the present invention.

[0095] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0096] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A material utilization ratio-based automatic tape-laying path planning method, characterized by, The method comprises the following steps: Obtaining the geometric information of all parts to be processed and the performance parameters of the laying equipment; generating an initial layout scheme containing the position information of each part based on a heuristic algorithm, and constructing an initial population of a genetic algorithm in combination with a random processing order and equipment allocation; In the iterative process of the genetic algorithm, a dynamic layout optimization operator is executed to adjust the initial layout scheme to obtain an adjusted layout scheme, the dynamic layout optimization operator comprising: calculating the geometric agility of each part, the geometric agility being negatively correlated with the area of the part and positively correlated with the square of the perimeter of the part; Dividing the plate into a plurality of local regions; calculating the local optimizable degree of each local region, the local optimizable degree being positively correlated with the average of the vacant area of the corresponding local region and the geometric agility of the adjacent parts thereof; taking the local region with the largest local optimizable degree as a target region, and taking the adjacent part with the largest geometric agility in the target region as a target part; moving the target part within a set search range to reduce the vacant area of the target region; calculating the material utilization rate based on the adjusted layout scheme and obtaining the fitness value of each individual; operating the population based on the fitness value until a termination condition is met, and decoding the individual with the highest fitness value to obtain the final laying path.

2. The material utilization-based automated tape-laying path planning method of claim 1, wherein, The heuristic algorithm is specifically a best-fit method, comprising: Sorting all parts to be processed in descending order of area; for each sorted part, traversing all available blank regions on the plate and placing the part in a position that minimizes the fragmentation of the remaining blank regions; repeating the placement process until all parts are placed to obtain the initial layout scheme.

3. The material utilization-based automated tape-laying path planning method of claim 1, wherein, The geometric agility satisfies the expression: ； wherein denotes the geometric agility of the i-th part; denotes the area of the i-th part; denotes the perimeter of the i-th part; denotes the circle constant.

4. The material utilization-based automated tape-laying path planning method of claim 1, wherein, The local optimizable degree satisfies the expression: ； wherein represents the local optimizability degree of the r-th local region; represents the vacant area of the r-th local region; represents the number of parts that adjoin or partially overlap with the local region represents the number of parts that adjoin or partially overlap with the local region represents the geometric agility of the j-th adjoining part of the r-th local region; represents a very small positive value.

5. The material utilization based automated tape- laying path planning method of claim 1, wherein, The division of the plate into a plurality of local regions comprises: Grid dividing the plate region, and taking each grid cell as a local region.

6. The material utilization-based automated tape-laying path-planning method of claim 5, wherein, The grid division comprises: The side length of the grid cell is the minimum value of the short side length of the envelope rectangle of all parts to be processed.

7. The material utilization based automated tape- laying path planning method of claim 1, wherein, The moving of the target part within a set search range to reduce the vacant area of the target region comprises: Generating a plurality of candidate fine-tuning perturbation vectors within the set search range with the current position of the target part as the center, each vector containing a translation component and a rotation component; applying each candidate fine-tuning perturbation vector to the target part and calculating the reduction amount of the vacant area of the target region; selecting the candidate fine-tuning perturbation vector with the largest reduction amount as the final fine-tuning perturbation vector and adjusting the target part.

8. The material utilization-based automated tape-laying path-planning method of claim 7, wherein, The search range comprises: With a circular translational search range with a radius of 1.5, and a sectorial rotational search range with an included angle of 90 degrees. 9.The material utilization based automated tape-laying path planning method of claim 1, wherein, The obtaining of the fitness value of each individual comprises: Calculating the material utilization rate and the total processing time based on the adjusted layout scheme; setting a total processing time threshold; taking the material utilization rate as the fitness value of the individual; when the total processing time of the individual exceeds the threshold, correcting the fitness value of the individual to a set minimum positive value.

10. A material utilization ratio-based automated tape-laying path planning system, comprising: The method comprises the following steps: A processor and a memory, the memory storing computer program instructions which, when executed by the processor, implement the material utilization rate-based automatic laying path planning method according to any one of claims 1-9.

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