Lightweight design method for main beam of bridge crane
By improving the Grey Wolf optimization algorithm for crane main beam design, the problem of crane design relying on manual experience was solved, achieving efficient and lightweight design and reducing manufacturing costs.
Patent Information
- Application Number
- CN202511928957.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-19
- Publication Date
- 2026-03-24
AI Technical Summary
Existing crane designs rely on manual experience, have lengthy design cycles, and are difficult to achieve the theoretically optimal solution, resulting in structural redundancy, excessive material consumption, and increased manufacturing costs.
An improved gray wolf optimization algorithm is adopted. Based on the crane main beam section model, the design variables of the crane main beam are encoded and optimized by constructing a fitness function and adaptively updating control parameters. This satisfies the constraints of vertical static stiffness, normal stress, shear stress, fatigue strength and overall stability, thus realizing intelligent lightweight design.
It improves design efficiency, outputs the optimal solution, avoids structural redundancy, reduces manufacturing costs, enhances the design efficiency of the crane main beam, and meets lightweight requirements.
Smart Images
Figure CN121723602A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of mechanical component design technology, and in particular relates to a lightweight design method for the main beam of a bridge crane. Background Technology
[0002] As an indispensable core equipment in modern industrial production, cranes undertake critical operations such as material handling and equipment installation in various fields such as building materials, metallurgy, and port logistics. With the diversification of industrial production scenarios, the market demand for cranes is becoming increasingly customized. Core technical parameters such as rated lifting capacity, working span, and lifting height often vary significantly depending on the application scenario, requiring each crane to be designed specifically for the specific application.
[0003] Under the existing design model, crane design relies heavily on the experience of senior engineers for manual design. This model requires repeated drawing, mechanical calculations, and parameter adjustments, resulting in a lengthy design cycle that is difficult to match the efficiency requirements of modern industry. Furthermore, it is limited by human judgment, making it difficult for the design to achieve the theoretically optimal solution. Taking crane bridge design as an example, the determination of its key section parameters requires multiple rounds of trial calculations and corrections. Engineers, based on safety considerations, often adopt conservative parameter values, which can easily lead to structural redundancy, excessive weight of the entire machine, and excessive material consumption, thereby increasing the manufacturing cost of the crane. Summary of the Invention
[0004] To address some or all of the technical problems existing in the prior art, the present invention provides a lightweight design method for the main beam of a bridge crane.
[0005] The technical solution of the present invention is as follows: A lightweight design method for the main beam of a bridge crane is provided, including: The cross-sectional data of the crane main beam is obtained as a design variable, and the optimization objective is to reduce the mass of the crane main beam. Constraints are generated based on vertical static stiffness, normal stress, shear stress, fatigue strength and overall stability to construct the cross-sectional model of the crane main beam. The design variables of the crane main beam section model are encoded and initialized based on the improved gray wolf optimization algorithm. The alpha wolf is selected by constructing a fitness function, and adaptive update control parameters are introduced to update the position of gray wolves in the population. When the termination condition is met, the potential solution corresponding to the position of the gray wolf is output as the optimal solution.
[0006] In some optional implementations, the step of: obtaining the cross-sectional data of the crane main beam as a design variable, using the reduction of the crane main beam's mass as an optimization objective, and generating constraints based on vertical static stiffness, normal stress, shear stress, fatigue strength, and overall stability to construct a crane main beam cross-sectional model; includes: Box girders are selected as the research object, and their cross-sectional data are used as design variables: ; in, This refers to the thickness data of the top cover plate of the box girder. This refers to the thickness data of the bottom cover plate of the box girder. This refers to the thickness data of the main web of the box girder. Thickness data of the secondary web of the box girder. The spacing between the webs of the box girder. This refers to the beam height of the box girder; With reducing the mass of the crane's main beam as the optimization objective, the objective function is constructed as follows: ; in, The objective function value represents the cross-sectional area of the main beam of the bridge crane.
[0007] In some optional implementations, the step of: obtaining the cross-sectional data of the crane main beam as a design variable, using the reduction of the crane main beam's mass as an optimization objective, and generating constraints based on vertical static stiffness, normal stress, shear stress, fatigue strength, and overall stability to construct a crane main beam cross-sectional model; includes: When the fully loaded trolley acts at the mid-span of the crane's main beam, the deflection of the crane's main beam should satisfy the vertical static stiffness constraint condition: ; in, This is the actual static displacement. For allowable static displacement, For the first wheel pressure of a fully loaded trolley, For the second wheel pressure of the fully loaded trolley, The elastic modulus of the material. The moment of inertia of the main beam section about the neutral axis. For the span of the crane, This refers to the wheel track of the car. When a fully loaded trolley acts at the mid-span of the crane's main beam, the main beam is subjected to the combined effects of a uniformly distributed load caused by its own weight and a concentrated load caused by the fully loaded trolley. The main web of the box girder experiences the maximum normal stress, and the normal stress constraint condition must be met. ; ; in, This represents the maximum normal stress at the first calculation point where the main web of the box girder connects to the lower cover plate. For allowable normal stress, The maximum bending moment of the crane's main beam section, The perpendicular distance from the first calculation point to the neutral axis is denoted as . The uniformly distributed load caused by the self-weight of the crane's main beam; When the fully loaded trolley acts at the mid-span of the crane's main beam, the shear stress at the second calculation point on the main web of the box girder should satisfy the shear stress constraint condition: ; in, The shear stress at the second calculation point, This represents the maximum static moment of shear stress in the direction perpendicular to the web. , Allowable shear stress; The first calculation point at the connection between the main web and the lower cover plate of the box girder must meet the fatigue strength constraint condition: ; in, This is the fatigue strength converted value for the first calculation point. For fatigue allowable normal stress, Allowable fatigue shear stress; The ratio of the height of the box girder to the distance between the outer sides of the main web of the box girder should satisfy the overall stability constraint condition: ; in, This is the ratio between the overall height of the crane's main beam and the distance between the outer sides of the main and auxiliary webs of the box girder.
[0008] In some optional implementations, the steps include: encoding and initializing the design variables of the crane main beam section model based on the improved gray wolf optimization algorithm; selecting the alpha wolf by constructing a fitness function; introducing adaptive update control parameters; updating the positions of gray wolves in the population; and outputting the potential solution corresponding to the gray wolf position as the optimal solution when the termination condition is met; including: The design variables of the crane main beam in the crane main beam section model are encoded using fixed-length encoding to generate the initial parameters of the gray wolf. Initialize and set the population size, number of population iterations, and adjustment coefficient; Based on the objective function and the constraints, construct the fitness function: ; in, For the first The fitness value of Sekiro: Shadows Die Twice For the first The objective function value of Sekiro: Shadows Die Twice. For the first Only the gray wolf satisfies the constraints. For the first The gray wolf does not satisfy at least one constraint condition; Initialize the location settings of gray wolves in the gray wolf population; ; in, For the first In the nth iteration Chaotic values of individual variables For control parameters, For the first In the nth iteration Chaotic values of individual variables For the first Grey Wolf The values of the variables, For the first The lower limit of the values that a variable can take. For the first The upper limit of the values that a variable can take; The fitness value of each gray wolf in the population was calculated based on the fitness function. The three non-dominated solutions with the best fitness values were selected as the three alpha wolves and labeled as follows: , , ; Adaptive updates to control parameters: ; in, For control parameters, This represents the current iteration number. The maximum number of iterations, The first adjustment coefficient, This is the second adjustment coefficient; The position of the gray wolf is updated based on the control parameters: ; in, For the first Sekiro's location updated The values of the variables, , , The first After each gray wolf updates its position to the three alpha wolves with the best fitness values, the position of the 1st gray wolf is... The values of the variables: ; in, , , These are the three alpha wolves with the best current fitness values. The values of the variables, , , These are the three alpha wolves with the best current fitness values. The value of the first variable and the first Grey Wolf Values of variables distance, For the coefficient vector: ; in, for The first random number within the interval; ; in, For the coefficient vector: ; in, for The second random number within the interval; The position of the gray wolf after the position is updated is taken as a potential solution. It is then determined whether the termination condition is met. If the termination condition is met, the potential solution is output as the optimal solution.
[0009] In some optional implementations, the steps of: encoding and initializing the design variables of the crane main beam section model based on the improved gray wolf optimization algorithm, selecting the alpha wolf by constructing a fitness function, introducing adaptive update control parameters, updating the position of the gray wolves in the population, and outputting the potential solution corresponding to the gray wolf position as the optimal solution when the termination condition is met; further include: When the potential solution does not meet the termination condition, the population is optimized based on the elite strategy, the alpha wolf is reselected, and the position of the gray wolves is updated until the maximum number of iterations is reached or the termination condition is met.
[0010] The main advantages of the technical solution of this invention are as follows: The lightweight design method for the main beam of a bridge crane of the present invention is based on the cross-sectional model of the main beam of the crane and an improved gray wolf optimization algorithm. Under the premise of meeting the structural performance constraints, it realizes the intelligent lightweight design of the main beam of the crane without manual intervention. It solves the problems of long design cycle and human-induced limitations caused by manual design relying on human experience in traditional technology, improves the efficiency of scheme design, and the output optimal solution can reach the theoretical optimal. In this way, it avoids the structural redundancy in traditional design, which leads to the problem of excessive weight of the whole machine and excessive material consumption. It effectively reduces the manufacturing cost of the crane, improves the design efficiency of the main beam of the crane, and meets the requirements of lightweight design of the main beam of the crane. Attached Figure Description
[0011] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and constitute a part of this invention, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 A flowchart illustrating a lightweight design method for the main beam of a bridge crane provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the box girder main beam section in a lightweight design method for the main beam of a bridge crane provided in an embodiment of the present invention; Figure 3 A schematic diagram showing the deflection of the main beam when the fully loaded trolley is located at the mid-span of the crane's main beam; Figure 4 A schematic diagram of the forces acting on the main beam of a crane when the fully loaded trolley is located at the mid-span of the main beam. Figure 5 The flowchart shows the improved gray wolf optimization algorithm used in a lightweight design method for the main beam of a bridge crane provided in an embodiment of the present invention. Detailed Implementation
[0012] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0013] The technical solutions provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0014] refer to Figure 1 This invention provides a lightweight design method for the main beam of a bridge crane, comprising: The cross-sectional data of the crane main beam is obtained as a design variable, and the optimization objective is to reduce the mass of the crane main beam. Constraints are generated based on vertical static stiffness, normal stress, shear stress, fatigue strength and overall stability to construct the cross-sectional model of the crane main beam. The design variables of the crane main beam section model are encoded and initialized based on the improved gray wolf optimization algorithm. The alpha wolf is selected by constructing a fitness function, and adaptive update control parameters are introduced to update the position of gray wolves in the population. When the termination condition is met, the potential solution corresponding to the position of the gray wolf is output as the optimal solution.
[0015] In this embodiment of the invention, the cross-sectional data of the crane main beam is obtained as a design variable, and the objective function is constructed with the goal of reducing the mass of the crane main beam. Constraints are generated based on vertical static stiffness, normal stress, shear stress, fatigue strength and overall stability, and a cross-sectional model of the crane main beam is built. The improved Grey Wolf optimization algorithm optimizes the design parameters of the crane main beam based on the cross-sectional model of the crane main beam, and outputs the optimal solution when the termination condition is met.
[0016] In this embodiment of the invention, the cross-sectional data of the crane main beam is used as the design variable, and the objective function is constructed to optimize the mass of the crane main beam. Vertical static stiffness, normal stress, shear stress, fatigue strength, and overall stability are used as constraints to realize the construction of the crane main beam cross-sectional model. Based on the improved Grey Wolf optimization algorithm, the design parameters of the crane main beam are optimized to obtain the optimal solution, thereby realizing the lightweight design of the crane main beam. The above method, based on the crane main beam cross-sectional model and the improved Grey Wolf optimization algorithm, realizes the intelligent lightweight design of the crane main beam under the premise of meeting the structural performance constraints. It does not require manual intervention and solves the problems of long design cycles and human-induced limitations caused by manual design relying on human experience in traditional technology. It improves the efficiency of scheme design, and the output optimal solution can reach the theoretical optimality. In this way, it avoids the structural redundancy in traditional design, which leads to excessive weight of the whole machine and excessive material consumption. It effectively reduces the manufacturing cost of the crane, improves the design efficiency of the crane main beam, and meets the requirements of lightweight design of the crane main beam.
[0017] In this embodiment of the invention, the steps include: obtaining the cross-sectional data of the crane main beam as a design variable, using the reduction of the crane main beam's mass as an optimization objective, and generating constraints based on vertical static stiffness, normal stress, shear stress, fatigue strength, and overall stability to construct a crane main beam cross-sectional model; including: refer to Figure 2 We selected box girders as the research object and used the cross-sectional data of box girders as design variables: ; in, The thickness data is for the top cover plate 1 of the box girder. The thickness data is for the lower cover plate 2 of the box girder. The thickness data is for the main web 3 of the box girder. Thickness data of the secondary web 4 of the box girder. The spacing between the webs of the box girder. This refers to the beam height of the box girder; With reducing the mass of the crane's main beam as the optimization objective, the objective function is constructed as follows: ; in, The objective function value represents the cross-sectional area of the main beam of the bridge crane.
[0018] In this embodiment of the invention, a box girder is taken as the research object, a crane main beam cross-section model is constructed, the cross-sectional data of the box girder is used as the design variable, and reducing the mass of the crane main beam is taken as the optimization objective, thus constructing an objective function.
[0019] In this embodiment of the invention, the cross-sectional data of the box girder is used as the design variable, and the mass of the lightweight crane main beam is used to construct the objective function. The cross-sectional model of the crane main beam is built, which provides a model basis for the design based on the cross-sectional model of the crane main beam and constructs a lightweight design model with "quantifiable objectives and computable constraints".
[0020] In this embodiment of the invention, the steps include: obtaining the cross-sectional data of the crane main beam as a design variable, using the reduction of the crane main beam's mass as an optimization objective, and generating constraints based on vertical static stiffness, normal stress, shear stress, fatigue strength, and overall stability to construct a crane main beam cross-sectional model; including: When the fully loaded trolley acts at the mid-span position of the crane's main beam, such as Figure 3 As shown, the deflection of the crane's main beam should meet the vertical static stiffness constraint condition: ; in, This is the actual static displacement. For allowable static displacement, For the first wheel pressure of a fully loaded trolley, For the second wheel pressure of the fully loaded trolley, The elastic modulus of the material. The moment of inertia of the main beam section about the neutral axis. For the span of the crane, This refers to the wheel track of the car. refer to Figure 2 The neutral axis of the box girder is the dashed line in the figure.
[0021] When a fully loaded trolley acts at the mid-span of the crane's main beam, the crane's main beam is subjected to the combined effects of a uniformly distributed load caused by its own weight and a concentrated load caused by the fully loaded trolley. Figure 4 As shown, the main web of the box girder experiences the greatest normal stress and should satisfy the normal stress constraint condition: ; ; in, This represents the maximum normal stress at the first calculation point 51, located at the connection between the main web and the lower cover plate of the box girder. For allowable normal stress, The maximum bending moment of the crane's main beam section, The perpendicular distance from the first calculation point 51 to the neutral axis. The uniformly distributed load caused by the self-weight of the crane's main beam; When the fully loaded trolley acts at the mid-span position of the crane's main beam, the shear stress at the second calculation point 52 on the main web of the box girder, located at the neutral axis, should satisfy the shear stress constraint condition: ; in, For the shear stress at the second calculation point 52, This represents the maximum static moment of shear stress in the direction perpendicular to the web. , Allowable shear stress; The first calculation point 51 at the connection between the main web and the lower cover plate of the box girder must meet the fatigue strength constraint condition: ; in, This is the fatigue strength converted value for the first calculation point 51. For fatigue allowable normal stress, Allowable fatigue shear stress; The ratio of the height of the box girder to the distance between the outer sides of the main web of the box girder should satisfy the overall stability constraint condition: ; in, This is the ratio between the overall height of the crane's main beam and the distance between the outer sides of the main and auxiliary webs of the box girder.
[0022] In this embodiment of the invention, a vertical static stiffness constraint condition for the main beam of the crane is established, namely, when the fully loaded trolley acts at the mid-span position of the main beam of the crane, the deflection of the main beam of the crane should meet the allowable static displacement.
[0023] In this embodiment of the invention, the normal stress constraint condition of the main beam of the crane is constructed, that is, when the fully loaded trolley acts on the mid-span position of the main beam of the crane, the normal stress on the main web of the box girder should meet the allowable normal stress.
[0024] In this embodiment of the invention, a shear stress constraint condition for the main beam of the crane is constructed, namely, when the fully loaded trolley acts on the mid-span position of the main beam of the crane, the shear stress at the second calculation point at the main web of the box girder should meet the allowable shear stress.
[0025] In this embodiment of the invention, fatigue strength constraints are constructed for the main beam of the crane, namely, the fatigue strength conversion value of the first calculation point at the connection position between the main web of the box girder and the lower cover plate meets the preset value requirement.
[0026] In this embodiment of the invention, an overall stability constraint condition for the main beam of the crane is constructed, namely, the ratio of the height of the beam to the distance between the outer sides of the main web of the box girder is less than a preset ratio.
[0027] In this embodiment of the invention, constraints are constructed on the crane main beam cross-section model based on the vertical static stiffness, normal stress, shear stress, fatigue strength and overall stability of the crane main beam, so that the design based on the crane main beam cross-section model meets the requirements of crane working strength.
[0028] like Figure 5 As shown, in this embodiment of the invention, the steps are as follows: Encoding and initializing the design variables of the crane main beam cross-section model based on the improved gray wolf optimization algorithm; selecting the alpha wolf by constructing a fitness function; introducing adaptive update control parameters; updating the positions of gray wolves in the population; and outputting the potential solution corresponding to the gray wolf position as the optimal solution when the termination condition is met; including: Fixed-length encoding is used to encode the design variables of the crane main beam in the crane main beam section model, generating the initial parameters for the gray wolf: ; Initialize and set the population size, number of population iterations, and adjustment coefficient; Based on the objective function and constraints, construct the fitness function: ; in, For the first The fitness value of Sekiro: Shadows Die Twice For the first The objective function value of Sekiro: Shadows Die Twice. For the first Only the gray wolf satisfies the constraints. For the first The gray wolf does not satisfy at least one constraint condition; Initialize the positions of gray wolves within the gray wolf population: ; in, For the first In the nth iteration Chaotic values of individual variables For control parameters, For the first In the nth iteration Chaotic values of individual variables For the first Grey Wolf The values of the variables, For the first The lower limit of the values that a variable can take. For the first The upper limit of the values that a variable can take; The fitness value of each gray wolf in the population was calculated based on the fitness function. The three non-dominated solutions with the best fitness values were selected as the three alpha wolves and labeled as follows: , , ; Adaptive updates to control parameters: ; in, For control parameters, This represents the current iteration number. The maximum number of iterations, The first adjustment coefficient, This is the second adjustment coefficient; The position of the gray wolf is updated based on the control parameters: ; in, For the first Sekiro's location updated The values of the variables, , , The first After each gray wolf updates its position to the three alpha wolves with the best fitness values, the position of the 1st gray wolf is... The values of the variables: ; in, , , These are the three alpha wolves with the best current fitness values. The values of the variables, , , These are the three alpha wolves with the best current fitness values. The value of the first variable and the first Grey Wolf Values of variables distance, For the coefficient vector: ; in, for The first random number within the interval; ; in, For the coefficient vector: ; in, for The second random number within the interval; The position of the gray wolf after the position is updated is taken as a potential solution. It is then determined whether the termination condition is met. If the termination condition is met, the potential solution is output as the optimal solution.
[0029] In this embodiment of the invention, a fixed-length encoding is used to encode the design variables of the crane main beam in the crane main beam section model, generating initial parameters for the gray wolves. The population size, number of population iterations, and adjustment coefficient are initialized. The positions of the gray wolves in the gray wolf population are initialized based on the chaotic Cubic mapping. The fitness value of each gray wolf in the gray wolf population is calculated through the fitness function. Three alpha wolves are selected, and the control parameters are adaptively updated. The positions of the gray wolves are updated as potential solutions. When the potential solution meets the termination condition, it is output as the optimal solution.
[0030] In this embodiment of the invention, the traditional gray wolf algorithm has the problem of poor global search capability. By introducing an adaptive update control parameter adjustment strategy, the global search capability of the algorithm can be improved.
[0031] In this embodiment of the invention, the first adjustment coefficient Second adjustment coefficient , ( .
[0032] In this embodiment of the invention, an improved gray wolf optimization algorithm is used to optimize the design parameters of the crane main beam section model and obtain the optimal solution. The improved gray wolf optimization algorithm integrates chaotic Cubic mapping and adaptive update strategy of control parameters into the gray wolf optimization algorithm, thereby improving the gray wolf optimization algorithm. Chaotic Cubic mapping enhances the diversity of the initial population of the algorithm and avoids getting stuck in local optima in the early stage of optimization. The adaptive update strategy dynamically adjusts the algorithm parameters, improves the convergence speed and optimization accuracy in the later stage, and makes the algorithm more suitable for the lightweight design requirements of the main beam, further improving the optimization efficiency and accuracy.
[0033] like Figure 5As shown, in this embodiment of the invention, the steps are as follows: Encoding and initializing the design variables of the crane main beam cross-section model based on the improved gray wolf optimization algorithm; selecting the alpha wolf by constructing a fitness function; introducing adaptive update control parameters; updating the positions of gray wolves in the population; and outputting the potential solution corresponding to the gray wolf position as the optimal solution when the termination condition is met; The method also includes: When a potential solution does not meet the termination condition, the population is optimized based on an elite strategy, a new alpha wolf is selected, and the position of the gray wolves is updated until the maximum number of iterations is reached or the termination condition is met.
[0034] In this embodiment of the invention, when the potential solution does not meet the termination condition, the fitness function value is recalculated, three alpha wolves are selected, and the gray wolf positions are updated until the maximum number of iterations is reached or the termination condition is met, thereby obtaining the optimal solution and realizing the acquisition of crane main beam design data.
[0035] Example 1: The crane's design parameters are: lifting capacity 75t, working class A6, crane span 25500mm, main load-bearing steel plate material Q355B, trolley wheel track 3000mm, uniformly distributed load 8kN / mm, and first wheel pressure of the fully loaded trolley. The second wheel of the fully loaded car The first wheel of the unloaded trolley The second wheel of the empty trolley .
[0036] The main beam section data of the box girder is as follows: the thickness data of the top cover plate of the box girder. The minimum value is 6mm, and the maximum value is 16mm; data on the thickness of the lower cover plate of the box girder. Minimum value is 6mm, maximum value is 16mm; Main web thickness data for box girder Minimum value is 8mm, maximum value is 12mm; Sub-web thickness data for box girder The minimum value is 6mm, and the maximum value is 10mm; the web spacing of the box girder The minimum value is 600mm, and the maximum value is 1200mm; the beam height of the box girder The minimum value is 1200mm, and the maximum value is 2400mm.
[0037] The parameters of the improved gray wolf optimization algorithm are set as follows: population size of 20, maximum number of generations of gray wolf population iteration of 100, and first adjustment coefficient. Second adjustment coefficient .
[0038] As shown in the table below, the lightweight design method for the main beam of the bridge crane of the present invention builds a cross-sectional model of the main beam of the crane based on the above parameters, wherein the thickness data of the upper cover plate of the box girder is... Box girder lower cover plate thickness data Main web thickness data of box girder Data on the thickness of the secondary web of a box girder The web spacing of the box girder The height of the box girder The optimal solution for the design parameters is obtained by improving the Grey Wolf optimization algorithm.
[0039] As a comparative example for verification, the table below lists the basic gray wolf algorithm and particle swarm optimization algorithm using existing technologies, each finding an optimal solution that satisfies the constraints based on the corresponding design parameters. This comparison shows that the optimal solution for representing the cross-sectional area of the main beam of the bridge crane obtained by this invention... Compared to the optimal solutions for representing the cross-sectional area of the main beam of a bridge crane obtained by the basic gray wolf algorithm and particle swarm optimization algorithm in existing technologies. The weight reductions were 5.8% and 9.3% respectively, thus verifying the effectiveness of the lightweight design method for the main beam of the bridge crane of the present invention.
[0040]
[0041] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Additionally, the terms "front," "back," "left," "right," "upper," and "lower" in this document refer to the placement shown in the accompanying drawings.
[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A lightweight design method for the main beam of a bridge crane, characterized in that, include: The cross-sectional data of the crane main beam is obtained as a design variable, and the optimization objective is to reduce the mass of the crane main beam. Constraints are generated based on vertical static stiffness, normal stress, shear stress, fatigue strength and overall stability to construct the cross-sectional model of the crane main beam. The design variables of the crane main beam section model are encoded and initialized based on the improved gray wolf optimization algorithm. The alpha wolf is selected by constructing a fitness function, and adaptive update control parameters are introduced to update the position of gray wolves in the population. When the termination condition is met, the potential solution corresponding to the position of the gray wolf is output as the optimal solution.
2. The lightweight design method for the main beam of a bridge crane according to claim 1, characterized in that, The steps include: obtaining the cross-sectional data of the crane main beam as a design variable, using the reduction of the crane main beam's mass as an optimization objective, and generating constraints based on vertical static stiffness, normal stress, shear stress, fatigue strength, and overall stability to construct the crane main beam cross-sectional model; including: We selected box girders as the research object and used their cross-sectional data as design variables: ； in, This refers to the thickness data of the top cover plate of the box girder. This refers to the thickness data of the bottom cover plate of the box girder. This refers to the thickness data of the main web of the box girder. Thickness data of the secondary web of the box girder. The spacing between the webs of the box girder. This refers to the beam height of the box girder; With reducing the mass of the crane's main beam as the optimization objective, the objective function is constructed as follows: ； in, The objective function value represents the cross-sectional area of the main beam of the bridge crane.
3. The lightweight design method for the main beam of a bridge crane according to claim 2, characterized in that, The steps include: obtaining the cross-sectional data of the crane main beam as a design variable, using the reduction of the crane main beam's mass as an optimization objective, and generating constraints based on vertical static stiffness, normal stress, shear stress, fatigue strength, and overall stability to construct the crane main beam cross-sectional model; including: When the fully loaded trolley acts at the mid-span of the crane's main beam, the deflection of the crane's main beam should satisfy the vertical static stiffness constraint condition: ； in, This is the actual static displacement. For allowable static displacement, For the first wheel pressure of the fully loaded trolley, For the second wheel pressure of the fully loaded trolley, The elastic modulus of the material. The moment of inertia of the main beam section about the neutral axis. For the span of the crane, This refers to the wheel track of the car. When a fully loaded trolley acts at the mid-span of the crane's main beam, the main beam is subjected to the combined effects of a uniformly distributed load caused by its own weight and a concentrated load caused by the fully loaded trolley. The main web of the box girder experiences the maximum normal stress, and the normal stress constraint condition must be met. ； ； in, This represents the maximum normal stress at the first calculation point where the main web of the box girder connects to the lower cover plate. For allowable normal stress, The maximum bending moment of the crane's main beam section, The perpendicular distance from the first calculation point to the neutral axis is denoted as . The uniformly distributed load caused by the self-weight of the crane's main beam; When the fully loaded trolley acts at the mid-span of the crane's main beam, the shear stress at the second calculation point on the main web of the box girder should satisfy the shear stress constraint condition: ； in, The shear stress at the second calculation point, This represents the maximum static moment of shear stress in the direction perpendicular to the web. , Allowable shear stress; The first calculation point at the connection between the main web and the lower cover plate of the box girder must meet the fatigue strength constraint condition: ； in, This is the fatigue strength converted value for the first calculation point. For fatigue allowable normal stress, Allowable fatigue shear stress; The ratio of the height of the box girder to the distance between the outer sides of the main web of the box girder should satisfy the overall stability constraint condition: ； in, This is the ratio between the overall height of the crane's main beam and the distance between the outer sides of the main and auxiliary webs of the box girder.
4. The lightweight design method for the main beam of a bridge crane according to claim 2, characterized in that, The steps are as follows: The design variables of the crane main beam section model are encoded and initialized based on the improved gray wolf optimization algorithm. The alpha wolf is selected by constructing a fitness function, adaptive update control parameters are introduced, the position of gray wolves in the population is updated, and when the termination condition is met, the potential solution corresponding to the position of the gray wolf is output as the optimal solution. include: The design variables of the crane main beam in the crane main beam section model are encoded using fixed-length encoding to generate the initial parameters of the gray wolf. Initialize and set the population size, number of population iterations, and adjustment coefficient; Based on the objective function and the constraints, construct the fitness function: ； in, For the first The fitness value of Sekiro: Shadows Die Twice For the first The objective function value of Sekiro: Shadows Die Twice. For the first Only the gray wolf satisfies the constraints. For the first The gray wolf does not satisfy at least one constraint condition; Initialize the positions of gray wolves within the gray wolf population: ； in, For the first In the nth iteration Chaotic values of individual variables For control parameters, For the first In the nth iteration Chaotic values of individual variables For the first Grey Wolf The values of the variables, For the first The lower limit of the values that a variable can take. For the first The upper limit of the values that a variable can take; The fitness value of each gray wolf in the population was calculated based on the fitness function. The three non-dominated solutions with the best fitness values were selected as the three alpha wolves and labeled as follows: , , ; Adaptive updates to control parameters: ； in, For control parameters, This represents the current iteration number. The maximum number of iterations, The first adjustment coefficient, This is the second adjustment coefficient; The position of the gray wolf is updated based on the control parameters: ； in, For the first Sekiro's location updated The values of the variables, , , The first After each gray wolf updates its position to the three alpha wolves with the best fitness values, the position of the 1st gray wolf is... The values of the variables: ； in, , , These are the three alpha wolves with the best current fitness values. The values of the variables, , , These are the three alpha wolves with the best current fitness values. The value of the first variable and the first Grey Wolf Values of variables distance, For the coefficient vector: ； in, for The first random number within the interval; ； in, For the coefficient vector: ； in, for The second random number within the interval; The position of the gray wolf after the position is updated is taken as a potential solution. It is then determined whether the termination condition is met. If the termination condition is met, the potential solution is output as the optimal solution.
5. The lightweight design method for the main beam of a bridge crane according to claim 4, characterized in that, The steps include: encoding and initializing the design variables of the crane main beam cross-section model based on the improved gray wolf optimization algorithm; selecting the alpha wolf by constructing a fitness function; introducing adaptive update control parameters to update the positions of gray wolves in the population; and outputting the potential solution corresponding to the gray wolf position as the optimal solution when the termination condition is met; it also includes: When the potential solution does not meet the termination condition, the population is optimized based on the elite strategy, the alpha wolf is reselected, and the position of the gray wolves is updated until the maximum number of iterations is reached or the termination condition is met.