A method for optimizing the cantilever structure of an automatic car cleaning robot

Abstract

The application provides an automatic car cleaning robot cantilever structure optimization design method, comprising: for the candidate high-strength lightweight alloy material, using the finite element analysis method, simulating the stress state of the cantilever under different working conditions, obtaining the stress-strain nephogram, and judging whether the material meets the strength and stiffness requirements; at the connection between the cantilever and the cleaning tool, a standardized quick-change interface is designed, the cleaning tool is divided into several functional modules, and each module is connected with the cantilever through the quick-change interface; a three-dimensional model of the quick-change interface is established, a precise positioning and locking mechanism is adopted, the quick positioning and reliable connection of the cleaning tool module and the cantilever are realized, and the interface gap after connection is less than a preset threshold; using a three-dimensional CAD software, a parameterized design model of the cantilever structure is established, the key size and geometric feature parameters of the cantilever are parameterized, and a parameterized driving framework is formed.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an optimization design method for the cantilever structure of an automated cleaning robot. Background Technology

[0002] Background of the problem:

[0003] A key technical challenge in applying automated cleaning robot technology to empty tippler coal cleaning is designing a standardized cantilever interface and module that allows different robot models to share cantilever components and achieve fast and convenient cantilever access. Solving this problem is crucial for reducing the manufacturing cost and maintenance complexity of automated cleaning robots.

[0004] Currently, different models of cleaning robots typically employ different cantilever designs, resulting in non-interchangeable parts and increased production and maintenance costs. Furthermore, replacing or repairing the cantilever often requires significant equipment downtime, impacting cleaning efficiency. Therefore, there is an urgent need to research and design a standardized cantilever interface and module that allows for interchangeability across different cleaning robot models, while also incorporating a quick-locking device for rapid cantilever installation and removal, minimizing equipment downtime.

[0005] Solving this technical problem requires a thorough analysis of the empty car coal cleaning process of tippler and the operating characteristics of the automatic cleaning robot. It also requires comprehensive consideration of factors such as the mechanical properties of the cantilever, material selection, standardized interface design, and quick-locking device design. Repeated testing, verification, optimization, and improvement are needed to obtain a standardized cantilever solution that balances versatility, reliability, and efficiency, thereby promoting the widespread application of automatic cleaning robot technology in the field of empty car coal cleaning of tippler. Summary of the Invention

[0006] This invention provides a method for optimizing the cantilever structure of an automated cleaning robot, mainly including:

[0007] Information on the cantilever mechanical performance requirements and working environment characteristics of different models of automatic cleaning robots is obtained. Based on this information, a material selection index system is established by comprehensively considering the strength, stiffness, toughness, wear resistance and corrosion resistance of the materials.

[0008] For candidate high-strength lightweight alloy materials, the finite element analysis method is used to simulate the stress state of the cantilever under different working conditions, obtain stress-strain cloud diagrams, and determine whether the material meets the strength and stiffness requirements.

[0009] For candidate materials that meet the strength and stiffness requirements, cantilever fatigue life and fracture toughness tests are conducted. Based on the test results, combined with the wear resistance and corrosion resistance of the materials, the optimal cantilever material selection scheme is determined.

[0010] At the connection between the cantilever and the cleaning tool, a standardized quick-change interface is designed to divide the cleaning tool into several functional modules, each of which is connected to the cantilever through the quick-change interface.

[0011] A 3D model of the quick-change interface is established, and a precision positioning and locking mechanism is adopted to achieve rapid positioning and reliable connection between the cleaning tool module and the cantilever. After connection, the interface gap is less than a preset threshold.

[0012] Using 3D CAD software, a parametric design model of the cantilever structure is established, and the key dimensions and geometric features of the cantilever are parameterized to form a parametric driving framework;

[0013] In the parametric design model, based on the usage requirements of a specific model of automatic cleaning robot, the target cantilever performance parameters are input, the optimization algorithm is called, and the cantilever structure size and shape are automatically optimized, generating a 3D model and engineering drawings of the cantilever. If the optimization results meet the performance requirements, the cantilever structure design is completed.

[0014] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0015] This invention discloses a method for optimizing the cantilever structure of an automated cleaning robot. The method first establishes a material selection index system, comprehensively considering factors such as strength, stiffness, and toughness, and then selects the optimal cantilever material through finite element analysis and fatigue testing. Next, a standardized quick-change interface is designed to achieve modular connection of the cleaning tools. Finally, a parametric design model of the cantilever structure is established. For specific robot models, target performance parameters are input, and an optimization algorithm is invoked to automatically complete structural optimization. This invention, through a combination of material selection, modular design, and parametric optimization, achieves efficient and customized design of the cantilever of an automated cleaning robot, improving the performance and adaptability of the cantilever structure and providing reliable technical support for cleaning operations under different working conditions. Attached Figure Description

[0016] Figure 1 This is a flowchart of an optimized design method for the cantilever structure of an automatic cleaning robot according to the present invention.

[0017] Figure 2 This is a schematic diagram of an optimized design method for the cantilever structure of an automatic cleaning robot according to the present invention.

[0018] Figure 3 This is another schematic diagram of the cantilever structure optimization design method for an automatic cleaning robot according to the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1-3 The method for optimizing the cantilever structure of an automated cleaning robot, as described in this embodiment, may specifically include:

[0021] S101. Obtain information on the cantilever mechanical performance requirements and working environment characteristics of different models of automatic cleaning robots. Based on the information, comprehensively consider the strength, stiffness, toughness, wear resistance and corrosion resistance of the materials, and establish a material selection index system.

[0022] Three-dimensional model data of cantilever arms of different models of automated cleaning robots were acquired. Static and dynamic simulation analyses of the cantilever arms were performed using finite element analysis software to obtain the stress-strain distribution and vibration characteristic parameters of the cantilever arms under different working conditions. The working environment parameters of the automated cleaning robot were obtained to determine the degree of influence of the environment on material performance and to identify the required performance requirements of the materials. An analytic hierarchy process (AHP) was used to establish a comprehensive material performance evaluation system and to score and rank candidate materials. Using the cantilever structure parameters obtained from the simulation analysis, the Apriori association rule mining algorithm was used to analyze the correlation between material performance and the cantilever structure parameters, optimizing the cantilever structure design scheme. Using the performance parameters of the candidate materials and environmental factors as input, a support vector machine regression model was constructed to predict the comprehensive performance of different materials in the actual working environment, selecting the material with the best adaptability. Virtual assembly and motion analysis of the cantilever arms made of different materials were performed using simulation technology to verify the mechanical reliability of the material selection scheme. A comprehensive analysis of the results was conducted to balance material performance and the cantilever structure design requirements, determining the optimal material selection scheme and generating a material selection report and optimization suggestions for the cantilever structure.

[0023] Specifically, the process involves acquiring 3D model data of cantilever arms for different models of automated cleaning robots, performing static and dynamic simulations using finite element analysis software to obtain stress-strain distribution and vibration characteristic parameters under different working conditions, acquiring environmental parameters of the automated cleaning robot, assessing the impact of the environment on material performance, and determining the required material performance. A comprehensive material performance evaluation system is established using the analytic hierarchy process (AHP) to score and rank candidate materials. Using the cantilever structure parameters obtained from simulation analysis, the Apriori association rule mining algorithm is employed to analyze the correlation between material performance and cantilever structure parameters, optimizing the cantilever structure design. Support vector machine regression models are constructed using the performance parameters of candidate materials and environmental factors as input to predict the comprehensive performance of different materials in actual working environments, selecting the most adaptable materials. Virtual assembly and motion analysis of cantilever arms made of different materials are performed using simulation technology to verify the mechanical reliability of the material selection scheme. Finally, a comprehensive analysis of all results is conducted to balance material performance and cantilever structure design requirements, determine the optimal material selection scheme, and generate a material selection report and cantilever structure optimization suggestions.

[0024] S102. For candidate high-strength lightweight alloy materials, the finite element analysis method is used to simulate the stress state of the cantilever under different working conditions, obtain stress-strain cloud diagrams, and determine whether the material meets the strength and stiffness requirements.

[0025] The physical and mechanical parameters of candidate high-strength lightweight alloy materials are obtained. An isotropic linear elastic constitutive model is used to establish the constitutive relation of the material, yielding its elastic modulus, Poisson's ratio, and yield strength. A geometric model of the cantilever structure is constructed using 3D modeling software, and the material's mechanical properties are assigned to this model, defining its properties. The geometric model is then meshed to generate a finite element mesh model. Loads and boundary conditions are applied to the finite element model based on the cantilever's actual working conditions. Static finite element analysis is used to calculate the stress-strain distribution of the cantilever under these conditions, obtaining stress-strain and deformation contour maps. Based on the cantilever's stress state and deformation characteristics, it is determined whether the material meets the strength and stiffness requirements under these conditions. If the material meets the strength and stiffness requirements, it is selected as a candidate material for further optimization design. If the material does not meet the strength and stiffness requirements, the chemical composition is adjusted or other materials are selected, and finite element analysis is performed again. Based on the finite element analysis results, structural optimization design is carried out on the stress concentration areas of the cantilever to improve its strength and reduce stress concentration.

[0026] Specifically, based on the physical and mechanical parameters of the candidate high-strength lightweight alloy materials, a suitable constitutive model, such as an isotropic linear elastic constitutive model, is used to establish the material constitutive relationship and obtain the material's elastic modulus, Poisson's ratio, yield strength, and other mechanical property parameters. For the cantilever structure, a geometric model is constructed using 3D modeling software such as SolidWorks, and the material's mechanical property parameters are assigned to the geometric model to define material properties. Finite element preprocessing software such as ANSYS Workbench is used to mesh the geometric model and generate mesh elements. Based on the actual working conditions of the cantilever, such as applying a downward concentrated force at the free end of the cantilever and applying full constraint at the fixed end, loads and boundary conditions are set in the finite element model. Using a finite element solver, static analysis is performed to calculate the stress and strain distribution of the cantilever under these conditions, obtaining stress-strain contour maps and deformation contour maps, and analyzing stress and strain concentration areas. Based on the stress state and deformation characteristics of the cantilever, the yield strength of the material is compared to determine whether the high-strength lightweight alloy material yields under these conditions. If the maximum stress is less than the material's yield strength and the maximum deformation meets the stiffness requirements, the material is considered to meet both strength and stiffness requirements and can be used as a candidate material for cantilever construction, allowing for further optimization design and experimental verification. If the maximum stress exceeds the material's yield strength, or the maximum deformation does not meet the stiffness requirements, the chemical composition of the material needs to be adjusted to increase its strength, or other lightweight alloy materials with higher strength and stiffness should be selected, requiring a new finite element analysis and evaluation. Based on the finite element analysis results, structural optimization design is performed on the stress-strain concentration areas of the cantilever, such as adding stiffeners or optimizing the cross-sectional shape, to improve the strength of the cantilever in these areas, reduce the degree of stress concentration, and ensure the cantilever meets both strength and stiffness requirements. Through the above analysis and optimization design, a cantilever structure that meets both strength and stiffness requirements can be obtained, providing a design reference for the manufacture and application of cantilever structures.

[0027] S103. For candidate materials that meet the strength and stiffness requirements, conduct cantilever fatigue life and fracture toughness tests. Based on the test results, combined with the wear resistance and corrosion resistance of the materials, determine the optimal cantilever material selection scheme.

[0028] Obtain a list of candidate materials that meet the strength and stiffness requirements; design fatigue life test schemes and fracture toughness test schemes for the materials in the candidate material list, wherein the fatigue life test scheme includes stress level and cycle number test parameters, and the fracture toughness test scheme includes load and crack size test parameters; conduct fatigue life tests and fracture toughness tests on the candidate materials to obtain quantitative results of fatigue life and fracture toughness; based on the quantitative results of fatigue life and fracture toughness, select preferred materials with long fatigue life and high fracture toughness; obtain wear resistance and corrosion resistance data of the candidate materials, calculate the wear rate of the candidate materials using the wear rate calculation method, and test the corrosion rate of the candidate materials using the corrosion rate test method; combine the fatigue life, fracture toughness, wear resistance, and corrosion resistance of the candidate materials to establish a hierarchical structure model of material performance indicators, use the analytic hierarchy process (AHP) to determine the weight of each material performance indicator, and calculate the comprehensive performance weighted score of each candidate material; based on the ranking results of the comprehensive performance weighted score, and combined with the actual working environment conditions and usage requirements of the cantilever, determine the optimal cantilever material selection scheme.

[0029] Specifically, based on the material performance database, a list of candidate materials meeting the strength and stiffness requirements is obtained. For the materials in the candidate list, fatigue life and fracture toughness test schemes are designed, and test parameters and evaluation indicators are established, such as stress level and cycle number for fatigue life tests, and load and crack size for fracture toughness tests. Fatigue life and fracture toughness tests are conducted on the candidate materials, and test process data is collected to obtain quantitative results of fatigue life and fracture toughness. Based on the fatigue life and fracture toughness test results, the fatigue performance and fracture performance of the candidate materials are evaluated, and preferred materials with long fatigue life and high fracture toughness are selected. By consulting literature and material handbooks, wear resistance and corrosion resistance data of the candidate materials are obtained. Combined with the existing material performance database, quantitative evaluation methods such as wear rate calculation and corrosion rate testing are used to evaluate the wear resistance and corrosion resistance of the materials. Based on a comprehensive consideration of material fatigue life, fracture toughness, wear resistance, and corrosion resistance, a hierarchical structure model of material performance indicators is established using the analytic hierarchy process (AHP). The weight of each indicator is determined through pairwise comparisons, and the weighted score of the comprehensive performance of each candidate material is calculated. Based on the comprehensive performance score ranking results, combined with the actual working environment conditions of the cantilever such as load spectrum, temperature, humidity, etc., as well as the usage requirements such as design life, reliability, etc., the mechanical properties, environmental adaptability and economic cost of the material are weighed to determine the optimal cantilever material selection scheme and complete the material selection process.

[0030] S104. At the connection between the cantilever and the cleaning tool, a standardized quick-change interface is designed to divide the cleaning tool into several functional modules, and each module is connected to the cantilever through the quick-change interface.

[0031] The process involves: acquiring preset functional module division rules, including the material of the cleaning object, the size of the cleaning area, and cleaning frequency factors; dividing the cleaning tool into several functional modules according to these rules, with each module responsible for a specific cleaning task; designing a corresponding standardized quick-change interface for each functional module, incorporating the module's size, weight, and power supply requirements; acquiring functional module data, including the applicable scenarios, performance parameters, and historical usage data for each module; training the functional module data using a support vector machine algorithm to obtain a functional module selection model; acquiring attribute data for the cleaning task to be executed, inputting this data into the functional module selection model to obtain the required combination of functional modules for the task; and acquiring the real-time operation data of each functional module. The system uses real-time operational status data, including energy consumption, noise, and vibration. A random forest algorithm is employed to analyze this data. When a functional module's performance deteriorates or malfunctions, the cantilever is moved to a designated position, and a new functional module is automatically replaced via the standardized quick-change interface. Based on the functional module's attribute parameters and the real-time operational status data, the cantilever's motion parameters, including speed, acceleration, and trajectory, are dynamically adjusted to improve cleaning efficiency and reduce energy consumption while maintaining cleaning effectiveness. A reinforcement learning model is constructed, using the time, energy consumption, and noise levels for completing the cleaning task as optimization objectives and corresponding reward functions. The selection and combination of functional modules constitute the action space. Through continuous trial and learning, the optimal decision path that maximizes cumulative rewards is obtained. This optimal decision path is then applied to actual cleaning operations.

[0032] Specifically, based on preset functional module division rules, the cleaning tool is divided into several functional modules, each responsible for a specific cleaning task. The division is based on factors such as the material of the object being cleaned, the size of the cleaning area, and the cleaning frequency. For each functional module, a corresponding standardized quick-change interface is designed. The interface design needs to consider parameters such as the size, weight, and power requirements of the functional module to ensure quick and reliable connection and disconnection between the module and the cantilever. The interface specifications of different functional modules should be as uniform as possible to reduce the complexity of replacement. A functional module database is established to record the applicable scenarios, performance parameters, and historical usage data for each module. A support vector machine algorithm is used to train the data to generate a functional module selection model. Before executing a cleaning task, relevant data is retrieved from the database based on task attributes and input into the selection model to automatically determine the required combination of functional modules. During the cleaning process, real-time operating status data of each functional module is acquired, including energy consumption, noise, and vibration. The system analyzes state data using a random forest algorithm. When a functional module's performance deteriorates or malfunctions, the cantilever is moved to a designated position, and a new functional module is automatically replaced via a quick-switch interface. Based on the functional module's attribute parameters and actual working status, the cantilever's motion parameters, including speed, acceleration, and trajectory, are dynamically adjusted. The optimization goal is to improve cleaning efficiency and reduce energy consumption while maintaining cleaning effectiveness. The adjustment strategy is generated according to pre-set rules and continuously self-optimizes with the accumulation of actual work data. In the reinforcement learning model, the time, energy consumption, and noise levels for completing the cleaning task are used as optimization objectives, with corresponding reward functions set. The selection and combination of functional modules constitute the action space. Through continuous trial and learning, the optimal decision path that maximizes cumulative rewards is found and applied to actual cleaning operations.

[0033] S105. Establish a 3D model of the quick-change interface, and adopt a precision positioning and locking mechanism to achieve rapid positioning and reliable connection between the cleaning tool module and the cantilever. After connection, the interface gap is less than a preset threshold.

[0034] A 3D model comprising the cleaning tool module and the cantilever is obtained. To meet the positioning requirements of the quick-change interface, positioning bosses and holes are added to the connecting surface. If the dimensions and shapes of the positioning bosses and holes are appropriately set, precise positioning of the cleaning tool module relative to the cantilever is achieved. Based on the reliable connection requirements of the quick-change interface, a locking device is arranged on the connecting surface, and an appropriate locking force is set. A pressure sensor is used to measure the locking force of the locking device, obtaining actual locking force data. This data is compared with a preset threshold to determine if the design requirements are met. To meet the sealing requirements of the quick-change interface, a sealing ring mounting groove is appropriately set on the connecting surface. By controlling the machining accuracy of the connecting surface, a CNC machining center is used to machine the connecting surface to ensure that the surface roughness meets the requirements, keeping the interface gap within a preset range after connection. A coordinate measuring machine is used to detect the positioning accuracy of the quick-change interface, obtaining the actual positioning deviation value, which is then compared with a preset threshold. If the positioning accuracy of the quick-change interface does not reach the preset threshold, finite element analysis software is used to optimize the structure of the precision positioning mechanism. By adjusting the parameters of the positioning boss and positioning holes, the positioning accuracy of the quick-change interface is improved. Then, 3D modeling and machining are performed again until the design requirements are met. Finite element analysis software is used to simulate the stress conditions of the quick-change interface under actual working conditions. The load is applied to the connection surface, and the stress distribution cloud map of the connection surface is obtained to determine the stress concentration areas. For the stress concentration areas, the structural design of the quick-change interface is optimized to improve its reliability. Finally, a prototype is manufactured, and through actual assembly and testing, the performance of the quick-change interface is verified to meet the design requirements.

[0035] Specifically, based on the functional requirements of the quick-change interface, a 3D model comprising the cleaning tool module and the cantilever was created using SolidWorks software, and the connection structure between the two was designed. To address the positioning requirements of the quick-change interface, positioning bosses and positioning holes were added to the connection surface to achieve precise positioning of the cleaning tool module relative to the cantilever. To ensure a reliable connection of the quick-change interface, a locking device was installed on the connection surface, and an appropriate locking force was set to ensure reliable fixation between the cleaning tool module and the cantilever. According to the sealing requirements of the quick-change interface, mounting grooves for the sealing ring were appropriately set on the connection surface, and the machining accuracy of the connection surface was controlled. A CNC machining center was used to machine the connection surface, ensuring that the surface roughness Ra was no greater than 6μm, so that the interface gap after connection was controlled within 1mm. A coordinate measuring machine was used to test the positioning accuracy of the quick-change interface, and a pressure sensor was used to measure the locking force of the locking device. The actual positioning deviation value and locking force data were obtained and compared with preset thresholds of 0.5mm and 1000N to determine whether the design requirements were met. If the positioning accuracy or locking reliability of the quick-change interface does not meet the preset threshold, ANSYS finite element analysis software is used to optimize the structure of the precision positioning mechanism and locking mechanism. This involves adjusting parameters such as the size and shape of the positioning boss and positioning holes, as well as the arrangement of the locking device, to improve the positioning accuracy and locking reliability of the quick-change interface. Then, 3D modeling and machining are performed again until the design requirements are met. ANSYS finite element analysis software is used to simulate the stress conditions of the quick-change interface under actual working conditions. Loads are applied to the connection surface, and the Von-Mises stress distribution cloud map of the connection surface is obtained to identify stress concentration areas. For these stress concentration areas, measures such as increasing the thickness of the connection surface and adding chamfers are used to optimize the structural design of the quick-change interface and improve its reliability. Finally, a prototype is machined, and actual assembly and testing are conducted to verify whether the performance of the quick-change interface meets the design requirements.

[0036] S106. Using 3D CAD software, establish a parametric design model for the cantilever structure, parameterize the key dimensions and geometric features of the cantilever, and form a parametric driving framework.

[0037] Obtain the geometric feature parameters of the cantilever structure, including length, width, and height; calculate the size ratio coefficient based on the geometric feature parameters and a preset calculation formula, the calculation formula including the length, width, and height; determine whether the size ratio coefficient meets a preset threshold range; if the size ratio coefficient meets the preset threshold range, adjust the key size parameters of the cantilever using the size ratio coefficient to obtain preliminary model data of the cantilever structure; extract shape feature vectors from the preliminary model data, the shape feature vectors including cross-sectional shape, cross-sectional dimensions, and length; group the shape feature vectors according to preset feature grouping rules to obtain feature grouping data, the feature grouping rules including classification according to cross-sectional shape and size range; associate the feature grouping data with a pre-established driving framework, and adjust the driving framework... The geometric topological relationships in the preliminary model data are integrated, including the connection methods and angles between various cross-sections, to obtain optimized geometric topological model data. Based on the geometric topological model data, a preset number of design schemes for the cantilever structure are automatically generated, covering combinations of different sizes and geometric features. The three-dimensional solid models of the preset number of design schemes are imported into finite element analysis software, material properties, constraints, and load conditions are set, and static and modal analyses are performed to obtain the stress, strain, deformation, and vibration frequency of the cantilever structure under different load conditions. The stress, strain, deformation, and vibration frequency are compared with preset strength and stability standards. If the stress and strain meet the preset strength standard, and the vibration frequency meets the preset stability standard, then the corresponding three-dimensional model drawing of the design scheme is output.

[0038] Specifically, a database of the geometric features and key dimensions of the cantilever structure in the parametric design model is pre-established. Geometric feature parameters of the cantilever structure, including length, width, and height, are acquired. A dimension scaling factor is calculated using these geometric feature parameters according to a pre-defined formula, which includes length, width, and height. After determining the dimension scaling factor, it is checked whether the factor meets a pre-defined threshold range. If it does, the key dimension parameters of the cantilever are adjusted using this factor, resulting in preliminary model data of the cantilever structure. Shape feature vectors, including cross-sectional shape, cross-sectional dimensions, and length, are extracted from the preliminary model data. These shape feature vectors are grouped according to pre-defined feature grouping rules, categorized by cross-sectional shape, dimension range, etc., resulting in feature grouping data. This feature grouping data is associated with a pre-established driving framework. The driving framework is used to adjust the geometric topology relationships in the preliminary model data, including adjusting the connection methods and angles between various cross-sections, resulting in optimized geometric topology model data. Multiple design schemes for the cantilever structure are automatically generated based on the geometric topology model data. These schemes cover combinations of different dimensions and geometric features. A 3D solid model is created using 3D modeling software such as SolidWorks based on the geometric topology data of the design scheme. This model is then imported into finite element analysis software such as ANSYS, where material properties, constraints, and load conditions are set. Static and modal analyses are performed to obtain strength and stability parameters of the cantilever structure under different load conditions, including stress, strain, deformation, and vibration frequency. These strength and stability parameters are compared with preset strength and stability standards. If the stress level and deformation meet the strength requirements, and the vibration frequency meets the stability requirements, then the corresponding 3D model drawing is output.

[0039] S107. In the parametric design model, based on the usage requirements of a specific model of automatic cleaning robot, input the target cantilever performance parameters, call the optimization algorithm, automatically complete the optimization of the cantilever structure size and shape, and generate a three-dimensional model and engineering drawing of the cantilever. If the optimization result meets the performance requirements, the cantilever structure design is completed.

[0040] The system acquires the target cantilever performance parameters and uses a pre-defined genetic algorithm to calculate and generate the optimal dimensions and shape data for the cantilever structure. The genetic algorithm searches for the optimal solution by simulating biological evolution, including selection, crossover, and mutation operations. Key geometric parameters are extracted from the generated dimension and shape data, and corresponding geometric models are created in 3D CAD software to construct a 3D solid model of the cantilever structure. Using the engineering drawing function of the 3D CAD software, projection views from different directions are extracted based on the generated 3D solid model, key dimensions are labeled, and 2D engineering drawings conforming to engineering drawing standards are automatically generated. The 3D solid model of the cantilever structure is imported into finite element analysis software, material properties, constraints, and load conditions are defined, and a tetrahedral mesh is used to simulate and calculate the stress distribution and deformation of the cantilever under actual stress conditions, obtaining the maximum stress and maximum deformation values ​​of the cantilever as key performance indicators. The performance indicator values ​​obtained from the finite element analysis are compared one by one with the input target performance parameters to determine whether each indicator meets the requirements. If all performance indicators reach or exceed the target values, the current design parameters, the generated 3D model, and the engineering drawings are output. If any performance indicators still fail to meet the requirements, return to the genetic algorithm optimization step, adjust the algorithm parameters, recalculate and generate new cantilever structure dimensions, and perform 3D modeling, finite element analysis, and performance evaluation again until the optimal design scheme that meets all performance requirements is found.

[0041] Specifically, based on the input target cantilever performance parameters, a pre-defined genetic algorithm is applied to calculate and generate the optimal size and shape data of the cantilever structure. The genetic algorithm searches for the optimal solution by simulating the biological evolution process, including operations such as selection, crossover, and mutation, making it suitable for solving complex structural optimization problems. Key geometric parameters, such as length, width, thickness, and cross-sectional shape, are extracted from the generated size and shape data. Corresponding geometric models are then created in the 3D CAD software SolidWorks to construct a 3D solid model of the cantilever structure. Using SolidWorks' engineering drawing function, projection views from different directions, such as the top view, front view, and left view, are extracted based on the generated 3D solid model, and key dimensions are labeled, automatically generating 2D engineering drawings that conform to engineering drawing standards. The 3D solid model of the cantilever structure is imported into the finite element analysis software ANSYS. Material properties, constraints, and load conditions are defined. Using ANSYS's static analysis module, a tetrahedral mesh is used to simulate and calculate the stress distribution and deformation of the cantilever under actual stress conditions, yielding key performance indicators such as the maximum stress and maximum deformation of the cantilever. The performance index values ​​obtained from the finite element analysis are compared one by one with the input target performance parameters to determine whether each index meets the requirements. If all performance indexes meet or exceed the target values, the optimization design of the cantilever structure is completed, and the current design parameters, the generated 3D model, and engineering drawings are output. If any performance indexes still fail to meet the standards, the process returns to the genetic algorithm optimization step, adjusting the algorithm parameters, such as increasing the population size, increasing the crossover probability and mutation probability, or using other optimization methods such as simulated annealing. New cantilever structure dimensions are recalculated, and 3D modeling, finite element analysis, and performance evaluation are performed again until the optimal design scheme that meets all performance requirements is found.

[0042] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the cantilever structure of an automated cleaning robot, characterized in that, The method includes: Information on the cantilever mechanical performance requirements and working environment characteristics of different models of automatic cleaning robots is obtained. Based on this information, a material selection index system is established by comprehensively considering the strength, stiffness, toughness, wear resistance and corrosion resistance of the materials. For candidate high-strength lightweight alloy materials, the finite element analysis method is used to simulate the stress state of the cantilever under different working conditions, obtain stress-strain cloud diagrams, and determine whether the material meets the strength and stiffness requirements. For candidate materials that meet the strength and stiffness requirements, cantilever fatigue life and fracture toughness tests are conducted. Based on the test results, combined with the wear resistance and corrosion resistance of the materials, the optimal cantilever material selection scheme is determined. At the connection between the cantilever and the cleaning tool, a standardized quick-change interface is designed to divide the cleaning tool into several functional modules, each of which is connected to the cantilever through the quick-change interface. A 3D model of the quick-change interface is established, and a precision positioning and locking mechanism is adopted to achieve rapid positioning and reliable connection between the cleaning tool module and the cantilever. After connection, the interface gap is less than a preset threshold. Using 3D CAD software, a parametric design model of the cantilever structure is established, and the key dimensions and geometric features of the cantilever are parameterized to form a parametric driving framework; In the parametric design model, based on the usage requirements of a specific model of automatic cleaning robot, the target cantilever performance parameters are input, the optimization algorithm is called, and the cantilever structure size and shape are automatically optimized, generating a 3D model and engineering drawings of the cantilever. If the optimization results meet the performance requirements, the cantilever structure design is completed.

2. The method according to claim 1, characterized in that, The process involves acquiring information on the cantilever mechanical performance requirements and working environment characteristics of different models of automated cleaning robots. Based on this information, and comprehensively considering factors such as material strength, stiffness, toughness, wear resistance, and corrosion resistance, a material selection index system is established, including: Three-dimensional model data of cantilever arms of different models of automatic cleaning robots are obtained. Static and dynamic simulation analysis of the cantilever arms is performed using finite element analysis software to obtain the stress and strain distribution and vibration characteristic parameters of the cantilever arms under different working conditions. Obtain the working environment parameters of the automatic cleaning robot, determine the degree of influence of the environment on the material properties, and determine the performance requirements that the material needs to possess. Using the analytic hierarchy process (AHP), a comprehensive evaluation system for material properties was established to score and rank candidate materials. Using the cantilever structure parameters obtained from the simulation analysis, the Apriori association rule mining algorithm is used to analyze the correlation between material properties and the cantilever structure parameters, and to optimize the cantilever structure design scheme. Using the performance parameters and environmental factors of the candidate materials as input, a support vector machine regression model is constructed to predict the comprehensive performance of different materials in actual working environments and to select the materials with the best adaptability. The mechanical reliability of the material selection scheme was verified by using simulation technology to virtually assemble and analyze the cantilever made of different materials. By comprehensively analyzing the results, balancing the material properties and the design requirements of the cantilever structure, the optimal material selection scheme is determined, and a material selection report and optimization suggestions for the cantilever structure are generated.

3. The method according to claim 1, characterized in that, For the candidate high-strength lightweight alloy materials, the finite element method is used to simulate the stress state of the cantilever under different working conditions, obtain stress-strain contour maps, and determine whether the material meets the strength and stiffness requirements, including: The physical and mechanical parameters of candidate high-strength lightweight alloy materials are obtained. An isotropic linear elastic constitutive model is used to establish the constitutive relation of the material, and the elastic modulus, Poisson's ratio and yield strength mechanical property parameters of the material are obtained. A geometric model of the cantilever structure is constructed using 3D modeling software, and the mechanical property parameters of the material are assigned to the geometric model to define the material properties. The geometric model is meshed to generate a finite element mesh model; Based on the actual working conditions of the cantilever, loads and boundary conditions are applied to the finite element model. Static finite element analysis was used to calculate the stress and strain distribution of the cantilever under this working condition, and stress-strain contour maps and deformation contour maps were obtained. Based on the stress state and deformation characteristics of the cantilever, determine whether the material meets the strength and stiffness requirements under this working condition; If the material meets the strength and stiffness requirements, it will be used as a candidate material for subsequent optimization design. If the material does not meet the strength and stiffness requirements, adjust the chemical composition ratio of the material or select other materials, and perform finite element analysis again; Based on the finite element analysis results, structural optimization design is carried out on the stress concentration area of ​​the cantilever to improve the strength of the cantilever and reduce the degree of stress concentration.

4. The method according to claim 1, characterized in that, For candidate materials that meet the strength and stiffness requirements, cantilever fatigue life and fracture toughness tests are conducted. Based on the test results, combined with the material's wear resistance and corrosion resistance, the optimal cantilever material selection scheme is determined, including: Obtain a list of candidate materials that meet the strength and stiffness requirements; For the materials in the candidate material list, fatigue life test schemes and fracture toughness test schemes are designed. The fatigue life test scheme includes test parameters for stress level and number of cycles, and the fracture toughness test scheme includes test parameters for load and crack size. Fatigue life tests and fracture toughness tests were conducted on the candidate materials to obtain quantitative results of fatigue life and fracture toughness of the candidate materials. Based on the quantitative results of fatigue life and fracture toughness, preferred materials with long fatigue life and high fracture toughness are selected. The wear resistance and corrosion resistance data of the candidate materials are obtained, the wear rate of the candidate materials is calculated using the wear rate calculation method, and the corrosion rate of the candidate materials is tested using the corrosion rate test method. Based on the fatigue life, fracture toughness, wear resistance and corrosion resistance of the candidate materials, a hierarchical structure model of material performance indicators is established. The weight of each material performance indicator is determined by the analytic hierarchy process (AHP), and the weighted score of the comprehensive performance of each candidate material is calculated. Based on the weighted score ranking of the comprehensive performance, and combined with the actual working environment conditions and usage requirements of the cantilever, the optimal cantilever material selection scheme is determined.

5. The method according to claim 1, characterized in that, A standardized quick-change interface is designed at the connection between the cantilever and the cleaning tool. The cleaning tool is divided into several functional modules, each of which is connected to the cantilever via the quick-change interface. These modules include: Obtain preset functional module division rules, which include the material of the cleaning object, the size of the cleaning area, and the cleaning frequency factors; According to the functional module division rules, the cleaning tool is divided into several functional modules, and each functional module is responsible for a specific cleaning task; For each of the aforementioned functional modules, a corresponding standardized quick-switch interface is designed, which incorporates the size, weight, and power supply requirements of the functional module. Obtain functional module data, which includes the applicable scenarios, performance parameters, and historical usage data for each functional module; The functional module selection model is obtained by training the data of the functional modules using the support vector machine algorithm. Obtain the attribute data of the cleaning task to be executed, input the attribute data into the functional module selection model, and obtain the combination of functional modules required for the cleaning task to be executed; Obtain real-time operating status data for each of the aforementioned functional modules, including energy consumption, noise, and vibration. The real-time working status data is analyzed using a random forest algorithm. When it is determined that the performance of a certain functional module has deteriorated or malfunctioned, the control arm is moved to a designated position and a new functional module is automatically replaced through the standardized quick-change interface. Based on the attribute parameters of the functional modules and the real-time working status data, the motion parameters of the cantilever are dynamically adjusted. The motion parameters include moving speed, acceleration, and motion trajectory, so as to improve cleaning efficiency and reduce energy consumption while ensuring cleaning effect. A reinforcement learning model is constructed, with the time, energy consumption, and noise levels for completing the cleaning task as optimization objectives, and corresponding reward functions are set. The selection and combination of the aforementioned functional modules shall be considered as the action space; Through continuous trial and learning, we can find the optimal decision path that yields the greatest cumulative reward. The optimal decision path is then applied to actual cleaning operations.

6. The method according to claim 1, characterized in that, The establishment of a 3D model of the quick-change interface, employing a precision positioning and locking mechanism, enables rapid positioning and reliable connection between the cleaning tool module and the cantilever. After connection, the interface gap is less than a preset threshold. This includes: Obtain a 3D model that includes the cleaning tool module and the cantilever. To meet the positioning requirements of the quick-change interface, set positioning bosses and positioning holes on the connection surface. If the size and shape of the positioning boss and positioning hole are set reasonably, the cleaning tool module can be accurately positioned relative to the cantilever. Based on the reliable connection requirements of the quick-connect interface, a locking device is arranged on the connection surface, and an appropriate locking force is set; A pressure sensor is used to measure the locking force of the locking device to obtain the actual locking force data. The actual locking force data is then compared with a preset threshold to determine whether the design requirements are met. To meet the sealing requirements of quick-change interfaces, a sealing ring mounting groove is reasonably set on the connecting surface. By controlling the machining accuracy of the connecting surface, the connecting surface is machined using a CNC machining center to ensure that the surface roughness meets the requirements, so that the interface gap after connection is controlled within a preset range. A coordinate measuring machine is used to test the positioning accuracy of the quick-change interface, obtain the actual positioning deviation value, and compare the actual positioning deviation value with a preset threshold. If the positioning accuracy of the quick-change interface does not reach the preset threshold, finite element analysis software is used to optimize the structure of the precision positioning mechanism. By adjusting the parameters of the positioning boss and positioning hole, the positioning accuracy of the quick-change interface is improved, and three-dimensional modeling and processing are carried out again until the design requirements are met. The stress conditions of the quick-connect interface under actual working conditions are simulated using finite element analysis software. The load is applied to the connection surface, and the stress distribution cloud map of the connection surface is obtained to determine the stress concentration area. For areas of stress concentration, the reliability of quick-change interfaces is improved by optimizing the structural design of the quick-change interfaces. Finally, a prototype was manufactured, and through actual assembly and testing, it was verified whether the performance of the quick-change interface met the design requirements.

7. The method according to claim 1, characterized in that, The process employs 3D CAD software to establish a parametric design model of the cantilever structure, parameterizing the key dimensions and geometric features of the cantilever to form a parametric driving framework, including: Obtain the geometric feature parameters of the cantilever structure, including length, width, and height; The size ratio coefficient is calculated based on the geometric feature parameters and the preset calculation formula, wherein the calculation formula includes the length, width and height; Determine whether the size ratio coefficient meets the preset threshold range; If the size ratio coefficient meets the preset threshold range, the key size parameters of the cantilever are adjusted using the size ratio coefficient to obtain preliminary model data of the cantilever structure. Shape feature vectors are extracted from the preliminary model data, and the shape feature vectors include cross-sectional shape, cross-sectional dimensions, and length. The shape feature vectors are grouped according to preset feature grouping rules to obtain feature grouping data. The feature grouping rules include classification according to cross-sectional shape and size range. The feature grouping data is associated with a pre-established driving framework, and the geometric topological relationships in the preliminary model data are adjusted through the driving framework. The geometric topological relationships include the connection methods and angles between each cross section, to obtain optimized geometric topological model data. Based on the geometric topology model data, a preset number of design schemes for cantilever structures are automatically generated, and the preset number of design schemes cover combinations of different sizes and geometric features; Import the three-dimensional solid models of the preset number of design schemes into the finite element analysis software, set the material properties, constraints and load conditions, and perform static and modal analysis to obtain the stress, strain, deformation and vibration frequency of the cantilever structure under different load conditions. The stress, strain, deformation, and vibration frequency are compared with preset strength and stability standards. If the stress and strain meet the preset strength standard and the vibration frequency meets the preset stability standard, then the corresponding three-dimensional model drawing of the design scheme will be output.

8. The method according to claim 1, characterized in that, In the parametric design model, based on the usage requirements of a specific model of automated cleaning robot, the target cantilever performance parameters are input, an optimization algorithm is invoked, and the cantilever structure dimensions and shape are automatically optimized, generating a 3D model and engineering drawings of the cantilever. If the optimization results meet the performance requirements, the cantilever structure design is completed, including: The target cantilever performance parameters are obtained, and the optimal size and shape data of the cantilever structure are calculated and generated using a preset genetic algorithm. The genetic algorithm searches for the optimal solution by simulating the biological evolution process, including selection, crossover, and mutation operations; Key geometric parameters are extracted from the generated size and shape data, and corresponding geometric models are created in 3D CAD software to construct a 3D solid model of the cantilever structure. Using the engineering drawing function of the 3D CAD software, based on the generated 3D solid model, projected views from different directions are extracted, key dimensions are marked, and 2D engineering drawings conforming to engineering drawing specifications are automatically generated. The three-dimensional solid model of the cantilever structure is imported into the finite element analysis software. Material properties, constraints and load conditions are defined. The model is divided into tetrahedral meshes. The stress distribution and deformation of the cantilever under actual stress conditions are simulated and calculated. The values ​​of the key performance indicators of the cantilever, such as the maximum stress and the maximum deformation, are obtained. The performance index values ​​obtained from the finite element analysis are compared one by one with the input target performance parameters to determine whether each index meets the requirements. If all performance indicators meet or exceed the target values, the current design parameters and the generated 3D model and engineering drawings will be output. If any performance indicators still fail to meet the requirements, return to the genetic algorithm optimization step, adjust the algorithm parameters, recalculate and generate new cantilever structure dimensions, and perform 3D modeling, finite element analysis, and performance evaluation again until the optimal design scheme that meets all performance requirements is found.

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