A new energy automobile valve body production process evaluation method and system

Abstract

The application discloses a new energy automobile valve body production process evaluation method and system. A target valve body three-dimensional design model and raw material three-dimensional scanning data are acquired, a design model and raw material deviation mapping relationship is constructed, and a machining allowance distribution is determined; based on the machining allowance distribution and cutting equipment performance data, raw material machining cutting tracks are generated and pre-machining is implemented, low-power and high-power organizational defect information of the pre-machining valve body is acquired; production process reliability evaluation is carried out according to the defect information, and an evaluation result is obtained; when the reliability is lower than a target requirement, a raw material defect distribution schematic diagram is constructed, machining equipment performance deviation and raw material defect characteristics are analyzed, and valve body production process is optimized accordingly, and a production process optimization strategy is formed. The method can realize accurate evaluation and optimization of the valve body machining process, and improve production process reliability and product quality.

Description

Technical Field

[0001] This invention relates to the field of equipment processing technology, and in particular to a method and system for evaluating the production process of valve bodies for new energy vehicles. Background Technology

[0002] As a key component of the power system and fluid control system, the valve body of new energy vehicles directly affects the performance stability and service life of the entire vehicle. In existing technologies, valve body machining is mostly carried out using CNC machining based on standard design drawings. However, in actual production, raw materials have dimensional deviations, internal defects, and differences in physical properties, resulting in uneven distribution of machining allowances, which affects machining accuracy.

[0003] Meanwhile, existing process optimization methods mostly rely on experience or single parameter adjustments, failing to provide systematic optimization for performance deviations in different batches of raw materials and equipment. This is especially true in the machining of valve bodies for new energy vehicles with complex geometries, where factors such as cutting path planning, cutting allowance distribution, and equipment performance errors interact, making it difficult for traditional methods to achieve closed-loop control for reliability prediction and production process optimization. This results in the inability to promptly report and correct machining defects during production, further impacting product consistency and batch production efficiency.

[0004] Therefore, there is an urgent need for a method and system for evaluating the production process of valve bodies for new energy vehicles that can perform accurate deviation analysis based on three-dimensional scanning data of raw materials and valve body design models, and combine cutting performance and defect identification, so as to achieve systematic evaluation and control of machining allowance, cutting trajectory, defect identification and production process optimization, thereby improving the reliability, accuracy and processing efficiency of valve body production. Summary of the Invention

[0005] To address at least one of the aforementioned technical problems, this invention proposes a method and system for evaluating the production process of valve bodies for new energy vehicles.

[0006] The first aspect of this invention provides a method for evaluating the manufacturing process of valve bodies for new energy vehicles, comprising:

[0007] A 3D design model of the target valve body of a new energy vehicle and 3D scanning data of the current batch of raw materials are obtained to construct a deviation mapping relationship between the raw materials and the design model, and the processing allowance distribution of the raw materials is determined based on the deviation mapping relationship.

[0008] The cutting thickness of the target valve body raw material at each position is determined based on the machining allowance distribution, the cutting performance data of the cutting equipment is obtained, and the machining cutting trajectory of the target valve body raw material is determined based on the cutting thickness and cutting performance data at each position.

[0009] The target valve body raw material is pre-processed according to the machining cutting trajectory to obtain low-magnification and high-magnification structural defects of the pre-processed target valve body, thereby obtaining defect information. The reliability of the target valve body production process is then evaluated based on the defect information to obtain the reliability evaluation result.

[0010] Based on the reliability assessment results, if the reliability is lower than the target valve body production quality requirements, the defect information is used to construct a defect distribution diagram of the current batch of raw materials, and the performance deviation of the processing equipment and the defect characteristics of the raw materials are determined based on the defect distribution diagram.

[0011] Based on the performance deviations and the defect characteristics of the raw materials, the production process of the target valve body is optimized to construct a production process optimization strategy.

[0012] In this solution, the process of acquiring the 3D design model of the target valve body for new energy vehicles and the 3D scanning data of the current batch of raw materials to construct a deviation mapping relationship between the raw materials and the design model, and determining the processing allowance distribution of the raw materials based on the deviation mapping relationship, specifically involves:

[0013] Obtain design drawing data of the target valve body of the new energy vehicle, construct a three-dimensional design model of the target valve body based on the design drawing data, obtain three-dimensional point cloud scanning data of the surface of the current batch of raw materials, and construct a three-dimensional model of the raw materials based on the three-dimensional point cloud scanning data;

[0014] Feature points of the 3D design model and the 3D model of the raw material are extracted. Based on the ICP point cloud registration algorithm, spatial registration operation is performed on the 3D design model and the 3D model of the raw material according to the feature points. The surface of the 3D design model is divided into grids according to the preset grid size, and the grid intersections are used as the edge points of the 3D design model.

[0015] After spatial registration, the edge points of the three-dimensional design model are horizontally extended and mapped to the intersection points of the raw material three-dimensional model. The deviation distance between each edge point position and the intersection point position is obtained. Based on the deviation distance, a three-dimensional deviation field between the surface of the raw material three-dimensional model and the three-dimensional design model is established.

[0016] The three-dimensional deviation field is processed to be continuous using the Kriging space interpolation method to construct a deviation mapping surface. The interference region of the raw material three-dimensional model relative to the three-dimensional design model is identified based on the deviation mapping surface.

[0017] Based on the interference region, a regional cutting repetition rate analysis is performed to construct a distinct cutting repetition heatmap. Based on the cutting repetition heatmap, spatial registration optimization is performed to obtain an optimized spatial registration three-dimensional model.

[0018] The three-dimensional deviation field is updated based on the optimized spatial registration three-dimensional model, and the processing allowance distribution of the target valve body raw material is determined based on the updated three-dimensional deviation field.

[0019] In this scheme, the step of performing regional cutting repetition rate analysis based on the interference region, constructing a distinct cutting repetition heatmap, and performing spatial registration optimization based on the cutting repetition heatmap to obtain an optimized spatial registration three-dimensional model is as follows:

[0020] Based on the octree spatial segmentation algorithm, the raw material is divided into several subspace units, the single cutting thickness information of the target valve body processing equipment is obtained, the number of cuttings of each subspace unit in the interference region is determined according to the single cutting thickness information, and a cutting repetition frequency heatmap is constructed based on the number of cuttings.

[0021] Thermodynamic coupling calculations are performed on the thermal map of the cutting repetition frequency based on the heat conduction equation. The cutting repetition frequency and cumulative cutting length are used as the equivalent heat source intensity, and the thermal conductivity of the raw material is used as the heat conduction parameter. The heat accumulation of the remaining subspace unit within the preset range of the cutting edge after cutting is estimated during the processing, and the heat accumulation temperature field distribution data is obtained.

[0022] Based on the heat accumulation temperature field distribution data, the area with heat accumulation greater than the preset value is marked as the processing heat-affected area, the interference area adjacent to the processing heat-affected area is marked as the cutting frequency optimization area, and the remaining interference areas are marked as the safe cutting area.

[0023] Calculate the difference between the optimization region of the cutting frequency to be optimized, the safe cutting region and the preset value respectively, and calculate the reduction frequency of cutting in the optimization region of the cutting frequency to be optimized and the increase frequency of cutting in the safe cutting region based on the difference and the heat conduction equation;

[0024] If the region to be optimized for cutting frequency and the safe cutting region are in a symmetrical relationship, a region cutting frequency transfer matrix is ​​established. The objective function is to make the total difference between the region to be optimized for cutting frequency and the safe cutting region and the preset value as close to zero. The objective optimization algorithm is used to iteratively optimize the cutting frequency transfer matrix to determine the optimal reduction frequency of the region to be optimized for cutting frequency and the optimal increase frequency of the safe cutting region.

[0025] Based on the optimal reduction or increase frequency and the single cutting thickness information of the target valve body processing equipment, the three-dimensional design model of the target valve body after spatial registration is shifted from the area to be cut frequency optimization to the safe cutting area to obtain the optimized spatial registration three-dimensional model.

[0026] In this solution, the step of determining the thickness to be cut at each position of the target valve body raw material based on the machining allowance distribution, obtaining the cutting performance data of the cutting equipment, and determining the machining cutting trajectory of the target valve body raw material based on the thickness to be cut at each position and the cutting performance data, specifically involves:

[0027] The normal depth of each grid node on the surface of the raw material is extracted based on the machining allowance distribution, and the thickness to be cut is determined according to the normal depth, and a thickness-position mapping matrix is ​​established.

[0028] Obtain the maximum spindle speed, tool feed rate, and radial depth of cut parameters of the cutting equipment, and construct a set of cutting performance parameters;

[0029] Cutting layer planning is performed based on the thickness to be cut and the set of cutting performance parameters of each node in the thickness-position mapping matrix. Based on the spatial spiral path planning algorithm, the cutting path with the largest thickness to be cut in the thickness-position mapping matrix is ​​used as the starting point, and a spiral descent machining cutting path is generated according to the cutting layer results.

[0030] In this solution, the pre-processing operation of the target valve body raw material according to the machining cutting trajectory is performed to obtain low-magnification and high-magnification structural defects of the pre-processed target valve body, thereby obtaining defect information. Based on this defect information, a reliability assessment of the target valve body manufacturing process is performed to obtain the reliability assessment result. Specifically:

[0031] Acquire low-magnification and high-magnification image data of structural defects in the target valve body, and label the image data with defect names to obtain labeled image data;

[0032] A defect recognition model is constructed based on a convolutional neural network. The labeled image data is imported into the defect recognition model for model training. The model includes an input layer, a feature extraction module, a spatial attention module, and a classification output layer. The feature extraction module is composed of multiple three-dimensional convolutional layers and three-dimensional max pooling layers stacked alternately. The spatial attention module obtains channel feature weights through three-dimensional global average pooling and performs weighted fusion with the labeled image data.

[0033] According to the processing cutting trajectory, a pre-processing operation is performed on the target valve body raw materials of the current production batch in a preset quantity. High-definition camera images and microscopic image data of the surface of the pre-processed target valve body are obtained. The high-definition camera images and microscopic image data are imported into the trained defect recognition model and the defect information is output.

[0034] The reliability of the target valve body manufacturing process is evaluated based on the defect information, and the reliability evaluation results are obtained.

[0035] In this solution, if the reliability is lower than the target valve body production quality requirements based on the reliability assessment results, a defect distribution diagram of the current batch of raw materials is constructed using the defect information. The performance deviation of the processing equipment and the defect characteristics of the raw materials are then determined based on the defect distribution diagram. Specifically:

[0036] Obtain production quality requirement data for the target valve body. Based on the reliability assessment results, if the reliability is lower than the production quality requirement data, construct a three-dimensional defect density field for the defect information using a kernel density estimation algorithm, and identify defect clustering areas through density gradient analysis.

[0037] Extract the geometric feature vector of the defect cluster region, including the principal axis direction of the defect distribution and spatial distribution characteristics, obtain the actual motion trajectory data of the target valve body pre-machining, and determine the instantaneous deviation data of the cutting trajectory based on the machining cutting path to construct the cutting path deviation field;

[0038] The spatial distribution characteristics of the defect cluster area are matched with the instantaneous deviation data. If the direction of the defect principal axis is consistent with the direction of the maximum gradient of the deviation field and the spatial distribution similarity exceeds the set threshold, it is determined to be a defect caused by the performance deviation of the processing equipment. The remaining defect areas are then labeled as raw material defect characteristics.

[0039] In this solution, the optimization of the production process of the target valve body based on the performance deviation and the defect characteristics of the raw materials, and the construction of a production process optimization strategy, specifically includes:

[0040] When the pre-processed target valve body has a defect caused by the performance deviation of the processing equipment, the processing equipment parameters are calibrated to obtain the maximum gradient direction and deviation amplitude of the cutting path deviation field. The tool feed direction of the machining cutting trajectory is adjusted according to the maximum gradient direction so that the tool feed direction is at a preset angle with the maximum gradient direction. The tool feed rate and spindle speed of the cutting equipment are adjusted according to the deviation amplitude to construct a set of equipment performance compensation cutting parameters, which constitutes the first production process optimization strategy.

[0041] When there are raw material defects in the pre-processed target valve body, the probability of the raw material defects is calculated based on the characteristics of the raw material defects, and the quality inspection frequency of the target valve body is determined based on the probability of occurrence, thus forming a second production process optimization strategy.

[0042] The actual production and processing operations of subsequent raw materials are carried out according to the first production process optimization strategy and the second production process optimization strategy.

[0043] A second aspect of the present invention also provides a new energy vehicle valve body manufacturing process evaluation system. The system includes a memory and a processor. The memory includes a new energy vehicle valve body manufacturing process evaluation method program. When the processor executes the new energy vehicle valve body manufacturing process evaluation method program, it performs the following steps:

[0044] A 3D design model of the target valve body of a new energy vehicle and 3D scanning data of the current batch of raw materials are obtained to construct a deviation mapping relationship between the raw materials and the design model, and the processing allowance distribution of the raw materials is determined based on the deviation mapping relationship.

[0045] The cutting thickness of the target valve body raw material at each position is determined based on the machining allowance distribution, the cutting performance data of the cutting equipment is obtained, and the machining cutting trajectory of the target valve body raw material is determined based on the cutting thickness and cutting performance data at each position.

[0046] The target valve body raw material is pre-processed according to the machining cutting trajectory to obtain low-magnification and high-magnification structural defects of the pre-processed target valve body, thereby obtaining defect information. The reliability of the target valve body production process is then evaluated based on the defect information to obtain the reliability evaluation result.

[0047] Based on the reliability assessment results, if the reliability is lower than the target valve body production quality requirements, the defect information is used to construct a defect distribution diagram of the current batch of raw materials, and the performance deviation of the processing equipment and the defect characteristics of the raw materials are determined based on the defect distribution diagram.

[0048] Based on the performance deviations and the defect characteristics of the raw materials, the production process of the target valve body is optimized to construct a production process optimization strategy.

[0049] This invention discloses a method and system for evaluating the manufacturing process of valve bodies for new energy vehicles. The method involves acquiring a 3D design model of the target valve body and 3D scanning data of the raw materials, constructing a deviation mapping relationship between the design model and the raw materials, and determining the machining allowance distribution. Based on the machining allowance distribution and cutting equipment performance data, a cutting trajectory for raw material processing is generated and pre-processing is performed to obtain low-magnification and high-magnification structural defect information of the pre-processed valve body. A reliability assessment of the manufacturing process is conducted based on the defect information, and the assessment results are obtained. When the reliability is lower than the target requirements, a schematic diagram of the raw material defect distribution is constructed, the performance deviation of the processing equipment and the characteristics of the raw material defects are analyzed, and the valve body manufacturing process is optimized accordingly to form a manufacturing process optimization strategy. This method can achieve accurate evaluation and optimization of the valve body processing process, improving the reliability of the manufacturing process and product quality. Attached Figure Description

[0050] Figure 1 A flowchart of a new energy vehicle valve body manufacturing process evaluation method according to the present invention is shown;

[0051] Figure 2 A flowchart illustrating the reliability assessment results obtained by this invention is shown;

[0052] Figure 3 A flowchart illustrating the present invention for determining the performance deviation of processing equipment and the defect characteristics of raw materials is shown.

[0053] Figure 4 A block diagram of a new energy vehicle valve body production process evaluation system according to the present invention is shown. Detailed Implementation

[0054] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0055] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0056] Figure 1 A flowchart of an evaluation method for the production process of valve bodies in new energy vehicles according to the present invention is shown.

[0057] like Figure 1 As shown, the first aspect of the present invention provides a method for evaluating the manufacturing process of valve bodies for new energy vehicles, comprising:

[0058] S102, acquire the three-dimensional design model of the target valve body of the new energy vehicle and the three-dimensional scanning data of the current batch of raw materials to construct the deviation mapping relationship between the raw materials and the design model, and determine the processing allowance distribution of the raw materials according to the deviation mapping relationship;

[0059] S104, determine the thickness to be cut at each position of the target valve body raw material according to the machining allowance distribution, obtain the cutting performance data of the cutting equipment, and determine the machining cutting trajectory of the target valve body raw material according to the thickness to be cut at each position and the cutting performance data;

[0060] S106, perform pre-processing operation on the target valve body raw material according to the processing cutting trajectory, obtain low-magnification and high-magnification structural defects of the pre-processed target valve body, obtain defect information, perform reliability assessment on the target valve body production process according to the defect information, and obtain reliability assessment results;

[0061] S108, Based on the reliability assessment results, if the reliability is lower than the target valve body production quality requirements, construct a defect distribution diagram of the current batch of raw materials using the defect information, and determine the performance deviation of the processing equipment and the defect characteristics of the raw materials based on the defect distribution diagram.

[0062] S110, optimize the production process of the target valve body based on the performance deviation and the defect characteristics of the raw materials, and construct a production process optimization strategy.

[0063] It should be noted that by acquiring the 3D design model of the valve body and the 3D scanning data of the raw materials and constructing a deviation mapping, the geometric differences between the raw materials and the design target can be accurately identified, achieving a refined distribution of machining allowance. Based on the machining allowance, the thickness to be cut at each location is determined, and the cutting trajectory is planned in conjunction with cutting performance data, which can optimize the cutting path, reduce machining stress and tool wear, and improve machining accuracy. Through pre-machining and acquiring low-magnification and high-magnification structural defect information, the defect types and distribution are quantitatively analyzed, enabling a reliability assessment of the production process. Based on the reliability assessment results, a defect distribution diagram is constructed, and equipment performance deviations and raw material defects are identified, accurately distinguishing the sources of defects. Combining equipment deviations and raw material characteristics, the production process is optimized, and targeted adjustment strategies are formulated to improve valve body machining consistency, reduce the defect rate, and enhance overall production reliability and efficiency. The target valve body includes speed control valves, brake regulating valves, etc.

[0064] According to an embodiment of the present invention, the step of acquiring the three-dimensional design model of the target valve body of the new energy vehicle and the three-dimensional scanning data of the current batch of raw materials to construct a deviation mapping relationship between the raw materials and the design model, and determining the processing allowance distribution of the raw materials based on the deviation mapping relationship, specifically involves:

[0065] Obtain design drawing data of the target valve body of the new energy vehicle, construct a three-dimensional design model of the target valve body based on the design drawing data, obtain three-dimensional point cloud scanning data of the surface of the current batch of raw materials, and construct a three-dimensional model of the raw materials based on the three-dimensional point cloud scanning data;

[0066] Feature points of the 3D design model and the 3D model of the raw material are extracted. Based on the ICP point cloud registration algorithm, spatial registration operation is performed on the 3D design model and the 3D model of the raw material according to the feature points. The surface of the 3D design model is divided into grids according to the preset grid size, and the grid intersections are used as the edge points of the 3D design model.

[0067] After spatial registration, the edge points of the three-dimensional design model are horizontally extended and mapped to the intersection points of the raw material three-dimensional model. The deviation distance between each edge point position and the intersection point position is obtained. Based on the deviation distance, a three-dimensional deviation field between the surface of the raw material three-dimensional model and the three-dimensional design model is established.

[0068] The three-dimensional deviation field is processed to be continuous using the Kriging space interpolation method to construct a deviation mapping surface. The interference region of the raw material three-dimensional model relative to the three-dimensional design model is identified based on the deviation mapping surface.

[0069] Based on the interference region, a regional cutting repetition rate analysis is performed to construct a distinct cutting repetition heatmap. Based on the cutting repetition heatmap, spatial registration optimization is performed to obtain an optimized spatial registration three-dimensional model.

[0070] The three-dimensional deviation field is updated based on the optimized spatial registration three-dimensional model, and the processing allowance distribution of the target valve body raw material is determined based on the updated three-dimensional deviation field.

[0071] It should be noted that the process involves acquiring surface data of the raw material through 3D point cloud scanning and constructing a 3D model of the raw material. This is then combined with a 3D design model generated from the design drawings. An ICP point cloud registration algorithm is used to achieve spatial alignment between the two. After registration, edge points of the design model are extracted through meshing, and the deviation distance between these points and the corresponding positions on the raw material model is calculated. This constructs a 3D deviation field, visually reflecting the geometric differences between the raw material and the design model. Furthermore, Kriging space interpolation is used to make the discrete deviation data continuous, forming a deviation mapping surface. This surface clearly identifies the interference area of ​​the raw material relative to the design model, i.e., the excess material that needs to be removed. This ensures that the material removal process meets the design dimensional requirements. The feature points of the 3D design model and the 3D raw material model include curvature change points, edge contour points, and surface concavity / convexity extreme points.

[0072] According to an embodiment of the present invention, the step of performing regional cutting repetition rate analysis based on the interference region, constructing a distinct cutting repetition heatmap, and performing spatial registration optimization based on the cutting repetition heatmap to obtain an optimized spatial registration three-dimensional model specifically includes:

[0073] Based on the octree spatial segmentation algorithm, the raw material is divided into several subspace units, the single cutting thickness information of the target valve body processing equipment is obtained, the number of cuttings of each subspace unit in the interference region is determined according to the single cutting thickness information, and a cutting repetition frequency heatmap is constructed based on the number of cuttings.

[0074] Thermodynamic coupling calculations are performed on the thermal map of the cutting repetition frequency based on the heat conduction equation. The cutting repetition frequency and cumulative cutting length are used as the equivalent heat source intensity, and the thermal conductivity of the raw material is used as the heat conduction parameter. The heat accumulation of the remaining subspace unit within the preset range of the cutting edge after cutting is estimated during the processing, and the heat accumulation temperature field distribution data is obtained.

[0075] Based on the heat accumulation temperature field distribution data, the area with heat accumulation greater than the preset value is marked as the processing heat-affected area, the interference area adjacent to the processing heat-affected area is marked as the cutting frequency optimization area, and the remaining interference areas are marked as the safe cutting area.

[0076] Calculate the difference between the optimization region of the cutting frequency to be optimized, the safe cutting region and the preset value respectively, and calculate the reduction frequency of cutting in the optimization region of the cutting frequency to be optimized and the increase frequency of cutting in the safe cutting region based on the difference and the heat conduction equation;

[0077] If the region to be optimized for cutting frequency and the safe cutting region are in a symmetrical relationship, a region cutting frequency transfer matrix is ​​established. The objective function is to minimize the absolute value of the total difference between the region to be optimized for cutting frequency and the safe cutting region and the preset value. The objective optimization algorithm is used to iteratively optimize the cutting frequency transfer matrix to determine the optimal reduction frequency of the region to be optimized for cutting frequency and the optimal increase frequency of the safe cutting region.

[0078] It should be noted that only when the region for optimizing the cutting frequency and the safe cutting region are in an opposing relationship can the reasonable redistribution of cutting frequency be achieved through the offset of the 3D design model, thereby optimizing the machining heat effect problem. This is because opposing regions are spatially complementary. When the 3D design model shifts towards the safe cutting region, the cutting amount in the region for optimizing the cutting frequency will decrease accordingly, while the cutting amount in the safe cutting region will increase. This achieves a balanced distribution of cutting heat without changing the overall material removal amount. For example, in the case of cutting a cube into a valve body, if the target valve body is placed at the bottom of the box, the upper region requires a large amount of cutting while the lower region requires very little cutting. In this case, the upper and lower parts are typical opposing relationships. Balance can be achieved by reducing the cutting frequency in the upper region and moderately increasing the cutting frequency in the lower region. The objective function is to minimize the absolute value of the total difference between the region for optimizing the cutting frequency and the safe cutting region and the preset value, which minimizes the area of ​​the region for optimizing the cutting frequency. The optimal reduction frequency is equal to the optimal increase frequency, which is equivalent to translating the target valve body from the region for optimizing the cutting frequency to the safe cutting region. The remaining subspace units constitute a target valve body. After the cutting is completed, the edge of the target valve body is within the preset range of the cutting edge. This is the place where heat conduction is most direct and contacted. These places are also the places with the greatest and most direct heat impact, and the places where the temperature is most likely to be too high, causing a decrease in valve body hardness or performance.

[0079] Based on the optimal reduction or increase frequency and the single cutting thickness information of the target valve body processing equipment, the three-dimensional design model of the target valve body after spatial registration is shifted from the area to be cut frequency optimization to the safe cutting area to obtain the optimized spatial registration three-dimensional model.

[0080] It should be noted that insufficient registration uniformity often leads to uneven distribution of cutting frequency in different locations, resulting in machining defects. When the cutting frequency is too high in certain areas, the workpiece will experience temperature accumulation during processing. The cutting edges may produce a quenching-like effect due to repeated exposure to high temperatures, ultimately causing a decline in workpiece performance. For example, in the process of cutting a cube of raw material into a target valve body, the registration process can be compared to placing the target valve body in a cube-shaped box. If the target valve body is placed at the bottom of the box, the upper part of the box will have excessive redundant space, requiring repeated cutting of a large amount of material, while the lower part will hardly need cutting. This significantly increases the number of contacts between the upper area and the cutting tool, causing the temperature in that area to continuously accumulate, eventually forming an overheated zone and damaging the material properties.

[0081] According to an embodiment of the present invention, the step of determining the thickness to be cut at each position of the target valve body raw material based on the machining allowance distribution, obtaining the cutting performance data of the cutting equipment, and determining the machining cutting trajectory of the target valve body raw material based on the thickness to be cut at each position and the cutting performance data, specifically includes:

[0082] The normal depth of each grid node on the surface of the raw material is extracted based on the machining allowance distribution, and the thickness to be cut is determined according to the normal depth, and a thickness-position mapping matrix is ​​established.

[0083] Obtain the maximum spindle speed, tool feed rate, and radial depth of cut parameters of the cutting equipment, and construct a set of cutting performance parameters;

[0084] Cutting layer planning is performed based on the thickness to be cut and the set of cutting performance parameters of each node in the thickness-position mapping matrix. Based on the spatial spiral path planning algorithm, the cutting path with the largest thickness to be cut in the thickness-position mapping matrix is ​​used as the starting point, and a spiral descent machining cutting path is generated according to the cutting layer results.

[0085] It should be noted that, through intelligent matching of the thickness-position mapping matrix and cutting performance parameters, the tool load is ensured to remain dynamically balanced during machining, avoiding tool vibration or chipping caused by sudden changes in local cutting thickness. Secondly, the progressive cutting trajectory generated based on the spatial helical path planning algorithm makes the material removal process exhibit gradient transition characteristics, which can effectively reduce the impact of sudden changes in cutting resistance on the machine tool transmission system, and reduce idle travel time through continuous helical tool path, significantly improving machining efficiency. Using the node with the maximum thickness to be cut as the starting point of the path, the tool enters the material under the most favorable initial working conditions, and the high-material-weight area is processed first during the sharp stage of the tool, which not only extends the tool life, but also achieves a smooth decay of cutting force through the natural transition of the subsequent helical descent path.

[0086] Figure 2 A flowchart illustrating the reliability assessment results obtained by the present invention is shown.

[0087] According to an embodiment of the present invention, the pre-processing operation of the target valve body raw material according to the machining cutting trajectory, obtaining low-magnification and high-magnification structural defects of the pre-processed target valve body, obtaining defect information, and performing a reliability assessment of the target valve body manufacturing process based on the defect information to obtain a reliability assessment result, specifically includes:

[0088] S202, acquire low-magnification and high-magnification image data of structural defects in the target valve body, and label the image data with defect names to obtain labeled image data;

[0089] S204, Construct a defect recognition model based on a convolutional neural network, and import the labeled image data into the defect recognition model for model training. The model includes an input layer, a feature extraction module, a spatial attention module, and a classification output layer. The feature extraction module is composed of multiple three-dimensional convolutional layers and three-dimensional max pooling layers stacked alternately. The spatial attention module obtains channel feature weights through three-dimensional global average pooling and performs weighted fusion with the labeled image data.

[0090] S206, according to the processing cutting trajectory, perform pre-processing operation on the target valve body raw materials of the current production batch in a preset quantity, obtain high-definition camera images and microscopic image data of the surface of the pre-processed target valve body, import the high-definition camera images and microscopic image data into the trained defect recognition model, and output defect information;

[0091] S208, Based on the defect information, a reliability assessment of the target valve body manufacturing process is performed to obtain the reliability assessment result.

[0092] It should be noted that the deep learning-based defect recognition model, through dual optimization of a 3D convolutional structure and a spatial attention mechanism, can accurately capture the microscopic features of various processing defects from multi-scale images of low-magnification and high-magnification structures, including subtle anomalies such as material cracks and porosity inclusions that are difficult to identify using traditional methods. By inputting multimodal image data of pre-processed samples into the fully trained model, the system can not only automatically classify defect types but also establish a correlation mapping between defect distribution and process parameters, avoiding the subjectivity and missed detection risks of manual inspection. Furthermore, statistical analysis of batches of pre-processed samples is also utilized. The low-magnification structural defects include processing dimensional deviations, porosity, shrinkage porosity, inclusions, cracks, and scratches; the high-magnification structural defects include grain boundary anomalies, precipitate segregation, and dislocation aggregation.

[0093] Figure 3 A flowchart illustrating the present invention for determining the performance deviation of processing equipment and the defect characteristics of raw materials is shown.

[0094] According to an embodiment of the present invention, if the reliability is lower than the target valve body production quality requirement based on the reliability assessment result, a defect distribution diagram of the current batch of raw materials is constructed using the defect information. The performance deviation of the processing equipment and the defect characteristics of the raw materials are then determined based on the defect distribution diagram. Specifically:

[0095] S302, acquire the production quality requirement data of the target valve body, and if the reliability is lower than the production quality requirement data according to the reliability assessment result, construct a three-dimensional defect density field for the defect information based on the kernel density estimation algorithm, and identify the defect clustering area through density gradient analysis.

[0096] S304, extract the geometric feature vector of the defect cluster area, including the main axis direction of the defect distribution and spatial distribution characteristics, obtain the actual motion trajectory data of the target valve body pre-processing, and determine the instantaneous deviation data of the cutting trajectory according to the machining cutting path to construct the cutting path deviation field;

[0097] S306, the spatial distribution characteristics of the defect cluster area are matched with the instantaneous deviation data. If the direction of the defect principal axis is consistent with the direction of the maximum gradient of the deviation field and the spatial distribution similarity exceeds the set threshold, it is determined to be a defect caused by the performance deviation of the processing equipment. The remaining defect areas are then labeled as raw material defect characteristics.

[0098] It should be noted that the three-dimensional defect density field constructed based on kernel density estimation can intuitively present the aggregation pattern of defects in the valve body space. Combined with density gradient analysis, it can accurately capture the dense areas and diffusion characteristics of defect distribution. By extracting the principal axis direction and spatial distribution pattern of defect aggregation areas and performing multi-dimensional matching with the actual cutting trajectory deviation data of the machining equipment, the system can intelligently distinguish between process defects and material defects. When the defect distribution and tool path deviation show a significant correlation, it can be identified as a systematic machining defect caused by equipment performance deviation, such as striped defects caused by tool wear or periodic defects caused by feed system deviation. For discrete defects without significant trajectory correlation, it is attributed to the microstructure inhomogeneity or inherent defect characteristics of the raw material itself. This defect tracing method based on spatial feature matching not only avoids the risk of misjudgment of traditional single detection methods, but also achieves closed-loop quality control from defect detection to root cause treatment by quantitatively analyzing the mapping relationship between defect distribution and machining parameters.

[0099] According to an embodiment of the present invention, the optimization of the production process of the target valve body based on the performance deviation and the defect characteristics of the raw materials, and the construction of a production process optimization strategy, specifically includes:

[0100] When the pre-processed target valve body has a defect caused by the performance deviation of the processing equipment, the processing equipment parameters are calibrated to obtain the maximum gradient direction and deviation amplitude of the cutting path deviation field. The tool feed direction of the machining cutting trajectory is adjusted according to the maximum gradient direction so that the tool feed direction is at a preset angle with the maximum gradient direction. The tool feed rate and spindle speed of the cutting equipment are adjusted according to the deviation amplitude to construct a set of equipment performance compensation cutting parameters, which constitutes the first production process optimization strategy.

[0101] When there are raw material defects in the pre-processed target valve body, the probability of the raw material defects is calculated based on the characteristics of the raw material defects, and the quality inspection frequency of the target valve body is determined based on the probability of occurrence, thus forming a second production process optimization strategy.

[0102] The actual production and processing operations of subsequent raw materials are carried out according to the first production process optimization strategy and the second production process optimization strategy.

[0103] It should be noted that, for defects caused by equipment performance deviations, the system intelligently adjusts the tool feed direction and cutting parameters by analyzing the gradient characteristics of the cutting path deviation field, forming an equipment performance compensation cutting scheme. This effectively suppresses systematic machining defects caused by factors such as machine tool vibration and tool wear. For inherent defects in raw materials, a defect prediction model is established through probability statistics, and the quality inspection frequency is dynamically adjusted to achieve precise control, avoiding resource waste caused by excessive inspection. This improves the dimensional accuracy and material performance consistency of valve body manufacturing, while reducing scrap rate and production costs.

[0104] According to an embodiment of the present invention, it further includes:

[0105] Obtain the cutting thickness and the set of performance parameters of the cutting equipment at each node in the cutting trajectory of the target valve body. Construct a cutting heat generation model based on the heat conduction equation and the tool friction coefficient. Calculate the instantaneous heat generation rate at each node on the cutting trajectory according to the cutting thickness and the set of performance parameters.

[0106] Based on the instantaneous heat generation rate combined with the material's specific heat capacity and thermal conductivity, the temperature field distribution data of the material surface during the cutting process is calculated through finite element thermo-mechanical coupling simulation, and the area where the temperature exceeds the preset threshold is marked as the potential heat-affected zone;

[0107] Based on the temperature field distribution data, the spatial coordinates and temperature gradient direction of the potential heat-affected zone are extracted. The machining cutting trajectory is then corrected based on the spatial coordinates and gradient direction to reduce the tool dwell time and cutting contact pressure in the potential heat-affected zone and generate a thermally optimized cutting path.

[0108] The target valve body is machined according to the thermally optimized cutting path. During the machining process, the tool temperature and infrared thermal imaging data of the material surface are collected in real time. The infrared thermal imaging data are compared with the simulated temperature field distribution data. If the deviation exceeds the allowable range, the tool feed rate and spindle speed are adjusted according to the deviation amplitude.

[0109] According to an embodiment of the present invention, the step of extracting the spatial coordinates and temperature gradient direction of the potential heat-affected zone based on the temperature field distribution data, and dynamically correcting the machining cutting path based on the spatial coordinates and gradient direction, specifically includes:

[0110] A three-dimensional heat accumulation risk map is established based on the temperature field distribution data. The direction of the maximum temperature gradient in the map is extracted as the main path of heat diffusion. The spatial coordinates of the main path and the machining cutting trajectory are overlapped and analyzed to identify the trajectory segments in the cutting trajectory that coincide with or intersect with the main path and mark them as high heat coupling areas.

[0111] Based on the geometric features and temperature peak of the high heat coupling zone, the original cutting trajectory is reconstructed using a path segmentation optimization algorithm. The spiral descent path of the high heat coupling zone is divided into multiple sub-paths, and a cooling transition section is inserted between adjacent sub-paths. The tool feed direction of the cooling transition section is orthogonal to the main heat diffusion path.

[0112] A new tool motion instruction set is generated based on the reconstructed sub-path and cooling transition section. The instruction set includes the tool's idle speed and cooling time parameters in the transition section. At the same time, the cutting parameters in the high heat coupling zone are adjusted according to the temperature peak, so that the cutting thickness decreases in the reverse proportion according to the temperature field gradient distribution.

[0113] During the cutting process, the temperature field suppression effect is verified by real-time infrared thermal imaging data. If the actual temperature of the high heat coupling zone still exceeds the correction threshold, the duration of the cooling transition section is increased.

[0114] It should be noted that high-precision cutting processes are extremely sensitive to temperature changes. Repeated cutting in localized areas can easily lead to heat accumulation, causing irreversible changes in the microstructure of the material surface and ultimately affecting the mechanical properties of the valve body. By constructing a cutting heat generation model and combining it with finite element thermo-mechanical coupling simulation, the instantaneous heat generation rate at each node on the cutting trajectory can be accurately calculated. This allows for the prediction and marking of potential heat-affected zones in advance, and the machining path can be dynamically optimized based on temperature field distribution data. By overlaying the main heat diffusion path with the cutting trajectory, high-heat coupling zones are intelligently identified and the cutting path is reconstructed. Orthogonal cooling transition sections are inserted in key areas, effectively reducing the tool's dwell time and contact pressure in heat-sensitive regions. This significantly reduces material defects caused by heat accumulation, improves the dimensional accuracy and surface quality of the valve body, and reduces machining energy consumption and extends tool life through intelligent path optimization.

[0115] Figure 4 A block diagram of a new energy vehicle valve body production process evaluation system according to the present invention is shown.

[0116] A second aspect of the present invention also provides a new energy vehicle valve body manufacturing process evaluation system. The system includes a memory 401, a processor 402, and a communication interface 403. The memory includes a new energy vehicle valve body manufacturing process evaluation method program. The communication interface is used for data connection and communication between the memory and the processor. When the new energy vehicle valve body manufacturing process evaluation method program is executed by the processor, it performs the following steps:

[0117] A 3D design model of the target valve body of a new energy vehicle and 3D scanning data of the current batch of raw materials are obtained to construct a deviation mapping relationship between the raw materials and the design model, and the processing allowance distribution of the raw materials is determined based on the deviation mapping relationship.

[0118] The cutting thickness of the target valve body raw material at each position is determined based on the machining allowance distribution, the cutting performance data of the cutting equipment is obtained, and the machining cutting trajectory of the target valve body raw material is determined based on the cutting thickness and cutting performance data at each position.

[0119] The target valve body raw material is pre-processed according to the machining cutting trajectory to obtain low-magnification and high-magnification structural defects of the pre-processed target valve body, thereby obtaining defect information. The reliability of the target valve body production process is then evaluated based on the defect information to obtain the reliability evaluation result.

[0120] Based on the reliability assessment results, if the reliability is lower than the target valve body production quality requirements, the defect information is used to construct a defect distribution diagram of the current batch of raw materials, and the performance deviation of the processing equipment and the defect characteristics of the raw materials are determined based on the defect distribution diagram.

[0121] Based on the performance deviations and the defect characteristics of the raw materials, the production process of the target valve body is optimized to construct a production process optimization strategy.

[0122] This invention discloses a method and system for evaluating the manufacturing process of valve bodies for new energy vehicles. The method involves acquiring a 3D design model of the target valve body and 3D scanning data of the raw materials, constructing a deviation mapping relationship between the design model and the raw materials, and determining the machining allowance distribution. Based on the machining allowance distribution and cutting equipment performance data, a cutting trajectory for raw material processing is generated and pre-processing is performed to obtain low-magnification and high-magnification structural defect information of the pre-processed valve body. A reliability assessment of the manufacturing process is conducted based on the defect information, and the assessment results are obtained. When the reliability is lower than the target requirements, a schematic diagram of the raw material defect distribution is constructed, the performance deviation of the processing equipment and the characteristics of the raw material defects are analyzed, and the valve body manufacturing process is optimized accordingly to form a manufacturing process optimization strategy. This method can achieve accurate evaluation and optimization of the valve body processing process, improving the reliability of the manufacturing process and product quality.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0124] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0125] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0126] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0128] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for evaluating the manufacturing process of valve bodies for new energy vehicles, characterized in that, Includes the following steps: A 3D design model of the target valve body for a new energy vehicle and 3D scanning data of the current batch of raw materials are obtained to construct a deviation mapping relationship between the raw materials and the design model. Based on this deviation mapping relationship, the processing allowance distribution of the raw materials is determined. Specifically: Obtain design drawing data of the target valve body of the new energy vehicle, construct a three-dimensional design model of the target valve body based on the design drawing data, obtain three-dimensional point cloud scanning data of the surface of the current batch of raw materials, and construct a three-dimensional model of the raw materials based on the three-dimensional point cloud scanning data; Feature points of the 3D design model and the 3D model of the raw material are extracted. Based on the ICP point cloud registration algorithm, spatial registration operation is performed on the 3D design model and the 3D model of the raw material according to the feature points. The surface of the 3D design model is divided into grids according to the preset grid size, and the grid intersections are used as the edge points of the 3D design model. After spatial registration, the edge points of the three-dimensional design model are horizontally extended and mapped to the intersection points of the raw material three-dimensional model. The deviation distance between each edge point position and the intersection point position is obtained. Based on the deviation distance, a three-dimensional deviation field between the surface of the raw material three-dimensional model and the three-dimensional design model is established. The three-dimensional deviation field is processed to be continuous using the Kriging space interpolation method to construct a deviation mapping surface. The interference region of the raw material three-dimensional model relative to the three-dimensional design model is identified based on the deviation mapping surface. Based on the interference region, a regional cutting repetition rate analysis is performed to construct a distinct cutting repetition heatmap. Based on the cutting repetition heatmap, spatial registration optimization is performed to obtain an optimized spatial registration three-dimensional model. The three-dimensional deviation field is updated based on the optimized spatial registration three-dimensional model, and the processing allowance distribution of the target valve body raw material is determined based on the updated three-dimensional deviation field. The cutting thickness of the target valve body raw material at each position is determined based on the machining allowance distribution, the cutting performance data of the cutting equipment is obtained, and the machining cutting trajectory of the target valve body raw material is determined based on the cutting thickness and cutting performance data at each position. The target valve body raw material is pre-processed according to the machining cutting trajectory to obtain low-magnification and high-magnification structural defects of the pre-processed target valve body, thereby obtaining defect information. The reliability of the target valve body production process is then evaluated based on the defect information to obtain the reliability evaluation result. Based on the reliability assessment results, if the reliability is lower than the target valve body production quality requirements, the defect information is used to construct a defect distribution diagram of the current batch of raw materials, and the performance deviation of the processing equipment and the defect characteristics of the raw materials are determined based on the defect distribution diagram. Based on the performance deviation and the defect characteristics of the raw materials, the production process of the target valve body is optimized, and a production process optimization strategy is constructed. The step involves analyzing the regional cutting repetition rate based on the interference region, constructing a distinct cutting repetition heatmap, and optimizing spatial registration based on the cutting repetition heatmap to obtain an optimized spatial registration 3D model. Specifically: Based on the octree spatial segmentation algorithm, the raw material is divided into several subspace units, the single cutting thickness information of the target valve body processing equipment is obtained, the number of cuttings of each subspace unit in the interference region is determined according to the single cutting thickness information, and a cutting repetition frequency heatmap is constructed based on the number of cuttings. Thermodynamic coupling calculations are performed on the thermal map of the cutting repetition frequency based on the heat conduction equation. The cutting repetition frequency and cumulative cutting length are used as the equivalent heat source intensity, and the thermal conductivity of the raw material is used as the heat conduction parameter. The heat accumulation of the remaining subspace unit within the preset range of the cutting edge after cutting is estimated during the processing, and the heat accumulation temperature field distribution data is obtained. Based on the heat accumulation temperature field distribution data, the area with heat accumulation greater than the preset value is marked as the processing heat-affected area, the interference area adjacent to the processing heat-affected area is marked as the cutting frequency optimization area, and the remaining interference areas are marked as the safe cutting area. Calculate the difference between the optimization region of the cutting frequency to be optimized, the safe cutting region and the preset value respectively, and calculate the reduction frequency of cutting in the optimization region of the cutting frequency to be optimized and the increase frequency of cutting in the safe cutting region based on the difference and the heat conduction equation; If the region to be optimized for cutting frequency and the safe cutting region are in a symmetrical relationship, a region cutting frequency transfer matrix is ​​established. The objective function is to make the total difference between the region to be optimized for cutting frequency and the safe cutting region and the preset value as close to zero. The objective optimization algorithm is used to iteratively optimize the cutting frequency transfer matrix to determine the optimal reduction frequency of the region to be optimized for cutting frequency and the optimal increase frequency of the safe cutting region. Based on the optimal reduction or increase frequency and the single cutting thickness information of the target valve body processing equipment, the three-dimensional design model of the target valve body after spatial registration is shifted from the area to be cut frequency optimization to the safe cutting area to obtain the optimized spatial registration three-dimensional model.

2. The method for evaluating the manufacturing process of a new energy vehicle valve body according to claim 1, characterized in that, The process involves determining the thickness to be cut at each position of the target valve body raw material based on the machining allowance distribution, acquiring the cutting performance data of the cutting equipment, and determining the machining cutting trajectory of the target valve body raw material based on the thickness to be cut at each position and the cutting performance data. Specifically: The normal depth of each grid node on the surface of the raw material is extracted based on the machining allowance distribution, and the thickness to be cut is determined according to the normal depth, and a thickness-position mapping matrix is ​​established. Obtain the maximum spindle speed, tool feed rate, and radial depth of cut parameters of the cutting equipment, and construct a set of cutting performance parameters; Cutting layer planning is performed based on the thickness to be cut and the set of cutting performance parameters of each node in the thickness-position mapping matrix. Based on the spatial spiral path planning algorithm, the cutting path with the largest thickness to be cut in the thickness-position mapping matrix is ​​used as the starting point, and a spiral descent machining cutting path is generated according to the cutting layer results.

3. The method for evaluating the manufacturing process of a new energy vehicle valve body according to claim 1, characterized in that, The process involves pre-processing the target valve body raw material according to the machining cutting trajectory to obtain low-magnification and high-magnification structural defects of the pre-processed target valve body, thereby obtaining defect information. Based on this defect information, a reliability assessment of the target valve body manufacturing process is performed to obtain the reliability assessment result. Specifically: Acquire low-magnification and high-magnification structural defects image data of the target valve body, and label the image data with defect names to obtain labeled image data; A defect recognition model is constructed based on a convolutional neural network. The labeled image data is imported into the defect recognition model for model training. The model includes an input layer, a feature extraction module, a spatial attention module, and a classification output layer. The feature extraction module is composed of multiple three-dimensional convolutional layers and three-dimensional max pooling layers stacked alternately. The spatial attention module obtains channel feature weights through three-dimensional global average pooling and performs weighted fusion with the labeled image data. According to the processing cutting trajectory, a pre-processing operation is performed on the target valve body raw materials of the current production batch in a preset quantity. High-definition camera images and microscopic image data of the surface of the pre-processed target valve body are obtained. The high-definition camera images and microscopic image data are imported into the trained defect recognition model and the defect information is output. The reliability of the target valve body manufacturing process is evaluated based on the defect information, and the reliability evaluation results are obtained.

4. The method for evaluating the manufacturing process of a new energy vehicle valve body according to claim 1, characterized in that, Based on the reliability assessment results, if the reliability is lower than the target valve body production quality requirements, a defect distribution diagram of the current batch of raw materials is constructed using the defect information. The performance deviation of the processing equipment and the defect characteristics of the raw materials are then determined based on the defect distribution diagram. Specifically: Obtain production quality requirement data for the target valve body. Based on the reliability assessment results, if the reliability is lower than the production quality requirement data, construct a three-dimensional defect density field for the defect information using a kernel density estimation algorithm, and identify defect clustering areas through density gradient analysis. Extract the geometric feature vector of the defect cluster region, including the principal axis direction of the defect distribution and spatial distribution characteristics, obtain the actual motion trajectory data of the target valve body pre-machining, and determine the instantaneous deviation data of the cutting trajectory based on the machining cutting path to construct the cutting path deviation field; The spatial distribution characteristics of the defect cluster area are matched with the instantaneous deviation data. If the direction of the defect principal axis is consistent with the direction of the maximum gradient of the deviation field and the spatial distribution similarity exceeds the set threshold, it is determined to be a defect caused by the performance deviation of the processing equipment. The remaining defect areas are then labeled as raw material defect characteristics.

5. The method for evaluating the manufacturing process of a new energy vehicle valve body according to claim 1, characterized in that, The optimization of the production process of the target valve body based on the performance deviation and the defect characteristics of the raw materials, and the construction of a production process optimization strategy, are as follows: When the pre-processed target valve body has a defect caused by the performance deviation of the processing equipment, the processing equipment parameters are calibrated to obtain the maximum gradient direction and deviation amplitude of the cutting path deviation field. The tool feed direction of the machining cutting trajectory is adjusted according to the maximum gradient direction so that the tool feed direction is at a preset angle with the maximum gradient direction. The tool feed rate and spindle speed of the cutting equipment are adjusted according to the deviation amplitude to construct a set of equipment performance compensation cutting parameters, which constitutes the first production process optimization strategy. When there are raw material defects in the pre-processed target valve body, the probability of the raw material defects is calculated based on the characteristics of the raw material defects, and the quality inspection frequency of the target valve body is determined based on the probability of occurrence, thus forming a second production process optimization strategy. The actual production and processing operations of subsequent raw materials are carried out according to the first production process optimization strategy and the second production process optimization strategy.

6. A new energy vehicle valve body manufacturing process evaluation system, characterized in that, The new energy vehicle valve body production process evaluation system includes a storage unit and a processor. The storage unit includes a new energy vehicle valve body production process evaluation method program. When the new energy vehicle valve body production process evaluation method program is executed by the processor, the following steps are achieved: A 3D design model of the target valve body for a new energy vehicle and 3D scanning data of the current batch of raw materials are obtained to construct a deviation mapping relationship between the raw materials and the design model. Based on this deviation mapping relationship, the processing allowance distribution of the raw materials is determined. Specifically: Obtain design drawing data of the target valve body of the new energy vehicle, construct a three-dimensional design model of the target valve body based on the design drawing data, obtain three-dimensional point cloud scanning data of the surface of the current batch of raw materials, and construct a three-dimensional model of the raw materials based on the three-dimensional point cloud scanning data; Feature points of the 3D design model and the 3D model of the raw material are extracted. Based on the ICP point cloud registration algorithm, spatial registration operation is performed on the 3D design model and the 3D model of the raw material according to the feature points. The surface of the 3D design model is divided into grids according to the preset grid size, and the grid intersections are used as the edge points of the 3D design model. After spatial registration, the edge points of the three-dimensional design model are horizontally extended and mapped to the intersection points of the raw material three-dimensional model. The deviation distance between each edge point position and the intersection point position is obtained. Based on the deviation distance, a three-dimensional deviation field between the surface of the raw material three-dimensional model and the three-dimensional design model is established. The three-dimensional deviation field is processed to be continuous using the Kriging space interpolation method to construct a deviation mapping surface. The interference region of the raw material three-dimensional model relative to the three-dimensional design model is identified based on the deviation mapping surface. Based on the interference region, a regional cutting repetition rate analysis is performed to construct a distinct cutting repetition heatmap. Based on the cutting repetition heatmap, spatial registration optimization is performed to obtain an optimized spatial registration three-dimensional model. The three-dimensional deviation field is updated based on the optimized spatial registration three-dimensional model, and the processing allowance distribution of the target valve body raw material is determined based on the updated three-dimensional deviation field. The cutting thickness of the target valve body raw material at each position is determined based on the machining allowance distribution, the cutting performance data of the cutting equipment is obtained, and the machining cutting trajectory of the target valve body raw material is determined based on the cutting thickness and cutting performance data at each position. The target valve body raw material is pre-processed according to the machining cutting trajectory to obtain low-magnification and high-magnification structural defects of the pre-processed target valve body, thereby obtaining defect information. The reliability of the target valve body production process is then evaluated based on the defect information to obtain the reliability evaluation result. Based on the reliability assessment results, if the reliability is lower than the target valve body production quality requirements, the defect information is used to construct a defect distribution diagram of the current batch of raw materials, and the performance deviation of the processing equipment and the defect characteristics of the raw materials are determined based on the defect distribution diagram. Based on the performance deviation and the defect characteristics of the raw materials, the production process of the target valve body is optimized, and a production process optimization strategy is constructed. The step involves analyzing the regional cutting repetition rate based on the interference region, constructing a distinct cutting repetition heatmap, and optimizing spatial registration based on the cutting repetition heatmap to obtain an optimized spatial registration 3D model. Specifically: Based on the octree spatial segmentation algorithm, the raw material is divided into several subspace units, the single cutting thickness information of the target valve body processing equipment is obtained, the number of cuttings of each subspace unit in the interference region is determined according to the single cutting thickness information, and a cutting repetition frequency heatmap is constructed based on the number of cuttings. Thermodynamic coupling calculations are performed on the thermal map of the cutting repetition frequency based on the heat conduction equation. The cutting repetition frequency and cumulative cutting length are used as the equivalent heat source intensity, and the thermal conductivity of the raw material is used as the heat conduction parameter. The heat accumulation of the remaining subspace unit within the preset range of the cutting edge after cutting is estimated during the processing, and the heat accumulation temperature field distribution data is obtained. Based on the heat accumulation temperature field distribution data, the area with heat accumulation greater than the preset value is marked as the processing heat-affected area, the interference area adjacent to the processing heat-affected area is marked as the cutting frequency optimization area, and the remaining interference areas are marked as the safe cutting area. Calculate the difference between the optimization region of the cutting frequency to be optimized, the safe cutting region and the preset value respectively, and calculate the reduction frequency of cutting in the optimization region of the cutting frequency to be optimized and the increase frequency of cutting in the safe cutting region based on the difference and the heat conduction equation; If the region to be optimized for cutting frequency and the safe cutting region are in a symmetrical relationship, a region cutting frequency transfer matrix is ​​established. The objective function is to make the total difference between the region to be optimized for cutting frequency and the safe cutting region and the preset value as close to zero. The objective optimization algorithm is used to iteratively optimize the cutting frequency transfer matrix to determine the optimal reduction frequency of the region to be optimized for cutting frequency and the optimal increase frequency of the safe cutting region. Based on the optimal reduction or increase frequency and the single cutting thickness information of the target valve body processing equipment, the three-dimensional design model of the target valve body after spatial registration is shifted from the area to be cut frequency optimization to the safe cutting area to obtain the optimized spatial registration three-dimensional model.

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