Modeling method for removing depth of multi-line tool path superposition material for abrasive belt grinding

Abstract

The invention discloses a multi-line tool path superposition material removal depth modeling method for abrasive belt grinding, and belongs to the technical field of abrasive belt grinding machining.The method comprises the steps that in a superposition area of multiple lines of tool paths, single-line material removal depth empirical models of all the lines of tool paths are added, and a single-line material removal depth empirical model of a non-superposition area is combined, so that a single-line material removal depth empirical model of the non-superposition area is obtained; establishing a depth empirical removal model; inputting the process parameters into the removal depth empirical model to obtain an initial removal depth predicted value, and taking a difference between the initial removal depth predicted value and a really measured removal depth value as a real residual error; the model is trained by using technological parameters during abrasive belt grinding, an error between a residual prediction value output by the model and a real residual serves as a loss function, training is conducted till convergence is achieved, and a deep residual removal model is obtained; and the depth empirical removal model and the depth residual removal model form a depth mixed removal model. According to the method, the superimposed effect and the prediction residual among multiple rows of tool paths are considered, so that the prediction precision and applicability are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of abrasive belt grinding, and more specifically, relates to a multi-line tool path superimposed material removal depth modeling method for abrasive belt grinding, which is suitable for high-precision grinding of complex curved surfaces such as aircraft engine blades. Background Art

[0002] Blades are a key component of aircraft engines. Because they significantly impact various aspects of an engine's aerodynamic performance and service life, strict precision requirements are often imposed on their production. However, due to factors such as the elastic contact characteristics of belt grinding, the multi-parameter coupling effect, and the superposition effect between multiple toolpaths, belt grinding often struggles to precisely control the depth of material removal, leading to over- or under-grinding and the resulting blade dimensions failing to meet tolerance requirements. Therefore, in the field of belt grinding, a method is needed to improve the precision of material removal depth control to avoid product failures caused by dimensional deviations after processing.

[0003] Numerous studies have been conducted domestically and internationally on the modeling of material removal depth during belt grinding. However, these studies primarily focus on single-line material removal models and utilize machine learning algorithms alone, rather than integrating them with traditional models. Consequently, these studies suffer from poor applicability and low accuracy. To date, there is a lack of high-precision material removal depth modeling methods suitable for the multi-line toolpath stacking method used in blade machining.

[0004] It can be seen that the existing depth removal modeling technology has technical problems such as poor applicability and low accuracy. Summary of the Invention

[0005] In response to the above defects or improvement needs of the prior art, the present invention provides a multi-line tool path superimposed material removal depth modeling method for belt grinding, thereby solving the technical problems of poor applicability and low precision of the existing removal depth modeling technology.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for modeling the depth of multi-path superposition material removal in belt grinding is provided, comprising:

[0007] In the overlapping area of multiple tool paths, the empirical models of the single-line material removal depth of each tool path are added together, and the empirical models of the single-line material removal depth in the non-overlapping area of the multiple tool paths are combined to establish an empirical model of the overlapping material removal depth of the multiple tool paths;

[0008] The process parameters of belt grinding are input into the empirical model of material removal depth superimposed on multiple tool paths to obtain the initial removal depth prediction value. The difference between the initial removal depth prediction value and the actual removal depth value measured during belt grinding is taken as the true residual.

[0009] The process parameters during belt grinding are used to train a model for establishing the mapping relationship between input and output. The error between the residual prediction value output by the model and the actual residual is used as the loss function for backpropagation, the model parameters are updated, and the training is carried out until convergence to obtain a model for removing deep residuals.

[0010] The multi-line tool path superposition material removal depth empirical model and the removal depth residual model constitute a multi-line tool path superposition material removal depth hybrid model.

[0011] Furthermore, the single-line material removal depth empirical model is constructed in the following manner:

[0012] The contact area between the grinding wheel and the workpiece in the grinding process is equivalent to an ellipse, and the pressure distribution in the contact area is calculated;

[0013] The relationship coefficient between grinding material removal rate and process parameters during belt grinding was determined by multivariate linear regression, and a nonlinear material removal rate formula was obtained.

[0014] When the abrasive belt wheel moves in the contact area, the pressure distribution in the contact area is integrated and multiplied by the nonlinear material removal rate formula to construct a single-line material removal depth empirical model.

[0015] Furthermore, the model used to establish the mapping relationship between input and output is LightGBM, XGBoost, CatBoost, convolutional neural network, recurrent neural network, Transformer, generalized linear model or support vector machine.

[0016] Furthermore, the process parameters include abrasive belt linear speed, workpiece feed speed and workpiece curvature.

[0017] According to another aspect of the present invention, a method for predicting the material removal depth of multiple tool paths in belt grinding is provided, comprising:

[0018] The process parameters of belt grinding are input into a multi-line tool path superimposed material removal depth hybrid model obtained by a multi-line tool path superimposed material removal depth modeling method for belt grinding. The initial removal depth prediction value output by the multi-line tool path superimposed material removal depth empirical model and the residual prediction value output by the removal depth residual model are added to obtain the final removal depth prediction value.

[0019] According to another aspect of the present invention, a method for planning a belt grinding process based on superposition of multiple tool paths is provided, comprising:

[0020] A multi-path superposition material removal depth prediction method for belt grinding is used to obtain the final removal depth prediction values of multiple sets of process parameters during belt grinding, and a query library with a mapping relationship between the process parameters during belt grinding and the final removal depth prediction values is established;

[0021] For a given multi-line tool path superimposed material removal depth path, the process parameters of the belt grinding corresponding to the removal depth of each path point in the multi-line tool path superimposed material removal depth path are obtained from the query library to form an abrasive belt grinding processing plan.

[0022] Furthermore, the method further comprises:

[0023] The error distribution between the actual size of the workpiece after multi-line tool path processing at different line spacings and the designed nominal size is calculated using mathematical calculation software. The error distribution is used to quantitatively analyze the uniformity of the removal depth of multi-line tool paths at different line spacings to obtain the optimal line spacing. The workpiece is then processed using the process parameters in the belt grinding processing plan at the optimal line spacing.

[0024] Furthermore, the error distribution includes error standard deviation, error range and error gradient.

[0025] According to another aspect of the present invention, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute a multi-line tool path superimposed material removal depth modeling method for belt grinding and / or a multi-line tool path superimposed material removal depth prediction method for belt grinding.

[0026] According to another aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a multi-line tool path superimposed material removal depth modeling method for belt grinding is implemented.

[0027] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0028] (1) The present invention further considers the superposition effect between multiple lines of tool paths on the basis of the single-line material removal depth empirical model, and the multi-line model finally established can be applied to the prediction of material removal depth under the multi-line tool path superposition processing mode in the actual processing of workpieces. Compared with the pure data-driven model established by only using machine learning algorithms, the present invention introduces the prediction error of the empirical model as the true value for training, and can still show high prediction accuracy under small-scale data sets. The hybrid model of the present invention can use the removal depth residual model to correct the prediction of the removal depth empirical model, ensuring that the hybrid model finally obtained has high prediction accuracy under various extreme working conditions and strong applicability.

[0029] (2) Since the abrasive belt wheel is made of elastic material, the geometric characteristics of the contact area change significantly due to local deformation when it contacts the curved workpiece. Elastic deformation not only expands the actual contact area, but also makes the contact patch morphology present complex nonlinear characteristics. Based on this, while adopting the nonlinear material removal rate formula, the contact area between the abrasive belt wheel and the workpiece in the grinding process is equivalent to an ellipse. The pressure distribution under the elastic contact between the abrasive belt wheel and the workpiece is calculated to obtain the final single-line material removal depth empirical model, which is more accurate than existing similar models.

[0030] (3) The present invention can use a variety of models for establishing a mapping relationship between input and output for training, and has strong applicability. Among them, LightGBM is a distributed gradient boosting framework based on decision trees, which is suitable for tasks with large-scale data, high-dimensional features, and low computing resource requirements. XGBoost is an optimized implementation of the traditional gradient boosting tree, which controls overfitting through a pre-sorting algorithm and regularization. Compared with LightGBM, it performs stably in structured data, but its memory consumption and training speed are slightly inferior to LightGBM. CatBoost has a built-in automatic encoding function for category features to reduce the complexity of feature engineering. The symmetric tree structure design improves the generalization ability of the model and is suitable for data sets containing a large number of categorical variables.

[0031] (4) The hybrid model established by the present invention can use the removal depth residual model to correct the prediction of the removal depth empirical model, so that the final removal depth prediction value obtained is highly accurate and has strong applicability.

[0032] (5) By using the hybrid model established by the present invention to make predictions, a query library with a mapping relationship between the process parameters during belt grinding and the predicted values of the final removal depth can be obtained. Based on the query library, the optimal belt grinding solution can be quickly queried for a given multi-line tool path superimposed material removal depth path.

[0033] (6) The present invention uses three error consistency evaluation indicators, namely error standard deviation, error range and error gradient, to quantitatively analyze the uniformity of material removal depth under different line spacings, and finally obtains the optimal line spacing. Through the coordinated use of the above three indicators, the consistency of blade grinding size error can be systematically evaluated. The error standard deviation quantifies the overall uniformity from a statistical level, and the range focuses on the influence range of extreme outliers. The combined analysis of the two can capture the overall discrete trend and extreme deviation risk at the same time, while the error gradient reveals the dynamic change law of spatial distribution and analyzes from the perspective of smoothness and continuity of the machined surface. Using the process parameters in the belt grinding processing scheme to process the workpiece under the optimal line spacing can significantly reduce the standard deviation of the blade size error after processing and reduce redundant tool paths, while improving processing accuracy and processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 1 is a flow chart of a method for depth modeling of multi-line tool path superposition material removal for belt grinding provided by an embodiment of the present invention;

[0035] Figure 2 Schematic diagram of the contact area profile of the abrasive belt grinding provided by an embodiment of the present invention;

[0036] Figure 3 Schematic diagram of the contact area of abrasive belt grinding provided by an embodiment of the present invention;

[0037] Figure 4 This is a graph showing a single-line tool path removal depth function provided by an embodiment of the present invention;

[0038] Figure 5 It is a schematic diagram of depth removal by superimposing multiple tool paths provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0040] like Figure 1 As shown in FIG, a multi-line tool path superposition material removal depth modeling method for belt grinding includes:

[0041] In the overlapping area of multiple tool paths, the empirical models of the single-line material removal depth of each tool path are added together, and the empirical models of the single-line material removal depth in the non-overlapping area of the multiple tool paths are combined to establish an empirical model of the overlapping material removal depth of the multiple tool paths;

[0042] The process parameters of belt grinding are input into the empirical model of material removal depth superimposed on multiple tool paths to obtain the initial removal depth prediction value. The difference between the initial removal depth prediction value and the actual removal depth value measured during belt grinding is taken as the true residual.

[0043] The process parameters during belt grinding are used to train a model for establishing the mapping relationship between input and output. The error between the residual prediction value output by the model and the actual residual is used as the loss function for backpropagation, the model parameters are updated, and the training is carried out until convergence to obtain a model for removing deep residuals.

[0044] The multi-line tool path superposition material removal depth empirical model and the removal depth residual model constitute a multi-line tool path superposition material removal depth hybrid model.

[0045] Models used to establish mapping relationships between inputs and outputs include:

[0046] 1. Gradient Boosting Tree Model

[0047] LightGBM: A distributed gradient boosting framework based on decision trees, supporting classification, regression, and ranking tasks. Its optimization strategy uses a histogram algorithm to reduce memory usage and a leaf-wise growth strategy to improve accuracy. It supports direct input of categorical features. It is suitable for tasks with large-scale data, high-dimensional features, and low computational resource requirements.

[0048] XGBoost: An optimized implementation of traditional gradient boosting trees, using a pre-sorting algorithm and regularization to control overfitting. Compared to LightGBM, it performs more stably on structured data, but its memory consumption and training speed are slightly lower than LightGBM.

[0049] CatBoost: Built-in automatic encoding of categorical features reduces feature engineering complexity. Its symmetrical tree structure improves model generalization and is suitable for datasets containing large numbers of categorical variables.

[0050] 2. Neural Network Model

[0051] Convolutional Neural Network (CNN): It extracts spatial features through convolution kernels and is good at tasks such as image classification and target detection.

[0052] Recurrent Neural Network (RNN): It has outstanding time series modeling capabilities and is suitable for scenarios such as natural language processing and time series prediction.

[0053] Transformer: Based on the self-attention mechanism, it replaces the traditional RNN structure and is good at processing long sequence dependencies.

[0054] 3. Other Models

[0055] Generalized Linear Model (GLM): Basic mapping model, including linear regression, logistic regression, etc., suitable for modeling simple linear relationships.

[0056] Support Vector Machine (SVM): It maps high-dimensional space through kernel functions to solve nonlinear classification / regression problems and is suitable for small sample data.

[0057] The workpiece processed by belt grinding in the present invention is a curved workpiece. For high-precision grinding of complex curved surfaces such as aircraft engine blades, the modeling process is described in detail in Example 1. There are many models for establishing the mapping relationship between input and output. Example 1 uses LightGBM.

[0058] Example 1

[0059] A multi-path superposition material removal depth modeling method for belt grinding, comprising:

[0060] S1: Determine the contour range and pressure distribution of the contact area between the grinding wheel and the blade during grinding based on Hertz contact theory;

[0061] Since the grinding wheel is made of elastic material, the geometric characteristics of the contact area will change significantly due to local deformation when it contacts the curved surface. Elastic deformation not only expands the actual contact area, but also makes the contact patch morphology present complex nonlinear characteristics. Based on Hertz contact theory, the surface contact area can be analyzed as an elliptical shape, and its outline diagram is shown as follows: Figure 2 The contact area can be expressed as:

[0062]

[0063] The mathematical expressions of each parameter are as follows:

[0064]

[0065] Where φ is the angle between the normal planes of the curvature radii R1 and R2, the relative elastic modulus E′ is determined by the contact wheel material parameters E1 and ν1 and the workpiece material parameters E2 and v2; A and B represent the relative principal curvatures of the abrasive wheel and the workpiece at the contact point, respectively; R1 and R1′ represent the maximum and minimum contact wheel normal curvature radii, R2 and R2′ represent the maximum and minimum workpiece normal curvature radii, and ε is the elliptic integral of the second kind.

[0066] Finally, the pressure distribution formula in the contact area is calculated as follows:

[0067]

[0068] S2: Determine the relationship between machining parameters and material removal rate through experiments and establish an empirical formula for nonlinear material removal rate;

[0069] By setting up a multiple linear regression experiment, the relationship coefficient between the grinding material removal rate and each processing parameter was measured, and finally the formula for the nonlinear material removal rate of belt grinding in this experiment was obtained as follows:

[0070]

[0071] Where r represents the material removal rate. g =C a K a k t , where C a is the correction constant of the grinding process, K a K is the combined constant of the workpiece wear resistance factor and the belt grinding ability factor, t is the grinding wheel wear coefficient, V b 、V f and F represent the belt linear speed, workpiece feed speed and normal pressure respectively.

[0072] S3: Establish a single-line material removal depth empirical model based on the pressure distribution in the contact area and the empirical formula of nonlinear material removal rate;

[0073] Schematic diagram of the microelement model of the contact area of belt grinding Figure 3 As shown. When the grinding wheel feeds at a speed of V f During the machining process, the three grinding contact areas in the figure are passed in sequence. When the grinding wheel is in contact area 1, the element Q on the blade is ground for the first time; when the grinding wheel moves to contact area 3, all machining processes of the element Q on the blade are completed. The total material removal of the element is the sum of the material removal at each instant when the grinding wheel moves between contact area 1 and contact area 3, which is equivalent to the sum of the material removal at each instant from the coordinate to The sum of the instantaneous material removal of each microelement in this dynamic contact area is expressed as follows:

[0074]

[0075] Finally, the material removal depth distribution formula along the y-axis in the elliptical contact area is calculated as follows:

[0076]

[0077] K is the curvature coefficient This value is only related to the blade curvature and increases with the curvature. The formula shows that the material removal depth distribution along the y-axis presents a parabolic shape, where the material removal depth at the center of the elliptical contact area is the largest. The single-line tool path removal depth function curve is as follows: Figure 4 shown.

[0078] S4: Based on the empirical model of single-line material removal depth, the superposition effect between multiple tool paths is considered to establish an empirical model of multi-line tool path superposition material removal depth;

[0079] In actual blade processing, multiple lines of superimposed toolpaths are usually used, and there is a partial overlap between two adjacent lines of toolpaths. The process of material removal depth under the superposition of multiple lines of toolpaths is as follows: Figure 5 As shown in Figure 2, b is the length of the minor semi-axis of the grinding contact area ellipse, and L is the machining path spacing. The removal depth in the overlapping area is affected by multiple machining paths, while the central area (i.e., the non-overlapping area) is only affected by a single machining path.

[0080] When planning the tool path, we first need to determine the processing parameters based on the blade margin distribution and use the single-line material removal depth model, that is, the belt linear speed V b and feed speed V f When the required removal allowances of two adjacent machining paths are different, the required machining parameters are also different. At this time, the formula for the material removal depth of multiple tool paths under the combined effect of the two is as follows:

[0081]

[0082] Among them, V b1 、V b2 、V f1 、V f2 are the machining parameters corresponding to the two tool paths. Since the two tool paths are adjacent and the line spacing is small, it can be approximately considered that the curvature of the grinding area of the two is equal, and constant force grinding is adopted, so the corresponding F n , K and b are equal.

[0083] When the removal allowance required for two adjacent machining paths is the same, the required machining parameters are also the same. At this time, the formula for the material removal depth of multiple tool paths under the combined action of the two is as follows:

[0084]

[0085] Among them, y1 is the working range of the first line of tool path in the Y-axis direction, and y2 is the working range of the second line of tool path in the Y-axis direction.

[0086] S5: The multi-line tool path superposition material removal depth empirical model obtained in S4 is optimized using the model accuracy improvement method based on LightGBM, and finally a high-precision multi-line tool path superposition material removal depth hybrid model is obtained.

[0087] The model accuracy improvement method based on LightGBM firstly calculates the predicted value of the multi-line tool path superposition material removal depth empirical model obtained in S4 under different working conditions and the actual processing value to obtain the residual, and then compares the residual with the belt linear speed V of the corresponding working condition. b , workpiece feed speed V f The blade curvature and other parameters are used as input features for LightGBM training to obtain a residual model of material removal depth. Finally, this residual model is combined with the empirical model obtained by S4 to obtain a high-precision multi-line tool path superposition material removal depth hybrid model.

[0088] Example 2

[0089] A belt grinding process planning method based on multi-line tool path superposition includes:

[0090] The process parameters of belt grinding are input into a multi-line tool path superimposed material removal depth hybrid model obtained by a multi-line tool path superimposed material removal depth modeling method for belt grinding. The initial removal depth prediction value output by the multi-line tool path superimposed material removal depth empirical model and the residual prediction value output by the removal depth residual model are added to obtain the final removal depth prediction value.

[0091] Establish a query library with mapping relationship between process parameters and final removal depth prediction values during belt grinding;

[0092] For a given multi-line tool path superimposed material removal depth path, the process parameters of the belt grinding corresponding to the removal depth of each path point in the multi-line tool path superimposed material removal depth path are obtained from the query library to form an abrasive belt grinding processing plan.

[0093] The error distribution between the actual size of the workpiece after multi-line tool path processing at different line spacings and the designed nominal size is calculated using mathematical calculation software. The error distribution is used to quantitatively analyze the uniformity of the removal depth of multi-line tool paths at different line spacings to obtain the optimal line spacing. The workpiece is then processed using the process parameters in the belt grinding processing plan at the optimal line spacing.

[0094] Specifically, the uniformity of material removal depth under different line spacings was quantitatively analyzed using the error consistency evaluation index, and the relationship between the optimal line spacing and blade curvature for multi-line toolpath superposition processing was obtained. Based on this, a variable line spacing toolpath planning method was proposed.

[0095] The material removal depth uniformity under different line spacings was quantitatively analyzed using three error consistency evaluation indicators: error standard deviation, error range, and error gradient. Finally, the optimal line spacing expression was obtained as follows:

[0096] L=1.42b

[0097] Where L is the machining tool path spacing, b is the length of the minor semi-axis of the elliptical contact area, and under constant pressure, this value is only determined by the blade curvature.

[0098] Finally, the line spacing L of different processing areas is calculated according to the measured blade curvature distribution, and the variable line spacing tool path planning of the blade is carried out based on it.

[0099] The present invention processes the workpiece using the process parameters in the belt grinding processing scheme at the optimal line spacing, which can significantly reduce the standard deviation of the blade size error after processing and reduce redundant tool paths, while improving the processing accuracy and processing efficiency.

[0100] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-line tool path superposition material removal depth modeling method for belt grinding, characterized in that: include: In the overlapping area of multiple tool paths, the empirical models of the single-line material removal depth of each tool path are added together, and the empirical models of the single-line material removal depth in the non-overlapping area of the multiple tool paths are combined to establish an empirical model of the overlapping material removal depth of the multiple tool paths; The process parameters of belt grinding are input into the empirical model of material removal depth superimposed on multiple tool paths to obtain the initial removal depth prediction value. The difference between the initial removal depth prediction value and the actual removal depth value measured during belt grinding is taken as the true residual. The process parameters during belt grinding are used to train a model for establishing the mapping relationship between input and output. The error between the residual prediction value output by the model and the actual residual is used as the loss function for backpropagation, the model parameters are updated, and the training is carried out until convergence to obtain a model for removing deep residuals. The multi-line tool path superposition material removal depth empirical model and the removal depth residual model constitute a multi-line tool path superposition material removal depth hybrid model.

2. A multi-path superimposed material removal depth modeling method for belt grinding according to claim 1, characterized in that: The single-line material removal depth empirical model is constructed as follows: The contact area between the grinding wheel and the workpiece in the grinding process is equivalent to an ellipse, and the pressure distribution in the contact area is calculated; The relationship coefficient between grinding material removal rate and process parameters during belt grinding was determined by multivariate linear regression, and a nonlinear material removal rate formula was obtained. When the abrasive belt wheel moves in the contact area, the pressure distribution in the contact area is integrated and multiplied by the nonlinear material removal rate formula to construct a single-line material removal depth empirical model.

3. A multi-path superimposed material removal depth modeling method for belt grinding according to claim 1 or 2, characterized in that: The model used to establish the mapping relationship between input and output is LightGBM, XGBoost, CatBoost, convolutional neural network, recurrent neural network, Transformer, generalized linear model or support vector machine.

4. A multi-path superimposed material removal depth modeling method for belt grinding according to claim 1 or 2, characterized in that: The process parameters include abrasive belt linear speed, workpiece feed speed and workpiece curvature.

5. A method for predicting the material removal depth of multiple tool paths superimposed on abrasive belt grinding, characterized in that: include: The process parameters during belt grinding are input into a multi-line tool path superimposed material removal depth hybrid model obtained by a multi-line tool path superimposed material removal depth modeling method for belt grinding as described in any one of claims 1-4, and the initial removal depth prediction value output by the multi-line tool path superimposed material removal depth empirical model is added to the residual prediction value output by the removal depth residual model to obtain the final removal depth prediction value.

6. A belt grinding process planning method based on multi-line tool path superposition, characterized in that: include: Using the multi-line tool path superposition material removal depth prediction method for belt grinding as described in claim 5, obtaining final removal depth prediction values of multiple sets of process parameters during belt grinding, and establishing a query library having a mapping relationship between the process parameters during belt grinding and the final removal depth prediction values; For a given multi-line tool path superimposed material removal depth path, the process parameters of the belt grinding corresponding to the removal depth of each path point in the multi-line tool path superimposed material removal depth path are obtained from the query library to form an abrasive belt grinding processing plan.

7. A belt grinding process planning method based on multi-line tool path superposition according to claim 6, characterized in that: The method further comprises: The error distribution between the actual size of the workpiece after multi-line tool path processing at different line spacings and the designed nominal size is calculated using mathematical calculation software. The error distribution is used to quantitatively analyze the uniformity of the removal depth of multi-line tool paths at different line spacings to obtain the optimal line spacing. The workpiece is then processed using the process parameters in the belt grinding processing plan at the optimal line spacing.

8. The belt grinding process planning method based on multi-line tool path superposition according to claim 7, characterized in that: The error distribution includes error standard deviation, error range and error gradient.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute a multi-line tool path superimposed material removal depth modeling method for belt grinding according to any one of claims 1 to 4 and / or a multi-line tool path superimposed material removal depth prediction method for belt grinding according to claim 5.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method for depth modeling of multi-line tool path superposition material removal for belt grinding according to any one of claims 1 to 4 is implemented.

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