Crankshaft quality evaluation method and device and server

Abstract

The invention provides a crankshaft quality evaluation method and device and a server, and relates to the technical field of quality evaluation, and the method comprises the steps: obtaining a crankshaft model, and receiving the interaction information of a user side for the crankshaft model; according to the interaction information, various internal components of the crankshaft are identified from the crankshaft model, and physical feature information of the internal components of the crankshaft is determined by identifying surface feature IDs of the internal components of the crankshaft; performing numerical analysis processing on the internal component types of the internal components of the crankshaft and the corresponding physical characteristic information, and determining constraint equations corresponding to the internal components of the crankshaft; and through a preset finite element solver, performing finite element numerical calculation on the crankshaft model based on the physical feature information and the constraint equation corresponding to each internal component of the crankshaft, and determining a crankshaft quality evaluation result. According to the method, the accuracy of crankshaft quality evaluation can be remarkably improved, the use threshold is reduced, and the evaluation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of quality assessment, and in particular to a crankshaft quality assessment method, device and server. Background Art

[0002] As a core internal component of power equipment such as transmitters and diesel engines, the quality and reliability of the crankshaft are key factors in ensuring the normal operation of mechanical equipment. At present, relevant technologies have proposed that the structural strength of the crankshaft can be tested through traditional physical experimental methods such as appearance inspection, strength test and fatigue test. However, the appearance inspection method is mainly used to check defects such as cracks and wear on the surface of the crankshaft. It cannot determine the structural strength of the crankshaft inside. The strength test must rely on the physical product, and the fatigue test has a long experimental cycle and high cost. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a crankshaft quality assessment method, device and server, which can significantly improve the accuracy of crankshaft quality assessment and improve the assessment efficiency.

[0004] In a first aspect, an embodiment of the present invention provides a crankshaft quality assessment method, the method comprising: acquiring a crankshaft model, and receiving interactive information from a user terminal regarding the crankshaft model; identifying various crankshaft internal components from the crankshaft model based on the interactive information, and determining physical feature information of each crankshaft internal component by identifying the surface feature ID of each crankshaft internal component; performing numerical analysis and processing on the internal component types and corresponding physical feature information of the crankshaft internal components, and determining constraint equations corresponding to each crankshaft internal component, wherein the constraint equations are used to simulate boundary constraints and load application conditions of each crankshaft internal component; performing finite element numerical calculations on the crankshaft model based on the physical feature information and constraint equations corresponding to each crankshaft internal component by a preset finite element solver, and determining a crankshaft quality assessment result.

[0005] In one embodiment, the step of identifying various crankshaft internal components from the crankshaft model based on the interactive information includes: acquiring surface feature data of the crankshaft model by traversing each surface of the crankshaft model; and determining the surface feature data associated with the target operating surface and the geometric features corresponding to the surface feature data by analyzing the target operating surface determined according to the interactive information to identify various crankshaft internal components.

[0006] In one embodiment, the step of determining the physical characteristic information of each internal component of the crankshaft by identifying the surface feature ID of each internal component of the crankshaft includes: based on the surface feature ID of each internal component of the crankshaft, configuring corresponding physical characteristic information from material data in a preset material library, wherein the physical characteristic information includes: density, elastic modulus, Poisson's ratio, yield strength, ultimate strength and SN curve data.

[0007] In one embodiment, a finite element solver is preset to perform finite element numerical calculation on a crankshaft model based on physical characteristic information and constraint equations corresponding to various internal components of the crankshaft, and the step of determining a crankshaft quality assessment result also includes: obtaining an external load vector of the crankshaft model, wherein the external load vector includes: the torque of the front end face of the crankshaft, the cylindrical support force of the crankshaft thrust surface, the cylindrical support force of the crankshaft support surface, and the non-uniformly distributed load on the crankshaft journal surface; by discretizing the continuous structure of the crankshaft model into multiple finite elements, and based on the external load vector and the finite element, constructing a basic equation for finite element solution corresponding to the crankshaft model; by a preset finite element solver, based on the physical characteristic information and constraint equations corresponding to various internal components of the crankshaft, solving the basic equation for finite element solution corresponding to the crankshaft model, determining the node displacement vector, stress vector, and strain vector of the crankshaft under different loads, and determining the node displacement vector, stress vector, and strain vector as the crankshaft quality assessment result.

[0008] In one embodiment, the basic equation for the finite element solution of the crankshaft model is:

[0009] in, is the global stiffness matrix, which is composed of the unit stiffness matrices. is the node displacement vector, representing the displacement of each mesh node, is the external load vector.

[0010] In one embodiment, the steps of determining the node displacement vector, stress vector and strain vector of the crankshaft under different loads include: determining the strain vector according to the node displacement vector, and obtaining the constitutive matrix of the material from a preset material library; and determining the stress vector using the linear proportional relationship between the strain vector and the stress vector and the constitutive matrix.

[0011] In one embodiment, after determining the node displacement vectors, stress vectors and strain vectors of the crankshaft under different loads, the method includes: using a preset image rendering model, using the node displacement vectors, stress vectors and strain vectors, generating a feature distribution cloud map corresponding to each evaluation feature, and using the crankshaft quality evaluation results to generate a digital verification report; sending the feature distribution cloud map and the digital verification report to the user end for display of the evaluation results.

[0012] In a second aspect, an embodiment of the present invention further provides a crankshaft quality assessment device, the device comprising: an information acquisition module, which acquires a crankshaft model and receives interactive information from a user terminal regarding the crankshaft model; a model interaction module, which identifies various crankshaft internal components from the crankshaft model according to the interactive information, and determines the physical feature information of each crankshaft internal component by identifying the surface feature ID of each crankshaft internal component; a numerical analysis module, which performs numerical analysis and processing on the internal component type and the corresponding physical feature information of the crankshaft internal components, and determines the constraint equations corresponding to each crankshaft internal component, wherein the constraint equations are used to simulate the boundary constraints and load application conditions of each crankshaft internal component; a quality assessment module, which performs finite element numerical calculation on the crankshaft model through a preset finite element solver based on the physical feature information and constraint equations corresponding to each crankshaft internal component, and determines the crankshaft quality assessment result.

[0013] In a third aspect, an embodiment of the present invention further provides a server, comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement any one of the methods provided in the first aspect.

[0014] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0015] The embodiments of the present invention bring the following beneficial effects: A crankshaft quality assessment method, device and server are provided in an embodiment of the present invention. After acquiring a crankshaft model and receiving interactive information from a user terminal regarding the crankshaft model, the method identifies various crankshaft internal components from the crankshaft model according to the interactive information, and determines the physical feature information of each crankshaft internal component by identifying the surface feature ID of each crankshaft internal component. Then, numerical analysis and processing are performed on the internal component type and the corresponding physical feature information of the crankshaft internal component to determine the constraint equations corresponding to each crankshaft internal component. Finally, a preset finite element solver is used to perform finite element numerical calculation on the crankshaft model based on the physical feature information and constraint equations corresponding to each crankshaft internal component to determine the crankshaft quality assessment result. The embodiment of the present invention can use a lightweight digital R&D process, with the help of a crankshaft three-dimensional model and interactive data, to quickly and efficiently perform accurate assessment and prediction of crankshaft quality and service life, thereby providing a new solution for crankshaft design, optimization and quality control.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A schematic flow chart of a crankshaft quality assessment method provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a crankshaft theoretical analysis model provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of a crankshaft model provided by an embodiment of the present invention; Figure 4 A schematic diagram of a specific process of a crankshaft quality assessment method provided by an embodiment of the present invention; Figure 5 A schematic diagram of a load parameter provided by an embodiment of the present invention; Figure 6A schematic diagram of a digital verification result provided by an embodiment of the present invention; Figure 7 A schematic diagram of the structure of a crankshaft quality assessment device provided by an embodiment of the present invention; Figure 8 A schematic diagram of the structure of a server provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] At present, in the field of modern mechanical engineering, power equipment such as engines and diesel engines are widely used in many key links such as transportation, industrial production and energy conversion. The crankshaft, as the core internal component of these mechanical equipment, is of self-evident importance. The crankshaft is subjected to huge mechanical stress and alternating loads in the complex power transmission process. Its performance directly determines the output efficiency and service life of the engine. However, due to the diversity of working environments, from high-speed automobile engines to heavy-loaded and low-speed ship diesel engines, different application scenarios have put forward extremely high requirements on the adaptability of the crankshaft. The quality and reliability of the crankshaft are key factors to ensure the normal operation of mechanical equipment, but traditional evaluation methods often rely on experience or tedious experimental tests, which make it difficult to quickly and accurately identify the weak points of the crankshaft.

[0022] In the field of crankshaft structural strength and quality assessment, traditional physical testing methods mainly include appearance inspection, strength test and fatigue test, but these methods have certain limitations: First, the appearance inspection method is mainly used to check the surface defects of the crankshaft, such as cracks and wear, but it is impossible to judge its internal structural strength. For example, the magnetic particle inspection technology commonly used in factories can detect the existence and length of cracks, but it cannot accurately assess the crack depth, especially for closed cracks in the fillet of the crankshaft journal. Conventional inspection methods have problems such as difficulty in detection and difficulty in quantification.

[0023] Secondly, strength testing is an important means to evaluate the structural strength of crankshafts, but its disadvantage is that it must rely on physical products. This means that during the product development phase, a complete crankshaft must be manufactured for testing, which not only increases R&D costs, but also prolongs the development cycle.

[0024] Finally, fatigue testing is a key method for evaluating the service life of crankshafts, but its cycle is long and the cost is high. For example, fatigue testing of automobile engine crankshafts usually requires thousands of hours of bench strengthening tests, which is not only time-consuming, but also requires a lot of experimental equipment and manpower investment. In addition, although traditional fatigue testing methods can provide relatively accurate results, it is difficult to monitor the fatigue state of the crankshaft in real time under actual working conditions.

[0025] In summary, traditional physical testing methods have obvious limitations in crankshaft structural strength and quality assessment, and cannot meet the needs of modern industry for efficient, low-cost and high-precision assessment. Based on this, the crankshaft quality assessment method, device and server provided by the present invention can significantly improve the accuracy of crankshaft quality assessment and improve the assessment efficiency.

[0026] See also Figure 1 The flowchart of a crankshaft quality assessment method shown in FIG. 1 mainly includes the following steps S102 to S108: Step S102, obtaining a crankshaft model, and receiving interactive information on the crankshaft model from the user end. In one embodiment, the crankshaft model is constructed based on a preset crankshaft theoretical analysis model. The crankshaft theoretical analysis model can be determined by simulating the actual use of the crankshaft internal components in the crankshaft-connecting rod-piston mechanism and theoretically analyzing and defining the crankshaft structure. See Figure 2 The structural schematic diagram of a crankshaft theoretical analysis model shown in the figure, the crankshaft is divided into several key internal components: the AB segment is defined as the thrust part of the crankshaft, the BC and EF segments are defined as the crank arm parts of the crankshaft, the CE segment is defined as the journal part of the crankshaft, the FG segment is defined as the support part of the crankshaft, and the GH segment is defined as the front end part of the crankshaft.

[0027] Under normal working conditions, the crankshaft thrust section (AB section) and its connected internal components jointly support the bearing. To simplify the analysis, this role is converted into the support force of the AB section in the x-axis and y-axis directions. and , so that the force condition of the thrust part can be accurately described in numerical calculation.

[0028] The crank arm part (BC and EF segments) is an important component of the crankshaft. Its main function is to convert the linear motion of the piston into rotational motion. The crank arm itself has no boundary freedom restrictions, but its strength verification is the focus of crankshaft quality assessment and life prediction.

[0029] The crankshaft journal part (CE section) is connected to the connecting rod. The driving force of the piston is transmitted to the journal through the connecting rod. In order to facilitate numerical calculation, this process is converted into the tangential force on the CE section. and radial force , so as to accurately describe the stress state of the journal part.

[0030] The crankshaft support part (FG section) plays a supporting role for the bearing. Similar to the thrust part, this role is converted into the supporting force of the FG section in the x-axis and y-axis directions, so that the force condition of the support part can be accurately described in the numerical calculation.

[0031] Finally, the front end of the crankshaft (GH section) will be subjected to external torque. In order to simplify the analysis, this effect is converted into the torque on the GH section. , so as to accurately describe the stress state of the front end part, and then establish a crankshaft model according to the crankshaft theoretical analysis model to perform numerical analysis and transformation on each part of the crankshaft model. For details of the crankshaft model, see Figure 3 A structural schematic diagram of a crankshaft model is shown.

[0032] Step S104, identifying various crankshaft internal components from the crankshaft model based on the interactive information, and determining the physical feature information of each crankshaft internal component by identifying the surface feature ID of each crankshaft internal component. The surface feature ID is the key data for subsequent finite element numerical calculations. The surface feature ID defines the position and mode of action of the load and boundary. In one embodiment, the surface feature data of the crankshaft model can be obtained by traversing each surface of the crankshaft model, and the target operating surface determined according to the interactive information is analyzed to determine the surface feature data associated with the target operating surface and the geometric features corresponding to the surface feature data, so as to identify various crankshaft internal components.

[0033] Specifically, when performing numerical calculation and evaluation of the crankshaft journal part, it is necessary to consider the complex alternating loads generated by the piston movement. In order to better transform and apply these loads, calculations can be performed based on actual engineering data. Since the piston movement will generate alternating loads, an extreme case evaluation will be taken here. The corresponding engineering data include the highest explosion pressure and cylinder liner diameter D during actual piston movement. The specific calculation formula is as follows:

[0034] This formula calculates the impact force generated by the piston on the crankshaft journal under the highest explosion pressure. Among them, Pi is the highest explosion pressure during actual piston movement, and D is the cylinder liner diameter. This impact force is a key parameter for evaluating the stress state of the crankshaft journal. It directly affects the deformation, stress distribution and fatigue life prediction of the crankshaft. In finite element analysis, the impact force F will be used as one of the boundary loads to simulate the stress of the crankshaft under actual working conditions. In this way, the performance of the crankshaft under extreme working conditions can be more accurately predicted.

[0035] During the application of sinusoidal non-uniform loads, the feature ID of the crankshaft journal surface can be obtained interactively. This can be achieved by selecting the crankshaft journal surface. After selecting the journal surface, the system can identify and obtain the corresponding surface feature ID, thereby providing the necessary information for the precise application of the load. This step is critical in the load transfer process because it ensures that the load can be correctly applied to the specific area of ​​the crankshaft.

[0036] Take the STEP file, a format for saving 3D CAD models, as an example. It can store geometry, topology, and feature information. To identify and obtain the face feature ID of the selected face, you first need to parse the STEP file and extract its geometry and topology data. Each face in a STEP file is usually represented by a unique identifier (ID), which is associated with geometric entities (such as planes, cylinders, spheres, etc.). To parse STEP files, you can use open source tools such as OpenCASCADE or PythonOCC, which can convert the geometry data in STEP files into programmable data structures, such as the boundary representation (BRep) model. The BRep model represents a 3D object as a collection of vertices, edges, and faces, each of which has a unique ID.

[0037] After obtaining the surface feature data of the model, the next step is to select a specific face interactively. Specifically, the algorithm will traverse all the faces of the model and select the determined face. It is necessary to identify its geometric features and obtain the corresponding feature ID. The faces in the STEP file are usually associated with geometric features (such as planes, cylindrical surfaces, etc.). These features are defined in the file by specific names (such as PLANE, CYLINDRICAL_SURFACE, etc.). By parsing the topological and geometric entity relationships in the STEP file, the selected face can be associated with its corresponding feature ID. For example, in the selection of this part of the crankshaft, if the selected face is a cylindrical surface, the geometric feature ID corresponding to the selected face can be found by traversing the CYLINDRICAL_SURFACE entity in the STEP file.

[0038] In another embodiment, the corresponding physical feature information can be configured from the material data of a preset material library based on the surface feature ID of each internal component of the crankshaft, where the physical feature information includes: density, elastic modulus, Poisson's ratio, yield strength, ultimate strength and SN curve data. Specifically, the material library is set up based on the configurable characteristics of the low-code platform. By importing predefined material data, the geometric model is given corresponding physical properties. These parameters directly determine the response behavior of the model to external loads during the simulation process. In addition, the material library also supports custom extensions. For example, the SN curve data will be expanded here to accurately describe the material behavior under more complex working conditions.

[0039] Step S106, performing numerical analysis on the internal component types and corresponding physical characteristic information of the internal components of the crankshaft, and determining the constraint equations corresponding to each internal component of the crankshaft, wherein the constraint equations are used to simulate the boundary constraints and load application conditions of each internal component of the crankshaft. In one embodiment, it is necessary to determine a reference direction based on the principle of non-uniform load. This reference direction is arbitrary, but in order to ensure the accuracy and consistency of the analysis, it is usually set with reference to the absolute coordinate system of the crankshaft model. After determining the reference direction, a sinusoidal waveform can be used to simulate the distribution of the load. This distribution method can better simulate the non-uniformity of the load under actual working conditions.

[0040] In numerical analysis, in order to accurately simulate the support force of segment AB in the x-axis and y-axis directions, cylindrical support can be used for transformation, so that constraints are imposed on the cylindrical surface in radial, axial and tangential dimensions to accurately simulate the actual support force. The cylindrical support is mainly based on the thrust surface of the crankshaft, and its geometric characteristics are used to impose corresponding constraints. The support force in the x-axis and y-axis directions can be achieved by limiting the radial and tangential degrees of freedom of the thrust surface. Among them, the restriction of radial degrees of freedom ensures that the displacement of the crankshaft in the radial direction is controlled, thereby effectively simulating the support effect in the x-axis and y-axis directions, while the constraint of the tangential degrees of freedom can prevent the thrust surface from slipping along the cylindrical surface, further enhancing the stability of the model.

[0041] In addition, considering the force characteristics of the thrust surface, the movement in the z-axis direction may be constrained under certain working conditions. Therefore, its axial degree of freedom can be further restricted to simulate the state of z-axis restriction under some working conditions, so as to effectively reproduce the supporting effect of the crankshaft on the thrust surface in actual working conditions, while ensuring the rationality of the force in the numerical calculation process and establishing the corresponding constraint equation.

[0042] In the finite element model of the cylindrical support, the constraint equation is used to simulate the force on the crankshaft thrust surface to ensure that the support forces in the x-axis and y-axis directions are reasonable, while limiting the z-axis degree of freedom under specific working conditions: 1. The radial constraint equation is used to limit the displacement of the thrust surface in the radial direction so that the node always remains on the cylindrical surface:

[0043] in, is the initial radius, ensuring that the thrust surface is constrained in the x and y directions.

[0044] 2. The tangential constraint equation is used to prevent the thrust surface from sliding along the tangential direction:

[0045] Make sure that the thrust surface cannot rotate about the axis or slide sideways.

[0046] 3. The axial constraint equation is used to further restrict the motion in the z direction under certain conditions:

[0047] Prevent the thrust surface from moving freely in the axial direction, simulating the restricted state of the z-axis.

[0048] In addition, by reasonably selecting the thrust surface of the crankshaft, appropriate boundary conditions can be imposed in the finite element model to make it conform to the stress conditions in actual engineering applications: for the support force of the FG segment in the x-axis and y-axis directions, the cylindrical support method is also used for transformation to ensure the accuracy of the numerical analysis and the stability of the calculation. This method simulates the support force in the x-axis and y-axis directions by limiting the radial degree of freedom of the crankshaft thrust surface, so that the thrust surface will not be displaced in the radial direction. By reasonably selecting the crankshaft support surface, finite element constraints can be effectively imposed to correctly simulate the support effect in the numerical analysis, thereby improving the overall calculation accuracy and the rationality of the model's force.

[0049] At the front end of the crankshaft (GH section), the torque It is a key action quantity, and its calculation and analysis directly affect the accuracy of numerical evaluation. In actual engineering, in order to more accurately convert and apply this torque, it is necessary to combine the actual engine working conditions and obtain the numerical value of the torque through engineering data to meet the numerical calculation requirements. These engineering data mainly include the engine's output power P (in kilowatts, kW) and speed n (in revolutions per minute, RPM), which can reflect the working characteristics of the engine under actual operating conditions. The torque calculation formula is:

[0050] in, is the torque at the front end of the crankshaft, in Newton meters (N·m); P is the engine output power, in kilowatts (kW); n is the engine speed, in revolutions per minute (RPM); 9549 is the constant for converting power to speed, which comes from the unit conversion (1kW = 1000W, 1RPM = rad / s).

[0051] Step S108, using a preset finite element solver, based on the physical characteristic information and constraint equations corresponding to each crankshaft internal component, finite element numerical calculation is performed on the crankshaft model to determine the crankshaft quality assessment result.

[0052] The crankshaft quality assessment method provided by the embodiment of the present invention can significantly improve the accuracy of crankshaft quality assessment and improve the assessment efficiency.

[0053] See also Figure 4The specific flow chart of a crankshaft quality assessment method shown in the figure starts with model import, and the geometric model is pre-processed in a general manner in combination with the corresponding grid setting and material library setting, and then various boundary and load conditions of the solution in the crankshaft specific scenario are introduced, including the crankshaft front end face moment, the crankshaft thrust face cylindrical support, the crankshaft support face cylindrical support and the crankshaft journal face non-uniformly distributed load. After completing the material library setting and applying the boundary conditions, the system calls the finite element solver for calculation. The finite element divides the continuous structure into multiple finite elements through discretization, and establishes corresponding solution equations to calculate the key results such as stress and deformation of the crankshaft under different loads. The embodiment of the present invention also provides an implementation method for performing finite element numerical calculation on the crankshaft model to determine the crankshaft quality assessment result. For details, see (1) to (3) below: (1) Obtaining an external load vector of the crankshaft model, wherein the external load vector includes: a moment on the front end face of the crankshaft, a cylindrical support force on the crankshaft thrust surface, a cylindrical support force on the crankshaft support surface, and a non-uniformly distributed load on the crankshaft journal surface.

[0054] (2) The continuous structure of the crankshaft model is discretized into multiple finite elements, and the basic equations for finite element solution of the crankshaft model are constructed based on the external load vector and the finite elements. The basic equations for finite element solution of the crankshaft model are:

[0055] in, is the global stiffness matrix, which is composed of the unit stiffness matrices. is the node displacement vector, representing the displacement of each mesh node, is the external load vector, including: the moment of the front end face of the crankshaft, the cylindrical support force of the crankshaft thrust surface, the cylindrical support force of the crankshaft support surface and the non-uniform load on the crankshaft journal surface.

[0056] (3) By using a preset finite element solver, based on the physical feature information and constraint equations corresponding to each crankshaft internal component, the basic equations of the finite element solution corresponding to the crankshaft model are solved to determine the node displacement vector, stress vector and strain vector of the crankshaft under different loads, and the node displacement vector, stress vector and strain vector are determined as the crankshaft quality assessment result. In one embodiment, the strain vector is determined according to the node displacement vector, and the constitutive matrix of the material is obtained from a preset material library. Then, the stress vector is determined using the linear proportional relationship between the strain vector and the stress vector and the constitutive matrix. In another embodiment, by using a preset image rendering model, the node displacement vector, stress vector and strain vector are used to generate a feature distribution cloud map corresponding to each assessment feature, and a digital verification report is generated using the crankshaft quality assessment result. Finally, the feature distribution cloud map and the digital verification report are sent to the user end for display of the assessment results.

[0057] Specifically, under the action of boundary conditions, the finite element solver solves the basic equations of the above finite element solution through matrix operations to obtain the displacement field of the crankshaft and further calculate the stress and strain:

[0058] in, is the stress vector, including normal stress and shear stress in all directions; is the constitutive matrix of the material, which depends on the elastic properties of the material (provided by the material library parameters); is the strain vector, which is calculated from the node displacement.

[0059] The evaluation results are presented in the form of rendered cloud graphs, and various data results that affect its structural strength and service life can be directly displayed, so that engineers can intuitively analyze the stress distribution, deformation mode and stress conditions of key parts of the crankshaft. For example, high stress concentration areas: indicate the parts of the crankshaft where fatigue failure may occur; maximum deformation areas: used to evaluate whether the stiffness and deformation of the crankshaft exceed the allowable range; contact pressure distribution: used to analyze the interaction between the journal surface and the bearing. Here, a complete report format will be used for automatic generation and display, thus forming a complete method for crankshaft quality evaluation and prediction.

[0060] In actual application, for the crankshaft model, various parts are selected interactively, including the crankshaft thrust surface, crankshaft journal surface, crankshaft support surface and crankshaft front end surface. This process can be realized through a graphical user interface. By selecting different parts of the crankshaft, the system can automatically identify and obtain the corresponding surface feature ID.

[0061] After obtaining the surface feature data, finite element numerical calculations can be performed, and after the calculation is completed, the results can be displayed in the form of cloud maps, such as total deformation distribution cloud maps, equivalent stress distribution cloud maps, etc. The color scale displayed in the cloud map adopts a red-green-blue-gray gradient mode, and a gray system that is more like the original color of the model is used to show that the model has small deformation or small stress, so as to avoid the interference of strong saturation colors on the distribution of weak points of the model. This visualization method helps engineers intuitively identify potential problem areas of the crankshaft, so as to carry out targeted design optimization.

[0062] In order to realize post-processing rendering and display of model files, the finite element result files can be converted into VTK visualization format files and transmitted to the Web side. VTK (Visualization Toolkit) is an open source software system for 3D computer graphics, image processing and visualization. In this way, users can perform interactive model analysis and result viewing on the Web side.

[0063] See also Figure 5 The following is a schematic diagram of load parameters. Subsequent reports and numerical feedback are automatically generated. In addition to the solidified project background and corresponding reference test standards in this scenario, the input power, speed, maximum burst pressure and cylinder diameter parameters can also be combined to generate corresponding tables and issue digital diagnosis results. See Figure 6 A schematic diagram of a digital verification result is shown, where, according to the input of material properties, the basis of digital diagnosis is as follows: If the maximum stress value is less than the material yield strength, it is considered that plastic deformation will not occur and the reference suggestion is qualified; if the material yield strength is ≤ the maximum stress value ≤ the material ultimate strength, it is considered that plastic deformation will occur but will not be damaged, and the reference suggestion is warning; if the maximum stress value is greater than the material ultimate strength, it is considered that damage will occur and the reference suggestion is unqualified.

[0064] The allowable number of crankshaft uses can also be judged by referring to its actual use standard, so as to make reference suggestions: if the maximum allowable number of uses is less than the actual use standard, the reference suggestion is unqualified; if the maximum allowable number of uses is less than the actual use standard, the reference suggestion is unqualified; The actual usage standard and reference recommendation are qualified.

[0065] In summary, the present invention can use a lightweight digital R&D process, with the help of crankshaft three-dimensional models and interactive data, to quickly and efficiently evaluate and predict the quality and service life of the crankshaft, providing a new solution for the design, optimization and quality control of the crankshaft.

[0066] For the crankshaft quality assessment method provided in the above embodiment, the present invention provides a crankshaft quality assessment device, see Figure 7 The structure diagram of a crankshaft quality assessment device shown in FIG. 1 includes the following parts: The information acquisition module 702 acquires the crankshaft model and receives the interactive information of the user end for the crankshaft model; The model interaction module 704 identifies various crankshaft internal components from the crankshaft model according to the interaction information, and determines the physical feature information of various crankshaft internal components by identifying the surface feature IDs of various crankshaft internal components; The numerical analysis module 706 performs numerical analysis on the internal component types and corresponding physical characteristic information of the internal components of the crankshaft to determine the constraint equations corresponding to the internal components of the crankshaft, wherein the constraint equations are used to simulate the boundary constraints and load application conditions of the internal components of the crankshaft; The quality assessment module 708 performs finite element numerical calculation on the crankshaft model through a preset finite element solver based on the physical characteristic information and constraint equations corresponding to each internal component of the crankshaft to determine the crankshaft quality assessment result.

[0067] The above-mentioned crankshaft quality assessment device provided in the embodiment of the present application can significantly improve the accuracy of crankshaft quality assessment and improve the assessment efficiency.

[0068] In one embodiment, when performing the step of identifying various crankshaft internal components from the crankshaft model based on the interactive information, the above-mentioned model interaction module 704 is also used to: obtain the surface feature data of the crankshaft model by traversing each surface of the crankshaft model; and determine the surface feature data associated with the target operating surface and the geometric features corresponding to the surface feature data by analyzing the target operating surface determined according to the interactive information to identify various crankshaft internal components.

[0069] In one embodiment, when performing the step of determining the physical characteristic information of each crankshaft internal component by identifying the surface feature ID of each crankshaft internal component, the above-mentioned model interaction module 704 is also used to: based on the surface feature ID of each crankshaft internal component, configure corresponding physical characteristic information from the material data of a preset material library, wherein the physical characteristic information includes: density, elastic modulus, Poisson's ratio, yield strength and ultimate strength.

[0070] In one embodiment, when performing finite element numerical calculation of the crankshaft model based on the physical characteristic information and constraint equations corresponding to each internal component of the crankshaft through a preset finite element solver to determine the crankshaft quality assessment result, the above-mentioned quality assessment module 708 is also used to: obtain the external load vector of the crankshaft model, wherein the external load vector includes: the torque of the front end face of the crankshaft, the cylindrical support force of the crankshaft thrust surface, the cylindrical support force of the crankshaft support surface and the non-uniform load on the crankshaft journal surface; discretize the continuous structure of the crankshaft model into multiple finite elements, and construct the basic equations for the finite element solution of the crankshaft model based on the external load vector and the finite element; solve the basic equations for the finite element solution of the crankshaft model based on the physical characteristic information and constraint equations corresponding to each internal component of the crankshaft through a preset finite element solver, determine the node displacement vector, stress vector and strain vector of the crankshaft under different loads, and determine the node displacement vector, stress vector and strain vector as the crankshaft quality assessment result.

[0071] In one implementation, the quality assessment module 708 further includes: a basic equation corresponding to the finite element solution of the crankshaft model is:

[0072] in, is the global stiffness matrix, which is composed of the unit stiffness matrices. is the node displacement vector, representing the displacement of each mesh node, is the external load vector.

[0073] In one embodiment, when determining the node displacement vector, stress vector and strain vector of the crankshaft under different loads, the quality assessment module 708 is also used to: determine the strain vector based on the node displacement vector, and obtain the constitutive matrix of the material from a preset material library; determine the stress vector using the linear proportional relationship between the strain vector and the stress vector and the constitutive matrix.

[0074] In one embodiment, after determining the node displacement vectors, stress vectors and strain vectors of the crankshaft under different loads, the quality assessment module 708 is also used to: generate a feature distribution cloud map corresponding to each assessment feature using the node displacement vectors, stress vectors and strain vectors through a preset image rendering model, and generate a digital verification report using the crankshaft quality assessment results; and send the feature distribution cloud map and the digital verification report to the user end for display of the assessment results.

[0075] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0076] An embodiment of the present invention provides a server. Specifically, the server includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned embodiments.

[0077] Figure 8 A structural diagram of a server provided in an embodiment of the present invention, the server 100 includes: a processor 80, a memory 81, a bus 82 and a communication interface 83, wherein the processor 80, the communication interface 83 and the memory 81 are connected via the bus 82; the processor 80 is used to execute an executable module stored in the memory 81, such as a computer program.

[0078] The memory 81 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 83 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0079] The bus 82 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0080] Among them, the memory 81 is used to store programs, and the processor 80 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiments of the present invention can be applied to the processor 80 or implemented by the processor 80.

[0081] The processor 80 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 80. The above processor 80 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 81, and the processor 80 reads the information in the memory 81 and completes the steps of the above method in combination with its hardware.

[0082] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0083] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0084] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A crankshaft quality assessment method, characterized in that: The method comprises: Obtain a crankshaft model and receive interactive information from the user end for the crankshaft model; Identifying various crankshaft internal components from the crankshaft model according to the interactive information, and determining physical feature information of various crankshaft internal components by identifying surface feature IDs of the various crankshaft internal components; Performing numerical analysis on the internal component types of the crankshaft internal components and the corresponding physical characteristic information to determine constraint equations corresponding to the various crankshaft internal components, wherein the constraint equations are used to simulate boundary constraints and load application conditions of the various crankshaft internal components; By using a preset finite element solver, based on the physical characteristic information corresponding to each of the crankshaft internal components and the constraint equations, finite element numerical calculation is performed on the crankshaft model to determine the crankshaft quality assessment result.

2. The crankshaft quality assessment method according to claim 1, characterized in that: The step of identifying various crankshaft internal components from the crankshaft model according to the interactive information comprises: Acquire surface feature data of the crankshaft model by traversing each surface of the crankshaft model; By analyzing the target operating surface determined according to the interactive information, surface feature data associated with the target operating surface and geometric features corresponding to the surface feature data are determined, so as to identify various crankshaft internal components.

3. The crankshaft quality assessment method according to claim 1, characterized in that: The step of determining the physical feature information of each crankshaft internal component by identifying the surface feature ID of each crankshaft internal component comprises: Based on the surface feature IDs of the various crankshaft internal components, corresponding physical feature information is configured from the material data of a preset material library, wherein the physical feature information includes: density, elastic modulus, Poisson's ratio, yield strength, ultimate strength and SN curve data.

4. The crankshaft quality assessment method according to claim 1, characterized in that: The step of performing finite element numerical calculation on the crankshaft model based on the physical characteristic information and constraint equations corresponding to the various crankshaft internal components by using a preset finite element solver to determine the crankshaft quality assessment result also includes: Obtaining an external load vector of the crankshaft model, wherein the external load vector includes: a moment of the front end face of the crankshaft, a cylindrical support force of the crankshaft thrust surface, a cylindrical support force of the crankshaft support surface, and a non-uniformly distributed load of the crankshaft journal surface; By discretizing the continuous structure of the crankshaft model into a plurality of finite elements, and constructing a basic equation for finite element solution corresponding to the crankshaft model based on the external load vector and the finite elements; By presetting a finite element solver, based on the physical feature information and constraint equations corresponding to the internal components of the crankshaft, the basic equations of the finite element solution corresponding to the crankshaft model are solved to determine the node displacement vectors, stress vectors and strain vectors of the crankshaft under different loads, and the node displacement vectors, stress vectors and strain vectors are determined as the crankshaft quality assessment results.

5. The crankshaft quality assessment method according to claim 4, characterized in that: The basic equation for the finite element solution of the crankshaft model is: in, is the global stiffness matrix, which is composed of the unit stiffness matrices. is the node displacement vector, representing the displacement of each mesh node, is the external load vector.

6. The crankshaft quality assessment method according to claim 4, characterized in that: The step of determining the node displacement vector, stress vector and strain vector of the crankshaft under different loads includes: Determine the strain vector according to the node displacement vector, and obtain the constitutive matrix of the material from a preset material library; The stress vector is determined by using the linear proportional relationship between the strain vector and the stress vector and the constitutive matrix.

7. The crankshaft quality assessment method according to claim 4, characterized in that: After the steps of determining the node displacement vectors, stress vectors and strain vectors of the crankshaft under different loads, including: By using a preset image rendering model, the node displacement vector, the stress vector and the strain vector are used to generate a feature distribution cloud map corresponding to each evaluation feature, and a digital verification report is generated using the crankshaft quality evaluation result; The characteristic distribution cloud map and the digital verification report are sent to the user end for display of the evaluation results.

8. A crankshaft quality assessment device, characterized in that: The device comprises: An information acquisition module, which acquires the crankshaft model and receives interactive information from the user end for the crankshaft model; A model interaction module, which identifies various crankshaft internal components from the crankshaft model according to the interaction information, and determines the physical feature information of the various crankshaft internal components by identifying the surface feature IDs of the various crankshaft internal components; A numerical analysis module performs numerical analysis on the internal component type of the crankshaft internal component and the corresponding physical characteristic information to determine the constraint equations corresponding to the various crankshaft internal components, wherein the constraint equations are used to simulate the boundary constraints and load application conditions of the various crankshaft internal components; The quality assessment module uses a preset finite element solver to perform finite element numerical calculation on the crankshaft model based on the physical feature information corresponding to each internal component of the crankshaft and the constraint equation to determine the crankshaft quality assessment result.

9. A server, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 7.

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