Simulation design method, system, medium, and program for a process scheme for welding an oil rail

Abstract

The present application relates to a kind of simulation design method, system, medium and program for the process scheme of welding oil rail, comprising: obtaining at least one candidate process scheme;For candidate process scheme, the finite element model of rail body is constructed, and the feature of potential defect associated with machining mode is created in model;The boundary condition and alternating load of simulated engine high pressure pulse working condition are applied to finite element model, and simulation solving is executed, to obtain the fatigue life index of rail body under the candidate process scheme;Based on the fatigue life index corresponding to each candidate process scheme, determine the optimal process scheme from at least one candidate process scheme and output.Compared with prior art, the fatigue life of rail body under different process schemes can be quantitatively evaluated with high fidelity before physical prototype manufacturing, and the optimal scheme is scientifically and efficiently screened, and reliability and economy are considered.

Description

Technical Field

[0001] This invention relates to the field of mechanical manufacturing process design and computer-aided engineering analysis technology, and in particular to a simulation design method, system, medium and program for welding oil rail process schemes. Background Technology

[0002] In high-pressure common rail fuel injection systems, the fuel rail, as the core component for storing and distributing high-pressure fuel, is subjected to alternating pulse pressures of up to 350 bar or even higher over long periods. To meet the requirements for structural integrity and fatigue durability under long-term high-pressure conditions, existing technologies typically favor a method of forging and machining the entire fuel rail body from integral stainless steel. However, while this forging and machining method offers high reliability, it suffers from significant drawbacks such as high manufacturing costs and large unit weight.

[0003] To reduce costs, the industry has begun exploring the use of welding processes to manufacture high-pressure fuel rails. The process of welding fuel rails involves various combinations of material selection (such as austenitic stainless steel or high-strength carbon steel) and rail manufacturing methods (such as seamless tubing or drilling solid bars). Different combinations introduce drastically different potential failure risks. For example, seamless tubing is highly prone to internal folding defects, while carbon steel is susceptible to electrochemical corrosion in environments containing alcohol-based gasoline. These microscopic defects and material degradation mechanisms can evolve into fatigue crack initiations under high-pressure pulse loads, ultimately leading to fuel rail leakage and failure.

[0004] In traditional product development processes, determining the optimal welding process for hydraulic rails often relies on extensive physical prototype manufacturing and lengthy bench durability testing. This approach is not only time-consuming and costly, but also makes it difficult to systematically and quantitatively assess the impact of potential defects in different process schemes on the product's fatigue life throughout its entire lifecycle in the early stages of design, thus failing to achieve an optimal trade-off between cost and reliability.

[0005] Therefore, there is an urgent need for a method that can efficiently and accurately pre-evaluate and screen different welding rail process schemes. Summary of the Invention

[0006] To address the problem that existing technologies struggle to systematically and quantitatively assess the impact of potential defects on fatigue life in different welding oil rail processes during the initial design phase, this application proposes a computer simulation design method and computer program product for welding oil rail processes. This method incorporates material types, machining methods, and their associated potential defect characteristics into a finite element simulation model, and solves the model under simulated high-pressure pulse conditions of an engine. This enables quantitative prediction and optimization of the durability performance of different process schemes, thereby reducing development costs and time while ensuring reliability.

[0007] The objective of this invention can be achieved through the following technical solutions: The first aspect of this invention provides a simulation design method for a welding oil rail process, comprising: Obtain at least one candidate process scheme, the candidate process scheme including the material type and machining method for forming the welded oil rail body; For the candidate process scheme, a finite element model of the track body is constructed, and features of potential defects associated with the machining method are created in the model; The boundary conditions and alternating loads simulating the high-pressure pulse condition of the engine are applied to the finite element model, and the simulation solution is performed to obtain the fatigue life index of the rail body under the candidate process scheme. Based on the fatigue life index corresponding to each candidate process scheme, the optimal process scheme is determined from the at least one candidate process scheme and output.

[0008] Furthermore, the material type of the candidate process is austenitic stainless steel or high-strength carbon steel, and the machining method is drilling of seamless tubing or solid bar stock. When the machining method is a seamless tube, the potential defect associated with the machining method is characterized by the introduction of a set of probabilistic initial cracks representing internal folding defects on the inner wall surface of the finite element model. The size and distribution of the initial cracks are randomly generated according to the statistical law of seamless tube process defects. The fatigue life index is the number of load cycles required for the crack to expand from the initial size to penetrate the wall thickness under the action of the alternating load.

[0009] Furthermore, the creation of features for potential defects associated with the machining method in the model also includes: When the material type is high-strength carbon steel and the machining method is seamless tubing, an additional electrochemical corrosion rate attenuation layer is set in the inner surface area of ​​the model to simulate the uniform thinning effect of alcohol-containing gasoline on the inner wall of the carbon steel rail body, and the thinning effect is coupled to the crack propagation calculation to solve for fatigue life index that is closer to the actual service conditions.

[0010] Furthermore, the determination of the optimal process scheme based on the fatigue life index corresponding to each candidate process scheme specifically includes: Multiple candidate process schemes are sorted from high to low according to the obtained fatigue life index, and schemes with index values ​​lower than the preset safety threshold are eliminated. From the remaining schemes, the scheme with the largest ratio of fatigue life index to estimated manufacturing cost is selected as the optimal process scheme.

[0011] Furthermore, the application of boundary conditions and alternating loads simulating the high-pressure pulse condition of the engine to the finite element model also includes: A hydraulic load is applied from zero to 350 bar on the inner wall of the rail and then periodically fluctuates. At the same time, a temperature field based on the engine compartment environment is applied to simulate the creep-fatigue interaction of the rail under high temperature and high pressure, thereby obtaining a more comprehensive fatigue life index.

[0012] Furthermore, the construction of the finite element model of the orbital body for the candidate process scheme further includes: The surface roughness data of the inner hole associated with the machining method is obtained, and the surface roughness is mapped to the stress concentration factor on the inner wall of the finite element model, so that the simulation can reflect the influence of the surface integrity difference caused by different machining methods on fatigue life.

[0013] A second aspect of the present invention provides a computer simulation design system for welding oil rail process schemes, comprising: The scheme acquisition module is used to acquire at least one candidate process scheme, the candidate process scheme including the material type and machining method for forming the welded oil rail body; The model building module is used to construct a finite element model of the track body for the candidate process scheme, and to create features of potential defects associated with the machining method in the model; The simulation solution module is used to apply boundary conditions and alternating loads simulating the high-pressure pulse condition of the engine to the finite element model and perform simulation solution to obtain the fatigue life index of the rail body under the candidate process scheme. The scheme output module is used to determine and output the optimal process scheme from the at least one candidate process scheme based on the fatigue life index corresponding to each candidate process scheme.

[0014] Furthermore, the model building module is specifically used for: When the machining method in the candidate process scheme is seamless tube, a set of probabilistic initial cracks representing internal folding defects are introduced on the inner wall surface of the finite element model. The size and distribution of the initial cracks are randomly generated according to the statistical law of seamless tube process defects. The simulation solution module is specifically used to calculate the number of load cycles required for the crack to expand from its initial size to penetrate the wall thickness under the alternating load, which serves as the fatigue life index.

[0015] A third aspect of the present invention provides a computer-readable storage medium, wherein the storage medium containing computer-executable instructions, when executed by a computer processor, is used to perform the simulation design method for a welding rail process scheme as described above.

[0016] A fourth aspect of the present invention provides a computer program product, including a computer program / instructions, characterized in that, when the computer program / instructions are executed by a processor, they implement the simulation design method for welding oil rail process schemes as described above.

[0017] Compared with the prior art, the technical solution provided by the present invention has at least the following beneficial effects: 1. By constructing a finite element model that includes the characteristics of potential process defects and simulating the high-pressure pulse condition of the engine to solve fatigue life, this invention transforms the traditional process verification process that relies on physical testing into a computer simulation process. It can quantitatively evaluate the durability performance of different material and process combinations in the early stage of product design, thereby avoiding the risk of later failure caused by selecting high-risk process solutions (such as carbon steel seamless tube solutions), and significantly improving the development efficiency and reliability of welded oil rail products.

[0018] 2. By analyzing the ratio of fatigue life index to estimated manufacturing cost and selecting the best option from those that meet the safety threshold, this invention achieves cost-optimal decision-making under the premise of ensuring structural safety. It overcomes the shortcomings of existing technologies that simply pursue high reliability leading to excessive costs or simply pursue low cost while ignoring long-term durability. It provides a scientific screening method that takes into account both reliability and economy for the engineering application of welded oil rails. Attached Figure Description

[0019] Figure 1 This is a logic block diagram of the simulation design method for the welding oil rail process scheme in Embodiment 1 of the present invention. Detailed Implementation

[0020] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Component models, material names, connection structures, circuit structures, control methods, algorithms, and other features not explicitly described in this technical solution are considered common technical features disclosed in the prior art.

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the embodiments of this invention will be described in detail below. This invention provides a simulation design method for welding oil rail process schemes. Its core concept lies in: constructing a refined finite element model that reflects the inherent defects of the combination of key process elements affecting the fatigue life of welded oil rails; and using this model to perform fatigue life simulation under simulated real service conditions, thereby achieving quantitative comparison and selection of the optimal process scheme from different options.

[0022] Example 1 This embodiment describes a simulation design method for a welding oil rail process scheme. See the logic diagram below. Figure 1Obtain at least one candidate process scheme. Each candidate process scheme explicitly includes the material type and machining method used to form the welded oil rail body. For example, the material type can be austenitic stainless steel, specifically grade 304 or 316L; or high-strength carbon steel, specifically grade E355. The machining method can be seamless tubing, i.e., prefabricated tubing formed by piercing bar stock and then cold rolling or cold drawing; or drilling solid bar stock, i.e., directly drilling the central oil passage hole from solid round steel bar stock using a deep hole drill (e.g., gun drill). These schemes are predefined and obtained as input data.

[0023] For each candidate process scheme obtained, a finite element model of the rail body was constructed. The geometric dimensions of the model were established based on the actual design drawings of the rail body. The mesh generation adopted a fine mesh suitable for crack propagation analysis, and local refinement was performed in the expected high stress gradient regions (such as the intersection of the inlet / outlet oil passage and the main oil passage, and the inner wall of the borehole). Material properties were assigned according to the selected material type. For example, if the material is 304 stainless steel, its Young's modulus (approximately 193 GPa), Poisson's ratio (approximately 0.3), yield strength, and SN curve parameters were input.

[0024] The key to this embodiment lies in creating features of potential defects associated with the machining methods included in the candidate process during the construction of the finite element model. This is not simply about building an idealized geometric model, but rather about introducing geometric or physical representations of the unique defects that may result from the machining process into the model, based on the technological characteristics of the machining method. This allows the model to reflect the impact of manufacturing quality on the final performance.

[0025] Specifically, if a candidate process involves machining seamless tubing, the characteristic of the potential defects associated with this machining method is the introduction of a set of probabilistic initial cracks representing inward folding defects on the inner wall surface of the finite element model. Inward folding is a high-risk inherent defect in seamless tubing, caused by improper piercing processes leading to metal tearing and folding into the inner wall. It is highly susceptible to becoming a fatigue crack initiation under high-pressure pulse conditions. During model construction, a set of initial cracks is randomly generated on nodes or elements of the inner wall surface, based on the statistical laws governing seamless tubing process defects. The size (e.g., length distributed with a specific probability in the range of 0.05mm to 0.2mm, and depth distributed with a specific probability in the range of 0.03mm to 0.15mm) and distribution (e.g., random locations along the length and circumference of the tubing) of these cracks are randomly generated based on statistical data obtained from numerous actual seamless tubing product inspections. The initial cracks in the model are defined as contact surfaces or sets of elements with initial separation, and the stress field around their tips becomes the starting point for subsequent crack propagation calculations. This probabilistic introduction of initial cracks can realistically simulate the fatigue life dispersion caused by the presence of randomly distributed internal folding defects in seamless tubular rails.

[0026] Conversely, if a candidate process involves drilling solid bar stock, the inner hole surface quality is controllable due to the material removal process, eliminating the internal fold defects common in seamless tubes. Therefore, the finite element model for this process will not create initial cracks representing internal folds on the inner hole wall. Instead, surface roughness data associated with the machining method is acquired, such as the arithmetic mean deviation of the profile measured by a surface roughness meter. This surface roughness is then mapped to the stress concentration factor on the inner wall of the finite element model. Specifically, based on the Petersen stress concentration factor empirical formula, the measured Ra value can be converted into an equivalent fatigue notch factor and applied to the stress calculation of the inner wall surface elements. This allows the simulation to accurately reflect the significant impact of surface integrity differences caused by different machining methods (such as drilling, which achieves low surface roughness, while the inner surface of a seamless tube is relatively rough) on fatigue life.

[0027] After the model is built, boundary conditions simulating the high-pressure pulse condition of the engine and alternating loads are applied to the finite element model. The boundary conditions include displacement constraints on the rail mounting supports to simulate their fixation on the engine. The alternating load is primarily a hydraulic load applied to the inner wall of the rail body. This hydraulic load is set to simulate the pressure pulse generated by the engine's fuel injection system, specifically a dynamic pressure curve that jumps from zero (or a low pre-pressure) to a peak pressure (e.g., 350 bar, corresponding to 35 MPa) and maintains periodic fluctuations. The waveform, frequency (e.g., the injection frequency corresponding to a specific engine speed), and amplitude of the pressure are set based on the engine's actual operating condition calibration data. Finally, the simulation is performed, and fatigue crack propagation analysis software (e.g., combining the extended finite element method or the Paris formula from fracture mechanics) is used to calculate the number of load cycles required for the crack to propagate from its initial size to penetrate the rail body wall thickness under the alternating load. This number of load cycles is then used as the fatigue life index of the rail body under this candidate process scheme.

[0028] Based on the fatigue life indices obtained from simulations of various candidate process schemes, the optimal process scheme is determined and output. There are several ways to determine the optimal scheme. For example, multiple candidate process schemes can be sorted from highest to lowest according to the obtained fatigue life indices, and a safety threshold based on engineering safety requirements can be pre-set. This safety threshold represents the minimum number of cycles the track body must reach within its target design life. Schemes with indices below this pre-set safety threshold are then eliminated to ensure that the final selected scheme meets basic safety and reliability requirements. Subsequently, from all remaining compliant schemes, the estimated manufacturing cost of each scheme is obtained, and the ratio of the fatigue life index to the estimated manufacturing cost, i.e., the cost-effectiveness, is calculated. Finally, the scheme with the largest ratio is selected as the optimal process scheme and output. This decision-making logic not only ensures product safety but also achieves the best balance between product reliability and economy.

[0029] Example 2 Based on Example 1, this embodiment specifies the material type and machining method of the candidate process scheme, and further refines the creation of defect features and the solution of fatigue life index.

[0030] In this embodiment, the material type of the candidate process is specifically selected as austenitic stainless steel (such as 304 or 316L) or high-strength carbon steel (such as E355), and the machining method is drilling of seamless tubing or solid bar stock.

[0031] When the candidate process scheme processed by the model building module involves machining seamless tubing, the specific operation for creating potential defect features is as follows: A set of probabilistic initial cracks representing inward folding defects are introduced onto the inner wall surface of the finite element model. The introduction of these initial cracks is the same as described in Example 1, and their size and distribution are randomly generated based entirely on the statistical laws of seamless tubing process defects. For example, according to the ultrasonic flaw detection data of a batch of cold-rolled seamless stainless steel tubing, the probability of an inward folding crack depth between 0.05 mm and 0.12 mm is 90%. Therefore, during model initialization, the crack depth can be set to follow a normal distribution within this range.

[0032] At this point, the solution process executed by the simulation solution module is specified as follows: Under the condition that these probabilistic initial cracks exist, when the boundary conditions simulating high-pressure pulse conditions of the engine and alternating loads are applied, the number of load cycles required for each initial crack to propagate from its initial size to penetrate the rail wall thickness is calculated. The rail wall thickness is a defined geometric design value (e.g., the main pipe wall thickness of a 350 bar oil rail is 5 mm). The simulation software simulates the stable propagation stage of the crack based on the stress intensity factor at the crack tip and the material fracture toughness, until the crack depth equals the wall thickness, at which point the rail fails due to leakage. The simulation solution module outputs the value of the initial crack that requires the fewest cycles to penetrate the wall thickness, as a conservative estimate of the fatigue life index for this scheme.

[0033] Example 3 Based on Example 2, this embodiment further considers the chemical compatibility between the material and the fuel medium, and supplements the method for creating defect features in the model.

[0034] When the candidate process schemes processed by the model building module involve high-strength carbon steel (such as E355) and seamless tubing, in addition to introducing an initial crack representing an internal fold defect as in Example 2, the corrosive effect of alcohol-containing gasoline on the inner wall of the carbon steel rail body also needs to be considered. Modern gasoline generally contains ethanol (such as E10 / E25) and trace amounts of water, which can cause electrochemical corrosion when in contact with carbon steel, leading to rust on the inner wall and uniform thinning of the wall thickness.

[0035] To simulate this effect, the model building module, when creating potential defect features, also adds an electrochemical corrosion rate attenuation layer to the inner surface region of the finite element model. This attenuation layer is not a real physical layer, but a material removal model defined by a secondary user subroutine (such as UMESHMOTION). Based on the electrochemical corrosion rate of carbon steel in a specific ethanol-gasoline mixture obtained from material corrosion tests (e.g., 3 micrometers / year), this subroutine automatically removes a layer of elements of a specified thickness from the inner surface of the model at each simulation increment step or a specific time step, thereby simulating the uniform thinning effect of the wall thickness.

[0036] In the crack propagation calculation performed by the simulation solver, this thinning effect is coupled in real time. As the simulation progresses, the rail body wall thickness continuously decreases due to corrosion, resulting in a reduction in the net cross-sectional area at the crack tip and an increase in stress level, thereby accelerating the crack propagation rate. By coupling corrosion thinning with crack propagation calculations, the simulation solver can obtain a more accurate comprehensive fatigue life index that considers both mechanical fatigue and electrochemical corrosion, closely resembling real service conditions.

[0037] Example 4 This embodiment provides a specific multi-objective decision-making method for determining the optimal process scheme.

[0038] In the solution output module, the specific process for determining the optimal process solution based on the fatigue life index corresponding to each candidate process solution is as follows: All candidate process schemes that have undergone simulation are ranked from highest to lowest according to their calculated fatigue life index values ​​(i.e., load cycle count). Then, a preset safety threshold is introduced, which is calculated by multiplying the total number of fuel pressure pulses the target vehicle may experience during its expected service life by a safety factor (e.g., 2.5). For example, if the target service life is 15 years, the expected total number of pulses is 1.5 × 10⁻⁶. 8 The safety threshold can be preset to 3.75 × 10⁻⁶. 8 The system automatically eliminates schemes with fatigue life values ​​below the preset safety threshold, as these schemes have an extremely high statistical risk of failure and are unacceptable. Next, from all remaining schemes that meet the safety requirements, the system retrieves the estimated manufacturing cost data associated with each scheme. This estimated manufacturing cost is a comprehensive value, including raw material costs, processing time costs, and the allocation of mold or tool wear under the corresponding materials and machining methods. The scheme output module calculates the cost-effectiveness of each remaining scheme, that is, by dividing its fatigue life value by its estimated manufacturing cost, and selects the scheme with the largest ratio as the final optimal process scheme. This method not only selects safe and reliable schemes but also ensures that the selected scheme is optimal in terms of engineering economics.

[0039] Example 5 This embodiment further refines the applied boundary conditions and loads to optimally simulate the engine's extreme service environment.

[0040] In the step of applying boundary conditions and alternating loads simulating high-pressure pulse conditions of the engine to the finite element model in the simulation solution module, not only is the hydraulic load as described in Example 1 applied, but the influence of the temperature field is further superimposed. Specifically, the applied hydraulic load is set as a pulse pressure that starts from the inner wall of the rail body, jumps from zero to 350 bar (35 MPa) in a very short time (e.g., 10 ms), and maintains a periodic fluctuation near this peak due to the opening and closing of the injector needle valve and the reflection of the pressure wave. At the same time, based on measured data of the engine compartment environment, a non-uniform temperature field is applied to the entire external surface of the finite element model. This temperature field simulates a gradient distribution where the temperature is highest at the end of the rail body near the engine cylinder head (e.g., reaching 130°C), while the temperature is relatively lower at the oil inlet end farther from the cylinder head (e.g., dropping to 90°C). By applying this temperature field, thermal stress can be introduced into the model, and the creep effect of the material at high temperatures can be considered. When solving the problem, the simulation solution module will use a calculation model of creep-fatigue interaction (for example, calculate the creep damage increment first in each cycle, and then add the fatigue damage increment), so as to obtain a more comprehensive fatigue life index that couples high temperature creep and high pressure fatigue, making the simulation prediction results accurate and reliable under extreme conditions.

[0041] Example 6 In this embodiment, the step of constructing a finite element model of the track body for candidate process schemes in the model building module includes a crucial surface quality digitization step, in addition to establishing the geometric model and meshing. The module first acquires the internal hole surface roughness data strongly correlated with the machining method in the current scheme. This data can come from actual process test measurement databases; for example, for drilling solid bar stock, the typical internal hole surface roughness Ra value is 1.6 μm; while for seamless tubing, whose inner surface has not undergone subsequent processing, the Ra value may be 6.3 μm. The model building module uses a mapping algorithm to convert this surface roughness value into stress concentration factors on the inner wall of the finite element model.

[0042] For example, based on the empirical relationship between the micro-geometry of the material surface and the fatigue notch coefficient, Ra = 1.6 μm can be mapped to a stress concentration factor Kt = 1.05, while Ra = 6.3 μm can be mapped to Kt = 1.25. In subsequent simulation solutions, when performing stress analysis, the nominal stress of the inner wall element calculated under each load step will be multiplied by this stress concentration factor, thus obtaining the local stress that truly reflects the micro-stress concentration. In this way, the simulation process can accurately reflect the differences in surface integrity caused by different machining methods, and their significant impact on fatigue crack initiation life and propagation rate is quantitatively incorporated into the final fatigue life index calculation.

[0043] Example 7 This embodiment describes a computer simulation design system for welding oil rail process schemes, the system comprising: The scheme acquisition module is used to acquire at least one candidate process scheme, which includes the material type and machining method used to form the welded oil rail body. This module provides a user interface or data interface that allows engineers to input, select, or load multiple predefined combinations of process schemes from a database.

[0044] The model building module is used to construct a finite element model of the rail body for the candidate process schemes, and to create features of potential defects associated with the machining method in the model. This module integrates finite element preprocessing functions and process defect modeling algorithms. For example, it contains embedded automated modeling subroutines, as detailed in Example 1, for introducing probabilistic initial cracks in seamless tubing and for mapping surface roughness as the stress concentration factor for "solid bar drilling".

[0045] The simulation solution module applies boundary conditions and alternating loads simulating the high-pressure pulse condition of the engine to the constructed finite element model and performs simulation solutions to obtain the fatigue life index of the rail body under the candidate process scheme. This module is the core of the numerical calculation of the system. It calls the finite element solver and fatigue analysis tools, automatically executes the simulation calculation process described in Example 1 according to the preset load spectrum and boundary conditions, and finally outputs the number of load cycles for each scheme.

[0046] The solution output module is used to determine and output the optimal process solution from the at least one candidate process solution based on the fatigue life index corresponding to each candidate process solution. This module implements a decision algorithm, such as the logic based on safety threshold and cost-effectiveness ranking described in Example 1. Its output can be a clear process solution code, a set of parameters, or an evaluation report containing comparative data, directly guiding engineers in making process decisions.

[0047] Example 8 This embodiment, as a modified implementation, provides a computer-readable storage medium implementation of the system described in Embodiment 1. A computer-readable storage medium may be a non-volatile storage medium, such as a hard disk drive, solid-state drive, read-only memory, flash memory, etc. The storage medium stores computer-executable instructions, which, when executed by one or more computer processors (e.g., a central processing unit installed on a high-performance workstation or cloud computing server), perform all the aforementioned steps. The processor loads instructions from the storage medium and executes all operations, including obtaining candidate solutions, constructing a defective finite element model, applying loads and boundary conditions for simulation solving, and outputting the optimal solution based on lifetime indicators.

[0048] Example 9 This embodiment, as another modified implementation, describes a computer program product implementation, namely, providing a computer program product including a computer program or instructions, which can be distributed in the form of a software installation package or code library. When the computer program or instructions are executed by a processor, they implement all the steps of the method described above. For example, this program product includes code for all functional modules such as a scheme acquisition module, a model building module, a simulation solution module, and a scheme output module, and can be integrated into a commercial finite element analysis software framework to form a dedicated welding rail process simulation and optimization plugin.

[0049] Application Example 1 To verify the technical effectiveness of the method of the present invention, the following comparative simulation tests were conducted.

[0050] Test conditions: The rail body of a certain model of 350bar welded hydraulic rail was selected as the analysis object. Its main pipe has a designed wall thickness of 5mm, and the material options are 304 austenitic stainless steel and E355 high-strength carbon steel. The machining options are seamless tubing and drilling of solid bar stock. Fatigue analysis was performed using the commercial finite element software ABAQUS combined with Fe-safe or Franc3D. The simulated operating conditions were alternating hydraulic pulses of 0-350bar at a frequency of 20Hz and an ambient temperature of 110℃. For the carbon steel option, a uniform corrosion rate of 3μm / year was set.

[0051] Data Presentation: The fatigue life index (number of cycles to wall penetration) of four typical process schemes was obtained through simulation and compared with the estimated unit manufacturing cost, as shown in the table below: As can be seen from the table above, Scheme B (machined stainless steel and bar stock) has the highest cost performance and has been determined as the optimal process scheme output in this application of the method of the present invention.

[0052] The underlying technical principle is that by selecting bar milling as the machining method, the unavoidable risk of internal folding defects in the seamless tube solution is fundamentally avoided, resulting in a significantly better fatigue life index than Solution A. This advantage is captured and quantified by the technical feature of introducing probabilistic initial cracks into the seamless tube and calculating its extended life.

[0053] Scheme C was eliminated because it had both internal folding defects and electrochemical corrosion coupled with thinning effect, resulting in a fatigue life far below the safety threshold. This proves that the method of the present invention can effectively identify high-risk schemes.

[0054] The fatigue life index of scheme B is 28.5 × 10⁻⁶. 7 (times) compared to the costly forging monolithic solution (comparative example, 30.0 × 10⁻⁶). 7The cost is almost equivalent to that of the previous method, but its estimated manufacturing cost index has decreased from 150 to 100. This proves that the welded, stainless steel bar machining solution selected using the method of this invention achieves the same safety level as the integral forging solution while successfully reducing costs significantly, thus perfectly solving the defects in the prior art.

[0055] The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. A simulation design method for welding oil rail process schemes, characterized in that, include: Obtain at least one candidate process scheme, the candidate process scheme including the material type and machining method for forming the welded oil rail body; For the candidate process scheme, a finite element model of the rail body is constructed, and features of potential defects associated with the machining method are created in the model; The boundary conditions and alternating loads simulating the high-pressure pulse condition of the engine are applied to the finite element model, and the simulation solution is performed to obtain the fatigue life index of the rail body under the candidate process scheme. Based on the fatigue life index corresponding to each candidate process scheme, the optimal process scheme is determined from the at least one candidate process scheme and output.

2. The simulation design method for welding oil rail process scheme according to claim 1, characterized in that, The material type of the candidate process is austenitic stainless steel or high-strength carbon steel, and the machining method is drilling of seamless tubing or solid bar stock. When the machining method is a seamless tube, the potential defect associated with the machining method is characterized by the introduction of a set of probabilistic initial cracks representing internal folding defects on the inner wall surface of the finite element model. The size and distribution of the initial cracks are randomly generated according to the statistical law of seamless tube process defects. The fatigue life index is the number of load cycles required for the crack to expand from the initial size to penetrate the wall thickness under the action of the alternating load.

3. The simulation design method for welding oil rail process scheme according to claim 2, characterized in that, The feature for creating potential defects associated with the machining method in the model also includes: When the material type is high-strength carbon steel and the machining method is seamless tubing, an additional electrochemical corrosion rate attenuation layer is set in the inner surface area of ​​the model to simulate the uniform thinning effect of alcohol-containing gasoline on the inner wall of the carbon steel rail body, and the thinning effect is coupled to the crack propagation calculation to solve for fatigue life index that is closer to the actual service conditions.

4. The simulation design method for welding oil rail process scheme according to claim 1, characterized in that, The determination of the optimal process scheme based on the fatigue life index corresponding to each candidate process scheme specifically includes: Multiple candidate process schemes are sorted from high to low according to the obtained fatigue life index, and schemes with index values ​​lower than the preset safety threshold are eliminated. From the remaining schemes, the scheme with the largest ratio of fatigue life index to estimated manufacturing cost is selected as the optimal process scheme.

5. The simulation design method for welding oil rail process scheme according to claim 1, characterized in that, The application of boundary conditions and alternating loads simulating high-pressure pulse conditions of an engine to the finite element model also includes: A hydraulic load is applied from zero to 350 bar on the inner wall of the rail and then periodically fluctuates. At the same time, a temperature field based on the engine compartment environment is applied to simulate the creep-fatigue interaction of the rail under high temperature and high pressure, thereby obtaining a more comprehensive fatigue life index.

6. The simulation design method for welding oil rail process scheme according to claim 1, characterized in that, The construction of the finite element model of the orbital body for the candidate process scheme further includes: The surface roughness data of the inner hole associated with the machining method is obtained, and the surface roughness is mapped to the stress concentration factor on the inner wall of the finite element model, so that the simulation can reflect the influence of the surface integrity difference caused by different machining methods on fatigue life.

7. A computer simulation design system for welding oil rail process schemes, characterized in that, include: The scheme acquisition module is used to acquire at least one candidate process scheme, the candidate process scheme including the material type and machining method for forming the welded oil rail body; The model building module is used to construct a finite element model of the track body for the candidate process scheme, and to create features of potential defects associated with the machining method in the model; The simulation solution module is used to apply boundary conditions and alternating loads simulating the high-pressure pulse condition of the engine to the finite element model and perform simulation solution to obtain the fatigue life index of the rail body under the candidate process scheme. The scheme output module is used to determine and output the optimal process scheme from the at least one candidate process scheme based on the fatigue life index corresponding to each candidate process scheme.

8. The computer simulation design system for welding oil rail process schemes according to claim 7, characterized in that, The model building module is specifically used for: When the machining method in the candidate process scheme is seamless tube, a set of probabilistic initial cracks representing internal folding defects are introduced on the inner wall surface of the finite element model. The size and distribution of the initial cracks are randomly generated according to the statistical law of seamless tube process defects. The simulation solution module is specifically used to calculate the number of load cycles required for the crack to expand from its initial size to penetrate the wall thickness under the alternating load, which serves as the fatigue life index.

9. A computer-readable storage medium, wherein the storage medium containing computer-executable instructions, when executed by a computer processor, is used to perform a simulation design method for a welding rail process scheme as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the simulation design method for welding rail process scheme as described in any one of claims 1 to 6.

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