Mobius ring production method based on artificial intelligence and Mobius ring support

Abstract

The invention relates to the technical field of artificial intelligence and the like, and provides a Mobius ring production method based on artificial intelligence and a Mobius ring support, a parameterized model of the Mobius ring support is established, the parameterized model is configured with various key process parameters, and according to the production requirements of finished products, the Mobius ring support can be produced according to the production requirements of the finished products. A multi-objective optimization AI algorithm is used for the multiple key process parameters to calculate an optimal blanking scheme and a welding spot distribution scheme; the first optimization objective of the optimization objectives of the multi-objective optimization AI algorithm is to minimize material waste, the second optimization objective is to meet radian consistency and 180-degree torsion requirements of an inner ring and an outer ring after forming, and the third optimization objective is to uniformly distribute welding positions, so that stability and strength during double-sided display of the bracket are ensured, and the welding quality is improved. The blanking of the material belt is carried out according to a better blanking scheme, and after the Mobius ring bracket is assembled and formed, the welding support strip is welded and hardened according to a welding spot distribution scheme, so that the labor experience cost is reduced, and the production efficiency of the Mobius ring bracket is improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of artificial intelligence, a Möbius ring bracket and the like, and in particular to a Möbius ring production method based on artificial intelligence and a Möbius ring bracket. Background Art

[0002] In the production of Möbius ring lamps and display screens, it is necessary to first design a Möbius ring bracket, and then assemble the display module or light-emitting module onto the Möbius ring bracket to form a Möbius ring lamp or display screen. Among them, when producing the Möbius ring bracket, several sections of strips need to be spliced ​​to obtain a continuous long strip connected end to end. In order to smoothly connect the ends of the long strip and form a twist, it is necessary to accurately calculate the length of each inner arc and outer arc when cutting. After the Möbius ring bracket is formed, it includes two parts, the inner ring and the outer ring. In order to achieve double-sided display, it is necessary to select multiple hardening welding positions at the edge positions of the inner ring and the outer ring to weld the support strips, so as to achieve hardening treatment of the entire Möbius ring bracket and support double-sided display. In practice, the selection of strip cutting and hardening welding positions mainly depends on manual experience. When facing the production of Möbius ring brackets of different models and lengths, it is necessary to pay a huge experience cost, which is not conducive to the efficient production of Möbius ring brackets.

[0003] In summary, the existing production technology of the Möbius ring stent has technical problems such as high labor and experience cost and low production efficiency. Summary of the invention

[0004] In view of the deficiencies in the above-mentioned prior art, the present invention provides a Möbius ring production method and a Möbius ring bracket based on artificial intelligence, so as to reduce the manpower experience cost and improve the production efficiency of the Möbius ring bracket.

[0005] In a first aspect, the present invention provides a method for producing a Möbius strip based on artificial intelligence, comprising:

[0006] Establishing a parametric model of a Möbius ring stent, wherein the parametric model is configured with a plurality of key process parameters, wherein the plurality of key process parameters include a single-segment strip length, a torsion angle, an inner ring radius, an outer ring radius, and a maximum weldable number of hardened welding support bars;

[0007] According to the production requirements of finished products, a multi-objective optimization AI algorithm is used to calculate the optimal material cutting plan and welding point distribution plan for the multiple key process parameters; the optimization goals of the multi-objective optimization AI algorithm include a first optimization goal, a second optimization goal and a third optimization goal, the first optimization goal is to minimize material waste, the second optimization goal is to meet the arc consistency of the inner ring and the outer ring after forming and the 180° torsion requirement, and the third optimization goal is to evenly distribute the welding positions to ensure the stability and strength of the bracket when it is displayed on both sides;

[0008] The material strips are cut according to the preferred cutting scheme, and after the material strips obtained by cutting are assembled to form a Mobius ring bracket with ends closed, the welding support strips are welded and hardened according to the welding point distribution scheme.

[0009] In a second aspect, the present invention provides a Möbius ring bracket, which is produced using the above-mentioned artificial intelligence-based Möbius ring production method.

[0010] Compared with the prior art, the present invention has the following beneficial effects:

[0011] The present invention provides a Möbius ring production method and a Möbius ring bracket based on artificial intelligence. A parameterized model of the Möbius ring bracket is established, and the parameterized model is configured with a plurality of key process parameters, wherein the plurality of key process parameters include a single-segment material strip length, a torsion angle, an inner ring radius, an outer ring radius, and a maximum weldable number of hardened welding support strips. According to the production requirements of finished products, a multi-objective optimization AI algorithm is used to calculate a better blanking scheme and a welding point distribution scheme for the plurality of key process parameters; the optimization targets of the multi-objective optimization AI algorithm include a first optimization target, a second optimization target, and a third optimization target, wherein the first optimization target is to minimize material waste, the second optimization target is to meet the arc consistency of the inner ring and the outer ring after forming and the 180° torsion requirement, the third optimization target is to evenly distribute the welding positions to ensure the stability and strength of the bracket when it is displayed on both sides, the material strip is blanked according to the better blanking scheme, and after the material strip obtained by blanking is assembled to form a Möbius ring bracket closed at both ends, the hardened welding support strip is welded according to the welding point distribution scheme, thereby reducing the human experience cost and improving the production efficiency of the Möbius ring bracket. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their description are used to explain the present invention and do not constitute an improper limitation of the present invention. Some specific embodiments of the present invention will be described in detail in an exemplary and non-restrictive manner with reference to the drawings. The same reference numerals in the drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the drawings:

[0013] Figure 1 It is a flow chart of a method for producing a Möbius ring based on artificial intelligence according to an embodiment of the present invention;

[0014] Figure 2 It is another schematic flow chart of a method for producing a Möbius ring based on artificial intelligence according to an embodiment of the present invention;

[0015] Figure 3 It is a structural schematic diagram of the Möbius ring bracket according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention.

[0017] Embodiment 1

[0018] See also Figure 1-Figure 3 This embodiment provides a method for producing a Möbius strip based on artificial intelligence, comprising the following steps:

[0019] S101, establishing a parameterized model of a Möbius ring stent, wherein the parameterized model is configured with a plurality of key process parameters, wherein the plurality of key process parameters include a single-segment material strip length, a torsion angle, an inner ring radius, an outer ring radius, and a maximum weldable number of hardened welding support bars;

[0020] S102. According to the production requirements of finished products, a multi-objective optimization AI algorithm is used to calculate the optimal material cutting plan and welding point distribution plan for the multiple key process parameters; the optimization goals of the multi-objective optimization AI algorithm include a first optimization goal, a second optimization goal and a third optimization goal, the first optimization goal is to minimize material waste, the second optimization goal is to meet the arc consistency of the inner ring and the outer ring after forming and the 180° torsion requirement, and the third optimization goal is to evenly distribute the welding positions to ensure the stability and strength of the bracket when it is displayed on both sides; wherein, the first optimization goal, the second optimization goal and the third optimization goal can be expressed by setting the corresponding objective function. After clarifying the definition of the first optimization goal, the second optimization goal and the third optimization goal in this embodiment, those skilled in the art can know the setting method of the specific objective function, which will not be repeated in this embodiment.

[0021] S103, cutting the material strip according to the preferred cutting scheme, and after the material strip obtained by cutting is assembled to form a Mobius ring bracket with ends closed, welding and hardening the welding support strip according to the welding point distribution scheme.

[0022] It should be noted that the Möbius ring bracket has unique application value in the fields of lamps and display screens. It is usually necessary to splice, twist and weld the material strip to form a ring structure that can display on both sides, is beautiful and has reasonable force. However, traditional production often relies on manual experience to determine the material cutting plan and welding position. When facing materials of various models, sizes and thicknesses, it is very easy to have problems such as material waste, inadequate twisting or insufficient strength. In this embodiment, through the parameterized model and the multi-objective optimization AI algorithm, it is possible to automatically and quickly find a better or approximately better production plan within a given process parameter range, thereby maximizing production efficiency and finished product quality. In step S101, the parameterized model is configured with a variety of key process parameters, including the length of a single-segment material strip, the torsion angle, the inner ring radius, the outer ring radius, and the maximum number of weldable hardened welding support strips. Among them, the forming of the Möbius ring needs to consider both the geometric shape (such as the torsion angle, the inner and outer ring radius, etc.) and the parameters related to the support strip (such as the upper limit of the number). If there is a lack of systematic management of these process parameters, subsequent production will face a large number of manual trial and error and the risk of difficulty in maintaining consistency. Centralizing all parameters into an adjustable parametric model can greatly improve the efficiency of design, modification and subsequent optimization. Different specifications of Möbius rings (different lengths, torsion angles, etc.) only need to adjust the corresponding parameters in the parametric model to quickly generate a digital model without having to re-model from scratch, which not only meets the needs of efficient batch production and reduces the cost of human experience, but also supports flexible customization. In step S102, according to the production requirements of the finished product, a multi-objective optimization AI algorithm is used to calculate the optimal cutting scheme and weld point distribution scheme for the various key process parameters. The optimization objectives of the multi-objective optimization AI algorithm include a first optimization objective, a second optimization objective and a third optimization objective. The first optimization objective is to minimize material waste, the second optimization objective is to meet the arc consistency of the inner ring and the outer ring after forming and the 180° torsion requirement, and the third optimization objective is to evenly distribute the welding positions to ensure the stability and strength of the bracket when it is displayed on both sides. Among them, when producing the Möbius ring bracket, it is necessary to pay attention to: material utilization (reducing waste); forming accuracy (meeting 180° torsion and ensuring the consistency of the inner and outer ring curvature); welding support stability (even distribution, no deformation or looseness when supporting double-sided display). These goals often restrict each other: for example, in order to save materials, it may lead to difficulty in forming or poor distribution of support strips; in order to meet high-strength support, it is possible to increase the amount of splicing of material strips and cause material waste. Multi-objective optimization AI algorithms (such as genetic algorithms, particle swarm algorithms, etc.) can perform global search and iteration in complex multi-dimensional spaces, and can find a Pareto optimal solution that takes all parties into consideration better than manual experience or single-objective optimization. Traditional methods usually rely on the personal experience of technicians to debug the cutting plan, or repeatedly explore the welding position; this makes it difficult to guarantee efficiency and consistency for new models or large-scale production.In this embodiment, through the multi-objective optimization AI algorithm, only the finished product production requirements (such as the circumference of the finished product) need to be input, and the algorithm can automatically calculate the optimal cutting plan and the optimal welding point distribution, significantly shortening the trial and error process and improving product consistency. In addition, different companies or products have different emphases on the torsion accuracy, appearance requirements, and support strength of the Möbius ring. Multi-objective optimization can flexibly set weights or restrictions, and automatically output corresponding optimal solutions in different scenarios without having to rewrite the logic. In step S103, the material strip is cut according to the optimal cutting plan, and after the material strip obtained by cutting is assembled to form a Möbius ring bracket with closed ends, the hardened welding support strip is welded according to the welding point distribution plan. In step S102, the multi-objective optimization AI algorithm has given a cutting plan and welding position that meets the multi-objective balance. Step S103 cuts the material according to the optimized specified information such as the number, length, and cross-section cutting of the material strips, continuously splices the cut material strips, twists them end to end, and forms the prototype of a Möbius strip; then installs and welds the hardened support strips at the welding point coordinates (i.e., welding point distribution plan) recommended by AI to ensure the stability of the double-sided display. In the field of lamps or display screens, Möbius strips often need to emit light / display on both sides, so the distribution of welding support strips is particularly important. If the distribution is too sparse or uneven, the double-sided display panel may become loose or deformed; if the distribution is too dense or the position is improper, materials will be wasted and the aesthetics will be damaged. According to the optimal welding layout in step S103, both the strength of the finished product can be ensured and a better appearance and double-sided function can be achieved.

[0023] In some further embodiments, the finished product production requirements include target size requirements, tolerance and surface requirements, installation methods, load weight or optical layout requirements, currently available material specifications, thickness and quantity requirements. It should be noted that the multi-objective optimization AI algorithm needs to find a better solution on the premise of meeting the production requirements of the finished product. Among them, the target size requirements (for example, total circumference, outer / inner ring diameter or radius, 180° torsion angle) directly determine the geometric characteristics that the strip should present after forming. The AI ​​algorithm needs to use these parameters as a reference when calculating segmented blanking or torsion; at the same time, the distribution of welding positions must also match the geometric curvature of the final ring to ensure that the finished product is correctly spliced. In addition, tolerance and surface requirements can be used in multi-objective optimization to improve forming accuracy and appearance. Tolerance requirements determine the allowable range of material strip cutting and welding position accuracy; surface requirements affect whether the welding position of more hardened welding support strips is acceptable, thereby affecting the optimization of the number and position of welding strips. In addition, the installation method, load weight or optical layout requirements determine the actual function that the Möbius strip ultimately carries (such as display module, lighting module, etc.), which affects the distribution of welding positions, the number of support strips and the selection of material strip thickness. In addition, the currently available material specifications, thickness and quantity requirements are based on the consideration of arranging the optimal material layout based on the known material availability to avoid waste or short materials.

[0024] It should be noted that after obtaining the target size requirements, tolerance and surface requirements, installation method, load weight or optical layout requirements, currently available material specifications, thickness and quantity requirements and other specific finished product production requirements and the above-mentioned multiple key process parameters, the specific finished product production requirements can be used as the optimization limit variables of the multi-objective optimization AI algorithm, and input into the trained multi-objective optimization AI algorithm model together with the above-mentioned multiple key process parameters. The multi-objective optimization AI algorithm model calculates the optimal material cutting scheme and solder joint distribution scheme according to the optimization limit variables and the single-segment material length, torsion angle, inner ring radius, outer ring radius, material cross-sectional size, material thickness, material material, hardened welding support bar size, hardened welding support bar material and the maximum weldable number of hardened welding support bars in the above-mentioned multiple key process parameters. Among them, the multi-objective optimization AI algorithm can adopt genetic algorithm, particle swarm algorithm or Bayesian optimization algorithm.

[0025] In some further embodiments, the multi-objective optimization AI algorithm adopts a genetic algorithm model; when using the genetic algorithm model to calculate a better cutting plan and a weld point distribution plan, it includes: determining the decision variables required by the genetic algorithm model, the decision variables include the specific lengths and splicing sequences of several sections of material strips under the premise of satisfying the total circumference of the ring, the distribution coordinates of the hardened welding support strips, and the number of hardened welding support strips that does not exceed the maximum weldable number of the hardened welding support strips; the genetic algorithm model randomly generates a certain number of initial plans within the constraint range based on the decision variables, the finished product production requirements as optimization limiting variables and the multiple key process parameters; each of the initial plans includes a combination of material strip segmentation and length, and the distribution coordinates and number of hardened welding support strips; the genetic algorithm model evaluates each of the initial plans through the first optimization goal, the second optimization goal and the third optimization goal to obtain The evaluation results are saved in the fitness information of each of the initial solutions; through selection processing, the initial solutions with good fitness (i.e., less material waste, high forming accuracy, and reasonable distribution of solder joints) are retained; through cross processing, two excellent initial solutions are allowed to exchange some of the decision variables (such as the length setting of a certain section of the material strip, the layout of solder joints) with each other to generate new offspring solutions; through mutation processing, some of the initial solutions are randomly adjusted in a small range to increase the algorithm exploration range, such as slightly changing the length of a certain section of the material strip, adding / reducing solder joints in certain ring segments, etc.; the evaluation processing, the selection processing, the cross processing, and the mutation processing are continuously repeated to iterate and evolve, eliminate the poor initial solutions generation by generation, and retain and improve the better solutions; if the initial solution as a whole has converged or reached a preset excellent level after a certain generation or exceeds the maximum number of generations, the genetic algorithm model stops iterative evolution and outputs several better material cutting solutions and solder joint distribution solutions.

[0026] It should be noted that in the production of the Möbius ring bracket, the length and splicing order of several sections of material strips, the number and distribution position of support strips, all belong to combinatorial optimization problems. Genetic algorithm (GA) is good at global search by iteratively evolving the population individuals, can effectively avoid falling into local optimality, and can handle both continuous and discrete parameters. In this embodiment, the first optimization goal pursues less material waste, the second optimization goal pursues high forming accuracy, and the third optimization goal pursues reasonable distribution of welds. The genetic algorithm can process multiple goals in parallel in the same iteration, output one or more groups of better solutions, and can flexibly configure various screening mechanisms (such as Pareto sorting, etc.). The selection, crossover, mutation and other steps of the genetic algorithm are relatively simple and easy to implement at the software level; they can also be easily connected with other algorithm components (such as mechanical simulation or CAD interface), which is suitable for industrial application scenarios. In addition, in this embodiment, the decision variables include the specific length and splicing order of several sections of material strips under the premise of meeting the total circumference of the ring, the distribution coordinates of the hardened welding support strips, and the number of hardened welding support strips that does not exceed the maximum weldable number of the hardened welding support strips. In the Möbius ring, the total circumference of the ring is the basic limitation. The length combination and splicing order of each strip directly determine the material cutting method and material utilization rate; the distribution coordinates and number of hardened welding support strips affect the strength of the overall structure, the installation position of the double-sided display and the welding cost. These factors are set as decision variables, so that the genetic algorithm can freely try different segmentation and solder joint layout schemes, and then find a better solution that takes into account material waste, forming accuracy and force rationality in iterative evolution. In addition, the genetic algorithm model randomly generates a certain number of initial solutions within the constraint range based on the decision variables, the finished product production requirements as optimization limiting variables and the multiple key process parameters. Among them, random initialization is a common practice of genetic algorithms, which can disperse the initial solutions in the solution space of the problem to avoid local optimization caused by starting from a single point. At the same time, it is necessary to ensure that the initial solution does not violate the basic constraints (such as total circumference, tolerance requirements, available material specifications, and maximum weldable quantity) to ensure that each individual has basic feasibility. In addition, the genetic algorithm model evaluates each of the initial solutions through the first optimization goal, the second optimization goal, and the third optimization goal to obtain the evaluation results and save them in the fitness information of each initial solution. Among them, the genetic algorithm needs to use fitness to measure the performance of each solution in terms of multiple objectives. Only in this way can the algorithm give priority to retaining excellent individuals with less material waste, more precise molding, and more reasonable support distribution in the subsequent selection process, and gradually approach a better solution. The evaluation results are written into the fitness information, and the survival of the fittest can be carried out according to multi-objective strategies (such as Pareto sorting or weighted summation) in the selection, crossover, and mutation stages.In addition, through selection processing, the initial scheme with good fitness is retained; through crossover processing, two excellent initial schemes are allowed to exchange some of the decision variables with each other; through mutation processing, some of the initial schemes are randomly adjusted in a small range. Among them, one of the core ideas of genetic algorithms is selection. In multi-objective optimization, Pareto sorting or fitness ranking can be combined to retain solutions with excellent performance; crossover means splicing some decision variables (such as material strip length, welding point coordinates) of two excellent solutions together, hoping to produce more potential new solutions; mutation means allowing a small amount of random disturbance, such as adding or removing welding points in a certain ring segment, changing the length of the material strip, etc., to avoid premature convergence of the algorithm and maintain population diversity. The repeated execution of these "selection-crossover-mutation" steps can simulate the process of biological evolution, continuously improve the scheme, and improve the overall population fitness. In addition, if the initial scheme has converged as a whole or reached a preset excellent level after a certain generation or exceeding the maximum number of generations, the genetic algorithm model stops iterative evolution and outputs several better cutting schemes and welding point distribution schemes. Among them, the essence of genetic algorithms is iterative search. After multiple rounds of "evaluation-selection-crossover-mutation", the population gradually approaches the high fitness area. When the algorithm meets the convergence conditions (such as the objective function changes very little) or reaches the accuracy required by engineering, there is no need to waste computing resources. Finally, multiple groups of better solutions are output, allowing producers to select the best solution based on actual production priorities (material cost, time, strength, etc.).

[0027] In some preferred embodiments, when calculating the preferred cutting scheme and the weld distribution scheme, a mechanical simulation module is used in conjunction with the multi-objective optimization AI algorithm to automatically evaluate the influence of the splicing of strips of different strip lengths and the distribution of welding positions on the strength of the finished product. It should be noted that after the Möbius ring is flipped and formed, the inner and outer ring structures of the bracket will produce a specific stress distribution; if the weld position and the strip splicing method are not properly selected, stress concentration and insufficient strength may occur in certain ring segments. Therefore, in addition to achieving material savings or appearance accuracy in the cutting layout and the number of welds, it is also necessary to pay attention to the final mechanical properties to ensure that the finished product can bear the weight of the lamp or display and meet the safety requirements in the use environment. When using the multi-objective optimization AI algorithm, the algorithm focuses on aspects such as material utilization, forming accuracy (torsion angle and circumferential error) and uniformity of welding distribution. Through the mechanical simulation module, the overall or local strength of the finished product can also be included in the optimization evaluation: when the multi-objective optimization AI algorithm generates a set of material strip segmentation and welding coordinate solutions, the mechanical simulation module can automatically test whether there are high stress areas, excessive deformation or potential failure risks under stress.

[0028] In some preferred embodiments, when establishing a parametric model of a Möbius band bracket, the method includes: using SolidWorks software to perform parametric modeling on the Möbius band bracket to obtain a parametric model of the Möbius band bracket. It should be noted that SolidWorks, as a common three-dimensional design software, allows designers to quickly create and modify the geometric structure of a Möbius band bracket. Under its parametric modeling function, only key parameters (such as inner and outer ring radii, torsion angles, strip width, etc.) need to be input or adjusted to automatically update the entire three-dimensional model and intuitively present structural changes, thereby greatly reducing the workload of manual modeling and reducing the design error rate.

[0029] In some preferred embodiments, after welding the hardened welding support strip according to the welding spot distribution scheme, the method includes: increasing or decreasing the number of hardened welding support strips to obtain a Mobius ring bracket after the hardened welding support strip is adjusted; comparing the structural stability of the adjusted Mobius ring bracket with the structural stability of the Mobius ring bracket before adjustment; if the structural stability of the adjusted Mobius ring bracket is better than the structural stability of the Mobius ring bracket before adjustment, then updating the welding spot distribution scheme; if the structural stability of the adjusted Mobius ring bracket is worse than the structural stability of the Mobius ring bracket before adjustment, then maintaining the welding spot distribution scheme. It should be noted that although the multi-objective optimization AI algorithm can theoretically output a better welding spot distribution scheme, some details that are not fully considered may appear at the production site (such as material batch difference, welding process deviation, appearance or specific installation requirements change, etc.). In this embodiment, by appropriately increasing or decreasing the number of hardened welding support strips after welding is completed, the local area can be quickly reinforced or streamlined, so that the stability effect of the bracket under actual working conditions can be tested and observed in the shortest time and with the least modification. When the stability result of the adjusted bracket is better (or worse) than that of the bracket before adjustment, the test result can be fed back to the solder joint distribution plan in time. If it is better, the plan will be updated; if the effect is worse, the original plan will be retained, thus providing the production line with a closed-loop mechanism of on-site trial and error and improvement, which can not only continuously improve the solder joint distribution strategy, but also accumulate experience in subsequent batch production or repeated manufacturing of the same model, thereby continuously improving the overall quality and reliability of the Möbius ring bracket.

[0030] In some preferred embodiments, after adjusting to obtain a Möbius ring bracket with better structural stability, a reinforcing sealing plate is welded at the welding position of the hardened welded support bar to cover the hardened welded support bar, thereby obtaining a finished Möbius ring bracket. It should be noted that after multiple tests or fine-tuning, a Möbius ring bracket with better structural stability has been obtained. At this time, by welding a reinforcing sealing plate at the welding position of the hardened welded support bar, the support bar and the main frame are more firmly covered in a closed manner, the strength or protective performance of the local connection is further improved, and the effect of aesthetics and decoration is achieved, which prevents the bracket from being subjected to external collision or environmental corrosion during use, and makes the overall shape more complete.

[0031] In some preferred embodiments, when a multi-objective optimization AI algorithm is used to calculate a better blanking solution for the multiple key process parameters, the length of the single-segment strip includes the length of the single-segment strip of the inner ring of the Möbius ring bracket and the length of the single-segment strip of the outer ring of the Möbius ring bracket, and the length of the single-segment strip of the inner ring of the Möbius ring bracket is less than the length of the single-segment strip of the outer ring of the Möbius ring bracket. It should be noted that the inner ring of the Möbius ring bracket usually has a smaller radius of curvature and is more compact than the outer ring. If the length of the inner ring strip is consistent with that of the outer ring, a large curvature error or local stress concentration may be formed during assembly and torsion, affecting the forming accuracy and overall strength. In this embodiment, the inner ring single-segment strip is set shorter than the outer ring single-segment strip, which helps to fit the inner ring surface with a smaller radius more finely, reduce the problem of pulling or difficulty in smooth splicing during torsional deformation, thereby improving the curve accuracy and force uniformity of the final product.

[0032] In some preferred embodiments, in the preferred material cutting scheme, the total number of segments of the single-segment material strip of the inner ring of the Möbius ring bracket is the same as the total number of segments of the single-segment material strip of the outer ring of the Möbius ring bracket. It should be noted that when the number of segments of the single-segment material strip of the inner ring is the same as that of the outer ring, each segment of the inner ring material strip can be one-to-one corresponding or synchronously connected with the corresponding outer ring material strip during the splicing and flipping process, avoiding the problem of misalignment of the inner ring and the outer ring in the assembly sequence.

[0033] In some preferred embodiments, before welding the hardened welding support strips according to the welding spot distribution scheme, the Möbius ring bracket is first tested to see whether it is successfully closed end to end. It should be noted that when the Möbius ring bracket forms a 180° twist and attempts to connect end to end, if there are problems such as segment length error, inadequate flipping, and misalignment of the inner and outer ring splicing, the bracket may not be closed smoothly. If hardening welding is performed in advance before the ends are completely and accurately connected, once a deviation in the closure is found, it is often necessary to cut off the welds and flip or rework. This not only wastes work time, but also easily damages materials. Therefore, by testing whether the ends are successfully closed before welding the support strips, it is possible to avoid rushing to weld on structures with assembly errors or dimensional errors, and reduce the risk of repeated disassembly and assembly.

[0034] Embodiment 2

[0035] See also Figure 1 A Möbius ring bracket is produced by the Möbius ring production method based on artificial intelligence described in any of the above embodiments, and a parameterized model of the Möbius ring bracket is established. The parameterized model is configured with multiple key process parameters, and the multiple key process parameters include the length of a single-segment material strip, a torsion angle, an inner ring radius, an outer ring radius, and a maximum number of weldable hardened welding support strips. According to the production requirements of the finished product, a multi-objective optimization AI algorithm is used to calculate a better blanking scheme and a welding point distribution scheme for the multiple key process parameters; the optimization target of the multi-objective optimization AI algorithm is The invention comprises a first optimization target, a second optimization target and a third optimization target, wherein the first optimization target is to minimize material waste, the second optimization target is to meet the curvature consistency of the inner ring and the outer ring after forming and the 180° torsion requirement, and the third optimization target is to evenly distribute the welding positions to ensure the stability and strength of the bracket when the bracket is displayed on both sides. The material strip is cut according to the preferred cutting scheme, and after the material strip obtained by cutting is assembled to form a Möbius ring bracket with the ends closed, the welding support strip is welded and hardened according to the welding point distribution scheme, thereby reducing the labor experience cost and improving the production efficiency of the Möbius ring bracket.

[0036] It should be pointed out that the above embodiments are only preferred specific implementation modes of the present invention, and the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for producing a Mobius ring based on artificial intelligence, characterized in that: include: Establishing a parametric model of a Möbius ring stent, wherein the parametric model is configured with a plurality of key process parameters, wherein the plurality of key process parameters include a single-segment strip length, a torsion angle, an inner ring radius, an outer ring radius, and a maximum weldable number of hardened welding support bars; According to the production requirements of finished products, a multi-objective optimization AI algorithm is used to calculate the optimal material cutting plan and welding point distribution plan for the multiple key process parameters; the optimization goals of the multi-objective optimization AI algorithm include a first optimization goal, a second optimization goal and a third optimization goal, the first optimization goal is to minimize material waste, the second optimization goal is to meet the arc consistency of the inner ring and the outer ring after forming and the 180° torsion requirement, and the third optimization goal is to evenly distribute the welding positions to ensure the stability and strength of the bracket when it is displayed on both sides; The material strips are cut according to the preferred cutting scheme, and after the material strips obtained by cutting are assembled to form a Mobius ring bracket with ends closed, the welding support strips are welded and hardened according to the welding point distribution scheme.

2. The method for producing a Möbius strip based on artificial intelligence according to claim 1, characterized in that: Also includes: When calculating the more optimal cutting plan and the weld point distribution plan, a mechanical simulation module is used in conjunction with the multi-objective optimization AI algorithm to automatically evaluate the effects of strip splicing and welding position distribution of strips of different lengths on the strength of the finished product.

3. The method for producing a Möbius strip based on artificial intelligence according to claim 1, characterized in that: When establishing the parametric model of the Möbius ring bracket, the method includes: using SolidWorks software to perform parametric modeling on the Möbius ring bracket to obtain the parametric model of the Möbius ring bracket.

4. The method for producing a Mobius strip based on artificial intelligence according to claim 1, characterized in that: The multi-objective optimization AI algorithm adopts a genetic algorithm, a particle swarm algorithm or a Bayesian optimization algorithm.

5. The method for producing a Mobius strip based on artificial intelligence according to claim 1, characterized in that: After welding the hardened welding support strips according to the welding spot distribution scheme, the method includes: increasing or decreasing the number of hardened welding support strips to obtain a Möbius ring bracket after the hardened welding support strips are adjusted; comparing the structural stability of the adjusted Möbius ring bracket with that of the Möbius ring bracket before the adjustment; if the structural stability of the Möbius ring bracket after the adjustment is better than the structural stability of the Möbius ring bracket before the adjustment, updating the welding spot distribution scheme; if the structural stability of the Möbius ring bracket after the adjustment is worse than the structural stability of the Möbius ring bracket before the adjustment, maintaining the welding spot distribution scheme.

6. The method for producing a Mobius strip based on artificial intelligence according to claim 5, characterized in that: After adjusting to obtain a Möbius ring bracket with better structural stability, a reinforcing sealing plate is welded at the welding position of the hardened welding support bar to cover the hardened welding support bar to obtain a finished Möbius ring bracket.

7. The method for producing a Mobius strip based on artificial intelligence according to claim 1, characterized in that: When a multi-objective optimization AI algorithm is used to calculate a better cutting plan for the multiple key process parameters, the single-segment material strip length includes the single-segment material strip length of the inner ring of the Möbius ring bracket and the single-segment material strip length of the outer ring of the Möbius ring bracket, and the single-segment material strip length of the inner ring of the Möbius ring bracket is less than the single-segment material strip length of the outer ring of the Möbius ring bracket.

8. The method for producing a Mobius strip based on artificial intelligence according to claim 7, characterized in that: In the preferred material cutting scheme, the total number of sections of the single-section material strip of the inner ring of the Möbius ring bracket is the same as the total number of sections of the single-section material strip of the outer ring of the Möbius ring bracket.

9. The method for producing a Möbius strip based on artificial intelligence according to any one of claims 1 to 8, characterized in that: Before welding and hardening the welding support strip according to the welding point distribution scheme, a test is first performed to determine whether the Möbius ring bracket is successfully closed end to end.

10. A Möbius ring bracket, characterized in that: The Möbius ring bracket is produced using the artificial intelligence-based Möbius ring production method as described in any one of claims 1 to 9.

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