Part layout optimization method and system for pipe laser cutting

Abstract

The invention discloses a part layout optimization method and system for pipe laser cutting. The method comprises the steps that discrete sampling and structured modeling are conducted on end face information of all parts, the end face attaching relation between the parts is calculated, and an end face relation database containing the attaching distance, the common edge relation, the rotating posture and other information is established; generating an initial solution according to a stock layout strategy set by a user, wherein the strategy comprises material saving priority, long material priority or short material priority and the like; on the basis of an initial solution, an improved genetic algorithm is applied to perform layout optimization, and the algorithm fuses directional sequential crossover, greedy variation, local optimization and a nonlinear penalty mechanism to realize global optimization of a layout result. And the system layer provides a visual interface or an API (Application Program Interface) for configuring strategy parameters and adjusting and optimizing weights. According to the method, the material utilization rate, the assembly reasonability and the optimization efficiency are considered, and the method is suitable for the efficient stock layout task of pipe laser cutting under the multi-specification and multi-strategy requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of tube laser cutting, and in particular relates to a parts layout optimization method and system for tube laser cutting. Background Art

[0002] In modern manufacturing, laser cutting technology, due to its high efficiency, precision, and flexibility, is widely used in the processing of various metal materials. In particular, it has gradually replaced traditional mechanical cutting processes in the field of pipe processing. Laser pipe cutting not only places higher demands on cutting accuracy and processing efficiency, but also presents more complex challenges in layout methods. Especially in the context of mass-produced, customized production, faced with the demand for a wide variety of parts of various types and specifications, optimally arranging them within a limited material length becomes a key factor affecting production efficiency and material utilization.

[0003] Currently, the most widely used method for laser cutting of pipes is automated nesting based on cutting files. This involves controlling the equipment through pre-generated nesting drawings or cutting path files. While this method can meet processing needs in general scenarios, it relies on static cutting diagrams and lacks the ability to adapt to dynamic production conditions. Traditional nesting methods typically only consider the single goal of material utilization, using fixed algorithms to generate nesting plans. These methods are unable to flexibly adjust to different user needs or actual production strategies (such as long material priority, short material priority, etc.). This limitation leads to a single nesting result, making it difficult to take into account multiple factors such as processing efficiency, equipment characteristics, and subsequent process coordination, limiting the wide adaptability of nesting solutions in intelligent manufacturing environments.

[0004] Furthermore, existing automatic nesting systems often suffer from low efficiency and poor optimization capabilities when handling large-volume, diverse parts tasks. As the scale of nesting tasks increases, the complexity of part arrangements and combinations increases exponentially. Traditional optimization algorithms are prone to falling into local optimality when solving large-scale combinatorial problems, and their convergence speed is slow, making the quality of optimization results difficult to guarantee. In this context, some systems have introduced intelligent optimization methods such as genetic algorithms to improve nesting results. However, most of these methods still use standard genetic algorithm processes and fail to effectively improve the unique constraints of nesting problems. This results in efficiency bottlenecks and unstable nesting results in practical applications.

[0005] Furthermore, current nesting strategies generally rely on geometric shape matching for end-face splicing, lacking in-depth modeling and analysis of the fit relationship between part end faces. For example, when considering the butt joint of multiple part end faces, using only the geometric similarity of the end face shapes as the matching criterion fails to accurately reflect their true fit, easily leading to nesting errors and material waste. For parts with irregular end faces or complex contours, existing methods are particularly weak in analyzing fit and determining common edges, further restricting nesting accuracy and assembly quality.

[0006] In summary, with the increasing intelligence of the manufacturing industry and the rapid growth of customized production demands, traditional pipe nesting methods and systems are no longer able to meet the requirements of efficient, precise, and diversified cutting production. Therefore, there is an urgent need to propose a new part nesting optimization method and system that can overcome the limitations of existing technologies in optimization strategies, nesting efficiency, close modeling, and multi-strategy adaptation, and achieve high-quality and efficient support for complex nesting tasks, thereby improving the flexibility and intelligence of the overall manufacturing process. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides a parts layout optimization method and system for tube laser cutting, which realizes multi-strategy configurable customized layout while ensuring high material utilization.

[0008] Specifically, the technical solutions provided by the present invention are as follows: A method for optimizing part layout for tube laser cutting, comprising: Obtain basic information and end face information of each type of part respectively to build a part feature database; the basic information includes part ID and part length, and the end face information includes the type, rotation angle and contour discrete sampling data of the left and right end faces of the part; Based on the part feature database, the end face relationship between any two parts is obtained and an end face relationship database is constructed; the end face relationship includes the end face proximity distance, co-edge relationship and rotational posture between the two parts. The proximity distance refers to the depth of the minimum circumscribed rectangle of two adjacent parts embedded in each other; For several parts to be arranged, the arrangement order of the parts under the selected arrangement strategy is obtained through an optimization algorithm based on the end face relationship database; the arrangement strategy includes a long material priority strategy, a short material priority strategy and a material saving priority strategy.

[0009] Furthermore, the optimization algorithm includes a fast optimization algorithm for generating regular solutions: when the long material priority strategy or the short material priority strategy is selected, the parts are arranged from long to short or from short to long according to their lengths, and adjacent parts are rotated and / or flipped according to the end face relationships recorded in the end face relationship library to achieve the purpose of saving material.

[0010] Furthermore, the optimization algorithm also includes a fast optimization algorithm for generating a greedy solution: randomly selecting the starting part for nesting, and for the parts in the front row, according to the end face relationship recorded in the end face relationship library, selecting the part with the largest distance to its end face from the remaining parts to be nested as its subsequent nesting part, and proceeding in sequence until all the parts to be nested are nested.

[0011] Furthermore, the optimization algorithm also includes an improved genetic algorithm for generating a global optimal solution, comprising the steps of: S1. Initialize the population Randomly select multiple different parts from a number of parts to be arranged as starting parts to generate multiple greedy solutions, and use these multiple greedy solutions as initial population individuals; S2. Fitness evaluation The fitness function is used to quantify the quality of the parts arrangement sequence represented by each individual in the population; S3. Select an operation Randomly select from the population k Individuals are selected, their fitness function values ​​are compared, and the individual with the highest fitness function value is retained; the process is repeated until the total number of retained individuals meets the set population size; S4. Crossover Operation First, two individuals are randomly selected from the retained individuals as parents, called parent 1 and parent 2; Then, in the order of parts represented by parent 1, continuous segments are selected as high-quality segments according to the length priority principle, and the high-quality segments are directly retained in the offspring. The parts not included in the high-quality segments from parent 2 are filled into the offspring in the order of parent 2 to form a complete offspring individual. S5. Mutation Operation Mutation probability P Randomly select an individual from the offspring individuals obtained by the crossover operation and randomly exchange the positions of two adjacent parts in the individual; Repeat S2~S5 and continuously iterate the population until the iteration termination condition is met. The part arrangement order represented by the individual with the maximum fitness function value in the population when the iteration terminates is taken as the global optimal solution.

[0012] Preferably, the fitness function is: , Indicates the total proximity distance, N is the total number of parts to be arranged, For the i Parts and i+ 1. The end face of a part is close to each other; Weight parameters αUsed to control the balance between material saving and length priority. α = 0, completely ignore the length priority and only optimize the close distance; when α = 1, the order is strictly based on length priority, and close distance is only a secondary goal; when 0< α When <1, the two are dynamically balanced, and the smaller the value, the more likely it is to save material; Indicates length reward. When the long material priority strategy is selected, ; When choosing the short material priority strategy, ; and Respectively represent i Parts and i+ 1 part's length; is a nonlinear penalty term, and , β Indicates the intensity of punishment, Indicates the allowed length difference value, and Respectively represent the maximum and minimum length values ​​of the part.

[0013] Preferably, the mutation probability P It gradually decreases with the increase of the number of iterations to reduce interference in the late iteration and accelerate convergence.

[0014] Preferably, in the mutation operation, for the individual after the position exchange, all adjacent part pairs of the individual are traversed, and if the fitness function value of the individual can be increased after the position exchange, the exchange operation is performed.

[0015] Preferably, the allowed length difference value .

[0016] A parts nesting optimization system for laser cutting of tubes includes a parts feature database for storing basic information and end face information of various types of parts, and an end face relationship database for storing the end face relationship between any two parts; and also includes a nesting optimization module for implementing the steps of the parts nesting optimization method described above.

[0017] Furthermore, it also includes a human-computer interaction module for inputting the type and quantity of parts to be nested, selecting the nesting strategy and setting the parameters of the nesting optimization module, and also for displaying the output part nesting results.

[0018] By introducing a database modeling approach based on end face features and fit relationships, this invention achieves a refined description of the splicing relationships between parts, enabling the nesting process to balance geometric fit and assembly rationality, fundamentally improving nesting accuracy and material utilization efficiency. Compared to traditional methods that rely on geometric similarity to arrange end faces, this invention, through database support, achieves controllable and configurable fit between specific end face pairs, further enhancing the system's adaptability to irregular end faces or complex parts.

[0019] At the same time, the present invention innovatively integrates a variety of nesting strategies and weight control mechanisms, breaking the limitation of traditional nesting systems that only use material saving as a single goal, allowing users to dynamically adjust strategy priorities according to actual production needs, and achieve customization of nesting results under different goals such as "material saving first", "long material first", and "short material first", effectively improving the flexibility and intelligence of the system. In the nesting optimization engine part, the present invention adopts a targeted improved genetic algorithm, combines the rule solution and the greedy solution to generate the initial population, and introduces mechanisms such as directed sequential crossover, greedy mutation and nonlinear penalty, which significantly improves the convergence speed of the solution and the global optimal search ability, and overcomes the problem that traditional algorithms are prone to falling into local optimality. Overall, the solution of the present invention has achieved substantial improvements in nesting accuracy, material utilization, optimization efficiency and strategy flexibility, providing a more advanced, reliable and efficient solution for laser cutting nesting of pipes. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the present invention and are not intended to limit the present invention.

[0021] Figure 1 This is a schematic diagram of a parts layout optimization process according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the distance between the end faces of a part provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of the relationship between the end faces of parts provided by one embodiment of the present invention; Figure 4 This is a schematic diagram of an end face relationship database provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by ordinary technicians in this field without making creative efforts are all within the scope of protection of the present invention.

[0023] Example 1 This embodiment provides a method for optimizing the arrangement of parts for laser cutting of pipes. Figure 1 As shown, the method mainly includes the following steps: 1. Build a part feature database The end faces of each part type are analyzed to extract information about the left and right end faces. The end face profiles are discretely sampled to construct an end face feature structure. The end face feature structure corresponding to each part is then stored in a database, forming a part feature database containing information about the end faces of each part type.

[0024] The end face feature structure contains basic information such as part ID and part length, as well as end face information such as end face type, rotation angle, and contour discrete sampling. The data structure is generally as follows:

[0025] 2. Calculate the end face relationship of parts through the part feature database and build the end face relationship database like Figure 2 As shown in the figure, for two parts A and B on a pipe (actually three-dimensional, but 2D for ease of explanation), assuming A remains stationary and B is rotated or flipped, the end-face relationship information, including the end-face proximity distance, common edge relationship, and rotational posture, between A and B is obtained to form an end-face relationship structure. The end-face proximity distance refers to the depth of the minimum circumscribed rectangle of the two adjacent parts. The greater the end-face proximity distance, the smaller the gap between the two parts, which reduces material consumption.

[0026] The end face relationship structure is as follows:

[0027] like Figure 3 As shown in the figure, each part has two end faces, left and right, and there is a front-to-back arrangement order. Therefore, there are eight possible end face relationships between parts A and B: the right end face of A faces the left end of B, the right end face of A faces the right end of B, the left end face of A faces the left end of B, and the left end face of A faces the right end of B. The four arrangement methods with A last can be regarded as the left-right reversal of the four arrangement methods with A first.

[0028] like Figure 4 As shown, an end face relationship database is constructed to store the end face relationship between any two parts. Each end face relationship includes the end face proximity distance, common edge relationship and rotation posture, etc.

[0029] 3. Multi-strategy layout (1) Quickly generate initial solution for nesting Since it is slow to directly obtain the optimal solution when there are a large number of parts to be nested, a simple algorithm will be used to obtain the initial solution first. The initial solution is extremely fast, and a pop-up window will appear on the system interface to prompt "nesting in progress".

[0030] The initial solution can be divided into regular solution and greedy solution: Rule explanation: When the user selects the long material priority strategy or the short material priority strategy, the parts will be arranged strictly from long to short or from short to long according to their length. Adjacent parts can only be rotated and / or flipped according to the end face relationship recorded in the end face relationship library to achieve the purpose of saving material.

[0031] Greedy solution: Randomly select the starting part for nesting. For the part at the top of the list, select the part with the largest end distance from the remaining parts to be nested based on the end face relationship recorded in the end face relationship library. This part is used as the subsequent nesting part. Repeat this process until all parts are nested.

[0032] In addition, the user can also terminate the generation of the global optimal solution described below in advance and use the non-global optimal solution in the generation process as the initial solution to quickly obtain the nesting result display.

[0033] (2) Generate global optimal solution based on improved genetic algorithm Genetic algorithms simulate the biological evolution process, gradually optimizing individuals in a population (i.e., the order in which parts are arranged) through a "selection-crossover-mutation" mechanism. The core process is as follows: ① Initialize the population: generate a set of initial solutions (initial permutations).

[0034] ②Fitness evaluation: Calculate the quality of each individual (total proximity distance + length priority score).

[0035] ③Selection: retain high-quality individuals and eliminate low-quality individuals.

[0036] ④Crossover: Combining parental gene fragments to generate offspring.

[0037] ⑤ Mutation: Randomly change the genes of offspring to introduce diversity.

[0038] ⑥ Iteration: Repeat steps ②~⑤ until the termination condition is met.

[0039] 1) Initialize the population Randomly select multiple different parts as starting parts to generate multiple greedy solutions. Each greedy solution represents a pattern arrangement method. Multiple greedy solutions are used as initial individuals to form the initial population. Of course, regular solutions can also be used as individuals in the initial population.

[0040] 2) Fitness evaluation The fitness function is used to quantify the quality of the order of parts represented by each individual in the population, and comprehensively evaluates the balance between proximity and length priority through positive incentives and negative constraints. In this embodiment, the fitness function is expressed as:

[0041] in, Indicates the total proximity distance, N is the total number of parts to be arranged, For the i Parts and i+ The distance between the end faces of a part. Representation length bonus: When choosing the long material priority strategy, ; When choosing the short material priority strategy, ; in, and Respectively represent i Parts and i+ 1 part length.

[0042] Weight parameter α Used to control the balance between material saving and length priority: α = 0, completely ignore the length priority and only optimize the close distance; when α = 1, the order is strictly based on length priority, and close distance is only a secondary goal; when 0< α When <1, the two are dynamically balanced, and the smaller the value, the more likely it is to save material.

[0043] It is a nonlinear penalty term that imposes severe penalties on large deviations from the length priority to prevent the algorithm from generating solutions that obviously violate user needs. Small length differences can be moderately allowed, and dynamic matching of penalty intensity and deviation degree is achieved through nonlinear functions.

[0044] Specifically: , in, β Indicates the penalty intensity, and the value is adjusted according to the length range of the part; Indicates the allowed length difference value, such as ; and Respectively represent the maximum length value and the minimum length value of the parts in the parts library, which are used to normalize the penalty term.

[0045] 3) Select an operation Each time randomly selected from the population kIndividuals are selected, their fitness function values ​​are compared, and the individual with the highest fitness is retained; the above process is repeated until the total number of retained individuals meets the population size.

[0046] Compared with traditional roulette wheel selection, the selection operation in this embodiment can not only retain high-quality individuals, but also maintain diversity through random selection, so that there are both super-strong individuals among the population individuals participating in subsequent iterations, and other individuals also have the opportunity to be selected and retained.

[0047] 4) Crossover Operation In this embodiment, a directional sequential crossover is used: First, two individuals are randomly selected as parents (Parent 1 and Parent 2) from the individuals retained by the selection operation.

[0048] Then, in the parts arrangement order represented by parent generation 1, continuous segments are selected as high-quality segments according to the length priority principle (continuous segments with decreasing part lengths are selected under the long material priority strategy, and continuous segments with increasing part lengths are selected under the short material priority strategy); the high-quality segments are directly retained in the offspring, and the parts that are not included in the high-quality segments from parent generation 2 are filled into the offspring according to the order in parent generation 2 to form a complete offspring individual.

[0049] For example: the parts arrangement order of parent generation 1 is: AEBCDGF, among which segment BCD is a high-quality segment, so _ _BCD_ _ is retained in the offspring; the parts arrangement order of parent generation 2 is: BECAFGD, among which E, A, F, and G are not included in the high-quality segments of parent generation 1, so they are filled into the offspring in the order in which they are in parent generation 2 to form a complete offspring individual EABCDFG.

[0050] Compared with traditional sequential crossover, this embodiment enforces inheritance of the local optimal arrangement that conforms to the length priority, while avoiding random crossover that destroys high-fitness gene segments, thereby improving the convergence speed.

[0051] 5) Mutation Operation Mutation probability P Randomly select an individual from the offspring individuals obtained by the crossover operation and randomly exchange the positions of two adjacent parts in the individual, such as ABCD→ACBD.

[0052] After the mutation exchange position, all adjacent part pairs of the individual are traversed. If the exchange position can increase the fitness function value of the individual, the exchange operation is performed.

[0053] Mutation probability PAs the number of iterations increases, it gradually decreases (e.g., from 0.3 to 0.1) to reduce interference in the later stages of the iteration and accelerate convergence. In this embodiment, local optimization is performed immediately after the mutation exchange position to avoid generating inferior solutions; using directed adjustments instead of blind randomness effectively accelerates iterative convergence.

[0054] Repeat the above "fitness evaluation → selection operation → crossover operation → mutation operation" and continuously iterate the population until the set iteration limit is reached or the maximum fitness function value in the population is improved by less than the set threshold for multiple generations. The order of parts represented by the individual with the maximum fitness function value in the population at the time of iteration termination is regarded as the global optimal solution.

[0055] Example 2 Based on the above method, this embodiment provides a parts nesting optimization system for laser cutting of tubes, which includes a parts feature database for storing basic information and end face information of various types of parts, and an end face relationship database for storing the end face relationship between any two parts; it also includes a nesting optimization module for implementing the steps of the above-mentioned parts nesting optimization method; it also includes a human-computer interaction module for inputting the type and quantity of parts to be nested, selecting the nesting strategy and setting the parameters of the nesting optimization module, and also for displaying the output part nesting results.

[0056] The above-mentioned system or product can execute the part layout optimization method for tube laser cutting described in Example 1, and has the corresponding functional modules and beneficial effects of the method. For technical details not described in detail in this embodiment, please refer to the part layout optimization method for tube laser cutting provided in Example 1 of the present invention.

[0057] Through the description of the above embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a general hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the relevant technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Under the idea of ​​the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the present invention as described above. For the sake of simplicity, they are not provided in detail. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in this field should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing part layout for tube laser cutting, characterized in that: include: Obtain basic information and end face information of each type of part respectively to build a part feature database; the basic information includes part ID and part length, and the end face information includes the type, rotation angle and contour discrete sampling data of the left and right end faces of the part; Based on the part feature database, the end face relationship between any two parts is obtained and an end face relationship database is constructed; the end face relationship includes the end face proximity distance, co-edge relationship and rotational posture between the two parts. The proximity distance refers to the depth of the minimum circumscribed rectangle of two adjacent parts embedded in each other; For several parts to be arranged, the arrangement order of the parts under the selected arrangement strategy is obtained through an optimization algorithm based on the end face relationship database; the arrangement strategy includes a long material priority strategy, a short material priority strategy and a material saving priority strategy.

2. The parts layout optimization method according to claim 1, wherein: The optimization algorithm includes a fast optimization algorithm for generating a rule solution: when the long material priority strategy or the short material priority strategy is selected, the parts are arranged from long to short or from short to long according to their lengths, and adjacent parts are rotated and / or flipped according to the end face relationships recorded in the end face relationship library to achieve the purpose of saving material.

3. The parts layout optimization method according to claim 1, wherein: The optimization algorithm also includes a fast optimization algorithm for generating a greedy solution: randomly selecting the starting part for nesting, and for the part at the top of the list, selecting the part with the largest distance to its end face from the remaining parts to be nested according to the end face relationship recorded in the end face relationship library, as its subsequent nesting part, and repeating the process until all the parts to be nested are nested.

4. The parts layout optimization method according to claim 3, wherein: The optimization algorithm also includes an improved genetic algorithm for generating a global optimal solution, comprising the steps of: S1. Initialize the population Randomly select multiple different parts from a number of parts to be arranged as starting parts to generate multiple greedy solutions, and use these multiple greedy solutions as initial population individuals; S2. Fitness evaluation The fitness function is used to quantify the quality of the parts arrangement sequence represented by each individual in the population; S3. Select an operation Randomly select from the population k Individuals are selected, their fitness function values ​​are compared, and the individual with the highest fitness function value is retained; the process is repeated until the total number of retained individuals meets the set population size; S4. Crossover Operation First, two individuals are randomly selected from the retained individuals as parents, called parent 1 and parent 2; Then, in the order of parts represented by parent 1, continuous segments are selected as high-quality segments according to the length priority principle, and the high-quality segments are directly retained in the offspring. The parts not included in the high-quality segments from parent 2 are filled into the offspring in the order of parent 2 to form a complete offspring individual. S5. Mutation Operation Mutation probability P Randomly select an individual from the offspring individuals obtained by the crossover operation and randomly exchange the positions of two adjacent parts in the individual; Repeat S2~S5 and continuously iterate the population until the iteration termination condition is met. The part arrangement order represented by the individual with the maximum fitness function value in the population when the iteration terminates is taken as the global optimal solution.

5. The parts layout optimization method according to claim 4, characterized in that: The fitness function is: ， Indicates the total proximity distance, N is the total number of parts to be arranged, For the i Parts and i+ 1. The end face of a part is close to each other; Weight parameters α Used to control the balance between material saving and length priority. α = 0, completely ignore the length priority and only optimize the close distance; when α = 1, the order is strictly based on length priority, and close distance is only a secondary goal; when 0 < α When <1, the two are dynamically balanced, and the smaller the value, the more likely it is to save material; Indicates length reward. When the long material priority strategy is selected, ; When choosing the short material priority strategy, ; and Respectively represent i Parts and i+ 1 part's length; is a nonlinear penalty term, and , β Indicates the intensity of punishment, Indicates the allowed length difference value, and Respectively represent the maximum and minimum length values ​​of the part.

6. The parts layout optimization method according to claim 5, characterized in that: The mutation probability P It gradually decreases with the increase of the number of iterations to reduce interference in the late iteration and accelerate convergence.

7. The parts layout optimization method according to claim 5, characterized in that: In the mutation operation, for the individual after the exchange of positions, all adjacent part pairs of the individual are traversed. If the exchange of positions can increase the fitness function value of the individual, the exchange operation is performed.

8. The parts layout optimization method according to claim 5, characterized in that: Allowable length difference .

9. A parts layout optimization system for tube laser cutting, characterized in that: It includes a part feature database for storing basic information and end face information of various types of parts, and an end face relationship database for storing the end face relationship between any two parts; it also includes a layout optimization module for implementing the steps of the part layout optimization method described in any one of claims 1 to 8.

10. The parts layout optimization system according to claim 9, characterized in that: It also includes a human-computer interaction module for inputting the type and quantity of parts to be nested, selecting nesting strategies and setting parameters of the nesting optimization module, and for displaying the output part nesting results.

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