Turning optimization method and device, electronic equipment and storage medium

Abstract

The invention belongs to the technical field of turning optimization, and discloses a turning optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a roughness training sample of a lathe through a tool angle adjustment system, carrying out the calculation of a historical roughness calculation result corresponding to a historical workpiece surface image in the roughness training sample, and carrying out the calculation of the historical roughness. According to historical lathe machining parameters, historical tool machining angles and historical workpiece surface images in the roughness training samples and corresponding historical roughness calculation results, an optimal parameter model is constructed, the obtained to-be-machined target parameters are input into the optimal parameter model, and the optimal machining angles of the to-be-machined target parameters are obtained through calculation; according to the method, the to-be-machined target parameters are input into the optimal parameter model constructed by the roughness training sample obtained based on the tool angle adjusting system, the optimal machining angle is obtained through calculation, turning machining is optimized, and the turning machining efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of turning process optimization, and in particular to a turning process optimization method, device, electronic equipment and storage medium. Background Art

[0002] In the existing machining parameter optimization process, when calculating the turning surface roughness, the turning parameters such as coolant flow, feed amount, feed speed, speed parameters and other data are mainly used for calculation and optimization, and most of these parameters are obtained by summarizing experience in engineering applications.

[0003] However, in the prior art, there is little analysis of the tool processing angle. The spatial geometric position of the tool itself is an important factor affecting the processing quality, especially when excellent surface roughness effect is required, the tool processing angle is extremely important.

[0004] Therefore, in order to solve the technical problem that the existing machining parameter optimization method does not consider the influence of the tool machining angle on the roughness, a turning machining optimization method, device, electronic device and storage medium are urgently needed. Summary of the invention

[0005] The purpose of the present application is to provide a turning processing optimization method, device, electronic device and storage medium. By inputting the target parameters to be processed into an optimal parameter model constructed based on a roughness training sample obtained by a tool angle adjustment system, the optimal processing angle is calculated, thereby solving the problem that the existing processing parameter optimization method does not consider the influence of the tool processing angle on the roughness. The roughness can be analyzed by adjusting the tool processing angle to optimize the parameters, or the optimal roughness can be obtained by adjusting the tool processing angle under fixed parameters, thereby improving the turning processing efficiency.

[0006] In a first aspect, the present application provides a turning process optimization method, which is applied to a tool angle adjustment system to optimize the tool processing angle, wherein the tool angle adjustment system includes a pitch angle control platform, a rotation angle control platform, and an azimuth angle control platform; the turning process optimization method includes: Through the tool angle adjustment system, a roughness training sample of a lathe is obtained; the roughness training sample includes historical lathe processing parameters, historical tool processing angles and corresponding historical workpiece surface images; the historical lathe processing parameters include historical records of lathe rotation speed, feed amount, feed speed and coolant flow at the same processing time; the historical tool processing angle is adjusted by the tool angle adjustment system, and the historical tool processing angle includes historical records of tool pitch angle, tool rotation angle and tool orientation angle at the same processing time; Calculating and obtaining a historical roughness calculation result corresponding to the historical workpiece surface image; Constructing an optimal parameter model according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results; The acquired target parameters to be processed are input into the optimal parameter model, the optimal processing angle of the target parameters to be processed is calculated, and the processing angle of the tool is adjusted according to the optimal processing angle.

[0007] The turning processing optimization method provided in the present application can optimize the processing angle. The optimal processing angle is calculated by inputting the target parameters to be processed into the optimal parameter model constructed based on the roughness training samples obtained by the tool angle adjustment system. This solves the problem that the existing processing parameter optimization methods do not consider the influence of the tool processing angle on the roughness. The roughness can be analyzed by adjusting the tool processing angle to optimize the parameters, or the optimal roughness can be obtained by adjusting the tool processing angle under fixed parameters, thereby improving the turning processing efficiency.

[0008] Optionally, obtaining a roughness training sample of a lathe through the tool angle adjustment system includes: Through the tool angle adjustment system, lathe processing parameters with different values ​​and tool processing angles with different angles are selected and combined to process multiple sampled workpieces to obtain corresponding workpiece surface images; The lathe processing parameters, the tool processing angles and the workpiece surface images belonging to the same combination are divided into historical records of the same processing moment to obtain roughness training samples of the lathe.

[0009] Optionally, calculating a historical roughness calculation result corresponding to the historical workpiece surface image includes: extracting roughness image features from the historical workpiece surface image; The roughness value of the roughness image feature is detected by a detection algorithm to obtain a historical roughness calculation result corresponding to the historical workpiece surface image.

[0010] The turning processing optimization method provided in this application can optimize the processing angle. The roughness prediction model constructed by the detection algorithm detects the roughness image, and the historical roughness calculation results corresponding to the historical workpiece surface images are obtained. The optimal parameter model is constructed through the historical roughness calculation results to calculate the optimal processing angle, which is beneficial to improving the turning processing efficiency.

[0011] Optionally, constructing an optimal parameter model according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results includes: Using the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results, a long short-term memory network model is constructed to obtain a primary optimal parameter model; Training the primary optimal parameter model according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images, and the corresponding historical roughness calculation results for training data to obtain a trained primary optimal parameter model; Based on the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results, the trained primary optimal parameter model is verified to obtain the optimal parameter model.

[0012] The turning processing optimization method provided in this application can optimize the processing angle. The model is trained and verified through historical lathe processing parameters, historical tool processing angles, historical workpiece surface images and corresponding historical roughness calculation results to obtain the optimal parameter model. Processing is performed at the optimal processing angle calculated by the optimal parameter model, which is beneficial to improving the turning processing efficiency.

[0013] Optionally, the primary optimal parameter model is trained according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results for training data to obtain the trained primary optimal parameter model, including: Extracting the optimal historical roughness calculation result from the historical roughness calculation results corresponding to the same historical lathe processing parameters, and obtaining the optimal historical tool processing angle corresponding to the optimal historical roughness calculation result; Inputting the training data in the historical lathe processing parameters into the primary optimal parameter model to obtain corresponding roughness output and angle output; Determine a training error according to an optimal historical tool processing angle corresponding to an optimal historical roughness calculation result corresponding to data used for training in the historical lathe processing parameters and the corresponding roughness output and angle output; Based on the training errors, the parameters of the primary optimal parameter model are adjusted to obtain the optimal parameters, and the primary optimal parameter model is optimized using the optimal parameters to obtain the trained primary optimal parameter model.

[0014] Optionally, before inputting the data used for training in the historical lathe processing parameters into the primary optimal parameter model to obtain the corresponding roughness output and angle output, the method further includes: Initialize the parameters of the primary optimal parameter model.

[0015] Optionally, the target parameter to be processed includes a processing parameter to be measured or a target roughness to be processed; the optimal parameter model includes a first optimal parameter model that takes the processing parameter to be measured as input data, and takes an optimal roughness prediction result and a corresponding optimal processing angle as output, and a second optimal parameter model that takes the target roughness to be processed as input data, and takes an optimal processing parameter and an optimal processing angle as output; inputting the acquired target parameter to be processed into the optimal parameter model, calculating the optimal processing angle of the target parameter to be processed, and adjusting the processing angle of the tool according to the optimal processing angle, including: Acquiring the processing parameter to be measured or the target roughness to be processed; Inputting the processing parameters to be measured into the first optimal parameter model to obtain an optimal roughness prediction result and a corresponding optimal processing angle; Alternatively, the target roughness to be processed is converted into the second optimal parameter model to obtain optimal processing parameters and optimal processing angles.

[0016] In a second aspect, the present application provides a turning process optimization device, which is applied to a tool angle adjustment system to optimize the tool processing angle, wherein the tool angle adjustment system includes a pitch angle control platform, a rotation angle control platform, and an azimuth angle control platform; the turning process optimization device includes: An acquisition module is used to acquire a roughness training sample of a lathe through the tool angle adjustment system; the roughness training sample includes historical lathe processing parameters, historical tool processing angles and corresponding historical workpiece surface images; the historical lathe processing parameters include historical records of lathe rotation speed, feed amount, feed speed and coolant flow at the same processing time; the historical tool processing angle is obtained by adjusting the tool angle adjustment system, and the historical tool processing angle includes historical records of tool pitch angle, tool rotation angle and tool orientation angle at the same processing time; A calculation module, used for calculating and obtaining a historical roughness calculation result corresponding to the historical workpiece surface image; A construction module, for constructing an optimal parameter model according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results; The optimization module is used to input the acquired target parameters to be processed into the optimal parameter model, calculate the optimal processing angle of the target parameters to be processed, and adjust the processing angle of the tool according to the optimal processing angle.

[0017] The turning processing optimization device calculates the optimal processing angle by inputting the target parameters to be processed into the optimal parameter model constructed based on the roughness training samples obtained by the tool angle adjustment system, thereby solving the problem that the existing processing parameter optimization method does not consider the influence of the tool processing angle on the roughness. The roughness can be analyzed by adjusting the tool processing angle to optimize the parameters, or the optimal roughness can be obtained by adjusting the tool processing angle under fixed parameters, thereby improving the turning processing efficiency.

[0018] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, it runs the steps in the turning process optimization method described above.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the turning process optimization method described above are executed.

[0020] Beneficial effects: The turning processing optimization method, device, electronic device and storage medium provided in the present application calculate the optimal processing angle by inputting the target parameters to be processed into the optimal parameter model constructed based on the roughness training samples obtained by the tool angle adjustment system, thereby solving the problem that the existing processing parameter optimization method does not consider the influence of the tool processing angle on the roughness. The roughness can be analyzed by adjusting the tool processing angle to optimize the parameters, or the optimal roughness can be obtained by adjusting the tool processing angle under fixed parameters, thereby improving the turning processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a turning process optimization method provided in an embodiment of the present application.

[0022] Figure 2 A schematic diagram of the structure of a turning process optimization device provided in an embodiment of the present application.

[0023] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0024] Figure 4 This is a schematic diagram of the structure of the tool angle adjustment system.

[0025] Explanation of reference numerals: 1. Acquisition module; 2. Calculation module; 3. Construction module; 4. Optimization module; 301. Processor; 302. Memory; 303. Communication bus. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0027] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0028] Please refer to Figure 1 , Figure 1 A turning process optimization method in some embodiments of the present application is applied to a tool angle adjustment system to optimize the tool processing angle. The tool angle adjustment system includes a pitch angle control platform, a rotation angle control platform and an azimuth angle control platform. The turning process optimization method includes the steps of: Step S101, obtaining a roughness training sample of a lathe through a tool angle adjustment system; the roughness training sample includes historical lathe processing parameters, historical tool processing angles and corresponding historical workpiece surface images; the historical lathe processing parameters include historical records of lathe speed, feed amount, feed speed and coolant flow at the same processing time; the historical tool processing angle is adjusted by the tool angle adjustment system, and the historical tool processing angle includes historical records of tool pitch angle, tool rotation angle and tool orientation angle at the same processing time; Step S102, calculating and obtaining the historical roughness calculation result corresponding to the historical workpiece surface image; Step S103, constructing an optimal parameter model according to historical lathe processing parameters, historical tool processing angles, historical workpiece surface images and corresponding historical roughness calculation results; Step S104: input the acquired target parameters to be processed into the optimal parameter model, calculate the optimal processing angle of the target parameters to be processed, and adjust the processing angle of the tool according to the optimal processing angle.

[0029] The turning processing optimization method calculates the optimal processing angle by inputting the target parameters to be processed into the optimal parameter model constructed by obtaining the roughness training samples based on the tool angle adjustment system, thereby solving the problem that the existing processing parameter optimization methods do not consider the influence of the tool processing angle on the roughness. The roughness can be analyzed by adjusting the tool processing angle to optimize the parameters, or the optimal roughness can be obtained by adjusting the tool processing angle under fixed parameters, thereby improving the turning processing efficiency.

[0030] Specifically, in step S101, a roughness training sample of a lathe is obtained through a tool angle adjustment system, including: Through the tool angle adjustment system, lathe processing parameters with different values ​​and tool processing angles with different angles are selected and combined to process multiple sampled workpieces to obtain corresponding workpiece surface images; The lathe machining parameters, tool machining angles and workpiece surface images belonging to the same combination are divided into historical records of the same machining moment to obtain the roughness training samples of the lathe.

[0031] In step S101, lathe processing parameters with different values ​​and tool processing angles with different angles are selected for combination, and the tool processing angle is adjusted to the selected tool processing angle by the tool angle adjustment system to process multiple sampled workpieces to obtain corresponding workpiece surface images. Among them, the multiple sampled workpieces are workpieces of the same scale and the same material. In practical applications, different lathe processing parameters and tool processing angles can be selected to process workpieces of different scales and different materials in turn, and sufficient training data can be obtained by multiple sampling to make the roughness training samples richer.

[0032] The lathe processing parameters, tool processing angles and workpiece surface images belonging to the same combination (i.e., processed at the same time) are divided into historical records of the same processing time to obtain the roughness training samples of the lathe. Among them, the roughness training samples include historical lathe processing parameters, historical tool processing angles and corresponding historical workpiece surface images; the historical lathe processing parameters include the historical records of lathe speed, feed amount, feed speed and coolant flow at the same processing time; the historical tool processing angle is the historical record of the tool processing angle at the same processing time as the historical lathe processing parameters; the historical workpiece surface image is the workpiece surface image obtained by processing using the historical lathe processing parameters and historical tool processing angles at the same processing time, and the workpiece surface image can reflect the roughness after processing using the historical lathe processing parameters and historical tool processing angles at the corresponding processing time; The historical tool processing angle is adjusted by the tool angle adjustment system. The historical tool processing angle includes the historical records of the tool pitch angle, tool rotation angle and tool azimuth angle at the same processing time. The tool pitch angle refers to the angle between the tool and the horizontal plane, the tool rotation angle refers to the angle at which the tool rotates around its own central axis, and the tool azimuth angle refers to the angle at which the tool rotates around the vertical line corresponding to the processing position point of the workpiece (that is, the vertical line passing through the processing position point).

[0033] Among them, Figure 4 As shown, Figure 4 Schematic diagram of the structure of the tool angle adjustment system, wherein a is the pitch angle control platform, b is the rotation angle control platform, c is the azimuth angle control platform, d is the tool (machining tool), e is the workpiece (sampling workpiece), f is the lathe spindle chuck, g is the coolant nozzle with a flow meter, h is the shooting device, and i is the control system. The tool pitch angle of tool d can be adjusted by the pitch angle control platform a, the tool rotation angle of tool d can be adjusted by the rotation angle control platform b, the tool azimuth angle of tool d can be adjusted by the azimuth angle control platform c, the coolant flow rate is adjusted by the coolant nozzle g with a flow meter, and the surface image of workpiece e is obtained by shooting with the shooting device h.

[0034] Specifically, in step S102, the historical roughness calculation result corresponding to the historical workpiece surface image is calculated, including: Extracting roughness image features from historical workpiece surface images; The roughness value of the roughness image feature is detected through the detection algorithm, and the historical roughness calculation result corresponding to the historical workpiece surface image is obtained.

[0035] In step S102, the roughness image features are extracted from the historical workpiece surface image by using methods such as local binary pattern (LBP) or gray level co-occurrence matrix (GLCM), and the roughness values ​​of the roughness image features are calculated by detection algorithms such as contour arithmetic mean deviation (Ra), contour maximum height (Ry), micro roughness ten-point height (Rz), peak and valley (Rp and Rv) or root mean square (Rq), so as to obtain the historical roughness calculation results corresponding to the historical workpiece surface image. Among them, algorithms such as local binary pattern (LBP), gray level co-occurrence matrix (GLCM), contour arithmetic mean deviation (Ra), contour maximum height (Ry), micro roughness ten-point height (Rz), peak and valley (Rp and Rv) and root mean square (Rq) are existing technologies and will not be described in detail here.

[0036] Specifically, in step S103, an optimal parameter model is constructed according to historical lathe processing parameters, historical tool processing angles, historical workpiece surface images, and corresponding historical roughness calculation results, including: Using historical lathe processing parameters, historical tool processing angles, historical workpiece surface images and corresponding historical roughness calculation results, a long short-term memory network model is constructed to obtain a primary optimal parameter model. Training a primary optimal parameter model according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images, and the corresponding historical roughness calculation results for training data to obtain a trained primary optimal parameter model; Based on the data not used for training in historical lathe processing parameters, historical tool processing angles, historical workpiece surface images and corresponding historical roughness calculation results, the trained primary optimal parameter model is verified to obtain the optimal parameter model.

[0037] In step S103, a long short-term memory network (LSTM) model is constructed using historical lathe processing parameters, historical tool processing angles, historical workpiece surface images, and corresponding historical roughness calculation results to obtain a primary optimal parameter model. The long short-term memory network model can better capture the complexity and long-term trend of lathe processing parameters, tool processing angles, and processing effect data (roughness calculation results). After a large amount of data comparison (i.e., training with a large amount of data), the long short-term memory network model can better reflect the relationship between lathe processing parameters, tool processing angles, and roughness, and finally output the optimal roughness and the corresponding optimal tool processing angle. Among them, the long short-term memory network model is a prior art and will not be described in detail here.

[0038] Specifically, in step S103, the primary optimal parameter model is trained according to the data used for training in the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results, and the trained primary optimal parameter model is obtained, including: Extract the optimal historical roughness calculation result from the historical roughness calculation results corresponding to the same historical lathe processing parameters, and obtain the optimal historical tool processing angle corresponding to the optimal historical roughness calculation result; Input the training data in the historical lathe processing parameters into the primary optimal parameter model to obtain the corresponding roughness output and angle output; Determine the training error according to the optimal historical roughness calculation result and the optimal historical tool processing angle corresponding to the data used for training in the historical lathe processing parameters and the corresponding roughness output and angle output; Based on the training error, the parameters of the primary optimal parameter model are adjusted to obtain the optimal parameters, and the primary optimal parameter model is optimized using the optimal parameters to obtain the trained primary optimal parameter model.

[0039] In step S103, the minimum value of the historical roughness calculation results when the historical lathe processing parameters are the same is extracted as the optimal historical roughness calculation result, and the optimal historical tool processing angle corresponding to the optimal historical roughness calculation result is obtained. The optimal historical roughness calculation result and the optimal historical tool processing angle corresponding to the historical lathe processing parameters used for training are compared with the roughness output and the angle output obtained by inputting the historical lathe processing parameters used for training into the primary optimal parameter model to obtain a training error, such as the size degree of a specific value in the optimal historical roughness calculation result and the corresponding value in the roughness output, or the degree of deviation between a specific value in the roughness output and the corresponding value in the historical roughness calculation result (for example, the specific value of the roughness output is not the roughness value but other values, such as the length or width of the roughness image feature). The training error is used to adjust the parameters of the primary optimal parameter model. If the training error is the size degree of a specific value in the optimal historical roughness calculation result and the corresponding value in the roughness output, the corresponding model parameters are modified to make the value of the roughness output more accurate, so as to obtain the optimal parameters. The primary optimal parameter model is optimized through the optimal parameters to obtain the trained primary optimal parameter model.

[0040] In some optional embodiments, the historical lathe processing parameter with the smallest value can be extracted from the historical roughness calculation results with the same value as the optimal historical lathe processing parameter, and the historical tool processing angle corresponding to the optimal historical lathe processing parameter can be used as the optimal historical tool processing angle, and each historical roughness calculation result can be input into the primary optimal parameter model for training, so that the processing parameter output and angle output of the trained primary optimal parameter model tend to the optimal historical tool processing angle corresponding to the optimal historical lathe processing parameter.

[0041] In practical applications, the two primary optimal parameter models mentioned above can be constructed simultaneously, so that the actual processing parameters and the actual target roughness are respectively input into the two primary optimal parameter models to obtain the corresponding optimal tool processing angle.

[0042] In step S103, the data in the historical lathe processing parameters that are not used for training are input into the trained primary optimal parameter model to obtain corresponding roughness output and angle output. The roughness output and angle output are used as verification data. The verification data is compared with the optimal historical roughness calculation result and the optimal historical tool processing angle corresponding to the historical lathe processing parameters that are not used for training to determine whether the error is within an acceptable range (the acceptable error range is generally 0 to 3%, which can be modified according to actual needs), verify the accuracy of the trained primary optimal parameter model, and obtain the optimal parameter model.

[0043] In some optional embodiments, when the input is a historical roughness calculation result, the data in the historical roughness calculation result that is not used for training is input into the trained primary optimal parameter model to obtain the corresponding processing parameter output and angle output, and the processing parameter output and angle output are used as verification data. The verification data is compared with the optimal historical lathe processing parameters and the optimal historical tool processing angle corresponding to the historical roughness calculation result that is not used for training to determine whether the error is within an acceptable range (the acceptable error range is generally 0 to 3%, which can be modified according to actual needs), verify the accuracy of the trained primary optimal parameter model, and obtain the optimal parameter model.

[0044] Specifically, in step S103, before inputting the data used for training in the historical lathe processing parameters into the primary optimal parameter model to obtain the corresponding roughness output and angle output, the method further includes: Initialize the parameters of the primary optimal parameter model.

[0045] In step S103, before inputting data to train the model, the parameters of the primary optimal parameter model are initialized to ensure that the model is in normal use.

[0046] Specifically, the target parameters to be processed include the processing parameters to be measured or the target roughness to be processed; the optimal parameter model includes a first optimal parameter model that takes the processing parameters to be measured as input data, and takes the optimal roughness prediction result and the corresponding optimal processing angle as output, and a second optimal parameter model that takes the target roughness to be processed as input data, and takes the optimal processing parameters and the optimal processing angle as output; in step S104, the acquired target parameters to be processed are input into the optimal parameter model, the optimal processing angle of the target parameters to be processed is calculated, and the processing angle of the tool is adjusted according to the optimal processing angle, including: Obtaining the processing parameters to be measured or the target roughness to be processed; Inputting the processing parameters to be measured into the first optimal parameter model to obtain the optimal roughness prediction result and the corresponding optimal processing angle; Alternatively, the target roughness to be processed is converted to a second optimal parameter model to obtain optimal processing parameters and optimal processing angles.

[0047] In step S104, according to actual conditions, the processing parameters to be measured can be input into the first optimal parameter model to obtain the corresponding optimal roughness prediction result and the corresponding optimal processing angle. The workpiece to be processed is processed by the processing parameters to be measured and the optimal processing angle to obtain the optimal roughness prediction result.

[0048] Alternatively, the target roughness to be processed is input into the second optimal parameter model to obtain the optimal processing parameters and the optimal processing angle, and the workpiece to be processed is processed by the optimal processing parameters and the optimal processing angle to obtain the target roughness to be processed. The optimal processing parameters and the optimal processing angle include the optimal processing parameters (parameters with the least consumption) and the corresponding processing angle, or the optimal combination of processing parameters and processing angles, which can be set by adjusting the output results of the model during model training.

[0049] As can be seen from the above, the turning processing optimization method obtains the roughness training samples of the lathe through the tool angle adjustment system, calculates the historical roughness calculation results corresponding to the historical workpiece surface images in the roughness training samples, constructs the optimal parameter model according to the historical lathe processing parameters, historical tool processing angles and historical workpiece surface images and the corresponding historical roughness calculation results in the roughness training samples, inputs the obtained target parameters to be processed into the optimal parameter model, calculates the optimal processing angle of the target parameters to be processed, and adjusts the processing angle of the tool according to the optimal processing angle; thus, by inputting the target parameters to be processed into the optimal parameter model constructed based on the roughness training samples obtained by the tool angle adjustment system, the optimal processing angle is calculated, which solves the problem that the existing processing parameter optimization method does not consider the influence of the tool processing angle on the roughness, and can analyze the roughness by adjusting the tool processing angle to optimize the parameters, or obtain the optimal roughness by adjusting the tool processing angle under fixed parameters, thereby improving the turning processing efficiency.

[0050] refer to Figure 2 The present application provides a turning process optimization device, which is applied to a tool angle adjustment system to optimize the tool processing angle. The tool angle adjustment system includes a pitch angle control platform, a rotation angle control platform and an azimuth angle control platform. The turning process optimization device includes: Acquisition module 1 is used to acquire roughness training samples of a lathe through the tool angle adjustment system; the roughness training samples include historical lathe processing parameters, historical tool processing angles and corresponding historical workpiece surface images; the historical lathe processing parameters include historical records of lathe rotation speed, feed amount, feed speed and coolant flow at the same processing time; the historical tool processing angles are adjusted by the tool angle adjustment system, and the historical tool processing angles include historical records of tool pitch angles, tool rotation angles and tool orientation angles at the same processing time; Calculation module 2, used to calculate the historical roughness calculation result corresponding to the historical workpiece surface image; Construction module 3, used to construct an optimal parameter model according to historical lathe processing parameters, historical tool processing angles, historical workpiece surface images and corresponding historical roughness calculation results; The optimization module 4 is used to input the acquired target parameters to be processed into the optimal parameter model, calculate the optimal processing angle of the target parameters to be processed, and adjust the processing angle of the tool according to the optimal processing angle.

[0051] The turning processing optimization device calculates the optimal processing angle by inputting the target parameters to be processed into the optimal parameter model constructed based on the roughness training samples obtained by the tool angle adjustment system, thereby solving the problem that the existing processing parameter optimization method does not consider the influence of the tool processing angle on the roughness. The roughness can be analyzed by adjusting the tool processing angle to optimize the parameters, or the optimal roughness can be obtained by adjusting the tool processing angle under fixed parameters, thereby improving the turning processing efficiency.

[0052] Specifically, when the acquisition module 1 acquires the roughness training sample of the lathe through the tool angle adjustment system, it executes: Through the tool angle adjustment system, lathe processing parameters with different values ​​and tool processing angles with different angles are selected and combined to process multiple sampled workpieces to obtain corresponding workpiece surface images; The lathe machining parameters, tool machining angles and workpiece surface images belonging to the same combination are divided into historical records of the same machining moment to obtain the roughness training samples of the lathe.

[0053] When the acquisition module 1 is executed, lathe processing parameters with different values ​​and tool processing angles with different angles are selected for combination, and the tool processing angle is adjusted to the selected tool processing angle through the tool angle adjustment system to process multiple sampled workpieces to obtain corresponding workpiece surface images. Among them, the multiple sampled workpieces are workpieces of the same scale and the same material. In practical applications, different lathe processing parameters and tool processing angles can be selected to process workpieces of different scales and different materials in turn, and sufficient training data can be obtained by multiple sampling to make the roughness training samples richer.

[0054] The lathe processing parameters, tool processing angles and workpiece surface images belonging to the same combination (i.e., processed at the same time) are divided into historical records of the same processing time to obtain the roughness training samples of the lathe. Among them, the roughness training samples include historical lathe processing parameters, historical tool processing angles and corresponding historical workpiece surface images; the historical lathe processing parameters include the historical records of lathe speed, feed amount, feed speed and coolant flow at the same processing time; the historical tool processing angle is the historical record of the tool processing angle at the same processing time as the historical lathe processing parameters; the historical workpiece surface image is the workpiece surface image obtained by processing using the historical lathe processing parameters and historical tool processing angles at the same processing time, and the workpiece surface image can reflect the roughness after processing using the historical lathe processing parameters and historical tool processing angles at the corresponding processing time; The historical tool processing angle is adjusted by the tool angle adjustment system. The historical tool processing angle includes the historical records of the tool pitch angle, tool rotation angle and tool azimuth angle at the same processing time. The tool pitch angle refers to the angle between the tool and the horizontal plane, the tool rotation angle refers to the angle at which the tool rotates around its own central axis, and the tool azimuth angle refers to the angle at which the tool rotates around the vertical line corresponding to the processing position point of the workpiece (that is, the vertical line passing through the processing position point).

[0055] Among them, Figure 4 As shown, Figure 4 Schematic diagram of the structure of the tool angle adjustment system, wherein a is the pitch angle control platform, b is the rotation angle control platform, c is the azimuth angle control platform, d is the tool (machining tool), e is the workpiece (sampling workpiece), f is the lathe spindle chuck, g is the coolant nozzle with a flow meter, h is the shooting device, and i is the control system. The tool pitch angle of tool d can be adjusted by the pitch angle control platform a, the tool rotation angle of tool d can be adjusted by the rotation angle control platform b, the tool azimuth angle of tool d can be adjusted by the azimuth angle control platform c, the coolant flow rate is adjusted by the coolant nozzle g with a flow meter, and the surface image of workpiece e is obtained by shooting with the shooting device h.

[0056] Specifically, when calculating the historical roughness calculation result corresponding to the historical workpiece surface image, the calculation module 2 executes: Extracting roughness image features from historical workpiece surface images; The roughness value of the roughness image feature is detected through the detection algorithm, and the historical roughness calculation result corresponding to the historical workpiece surface image is obtained.

[0057] When the calculation module 2 is executed, the roughness image features are extracted from the historical workpiece surface image using methods such as local binary pattern (LBP) or gray level co-occurrence matrix (GLCM), and the roughness value of the roughness image features is calculated through detection algorithms such as contour arithmetic mean deviation (Ra), contour maximum height (Ry), micro roughness ten-point height (Rz), peak and valley (Rp and Rv) or root mean square (Rq), so as to obtain the historical roughness calculation result corresponding to the historical workpiece surface image. Among them, algorithms such as local binary pattern (LBP), gray level co-occurrence matrix (GLCM), contour arithmetic mean deviation (Ra), contour maximum height (Ry), micro roughness ten-point height (Rz), peak and valley (Rp and Rv) and root mean square (Rq) are existing technologies and will not be described in detail here.

[0058] Specifically, when constructing the optimal parameter model according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results, the construction module 3 executes: Using historical lathe processing parameters, historical tool processing angles, historical workpiece surface images and corresponding historical roughness calculation results, a long short-term memory network model is constructed to obtain a primary optimal parameter model. Training a primary optimal parameter model according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images, and the corresponding historical roughness calculation results for training data to obtain a trained primary optimal parameter model; Based on the data not used for training in historical lathe processing parameters, historical tool processing angles, historical workpiece surface images and corresponding historical roughness calculation results, the trained primary optimal parameter model is verified to obtain the optimal parameter model.

[0059] When construction module 3 is executed, the historical lathe processing parameters, historical tool processing angles, historical workpiece surface images and corresponding historical roughness calculation results are used to construct a long short-term memory network (LSTM) model to obtain a primary optimal parameter model. The long short-term memory network model can better capture the complexity and long-term trend of lathe processing parameters and tool processing angles and processing effect data (roughness calculation results). After a large amount of data comparison (i.e., training with a large amount of data), the long short-term memory network model can better reflect the relationship between lathe processing parameters and tool processing angles and roughness, and finally output the optimal roughness and the corresponding optimal tool processing angle. Among them, the long short-term memory network model is a prior art and will not be described in detail here.

[0060] Specifically, when the construction module 3 trains the primary optimal parameter model based on the data used for training in the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results, and obtains the trained primary optimal parameter model, it executes: Extract the optimal historical roughness calculation result from the historical roughness calculation results corresponding to the same historical lathe processing parameters, and obtain the optimal historical tool processing angle corresponding to the optimal historical roughness calculation result; Input the training data in the historical lathe processing parameters into the primary optimal parameter model to obtain the corresponding roughness output and angle output; Determine the training error according to the optimal historical roughness calculation result and the optimal historical tool processing angle corresponding to the data used for training in the historical lathe processing parameters and the corresponding roughness output and angle output; Based on the training error, the parameters of the primary optimal parameter model are adjusted to obtain the optimal parameters, and the primary optimal parameter model is optimized using the optimal parameters to obtain the trained primary optimal parameter model.

[0061] When construction module 3 is executed, the optimal historical roughness calculation results and the optimal historical tool processing angles corresponding to the historical lathe processing parameters used for training are compared with the roughness output and angle output obtained by inputting the historical lathe processing parameters used for training into the primary optimal parameter model, so as to obtain training errors, such as the degree of difference between a specific value in the optimal historical roughness calculation result and a corresponding value in the roughness output, or the degree of deviation between a specific value in the roughness output and a corresponding value in the historical roughness calculation result (for example, the specific value of the roughness output is not the roughness value but other values, such as the length or width of the roughness image feature), etc. The parameters of the primary optimal parameter model are adjusted using the training errors. If the training error is the degree of difference between a specific value in the optimal historical roughness calculation result and a corresponding value in the roughness output, the corresponding model parameters are modified to make the values ​​of the roughness output more accurate, so as to obtain the optimal parameters. The primary optimal parameter model is optimized using the optimal parameters to obtain the trained primary optimal parameter model.

[0062] In some optional embodiments, the historical lathe processing parameter with the smallest value can be extracted from the historical roughness calculation results with the same value as the optimal historical lathe processing parameter, and the historical tool processing angle corresponding to the optimal historical lathe processing parameter can be used as the optimal historical tool processing angle, and each historical roughness calculation result can be input into the primary optimal parameter model for training, so that the processing parameter output and angle output of the trained primary optimal parameter model tend to the optimal historical tool processing angle corresponding to the optimal historical lathe processing parameter.

[0063] In practical applications, the two primary optimal parameter models mentioned above can be constructed simultaneously, so that the actual processing parameters and the actual target roughness are respectively input into the two primary optimal parameter models to obtain the corresponding optimal tool processing angle.

[0064] When construction module 3 is executed, the data not used for training in the historical lathe processing parameters is input into the trained primary optimal parameter model to obtain the corresponding roughness output and angle output, and the roughness output and angle output are used as verification data. The verification data is compared with the optimal historical roughness calculation results and the optimal historical tool processing angle corresponding to the historical lathe processing parameters not used for training to determine whether the error is within an acceptable range (the acceptable error range is generally 0 to 3%, which can be modified according to actual needs), verify the accuracy of the trained primary optimal parameter model, and obtain the optimal parameter model.

[0065] In some optional embodiments, when the input is a historical roughness calculation result, the data in the historical roughness calculation result that is not used for training is input into the trained primary optimal parameter model to obtain the corresponding processing parameter output and angle output, and the processing parameter output and angle output are used as verification data. The verification data is compared with the optimal historical lathe processing parameters and the optimal historical tool processing angle corresponding to the historical roughness calculation result that is not used for training to determine whether the error is within an acceptable range (the acceptable error range is generally 0 to 3%, which can be modified according to actual needs), verify the accuracy of the trained primary optimal parameter model, and obtain the optimal parameter model.

[0066] Specifically, before inputting the training data in the historical lathe processing parameters into the primary optimal parameter model to obtain the corresponding roughness output and angle output, the construction module 3 also executes: Initialize the parameters of the primary optimal parameter model.

[0067] When constructing module 3, before inputting data to train the model, the parameters of the primary optimal parameter model are initialized to ensure that the model is in normal use.

[0068] Specifically, the target parameters to be processed include the processing parameters to be measured or the target roughness to be processed; the optimal parameter model includes a first optimal parameter model that takes the processing parameters to be measured as input data, and takes the optimal roughness prediction result and the corresponding optimal processing angle as output, and a second optimal parameter model that takes the target roughness to be processed as input data, and takes the optimal processing parameters and the optimal processing angle as output; when the optimization module 4 inputs the acquired target parameters to be processed into the optimal parameter model, calculates the optimal processing angle of the target parameters to be processed, and adjusts the processing angle of the tool according to the optimal processing angle, it executes: Obtaining the processing parameters to be measured or the target roughness to be processed; Inputting the processing parameters to be measured into the first optimal parameter model to obtain the optimal roughness prediction result and the corresponding optimal processing angle; Alternatively, the target roughness to be processed is converted to a second optimal parameter model to obtain optimal processing parameters and optimal processing angles.

[0069] When the optimization module 4 is executed, the processing parameters to be measured are input into the first optimal parameter model according to the actual situation, and the corresponding optimal roughness prediction result and the corresponding optimal processing angle are obtained. The workpiece to be processed is processed by the processing parameters to be measured and the optimal processing angle to obtain the optimal roughness prediction result.

[0070] Alternatively, the target roughness to be processed is input into the second optimal parameter model to obtain the optimal processing parameters and the optimal processing angle, and the workpiece to be processed is processed by the optimal processing parameters and the optimal processing angle to obtain the target roughness to be processed. The optimal processing parameters and the optimal processing angle include the optimal processing parameters (parameters with the least consumption) and the corresponding processing angle, or the optimal combination of processing parameters and processing angles, which can be set by adjusting the output results of the model during model training.

[0071] As can be seen from the above, the turning processing optimization device obtains the roughness training samples of the lathe through the tool angle adjustment system, calculates the historical roughness calculation results corresponding to the historical workpiece surface images in the roughness training samples, constructs the optimal parameter model according to the historical lathe processing parameters, historical tool processing angles and historical workpiece surface images and the corresponding historical roughness calculation results in the roughness training samples, inputs the obtained target parameters to be processed into the optimal parameter model, calculates the optimal processing angle of the target parameters to be processed, and adjusts the processing angle of the tool according to the optimal processing angle; thus, by inputting the target parameters to be processed into the optimal parameter model constructed based on the roughness training samples obtained by the tool angle adjustment system, the optimal processing angle is calculated, the problem that the existing processing parameter optimization method does not consider the influence of the tool processing angle on the roughness is solved, the roughness can be analyzed by adjusting the tool processing angle to optimize the parameters, or the optimal roughness can be obtained by adjusting the tool processing angle under fixed parameters, thereby improving the turning processing efficiency.

[0072] Please refer to Figure 3 , Figure 3A structural schematic diagram of an electronic device provided in an embodiment of the present application. The present application provides an electronic device, including: a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanisms (not shown). The memory 302 stores a computer program executable by the processor 301. When the electronic device is running, the processor 301 executes the computer program to execute the turning process optimization method in any optional implementation of the above embodiment to achieve the following functions: through the tool angle adjustment system, a roughness training sample of the lathe is obtained, and a historical roughness calculation result corresponding to the historical workpiece surface image in the roughness training sample is calculated, and an optimal parameter model is constructed according to the historical lathe processing parameters, historical tool processing angles, historical workpiece surface images and corresponding historical roughness calculation results in the roughness training sample, and the obtained target parameters to be processed are input into the optimal parameter model, and the optimal processing angle of the target parameters to be processed is calculated, and the processing angle of the tool is adjusted according to the optimal processing angle.

[0073] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the turning processing optimization method in any optional implementation of the above-mentioned embodiment is executed to achieve the following functions: a roughness training sample of a lathe is obtained through a tool angle adjustment system, and a historical roughness calculation result corresponding to a historical workpiece surface image in the roughness training sample is calculated; an optimal parameter model is constructed based on historical lathe processing parameters, historical tool processing angles, historical workpiece surface images and corresponding historical roughness calculation results in the roughness training sample; the obtained target parameters to be processed are input into the optimal parameter model, the optimal processing angle of the target parameters to be processed is calculated, and the processing angle of the tool is adjusted according to the optimal processing angle. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0074] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0075] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0077] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0078] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A turning process optimization method, applied to a tool angle adjustment system to optimize the tool processing angle, characterized in that: The tool angle adjustment system includes a pitch angle control platform, a rotation angle control platform and an azimuth angle control platform; the turning process optimization method includes the steps of: Through the tool angle adjustment system, a roughness training sample of a lathe is obtained; the roughness training sample includes historical lathe processing parameters, historical tool processing angles and corresponding historical workpiece surface images; the historical lathe processing parameters include historical records of lathe rotation speed, feed amount, feed speed and coolant flow at the same processing time; the historical tool processing angle is adjusted by the tool angle adjustment system, and the historical tool processing angle includes historical records of tool pitch angle, tool rotation angle and tool orientation angle at the same processing time; Calculating and obtaining a historical roughness calculation result corresponding to the historical workpiece surface image; Constructing an optimal parameter model according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results; The acquired target parameters to be processed are input into the optimal parameter model, the optimal processing angle of the target parameters to be processed is calculated, and the processing angle of the tool is adjusted according to the optimal processing angle.

2. The turning process optimization method according to claim 1, characterized in that: By using the tool angle adjustment system, a roughness training sample of a lathe is obtained, including: Through the tool angle adjustment system, lathe processing parameters with different values ​​and tool processing angles with different angles are selected and combined to process multiple sampled workpieces to obtain corresponding workpiece surface images; The lathe processing parameters, the tool processing angles and the workpiece surface images belonging to the same combination are divided into historical records of the same processing moment to obtain roughness training samples of the lathe.

3. The turning process optimization method according to claim 1, characterized in that: Calculating the historical roughness calculation result corresponding to the historical workpiece surface image includes: extracting roughness image features from the historical workpiece surface image; The roughness value of the roughness image feature is detected by a detection algorithm to obtain a historical roughness calculation result corresponding to the historical workpiece surface image.

4. The turning process optimization method according to claim 1, characterized in that: According to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results, an optimal parameter model is constructed, including: Using the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results, a long short-term memory network model is constructed to obtain a primary optimal parameter model; Training the primary optimal parameter model according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images, and the corresponding historical roughness calculation results for training data to obtain a trained primary optimal parameter model; Based on the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results, the trained primary optimal parameter model is verified to obtain the optimal parameter model.

5. The turning process optimization method according to claim 4, characterized in that: The primary optimal parameter model is trained according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results for training data to obtain the trained primary optimal parameter model, including: Extracting the optimal historical roughness calculation result from the historical roughness calculation results corresponding to the same historical lathe processing parameters, and obtaining the optimal historical tool processing angle corresponding to the optimal historical roughness calculation result; Inputting the training data in the historical lathe processing parameters into the primary optimal parameter model to obtain corresponding roughness output and angle output; Determine a training error according to an optimal historical tool processing angle corresponding to an optimal historical roughness calculation result corresponding to data used for training in the historical lathe processing parameters and the corresponding roughness output and angle output; Based on the training errors, the parameters of the primary optimal parameter model are adjusted to obtain the optimal parameters, and the primary optimal parameter model is optimized using the optimal parameters to obtain the trained primary optimal parameter model.

6. The turning process optimization method according to claim 5, characterized in that: Before inputting the training data in the historical lathe processing parameters into the primary optimal parameter model to obtain the corresponding roughness output and angle output, the method further includes: Initialize the parameters of the primary optimal parameter model.

7. The turning process optimization method according to claim 1, characterized in that: The target parameters to be processed include the processing parameters to be measured or the target roughness to be processed; the optimal parameter model includes a first optimal parameter model that takes the processing parameters to be measured as input data, and takes the optimal roughness prediction result and the corresponding optimal processing angle as output, and a second optimal parameter model that takes the target roughness to be processed as input data, and takes the optimal processing parameters and the optimal processing angle as output; the acquired target parameters to be processed are input into the optimal parameter model, the optimal processing angle of the target parameters to be processed is calculated, and the processing angle of the tool is adjusted according to the optimal processing angle, including: Acquiring the processing parameter to be measured or the target roughness to be processed; Inputting the processing parameters to be measured into the first optimal parameter model to obtain an optimal roughness prediction result and a corresponding optimal processing angle; Alternatively, the target roughness to be processed is converted into the second optimal parameter model to obtain optimal processing parameters and optimal processing angles.

8. A turning process optimization device, applied to a tool angle adjustment system to optimize the tool processing angle, characterized in that: The tool angle adjustment system includes a pitch angle control platform, a rotation angle control platform and an azimuth angle control platform; the turning process optimization device includes: An acquisition module is used to acquire a roughness training sample of a lathe through the tool angle adjustment system; the roughness training sample includes historical lathe processing parameters, historical tool processing angles and corresponding historical workpiece surface images; the historical lathe processing parameters include historical records of lathe rotation speed, feed amount, feed speed and coolant flow at the same processing time; the historical tool processing angle is obtained by adjusting the tool angle adjustment system, and the historical tool processing angle includes historical records of tool pitch angle, tool rotation angle and tool orientation angle at the same processing time; A calculation module, used for calculating and obtaining a historical roughness calculation result corresponding to the historical workpiece surface image; A construction module, for constructing an optimal parameter model according to the historical lathe processing parameters, the historical tool processing angles, the historical workpiece surface images and the corresponding historical roughness calculation results; The optimization module is used to input the acquired target parameters to be processed into the optimal parameter model, calculate the optimal processing angle of the target parameters to be processed, and adjust the processing angle of the tool according to the optimal processing angle.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, it runs the steps in the turning process optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the turning process optimization method according to any one of claims 1 to 7 are executed.

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