Evaluation method and system for residual stress distribution uniformity of machine tool casting

Abstract

The invention provides an evaluation method and system for residual stress distribution uniformity of a machine tool casting, and the method comprises the steps: 1, obtaining residual stress data of the machine tool casting in a three-dimensional space, and forming an initial residual stress matrix of the machine tool casting; 2, grouping the initial residual stress matrix according to a small pool dimension m * n * 2, and obtaining an initial residual stress variation coefficient matrix according to the variation coefficient of residual stress data in each small pool; 3, performing iterative pooling operation in the step 2 on the initial residual stress variation coefficient matrix to obtain a simplified matrix of a preset dimension; 4, performing mean pooling on the simplified matrix to obtain a two-dimensional partition variable coefficient matrix, and determining a total variable coefficient of the machine tool casting based on the two-dimensional partition variable coefficient matrix; and 5, evaluating whether the residual stress distribution of the machine tool casting is uniform or not according to the total variable coefficient and the two-dimensional partition variable coefficient matrix. According to the technical scheme provided by the invention, the uniformity of the machine tool casting can be accurately evaluated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of casting quality evaluation and process optimization guidance, and particularly relates to a method and system for evaluating the uniformity of residual stress distribution of a machine tool casting. BACKGROUND

[0002] When the as-delivered casting itself has a non-uniform residual stress field distribution, with changes in temperature, load and other working conditions and the accumulation of working time during the working process of the machine tool, the energy of the non-uniform residual stress field of the casting will gradually be released, thereby causing changes in assembly accuracy, gradual accumulation of assembly errors, and ultimately affecting the stability of the machine tool performance. Currently, the casting quality is usually evaluated by experimentally measuring residual stress at a small number of points on the as-delivered casting. This method has the disadvantages of few measurement points, high cost, and lack of a method and standard for overall evaluation of the casting quality.

[0003] Some methods for evaluating the uniformity of residual stress field of a part have been proposed in the prior art, but these methods are mostly directed to cylindrical rotary parts and are limited to one-dimensional linear evaluation or two-dimensional circular surface evaluation, which has great limitations for the casting quality evaluation of machine tool cubic castings, resulting in low accuracy of the uniformity evaluation of machine tool castings. Therefore, there is an urgent need to propose a three-dimensional comprehensive evaluation scheme for the uniformity of residual stress field distribution of machine tool castings. SUMMARY

[0004] The present application provides a method and system for evaluating the uniformity of residual stress distribution of a machine tool casting, to at least solve the technical problem of great limitations for the casting quality evaluation of machine tool cubic castings in the prior art, resulting in low accuracy of the uniformity evaluation of machine tool castings.

[0005] The first aspect embodiment of the present application provides a method for evaluating the uniformity of residual stress distribution of a machine tool casting, which comprises: Step 1: Obtain residual stress data in the three-dimensional space of the machine tool casting to form an initial residual stress matrix of the machine tool casting, wherein the dimension of the initial residual stress matrix is i x j x k, wherein i is the number of rows in the length direction, j is the number of columns in the width direction, and k is the number of layers in the height direction; Step 2: Group the initial residual stress matrix according to the small pool dimension m x n x 2, and then calculate the variation coefficient of the residual stress data in each small pool to obtain an initial residual stress variation coefficient matrix of the machine tool casting, wherein the dimension of the intermediate matrix is (k+1) x (k+1) x (k-1); Step 3: Perform the iteration pooling operation of step 2 on the initial residual stress variation coefficient matrix to obtain a simplified matrix of a preset dimension; Step 4: mean-pooling the simplified matrix to obtain a two-dimensional partitioned coefficient of variation matrix, and determining a total coefficient of variation of the machine tool casting based on the two-dimensional partitioned coefficient of variation matrix; Step 5: evaluating whether the residual stress distribution of the machine tool casting is uniform according to the total coefficient of variation and the two-dimensional partitioned coefficient of variation matrix.

[0006] Preferably, the values of i, j, and k of the initial residual stress matrix are determined based on the length, width, and height dimensions of the cubic machine tool casting.

[0007] Further, m1 and n1 are solved by using the formula , wherein m1 is a first positive integer and n1 is a second positive integer. The m is equal to m1+1, and the n is equal to n1+1. The preset dimension is 4x4x2.

[0008] Further, the determination of the total coefficient of variation of the machine tool casting based on the two-dimensional partitioned coefficient of variation matrix comprises: determining the residual stress standard deviation of each element in the two-dimensional partitioned coefficient of variation matrix and the residual stress average value of each element; determining the ratio of the residual stress standard deviation to the residual stress average value, and taking the ratio as the total coefficient of variation of the machine tool casting.

[0009] Further, the evaluation of whether the residual stress distribution of the machine tool casting is uniform according to the total coefficient of variation and the two-dimensional partitioned coefficient of variation matrix comprises: when the total coefficient of variation is less than or equal to a preset first coefficient of variation threshold, it is determined that the residual stress distribution of the machine tool casting is uniform, otherwise, the residual stress distribution of the machine tool casting is not uniform; determining the absolute value of the difference between the coefficient of variation of each region after mean-pooling and the coefficient of variation of its adjacent region based on the two-dimensional partitioned coefficient of variation matrix, and if the absolute value of the difference is greater than a preset difference threshold, it is determined that the residual stress distribution of the adjacent region is not uniform.

[0010] The second aspect embodiment of the present application proposes an evaluation system for the uniformity of residual stress distribution of a machine tool casting, comprising: an acquisition module configured to acquire residual stress data in a three-dimensional space of a machine tool casting, and to constitute an initial residual stress matrix M0 of the machine tool casting, wherein the dimension of the initial residual stress matrix M0 is i x j x k, i is the number of rows in the length direction, j is the number of columns in the width direction, and k is the number of layers in the height direction. The initial pooling module groups the initial residual stress matrix M0 according to the pool dimensions m×n×2, then calculates the coefficient of variation (CV) of the residual stress data in each pool to obtain the initial residual stress coefficient of variation matrix M of the machine tool casting. c0 The dimensions of the intermediate matrix M0 are (k+1)×(k+1)×(k-1); The iterative pooling module is used to process the initial residual stress variation coefficient matrix M. c0 Perform the iterative pooling operation in step 2 to obtain a simplified matrix M of the preset dimensions. cx ; The mean pooling module is used to simplify the matrix M. cx Mean pooling is performed to obtain a two-dimensional partitioned coefficient of variation matrix M, and the total coefficient of variation CV of the machine tool casting is determined based on the two-dimensional partitioned coefficient of variation matrix M. The evaluation module is used to evaluate whether the residual stress distribution of the machine tool casting is uniform based on the total coefficient of variation (CV) and the two-dimensional partitioned coefficient of variation matrix (M).

[0011] Preferably, the values ​​of i, j, and k of the initial residual stress matrix M0 are determined based on the length, width, and height dimensions of the cubic machine tool casting.

[0012] Furthermore, using the formula , Solving for m1 and n1 yields m1 and n1, where m1 is the first positive integer and n1 is the second positive integer. The m equals m1+1, and the n equals n1+1; The preset dimension is 4×4×2.

[0013] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method described in the first aspect embodiment.

[0014] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect.

[0015] The technical solutions provided by the embodiments of this application have at least the following beneficial effects: This application proposes a method and system for evaluating the uniformity of residual stress distribution in machine tool castings. The method includes: Step 1: acquiring residual stress data in the three-dimensional space of the machine tool casting to form an initial residual stress matrix of the machine tool casting. The dimension of the initial residual stress matrix is ​​i×j×k, where i is the number of rows in the length direction, j is the number of columns in the width direction, and k is the number of layers in the height direction; Step 2: grouping the initial residual stress matrix according to the pool dimension m×n×2, and then calculating the coefficient of variation of the residual stress data in each pool to obtain the initial residual stress coefficient of variation matrix of the machine tool casting. The dimension of the intermediate matrix is ​​(k+1)×(k+1)×(k-1); Step 3: performing iterative pooling operation of Step 2 on the initial residual stress coefficient of variation matrix to obtain a simplified matrix of a preset dimension; Step 4: performing mean pooling on the simplified matrix to obtain a two-dimensional partitioned coefficient of variation matrix, and determining the total coefficient of variation of the machine tool casting based on the two-dimensional partitioned coefficient of variation matrix; Step 5: evaluating whether the residual stress distribution of the machine tool casting is uniform based on the total coefficient of variation and the two-dimensional partitioned coefficient of variation matrix. The technical solution proposed in this application can accurately evaluate the uniformity of machine tool castings.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for evaluating the uniformity of residual stress distribution in machine tool castings according to an embodiment of this application; Figure 2 This is a schematic diagram showing the location of residual stress data sampling points for a machine tool slide casting according to an embodiment of this application; Figure 3 A detailed flowchart of a method for evaluating the uniformity of residual stress distribution in machine tool castings according to an embodiment of this application; Figure 4 This is a schematic diagram illustrating the process of simplifying casting partitioning using pooling theory according to an embodiment of this application; Figure 5 This is a residual stress distribution cloud map of a slide casting on a machine tool according to a process I provided in an embodiment of this application; Figure 6 This is a residual stress distribution cloud map of a slide casting on a machine tool according to a process II provided in one embodiment of this application; Figure 7This is a structural diagram of an evaluation system for the uniformity of residual stress distribution in machine tool castings, provided according to an embodiment of this application. Detailed Implementation

[0018] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0019] This application proposes a method and system for evaluating the uniformity of residual stress distribution in machine tool castings. The method includes: Step 1: acquiring residual stress data in the three-dimensional space of the machine tool casting to form an initial residual stress matrix of the machine tool casting. The dimension of the initial residual stress matrix is ​​i×j×k, where i is the number of rows in the length direction, j is the number of columns in the width direction, and k is the number of layers in the height direction; Step 2: grouping the initial residual stress matrix according to the pool dimension m×n×2, and then calculating the coefficient of variation of the residual stress data in each pool to obtain the initial residual stress coefficient of variation matrix of the machine tool casting. The dimension of the intermediate matrix is ​​(k+1)×(k+1)×(k-1); Step 3: performing iterative pooling operation of Step 2 on the initial residual stress coefficient of variation matrix to obtain a simplified matrix of a preset dimension; Step 4: performing mean pooling on the simplified matrix to obtain a two-dimensional partitioned coefficient of variation matrix, and determining the total coefficient of variation of the machine tool casting based on the two-dimensional partitioned coefficient of variation matrix; Step 5: evaluating whether the residual stress distribution of the machine tool casting is uniform based on the total coefficient of variation and the two-dimensional partitioned coefficient of variation matrix. The technical solution proposed in this application can accurately evaluate the uniformity of machine tool castings.

[0020] The following describes, with reference to the accompanying drawings, an embodiment of the present application of a method and system for evaluating the uniformity of residual stress distribution in machine tool castings.

[0021] Example 1 Figure 1 This is a flowchart of a method for evaluating the uniformity of residual stress distribution in machine tool castings according to an embodiment of this application, such as... Figure 1 As shown, the method includes: Step 1: Obtain the residual stress data in the three-dimensional space of the machine tool casting to form the initial residual stress matrix M0 of the machine tool casting. The dimension of the initial residual stress matrix M0 is i×j×k, where i is the number of rows in the length direction, j is the number of columns in the width direction, and k is the number of layers in the height direction.

[0022] It should be noted that the number and location of data points used in the evaluation method for the uniformity of residual stress distribution in machine tool castings should be selected and confirmed based on the casting structure and dimensions, such as... Figure 2As shown, in this method, data is collected from the casting's bottom surface as a reference, along three dimensions: length, width, and height. Data is collected in rows i along the length direction, columns j along the width direction, and layers k along the height direction. In this method, i and j determine the number of data groups in each pool during subsequent calculations, and k determines the number of pooling calculations. Therefore, for the subsequent pooling calculations to be performed, i, j, and k must satisfy the following relationship:

[0023] Subsequently, data is collected based on the selected data points, resulting in an initial residual stress data matrix M0 of dimension (i*j*k) for the casting:

[0024] In the formula, This represents the initial residual stress matrix of the machine tool casting. The residual stress data is for the i-th row and j-th column of layer k. Let be the residual stress vector of layer k.

[0025] Step 2: Group the initial residual stress matrix M0 according to the pool dimensions m×n×2, and then calculate the coefficient of variation (CV) of the residual stress data in each pool to obtain the initial residual stress coefficient of variation matrix M of the machine tool casting. c0 The dimensions of the intermediate matrix M0 are (k+1)×(k+1)×(k-1).

[0026] It should be noted that, using the formula , Solving for m1 and n1 yields m1 and n1, where m1 is the first positive integer and n1 is the second positive integer. The m is equal to m1+1, and the n is equal to n1+1.

[0027] It should be noted that step 2 is the initial pooling. In the initial pooling calculation, the dimension (m*n*2) of each sub-pooling matrix needs to be determined, based on m1 and n1:

[0028] Then, the initial residual stress data matrix M0 of the casting is used for the first pooling calculation, following the coefficient of variation formula:

[0029] in The standard deviation of the data in the small pool. This is the arithmetic mean of the data in the small pool.

[0030] The initial residual stress variation coefficient matrix M of the casting is obtained. c0 :

[0031] In the formula, The initial residual stress variation coefficient matrix of the casting. Let K be the vector of the initial residual stress variation coefficients corresponding to layer k-1. is the coefficient of variation in the (k+1)th row and (k+1)th column of layer k-1.

[0032] It should be noted that, as Figure 3 As shown, matrix M c0 The dimension should be [(k+1)*(k+1)*(k-1)]. If it does not meet this rule, the number of data collections should be replanned. If it does meet the rule, proceed to step 3 and continue pooling calculation using the coefficient of variation formula. Step 3: Calculate the initial residual stress variation coefficient matrix M. c0 Perform the iterative pooling operation in step 2 to obtain a simplified matrix M of the preset dimensions. cx ,like Figure 3 As shown; It should be noted that the preset dimension is 4×4×2.

[0033] It should be noted that the pooling calculation using the coefficient of variation formula will be performed (k-2) times, until the matrix M is iteratively updated. cx (x=0, 1, …, k-2) has a dimension of (4*4*2).

[0034] Step 4: Simplify matrix M cx Mean pooling is performed to obtain a two-dimensional partitioned coefficient of variation matrix M, and the total coefficient of variation CV of the machine tool casting is determined based on the two-dimensional partitioned coefficient of variation matrix M.

[0035] In this embodiment of the disclosure, step 4 specifically includes: Determine the standard deviation of residual stress and the average value of residual stress for each element in the two-dimensional partitioned coefficient of variation matrix M; The ratio of the standard deviation of the residual stress to the average value of the residual stress is determined, and the ratio is used as the total coefficient of variation of the machine tool casting.

[0036] It should be noted that, as Figure 3 As shown, the final residual stress variation coefficient matrix of the casting is then solved using mean pooling. And calculate the total coefficient of variation (CV).

[0037] Step 5: Evaluate whether the residual stress distribution of the machine tool casting is uniform based on the total coefficient of variation (CV) and the two-dimensional partitioned coefficient of variation matrix (M).

[0038] In this embodiment of the disclosure, step 5 specifically includes: When the total coefficient of variation is less than or equal to a preset first coefficient of variation threshold, it is determined that the residual stress distribution of the machine tool casting is uniform; otherwise, the residual stress distribution of the machine tool casting is not uniform. Based on the two-dimensional partitioned coefficient of variation matrix, the absolute value of the difference between the coefficient of variation of each region after mean pooling of the machine tool casting and the coefficient of variation of its adjacent regions is determined. If the absolute value of the difference is greater than the preset difference threshold, it is determined that the residual stress distribution in the adjacent regions is uneven.

[0039] It should be noted that the uniformity of residual stress distribution in machine tool castings is evaluated based on the final residual stress variation matrix M and the total coefficient of variation CV. The total coefficient of variation CV indicates the overall uniformity of residual stress distribution in the machine tool casting; a smaller CV value indicates better uniformity. The final residual stress matrix M, obtained after mean pooling, also provides information on the uniformity of residual stress distribution in different regions of the casting. Each data point in matrix M corresponds to an evaluation point. Figure 4 Different zones after the casting is pooled.

[0040] Specifically, in combination Figure 5 and Figure 6 The present invention will be described in detail using machine tool slide castings with different casting processes as examples.

[0041] Figure 5 The first process was used to perform casting simulation calculations on the slide parts of the machine tool to obtain the residual stress data of the casting. like Figure 2 As shown, taking the casting bottom surface as the reference, data is collected from left to right along the length direction (i=10 rows), from top to bottom along the width direction (j=6 columns), and from bottom to top along the height direction (k=4 layers). Therefore, according to the relationship satisfied by i, j, and k... We can obtain: .

[0042] Based on the selected data points, an initial residual stress data matrix M0 with dimensions (10*6*4) can be obtained for the casting. It consists of four two-dimensional matrices with dimensions (10*6).

[0043] :

[0044] :

[0045] :

[0046] :

[0047] Furthermore, the dimension (3*2*2) of each sub-pool matrix in the initial pooling calculation can be determined.

[0048] Then, a first pooling calculation was performed on the initial residual stress data matrix M0 of the casting to obtain the initial residual stress variation coefficient matrix M of the casting. c0 ;

[0049] :

[0050] :

[0051] :

[0052] In this embodiment, matrix M c0 The dimension is (5*5*3)=(k+1)*(k+1)*(k-1), which meets the conditions for continuing pooling calculation using the coefficient of variation formula; Then, a single pooling calculation is performed using the coefficient of variation formula, iteratively updating matrix M. c1 :

[0053] :

[0054] ：

[0055] In this case, matrix M c1 The dimension reaches (4*4*2), therefore we get M. c1 Then, mean-pooling is used to solve for the final residual stress variation coefficient matrix M of the casting:

[0056] And calculate the total coefficient of variation (CV):

[0057] In this case, the total coefficient of variation of the machine tool slide part cast by the first process is 0.232, which means that the overall residual stress distribution of the machine tool casting is uneven. According to its final residual stress coefficient of variation matrix M, the uneven distribution is more obvious in the four corner areas and the right side area of ​​the casting. This is because the casting structure has high sidewalls on the left and right sides. When the casting is poured from the bottom up and cooled, the sidewalls, especially the upper surface, cool more slowly, and are also affected by the riser design.

[0058] Figure 6 The second process was used to perform casting simulation calculations on the slide parts of the machine tool to obtain the residual stress data of the casting. Using the same data collection method as the previous case: collect data i=10 rows from left to right along the length direction, j=6 columns from top to bottom along the width direction, and k=4 layers from bottom to top along the height direction. This also ensures: .

[0059] The initial residual stress data matrix M0 of the casting has dimensions (10*6*4), which is also composed of four two-dimensional matrices with dimensions (10*6):

[0060] :

[0061] :

[0062] :

[0063] :

[0064] Similarly, the dimension (3*2*2) of each small pool matrix in the initial pooling calculation can be determined; Then, a first pooling calculation was performed on the initial residual stress data matrix M0 of the casting to obtain the initial residual stress variation coefficient matrix M of the casting. c0 ;

[0065] :

[0066] :

[0067] ：

[0068] Matrix M c0 The dimension is (5*5*3)=(k+1)*(k+1)*(k-1), which meets the conditions for continuing pooling calculation using the coefficient of variation formula; Then, the coefficient of variation formula is used to perform a single pooling calculation, iteratively updating matrix M. c1 :

[0069] :

[0070] :

[0071] In this case, matrix M c1 The dimension reaches (4*4*2), therefore we get M. c1 Then, mean-pooling is used to solve for the final residual stress variation coefficient matrix M of the casting:

[0072] In this case, the total coefficient of variation for the machine tool slide part cast using the second process is 0.162, indicating that the overall residual stress distribution of the machine tool casting still exhibits unevenness. However, compared to the casting obtained using the first process, its overall residual stress distribution is more uniform. According to its final residual stress coefficient of variation matrix M, the unevenness in the right-side region of the casting is more pronounced, but compared to the casting obtained using the first process, the coefficient of variation value in the right-side region is lower. This is because, after changing the casting temperature, speed, and demolding temperature in the second process, the overall cooling time of the casting is longer and more uniform, improving the stress unevenness of the right-side wall. However, the uneven residual stress distribution in this region due to its structure and riser design still exists.

[0073] In the evaluation method for the uniformity of residual stress distribution in machine tool castings proposed in this embodiment, the use of the coefficient of variation (CV) provides a dimensionless, relative measure of residual stress uniformity, while pooling theory enables feature extraction and dimensionality reduction simplification of the data. Specifically, the smaller the total coefficient of variation (CV) value, the better the overall uniformity of residual stress distribution in the machine tool casting. This provides a clear and traceable optimization objective for casting process optimization design—reducing the total coefficient of variation of residual stress in the casting. The final residual stress CV matrix M obtained after mean pooling also provides the possibility of defining regions of interest. The evaluation of residual stress uniformity at key locations in the casting structure can be optimized and tracked separately, such as the area around a hole or near a joint surface.

[0074] In summary, the evaluation method for the uniformity of residual stress distribution in machine tool castings proposed in this embodiment can accurately evaluate the uniformity of machine tool castings.

[0075] Example 2 Figure 7 This is a structural diagram of a system for evaluating the uniformity of residual stress distribution in machine tool castings according to an embodiment of this application, as shown below. Figure 7 As shown, the system includes: The acquisition module 100 is used to acquire residual stress data in the three-dimensional space of the machine tool casting to form an initial residual stress matrix of the machine tool casting. The dimension of the initial residual stress matrix is ​​i×j×k, where i is the number of rows in the length direction, j is the number of columns in the width direction, and k is the number of layers in the height direction. The values ​​of i, j, and k in the initial residual stress matrix are determined based on the length, width, and height dimensions of the cubic machine tool casting.

[0076] The initial pooling module 200 is used to group the initial residual stress matrix according to the smaller pool dimensions m×n×2, and then calculate the coefficient of variation of the residual stress data in each smaller pool to obtain the initial residual stress coefficient of variation matrix of the machine tool casting. The dimension of the intermediate matrix is ​​(k+1)×(k+1)×(k-1). Among them, using the formula , Solving for m1 and n1 yields m1 and n1, where m1 is the first positive integer and n1 is the second positive integer. The m equals m1+1, and the n equals n1+1; The preset dimension is 4×4×2.

[0077] The iterative pooling module 300 is used to perform the iterative pooling operation of step 2 on the initial residual stress variation coefficient matrix to obtain a simplified matrix of a preset dimension. Mean pooling module 400 is used to perform mean pooling on the simplified matrix to obtain a two-dimensional partitioned coefficient of variation matrix, and to determine the total coefficient of variation of the machine tool casting based on the two-dimensional partitioned coefficient of variation matrix; Evaluation module 500 is used to evaluate whether the residual stress distribution of the machine tool casting is uniform based on the total coefficient of variation and the two-dimensional partitioned coefficient of variation matrix.

[0078] In this embodiment of the disclosure, the mean pooling module 400 is further configured to: Determine the standard deviation of residual stress and the average value of residual stress for each element in the two-dimensional partitioned coefficient of variation matrix M; The ratio of the standard deviation of the residual stress to the average value of the residual stress is determined, and the ratio is used as the total coefficient of variation of the machine tool casting.

[0079] In this embodiment of the disclosure, the evaluation module 500 is further configured to: When the total coefficient of variation is less than or equal to a preset first coefficient of variation threshold, it is determined that the residual stress distribution of the machine tool casting is uniform; otherwise, the residual stress distribution of the machine tool casting is not uniform. Based on the two-dimensional partitioned coefficient of variation matrix, the absolute value of the difference between the coefficient of variation of each region after mean pooling of the machine tool casting and the coefficient of variation of its adjacent regions is determined. If the absolute value of the difference is greater than the preset difference threshold, it is determined that the residual stress distribution in the adjacent regions is uneven.

[0080] In summary, the evaluation system for the uniformity of residual stress distribution in machine tool castings proposed in this embodiment can accurately evaluate the uniformity of machine tool castings.

[0081] Example 3 To implement the above embodiments, this disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method described in Embodiment 1.

[0082] Example 4 To implement the above embodiments, this disclosure also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in Embodiment 1.

[0083] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0084] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0085] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for evaluating the uniformity of residual stress distribution in machine tool castings, characterized in that, The method includes: Step 1: Obtain the residual stress data in the three-dimensional space of the machine tool casting to form the initial residual stress matrix of the machine tool casting. The dimension of the initial residual stress matrix is ​​i×j×k, where i is the number of rows in the length direction, j is the number of columns in the width direction, and k is the number of layers in the height direction. Step 2: Group the initial residual stress matrix according to the pool dimension m×n×2, and then calculate the coefficient of variation of the residual stress data in each pool to obtain the initial residual stress coefficient of variation matrix of the machine tool casting. The dimension of the intermediate matrix is ​​(k+1)×(k+1)×(k-1). Step 3: Perform iterative pooling operation from Step 2 on the initial residual stress variation coefficient matrix to obtain a simplified matrix of the preset dimensions; Step 4: Perform mean pooling on the simplified matrix to obtain a two-dimensional partitioned coefficient of variation matrix, and determine the total coefficient of variation of the machine tool casting based on the two-dimensional partitioned coefficient of variation matrix; Step 5: Evaluate whether the residual stress distribution of the machine tool casting is uniform based on the total coefficient of variation and the two-dimensional partitioned coefficient of variation matrix.

2. The method as described in claim 1, characterized in that, The values ​​of i, j, and k in the initial residual stress matrix are determined based on the length, width, and height dimensions of the cubic machine tool casting.

3. The method as described in claim 2, characterized in that, Using formula , Solving for m1 and n1 yields m1 and n1, where m1 is the first positive integer and n1 is the second positive integer. The m equals m1+1, and the n equals n1+1; The preset dimension is 4×4×2.

4. The method as described in claim 3, characterized in that, The determination of the total coefficient of variation of the machine tool casting based on the two-dimensional partitioned coefficient of variation matrix includes: Determine the standard deviation of residual stress and the average value of residual stress for each element in the two-dimensional partitioned coefficient of variation matrix; The ratio of the standard deviation of the residual stress to the average value of the residual stress is determined, and the ratio is used as the total coefficient of variation of the machine tool casting.

5. The method as described in claim 4, characterized in that, The evaluation of the uniformity of residual stress distribution in the machine tool casting based on the total coefficient of variation and the two-dimensional partitioned coefficient of variation matrix includes: When the total coefficient of variation is less than or equal to a preset first coefficient of variation threshold, it is determined that the residual stress distribution of the machine tool casting is uniform; otherwise, the residual stress distribution of the machine tool casting is not uniform. Based on the two-dimensional partitioned coefficient of variation matrix, the absolute value of the difference between the coefficient of variation of each region after mean pooling of the machine tool casting and the coefficient of variation of its adjacent regions is determined. If the absolute value of the difference is greater than the preset difference threshold, it is determined that the residual stress distribution in the adjacent regions is uneven.

6. A system for evaluating the uniformity of residual stress distribution in machine tool castings, characterized in that, The system includes: The acquisition module is used to acquire residual stress data in the three-dimensional space of the machine tool casting to form an initial residual stress matrix of the machine tool casting. The dimension of the initial residual stress matrix is ​​i×j×k, where i is the number of rows in the length direction, j is the number of columns in the width direction, and k is the number of layers in the height direction. The initial pooling module is used to group the initial residual stress matrix according to the smaller pool dimensions m×n×2, and then calculate the coefficient of variation of the residual stress data in each smaller pool to obtain the initial residual stress coefficient of variation matrix of the machine tool casting. The dimensions of the intermediate matrix are (k+1)×(k+1)×(k-1). The iterative pooling module is used to perform the iterative pooling operation in step 2 on the initial residual stress variation coefficient matrix to obtain a simplified matrix of a preset dimension. The mean pooling module is used to perform mean pooling on the simplified matrix to obtain a two-dimensional partitioned coefficient of variation matrix, and to determine the total coefficient of variation of the machine tool casting based on the two-dimensional partitioned coefficient of variation matrix. The evaluation module is used to evaluate whether the residual stress distribution of the machine tool casting is uniform based on the total coefficient of variation and the two-dimensional partitioned coefficient of variation matrix.

7. The system as described in claim 6, characterized in that, The values ​​of i, j, and k in the initial residual stress matrix are determined based on the length, width, and height dimensions of the cubic machine tool casting.

8. The system as described in claim 7, characterized in that, Using formula , Solving for m1 and n1 yields m1 and n1, where m1 is the first positive integer and n1 is the second positive integer. The m equals m1+1, and the n equals n1+1; The preset dimension is 4×4×2.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

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