Collaborative machining method and device for spiral bevel gears based on spatial geometry optimization

Abstract

The present application provides a method and device for collaborative processing of spiral bevel gears based on spatial geometry optimization. The method includes: determining the distribution of motion characteristics based on the target spatial geometry arrangement model of the multi-gear system; then determining the positional relationship data set between each spiral bevel gear; and then determining the potential meshing error range; when the preset error threshold is exceeded, adjusting the spatial geometry arrangement model to obtain an optimized positional relationship data set, and determining the target processing parameter set; when the target processing parameter set meets the preset parameter threshold, determining the target spatial geometry optimization scheme based on the residual error distribution; then extracting the consistency optimization data from the target processing parameter set to generate the gear processing code; and performing collaborative processing of spiral bevel gears based on the gear processing code. By constructing a target spatial geometry arrangement model, optimizing the positional relationship of each spiral bevel gear, and generating a target spatial geometry optimization scheme for collaborative processing of spiral bevel gears, the processing accuracy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gear processing, and in particular to a method and device for collaborative processing of spiral bevel gears based on spatial geometry optimization. Background Art

[0002] Spiral bevel gear machining technology, as an important research direction in the field of mechanical transmission, occupies a key position in high-end manufacturing such as aerospace, automotive industry, and heavy equipment. Its machining accuracy and efficiency directly affect the performance and reliability of complex multi-gear systems. Existing machining methods mostly focus on the independent manufacturing of single gears, which generally have technical limitations. Traditional processes usually rely on assembly adjustments after machining to compensate for the matching errors between gears. This method is not only time-consuming and labor-intensive, but also makes it difficult to fundamentally guarantee the overall accuracy of the multi-gear system. In particular, the problem of error accumulation is particularly prominent under high load and high-speed operation conditions. Summary of the Invention

[0003] Based on this, a method and device for collaborative processing of spiral bevel gears based on spatial geometry optimization are provided, which improves the processing accuracy of spiral bevel gears.

[0004] In a first aspect, a method for collaborative machining of spiral bevel gears based on spatial geometry optimization is provided, the method comprising:

[0005] determining a kinematic characteristic distribution of the multi-gear system according to a target spatial geometric arrangement model of the multi-gear system, wherein the multi-gear system includes a plurality of spiral bevel gears, and the spatial geometric arrangement model is constructed based on design data of the multi-gear system, the design data including at least geometric parameters of each of the spiral bevel gears and meshing parameters of each of the spiral bevel gears;

[0006] determining a positional relationship data set between the spiral bevel gears of the multi-gear system according to the motion characteristic distribution;

[0007] Determining a potential meshing error range of the multi-gear system using an error prediction technique based on the position relationship data set;

[0008] When the maximum value of the potential meshing error exceeds a preset error threshold, adjusting the spatial geometric arrangement model through a motion characteristic simulation algorithm to obtain an optimized position relationship data set;

[0009] determining a target processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set;

[0010] determining a target space geometry optimization scheme based on a residual error distribution in the multi-gear system performance when the target machining parameter set satisfies a preset parameter threshold;

[0011] Based on the target space geometry optimization scheme, extracting consistency optimization data from the target processing parameter set to generate gear processing code;

[0012] The spiral bevel gear is collaboratively processed based on the gear processing code.

[0013] Optionally, before obtaining the motion characteristic distribution of the multi-gear system according to the target space geometric arrangement model of the multi-gear system, the method further comprises:

[0014] acquiring design data of the multi-gear system from the multi-gear system based on a pre-simulation technology;

[0015] determining spatial geometric parameters of the multi-gear system according to the design data;

[0016] constructing an initial spatial geometric arrangement model of the multi-gear system according to the spatial geometric parameters;

[0017] Determining first motion trajectory data of each of the spiral bevel gears based on the initial spatial geometric arrangement model;

[0018] When the first motion trajectory data exceeds a first preset threshold, the spatial geometric parameters are adjusted, and the initial spatial geometric arrangement model is updated based on the adjusted spatial geometric parameters to obtain the target spatial geometric arrangement model.

[0019] Optionally, obtaining the motion characteristic distribution of the multi-gear system according to the target space geometric arrangement model of the multi-gear system includes:

[0020] Determining a second motion trajectory of each of the spiral bevel gears based on the target space geometric arrangement model;

[0021] The motion characteristic distribution of the multi-gear system is extracted from the second motion trajectory, wherein the motion characteristics at least include the spatial distance between the spiral bevel gears, the angle between the axes of the spiral bevel gears, and the center distance between any two spiral bevel gears.

[0022] Optionally, based on the position relationship data set, an error prediction technology is used to determine a potential meshing error range of the multi-gear system, including:

[0023] Extracting spatial geometric description data of each spiral bevel gear from the position relationship data set;

[0024] Based on the geometric arrangement technology, an arrangement model between the spiral bevel gears is generated according to the spatial geometric description data;

[0025] According to the spatial geometric data in the arrangement model, the potential meshing error range of the multi-gear system is determined based on the error prediction technology.

[0026] Optionally, determining a target processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set includes:

[0027] Based on the consistency optimization method, generating a processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set;

[0028] Performing a machining simulation based on the machining parameter set, extracting a machining path for each spiral bevel gear, and obtaining a collaborative machining instruction sequence for the multi-gear system;

[0029] Performing motion characteristic simulation according to the collaborative machining instruction sequence to obtain a current meshing error of the multi-gear system;

[0030] In a case where a current meshing error of the multi-gear system is less than a preset error threshold, the target machining parameter set is obtained.

[0031] Optionally, based on a consistency optimization method, generating a processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set includes:

[0032] Adjusting the optimized position relationship data set based on the consistency optimization method to obtain a preliminary processing parameter set;

[0033] Based on the geometric analysis technology, according to the preliminary processing parameter set and the optimized position relationship data set, determining the adjusted position distribution of the spiral bevel gear;

[0034] determining the collaborative characteristic data of the multi-gear system according to the adjusted spiral bevel gear position distribution;

[0035] When the collaborative characteristic data exceeds a second preset threshold, the preliminary processing parameter set is updated to obtain the processing parameter set corresponding to the assembly accuracy of the multi-gear system.

[0036] Optionally, performing a machining simulation based on the machining parameter set, extracting a machining path for each spiral bevel gear, and obtaining a collaborative machining instruction sequence for the multi-gear system includes:

[0037] Performing machining simulation based on the machining parameter set, extracting machining paths for each spiral bevel gear, and obtaining an initial machining instruction set;

[0038] Determining a collaborative machining path for the multi-gear system using a geometric analysis technique based on the initial machining instruction set and the structural characteristics of the multi-gear system;

[0039] According to the collaborative processing paths and preset parameter control rules, dynamic distribution data of each collaborative processing path is obtained;

[0040] The dynamic distribution data is processed using dynamic simulation technology to obtain a collaborative processing instruction sequence for the multi-gear system.

[0041] In a second aspect, a device for collaboratively processing spiral bevel gears based on spatial geometry optimization is provided, the device comprising:

[0042] a first determining module, configured to determine a kinematic characteristic distribution of a multi-gear system based on a target spatial geometric arrangement model of the multi-gear system, wherein the multi-gear system includes a plurality of spiral bevel gears, and the spatial geometric arrangement model is constructed based on design data of the multi-gear system, the design data including at least geometric parameters of each of the spiral bevel gears and meshing parameters of each of the spiral bevel gears;

[0043] a second determining module, configured to determine a positional relationship dataset between the spiral bevel gears of the multi-gear system according to the motion characteristic distribution;

[0044] a third determining module, configured to determine a potential meshing error range of the multi-gear system using an error prediction technique based on the position relationship data set;

[0045] an adjustment module, configured to adjust the spatial geometric arrangement model by a motion characteristic simulation algorithm to obtain an optimized position relationship data set when the maximum value of the potential meshing error exceeds a preset error threshold;

[0046] a fourth determining module, configured to determine a target processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set;

[0047] a fifth determination module, configured to determine a target space geometry optimization scheme based on a residual error distribution in the multi-gear system performance when the target machining parameter set satisfies a preset parameter threshold;

[0048] An extraction module, configured to extract consistency optimization data from the target machining parameter set based on the target spatial geometry optimization solution and generate a gear machining code;

[0049] A processing module is used for collaborative processing of spiral bevel gears based on the gear processing code.

[0050] According to a third aspect, an electronic device is provided, including:

[0051] a memory configured to store instructions;

[0052] The processor is configured to call the instructions from the memory and implement the spiral bevel gear collaborative processing method based on spatial geometry optimization provided by the first aspect when executing the instructions.

[0053] In a fourth aspect, a machine-readable storage medium is provided, on which instructions are stored, and the instructions are used to enable a machine to execute the above-mentioned method for collaborative processing of spiral bevel gears based on spatial geometry optimization.

[0054] The above-mentioned method and device for collaborative processing of spiral bevel gears based on spatial geometry optimization determine the motion characteristic distribution of the gear system according to the target spatial geometry arrangement model of the multi-gear system, and determine the position relationship data set between each spiral bevel gear of the multi-gear system according to the motion characteristic distribution; according to the position relationship data set, the error prediction technology is used to determine the potential meshing error range of the multi-gear system; when the maximum value of the potential meshing error exceeds the preset error threshold, the spatial geometry arrangement model is adjusted by the motion characteristic simulation algorithm to obtain an optimized position relationship data set; according to the optimized position relationship data set, the target processing parameter set corresponding to the assembly accuracy of the multi-gear system is determined; when the target processing parameter set meets the preset parameter threshold, the target spatial geometry optimization scheme is determined according to the residual error distribution in the performance of the multi-gear system; based on the target spatial geometry optimization scheme, consistency optimization data is extracted from the target processing parameter set to generate a gear processing code; and the spiral bevel gears are collaboratively processed based on the gear processing code. The beneficial effects of the present application are as follows: by constructing a target spatial geometry arrangement model, optimizing the position relationship of each spiral bevel gear, generating a target spatial geometry optimization scheme for collaborative processing of spiral bevel gears, the processing accuracy of the spiral bevel gears can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 1 is a flow chart of a method for collaboratively machining spiral bevel gears based on spatial geometry optimization provided by an embodiment of the present application;

[0056] Figure 2 Schematic diagram of the structure of a spiral bevel gear collaborative processing device based on spatial geometry optimization provided by an embodiment of the present application;

[0057] Figure 3 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0059] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0060] The following describes in detail the method and device for collaborative processing of spiral bevel gears based on spatial geometry optimization provided by the embodiments of the present application through specific embodiments and their application scenarios in combination with the accompanying drawings.

[0061] See Figure 1 , is a flow chart of a method for collaboratively processing spiral bevel gears based on spatial geometry optimization provided by an embodiment of the present application, which is applied to electronic equipment. Figure 1 As shown, the method includes the following steps S100 to S800.

[0062] Step S100: Determine the motion characteristic distribution of the multi-gear system according to the target spatial geometric arrangement model of the multi-gear system, wherein the multi-gear system includes a plurality of spiral bevel gears, and the spatial geometric arrangement model is constructed based on the design data of the multi-gear system, and the design data includes at least geometric parameters of each of the spiral bevel gears and meshing parameters of each of the spiral bevel gears.

[0063] In an embodiment of the present application, the spiral bevel gear is a bevel gear whose tooth surface is spiral-shaped. Unlike ordinary bevel gears, spiral bevel gears have higher meshing efficiency and lower operating noise. The multi-gear system based on spiral bevel gears is a complex transmission system. The design data of the multi-gear system may include but is not limited to the geometric parameters of each spiral bevel gear and the meshing parameters of each spiral bevel gear. Based on the design data of the multi-gear system, a target spatial geometric arrangement model can be constructed. The spatial geometric arrangement model can be understood as a mathematical model that describes the relative position, motion relationship and geometric structure of an object or mechanical component in three-dimensional space. By constructing a target spatial geometric arrangement model of the multi-gear system, the motion characteristic distribution of the multi-gear system can be determined. The motion characteristics of the multi-gear system may include but are not limited to the spatial distance between each spiral bevel gear, the angle between the axes of each spiral bevel gear and the center distance between any two spiral bevel gears.

[0064] Step S200: determining a position relationship data set between the spiral bevel gears of the multi-gear system according to the motion characteristic distribution.

[0065] In an embodiment of the present application, after determining the kinematic characteristics distribution of a multi-gear system, the relative position data of each spiral bevel gear in the multi-gear system can be obtained based on the initial kinematic characteristics. Parameter extraction techniques are then used to obtain a dataset of positional relationships between the spiral bevel gears from the relative position data of each spiral bevel gear.

[0066] Step S300: Determine the potential meshing error range of the multi-gear system using error prediction technology based on the position relationship data set.

[0067] In an embodiment of the present application, after determining the position relationship data set, the spatial geometric description data of each spiral bevel gear is extracted from the position relationship data set. The spatial geometric description data can be understood as data used to accurately describe the position, shape, direction and mutual relationship of an object or multiple objects in three-dimensional space. Then, the arrangement model between each spiral bevel gear is generated by geometric arrangement technology. Geometric arrangement technology is generally used to describe the arrangement of objects in space, especially the relative position, form and mutual relationship between multiple objects. Finally, based on the spatial geometric data in the arrangement model, the error prediction technology is used to determine the potential meshing error range of the multi-gear system. Error prediction technology refers to a technology that analyzes and models the errors that may occur in the system, and predicts and evaluates the impact of these errors in advance, so as to take measures to correct or optimize them. The potential meshing error range of the multi-gear system can be understood as the range of incomplete contact between the gear tooth surfaces and the precision deviation of the meshing of the gears during the spiral bevel gear transmission process.

[0068] Step S400: When the maximum value of the potential meshing error exceeds a preset error threshold, the spatial geometric arrangement model is adjusted by a motion characteristic simulation algorithm to obtain an optimized position relationship data set.

[0069] In an embodiment of the present application, when the maximum potential meshing error exceeds a preset error threshold, the spatial geometric arrangement model is adjusted using a motion characteristic simulation algorithm. Specifically, a new geometric arrangement model can be generated by adjusting position parameters. Based on the adjusted geometric arrangement model, distributed characteristic data of the multi-gear system is obtained to obtain an optimized position relationship dataset. The distributed characteristic data is then classified using a cluster analysis algorithm to obtain a classification pattern of motion characteristics. Finally, key parameters are extracted from the classification pattern to determine the dynamic response range of the multi-gear system.

[0070] Step S500: determining a target processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set.

[0071] In an embodiment of the present application, to address the coordination requirements of a multi-gear system, a consistency optimization method can be used to generate a processing parameter set corresponding to assembly accuracy based on the optimized gear positional relationship data. A machining simulation is then performed using this processing parameter set, extracting the gear machining path from the individual manufacturing technology, and determining a collaborative machining instruction sequence for the multi-gear system. Finally, based on this collaborative machining instruction sequence, a motion characteristic simulation is performed in a virtual environment to determine whether the meshing error is below a preset error threshold. If the meshing error is below the preset error threshold, a target machining parameter set corresponding to the assembly accuracy of the multi-gear system can be obtained.

[0072] Step S600: When the target machining parameter set meets a preset parameter threshold, a target space geometry optimization scheme is determined according to a residual error distribution in the multi-gear system performance.

[0073] In an embodiment of the present application, the distribution data of the residual error in the multi-gear system can be first obtained by the error prediction technology, and it is determined whether the distribution data of the error meets the preset data threshold requirements. The operating status of the multi-gear system is analyzed based on the distribution data to obtain a characteristic description of the system performance. The characteristic description of the system performance is then used to adjust the processing parameters to determine the updated processing parameter set. The adjustment data of the spatial geometry is generated by the updated processing parameter set to obtain a preliminary plan for geometric optimization. The changing trend of the residual error is then analyzed based on the preliminary plan to determine the applicability of the optimization plan. Finally, the distribution data is updated based on the changing trend to determine the target spatial geometry optimization plan.

[0074] Step S700: Based on the target space geometry optimization solution, consistent optimization data is extracted from the target processing parameter set to generate a gear processing code.

[0075] In an embodiment of the present application, consistency data is first obtained from a processing parameter set. Consistency data can be understood as the consistency or stability of various data obtained in multiple measurements or multiple tests during a specific processing process. An extraction technique is used to determine the range of the optimization data, and the optimization data is extracted from the consistency data, and an initial gear processing code is generated by a numerical algorithm. A control algorithm is used to adjust the parameters of the initial gear processing code to obtain a processing code suitable for a multi-gear system. Spatial optimization data is generated based on the processing code to obtain a preliminary geometric solution. If the preliminary geometric solution meets the applicability requirements of the multi-gear system, the optimization data is updated through the consistency data, and the final geometric solution is determined. A gear processing code is generated for the final geometric solution.

[0076] Step S800: performing collaborative processing of spiral bevel gears based on the gear processing code.

[0077] In an embodiment of the present application, the collaborative processing of spiral bevel gears is driven by the gear processing code, and the operating data of the collaborative processing is obtained from the multi-gear system to obtain a preliminary operating data set. Based on the preliminary operating data set, data screening technology is used to extract feature data related to system accuracy and determine the accuracy feature set. For the accuracy feature set, by comparing the preset thresholds in the design standard, it is determined whether the system accuracy meets the requirements and the accuracy evaluation result is obtained. If the accuracy evaluation result is lower than the design standard, new operating data is generated by adjusting the gear processing code to obtain an updated data set. Based on the updated data set, a clustering algorithm is used to analyze the collaborative processing status of the multi-gear system and determine the optimization direction distribution. The processing parameters of the manufacturing equipment are adjusted by optimizing the direction distribution to obtain an optimization model suitable for the multi-gear system. For the optimization model, verification data is extracted from the operating data, and by comparing with the design standard, it is determined whether the multi-gear system has reached the optimization completion state.

[0078] Through the above steps S100 to S800, the kinematic characteristic distribution of the gear system is determined based on the target spatial geometric arrangement model of the multi-gear system. Based on the kinematic characteristic distribution, a positional relationship dataset between each spiral bevel gear of the multi-gear system is determined; based on the positional relationship dataset, an error prediction technique is used to determine the potential meshing error range of the multi-gear system; when the maximum value of the potential meshing error exceeds a preset error threshold, the spatial geometric arrangement model is adjusted using a kinematic characteristic simulation algorithm to obtain an optimized positional relationship dataset; based on the optimized positional relationship dataset, a target machining parameter set corresponding to the assembly accuracy of the multi-gear system is determined; when the target machining parameter set meets the preset parameter threshold, a target spatial geometric optimization scheme is determined based on the residual error distribution in the performance of the multi-gear system; based on the target spatial geometric optimization scheme, consistent optimization data is extracted from the target machining parameter set to generate a gear machining code; and spiral bevel gear collaborative machining is performed based on the gear machining code. In this way, by constructing a target spatial geometric arrangement model, optimizing the positional relationship of each spiral bevel gear, and generating a target spatial geometric optimization scheme for collaborative machining of spiral bevel gears, the machining accuracy of the spiral bevel gears can be improved.

[0079] In some embodiments, before obtaining the motion characteristic distribution of the multi-gear system according to the target spatial geometric arrangement model of the multi-gear system, the method further includes:

[0080] acquiring design data of the multi-gear system from the multi-gear system based on a pre-simulation technology;

[0081] determining spatial geometric parameters of the multi-gear system according to the design data;

[0082] constructing an initial spatial geometric arrangement model of the multi-gear system according to the spatial geometric parameters;

[0083] Determining first motion trajectory data of each of the spiral bevel gears based on the initial spatial geometric arrangement model;

[0084] When the first motion trajectory data exceeds a first preset threshold, the spatial geometric parameters are adjusted, and the initial spatial geometric arrangement model is updated based on the adjusted spatial geometric parameters to obtain the target spatial geometric arrangement model.

[0085] Specifically, a spiral bevel gear is a bevel gear whose tooth surface is spiral-shaped. Unlike ordinary bevel gears, spiral bevel gears have higher meshing efficiency and lower operating noise. A multi-gear system based on spiral bevel gears is a complex transmission system. The design data of the multi-gear system may include but is not limited to the geometric parameters of each spiral bevel gear and the meshing parameters of each spiral bevel gear. Based on the design data of the multi-gear system, the spatial geometric parameters of the multi-gear system can be determined. The spatial geometric parameters may include but are not limited to the size and position parameters of each spiral bevel gear in the multi-gear system. Then, an initial spatial geometric arrangement model of the multi-gear system is constructed based on the spatial geometric parameters of the multi-gear system. Based on the initial spatial geometric arrangement model, the first motion trajectory data of each spiral bevel gear can be determined, and it can be determined whether the first motion trajectory data exceeds a first preset threshold. In the case that the first motion trajectory data exceeds the first preset threshold, the spatial geometric parameters are adjusted, and the initial spatial geometric arrangement model is updated based on the adjusted spatial geometric parameters to obtain a target spatial geometric arrangement model.

[0086] In some embodiments, obtaining the motion characteristic distribution of the multi-gear system according to the target spatial geometric arrangement model of the multi-gear system includes:

[0087] Determining a second motion trajectory of each of the spiral bevel gears based on the target space geometric arrangement model;

[0088] The motion characteristic distribution of the multi-gear system is extracted from the second motion trajectory, wherein the motion characteristics at least include the spatial distance between the spiral bevel gears, the angle between the axes of the spiral bevel gears, and the center distance between any two spiral bevel gears.

[0089] Specifically, the second motion trajectory of each spiral bevel gear can be determined based on the target space geometric arrangement model, and the motion characteristic distribution of the multi-gear system can be extracted from the second motion trajectory.

[0090] Among them, the spatial distance between each spiral bevel gear can be expressed as:

[0091]

[0092] in, Indicates gear With gears The spatial distance between 、 、 Indicates gear The three-dimensional coordinate position of 、 、 Indicates gear The three-dimensional coordinate position of .

[0093] The angle between the axes of the spiral bevel gears can be expressed as:

[0094]

[0095] in, represents the angle between the two gear axes, and Represent the axial vectors of the two gears respectively.

[0096] The center distance between any two spiral bevel gears can be expressed as:

[0097]

[0098] in, Indicates the center distance between the two gears, and Represent the modules of the two gears respectively, and Respectively represent the number of teeth of the two gears.

[0099] It should be noted that the above-mentioned "first" and "second" are only used to distinguish two data of the same type.

[0100] In some embodiments, determining a potential meshing error range of the multi-gear system using an error prediction technique based on the position relationship dataset includes:

[0101] Extracting spatial geometric description data of each spiral bevel gear from the position relationship data set;

[0102] Based on the geometric arrangement technology, an arrangement model between the spiral bevel gears is generated according to the spatial geometric description data;

[0103] According to the spatial geometric data in the arrangement model, the potential meshing error range of the multi-gear system is determined based on the error prediction technology.

[0104] Specifically, after determining the positional relationship dataset, spatial geometric description data for each spiral bevel gear is extracted from the positional relationship dataset. Spatial geometric description data can be understood as data used to accurately describe the position, shape, orientation, and mutual relationship of an object or multiple objects in three-dimensional space. Then, a geometric arrangement technique is used to generate an arrangement model between each spiral bevel gear. The arrangement model between each spiral bevel gear can be understood as a static position sketch, which is used to qualitatively assess the potential meshing conflicts of each spiral bevel gear. Geometric arrangement techniques are generally used to describe the arrangement of objects in space, particularly the relative position, shape, and mutual relationship between multiple objects. Finally, based on the spatial geometric data in the arrangement model, error prediction techniques are used to determine the potential meshing error range of the multi-gear system. Error prediction techniques analyze and model potential errors in the system, predict and evaluate the impact of these errors in advance, and then take measures to correct or optimize them. The potential meshing error range of the multi-gear system can be understood as the range of incomplete contact between gear tooth surfaces and the precision deviation of the meshing of the gears during the spiral bevel gear transmission process.

[0105] In some embodiments, determining a target processing parameter set corresponding to the assembly accuracy of the multi-gear system based on the optimized position relationship data set includes:

[0106] Based on the consistency optimization method, generating a processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set;

[0107] Performing a machining simulation based on the machining parameter set, extracting a machining path for each spiral bevel gear, and obtaining a collaborative machining instruction sequence for the multi-gear system;

[0108] Performing motion characteristic simulation according to the collaborative machining instruction sequence to obtain a current meshing error of the multi-gear system;

[0109] In a case where a current meshing error of the multi-gear system is less than a preset error threshold, the target machining parameter set is obtained.

[0110] Specifically, to address the coordination requirements of multi-gear systems, a consistency optimization method can be used to generate a set of machining parameters corresponding to assembly accuracy based on the optimized gear positional relationship data. Machining simulations are then performed using this set of machining parameters, extracting the gear machining paths from the individual manufacturing technologies and determining the collaborative machining instruction sequence for the multi-gear system. Finally, based on this collaborative machining instruction sequence, kinematic simulations are performed in a virtual environment to determine whether the meshing error is below a preset error threshold. If this is the case, the target machining parameter set corresponding to the multi-gear system's assembly accuracy can be obtained.

[0111] In some embodiments, based on a consistency optimization method, generating a processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set includes:

[0112] Adjusting the optimized position relationship data set based on the consistency optimization method to obtain a preliminary processing parameter set;

[0113] Based on the geometric analysis technology, according to the preliminary processing parameter set and the optimized position relationship data set, determining the adjusted position distribution of the spiral bevel gear;

[0114] determining the collaborative characteristic data of the multi-gear system according to the adjusted spiral bevel gear position distribution;

[0115] When the collaborative characteristic data exceeds a second preset threshold, the preliminary processing parameter set is updated to obtain the processing parameter set corresponding to the assembly accuracy of the multi-gear system.

[0116] Specifically, a consistency optimization method is used to adjust the optimized positional relationship dataset to obtain a preliminary machining parameter set. Based on the preliminary machining parameter set and the optimized positional relationship dataset, geometric analysis techniques are then used to generate an adjusted spiral bevel gear position distribution. The adjusted spiral bevel gear position distribution is used to obtain synergy characteristic data for the multi-gear system, and a determination is made as to whether the synergy characteristic data exceeds a second preset threshold. If the synergy characteristic data exceeds the second preset threshold, the initial machining parameter set is adjusted by updating the preliminary machining parameter set to obtain a machining parameter set that corresponds to the assembly accuracy of the multi-gear system.

[0117] In this embodiment, motion simulation technology is used to generate dynamic distribution data for a multi-gear system based on a set of machining parameters corresponding to the assembly accuracy of the multi-gear system and the assembly requirements of the multi-gear system, thereby determining the stability of the system. This dynamic distribution data and the assembly accuracy of the multi-gear system are then analyzed to obtain a classified operating state pattern. Key parameter ranges are extracted from this classified operating state pattern to determine the overall coordination status of the multi-gear system.

[0118] In some embodiments, performing a machining simulation based on the machining parameter set, extracting a machining path for each spiral bevel gear, and obtaining a collaborative machining instruction sequence for the multi-gear system includes:

[0119] Performing machining simulation based on the machining parameter set, extracting machining paths for each spiral bevel gear, and obtaining an initial machining instruction set;

[0120] Determining a collaborative machining path for the multi-gear system using a geometric analysis technique based on the initial machining instruction set and the structural characteristics of the multi-gear system;

[0121] According to the collaborative processing paths and preset parameter control rules, dynamic distribution data of each collaborative processing path is obtained;

[0122] The dynamic distribution data is processed using dynamic simulation technology to obtain a collaborative processing instruction sequence for the multi-gear system.

[0123] Specifically, machining simulation is performed using a machining parameter set. The initial machining instruction set is determined from the machining paths of each spiral bevel gear in the individual machining data. Based on this initial machining instruction set and the structural characteristics of the multi-gear system, geometric analysis techniques are used to generate collaborative machining paths for the multi-gear system. Based on the collaborative machining paths and preset parameter control rules, dynamic distribution data for each collaborative machining path is obtained to determine sequence continuity. Motion simulation technology is used to process this dynamic distribution data, generate a collaborative machining instruction sequence for the multi-gear system, and determine the execution order of the instructions.

[0124] In the embodiment of the present application, the temporal and spatial distribution characteristics of gear manufacturing are obtained by the instruction execution sequence and the processing path distribution, and the system processing consistency is judged. If the temporal and spatial distribution characteristics exceed the preset range, the processing parameter set is updated through parameter adjustment technology to obtain an optimized instruction sequence. Based on the optimized instruction sequence and the collaborative processing requirements, the final processing path distribution is generated using data fusion technology to determine the processing status of the multi-gear system. The processing consistency index can be expressed as:

[0125]

[0126] in, Indicates the processing consistency index, Indicates the number of sampling points, Indicates the The processing parameter value of each sampling point, represents the mean value of the processing parameters, Indicates the standard deviation of processing parameters.

[0127] See Figure 2 , is a spiral bevel gear collaborative processing device based on spatial geometry optimization provided by an embodiment of the present application, the device comprising:

[0128] a first determining module, configured to determine a kinematic characteristic distribution of a multi-gear system based on a target spatial geometric arrangement model of the multi-gear system, wherein the multi-gear system includes a plurality of spiral bevel gears, and the spatial geometric arrangement model is constructed based on design data of the multi-gear system, the design data including at least geometric parameters of each of the spiral bevel gears and meshing parameters of each of the spiral bevel gears;

[0129] a second determining module, configured to determine a positional relationship dataset between the spiral bevel gears of the multi-gear system according to the motion characteristic distribution;

[0130] a third determining module, configured to determine a potential meshing error range of the multi-gear system using an error prediction technique based on the position relationship data set;

[0131] an adjustment module, configured to adjust the spatial geometric arrangement model by a motion characteristic simulation algorithm to obtain an optimized position relationship data set when the maximum value of the potential meshing error exceeds a preset error threshold;

[0132] a fourth determining module, configured to determine a target processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set;

[0133] a fifth determination module, configured to determine a target space geometry optimization scheme based on a residual error distribution in the multi-gear system performance when the target machining parameter set satisfies a preset parameter threshold;

[0134] An extraction module, configured to extract consistency optimization data from the target machining parameter set based on the target spatial geometry optimization solution and generate a gear machining code;

[0135] A processing module is used for collaborative processing of spiral bevel gears based on the gear processing code.

[0136] The spiral bevel gear collaborative processing device based on spatial geometry optimization provided in the second aspect of the embodiment of the present application can realize the various processes implemented in the above method embodiment and achieve the same beneficial effects. To avoid repetition, it will not be repeated here.

[0137] See Figure 3 , is a structural diagram of an electronic device provided in an embodiment of the present application. The third aspect of the embodiment of the present application provides an electronic device 3000, including a processor 3100 and a memory 3200. The memory 3200 stores machine executable instructions that can be executed by the processor 3100. The processor 3100 can execute the machine executable instructions to implement the above-mentioned collaborative processing method of spiral bevel gears based on spatial geometry optimization.

[0138] A fourth aspect of an embodiment of the present application provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by a processor, the processor implements the above-mentioned method for collaborative processing of spiral bevel gears based on spatial geometry optimization.

[0139] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0141] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0142] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0143] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0144] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0145] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

[0146] In addition, the various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A collaborative processing method for spiral bevel gears based on spatial geometry optimization, characterized in that: The method comprises: determining a kinematic characteristic distribution of the multi-gear system according to a target spatial geometric arrangement model of the multi-gear system, wherein the multi-gear system includes a plurality of spiral bevel gears, and the spatial geometric arrangement model is constructed based on design data of the multi-gear system, the design data including at least geometric parameters of each of the spiral bevel gears and meshing parameters of each of the spiral bevel gears; determining a positional relationship data set between the spiral bevel gears of the multi-gear system according to the motion characteristic distribution; Determining a potential meshing error range of the multi-gear system using an error prediction technique based on the position relationship data set; When the maximum value of the potential meshing error exceeds a preset error threshold, adjusting the spatial geometric arrangement model through a motion characteristic simulation algorithm to obtain an optimized position relationship data set; determining a target processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set; determining a target space geometry optimization scheme based on a residual error distribution in the multi-gear system performance when the target machining parameter set satisfies a preset parameter threshold; Based on the target space geometry optimization scheme, extracting consistency optimization data from the target processing parameter set to generate gear processing code; The spiral bevel gear is collaboratively processed based on the gear processing code.

2. The method according to claim 1, characterized in that Before obtaining the motion characteristic distribution of the multi-gear system according to the target spatial geometric arrangement model of the multi-gear system, the method further includes: acquiring design data of the multi-gear system from the multi-gear system based on a pre-simulation technology; determining spatial geometric parameters of the multi-gear system according to the design data; constructing an initial spatial geometric arrangement model of the multi-gear system according to the spatial geometric parameters; Determining first motion trajectory data of each of the spiral bevel gears based on the initial spatial geometric arrangement model; When the first motion trajectory data exceeds a first preset threshold, the spatial geometric parameters are adjusted, and the initial spatial geometric arrangement model is updated based on the adjusted spatial geometric parameters to obtain the target spatial geometric arrangement model.

3. The method according to claim 1, characterized in that The method of obtaining the motion characteristic distribution of the multi-gear system according to the target space geometric arrangement model of the multi-gear system includes: Determining a second motion trajectory of each of the spiral bevel gears based on the target space geometric arrangement model; The motion characteristic distribution of the multi-gear system is extracted from the second motion trajectory, wherein the motion characteristics at least include the spatial distance between the spiral bevel gears, the angle between the axes of the spiral bevel gears, and the center distance between any two spiral bevel gears.

4. The method according to claim 1, wherein Determining the potential meshing error range of the multi-gear system using an error prediction technique based on the position relationship data set includes: Extracting spatial geometric description data of each spiral bevel gear from the position relationship data set; Based on the geometric arrangement technology, an arrangement model between the spiral bevel gears is generated according to the spatial geometric description data; According to the spatial geometric data in the arrangement model, the potential meshing error range of the multi-gear system is determined based on the error prediction technology.

5. The method according to claim 1, wherein Determining a target processing parameter set corresponding to the assembly accuracy of the multi-gear system based on the optimized position relationship data set includes: Based on the consistency optimization method, generating a processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set; Performing a machining simulation based on the machining parameter set, extracting a machining path for each spiral bevel gear, and obtaining a collaborative machining instruction sequence for the multi-gear system; Performing motion characteristic simulation according to the collaborative machining instruction sequence to obtain a current meshing error of the multi-gear system; In a case where a current meshing error of the multi-gear system is less than a preset error threshold, the target machining parameter set is obtained.

6. The method according to claim 5, characterized in that The consistency-based optimization method generates a processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set, including: Adjusting the optimized position relationship data set based on the consistency optimization method to obtain a preliminary processing parameter set; Based on the geometric analysis technology, according to the preliminary processing parameter set and the optimized position relationship data set, determining the adjusted position distribution of the spiral bevel gear; determining the collaborative characteristic data of the multi-gear system according to the adjusted spiral bevel gear position distribution; When the collaborative characteristic data exceeds a second preset threshold, the preliminary processing parameter set is updated to obtain the processing parameter set corresponding to the assembly accuracy of the multi-gear system.

7. The method according to claim 5, characterized in that The processing simulation is performed based on the processing parameter set, the processing path of each spiral bevel gear is extracted, and the collaborative processing instruction sequence of the multi-gear system is obtained, including: Performing machining simulation based on the machining parameter set, extracting machining paths for each spiral bevel gear, and obtaining an initial machining instruction set; Determining a collaborative machining path for the multi-gear system using a geometric analysis technique based on the initial machining instruction set and the structural characteristics of the multi-gear system; According to the collaborative processing paths and preset parameter control rules, dynamic distribution data of each collaborative processing path is obtained; The dynamic distribution data is processed using dynamic simulation technology to obtain a collaborative processing instruction sequence for the multi-gear system.

8. A collaborative processing device for spiral bevel gears based on spatial geometry optimization, characterized in that: The device comprises: a first determining module, configured to determine a kinematic characteristic distribution of a multi-gear system based on a target spatial geometric arrangement model of the multi-gear system, wherein the multi-gear system includes a plurality of spiral bevel gears, and the spatial geometric arrangement model is constructed based on design data of the multi-gear system, the design data including at least geometric parameters of each of the spiral bevel gears and meshing parameters of each of the spiral bevel gears; a second determining module, configured to determine a positional relationship dataset between the spiral bevel gears of the multi-gear system according to the motion characteristic distribution; a third determining module, configured to determine a potential meshing error range of the multi-gear system using an error prediction technique based on the position relationship data set; an adjustment module, configured to adjust the spatial geometric arrangement model by a motion characteristic simulation algorithm to obtain an optimized position relationship data set when the maximum value of the potential meshing error exceeds a preset error threshold; a fourth determining module, configured to determine a target processing parameter set corresponding to the assembly accuracy of the multi-gear system according to the optimized position relationship data set; a fifth determination module, configured to determine a target space geometry optimization scheme based on a residual error distribution in the multi-gear system performance when the target machining parameter set satisfies a preset parameter threshold; An extraction module, configured to extract consistency optimization data from the target machining parameter set based on the target spatial geometry optimization solution and generate a gear machining code; A processing module is used for collaborative processing of spiral bevel gears based on the gear processing code.

9. An electronic device, characterized in that: include: a memory configured to store instructions; A processor is configured to call the instructions from the memory and implement the spiral bevel gear collaborative processing method based on spatial geometry optimization according to any one of claims 1 to 7 when executing the instructions.

10. A machine-readable storage medium, characterized in that The machine-readable storage medium stores instructions for enabling a machine to execute the method for collaborative machining of spiral bevel gears based on spatial geometry optimization according to any one of claims 1 to 7.

Images