A steel bar processing control system based on digital production

Abstract

The present invention relates to the technical field of steel bar cutting, and specifically discloses a steel bar processing control system based on digital production, including: Initial module: Set time nodes; Set an initial speed and obtain an initial sorting; Calibration module: Cut the steel bar at the initial speed V1; After cutting is completed, calculate a cutting score, generate coordinate points; Obtain a new initial sorting, generate new coordinate points, fit the coordinate points to obtain a fitting curve; Substitute the cutting score threshold into the fitting curve to obtain a first speed, and record the maximum first speed as the theoretical speed; Control module: Obtain the actual duration, calculate the time difference, and determine the theoretical speed VDY based on the time difference. The present invention can ensure the quality of the steel bar cut while avoiding a decrease in cutting efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of steel bar cutting, and particularly relates to a steel bar processing control system based on digital production. Background Art

[0002] In the field of construction, steel bars are one of the key materials to ensure the strength and stability of building structures, and play a crucial role in the safety and durability of buildings. During the construction process, it is necessary to process steel bars, such as cutting the steel bars into appropriate lengths to meet specific construction design and structural requirements.

[0003] The mechanical cutting method has the characteristics of high efficiency and precision. It can quickly and accurately cut steel bars according to a predetermined length through professional cutting equipment. Specifically, after starting the cutting equipment, the cutting tool starts to rotate under the drive of power. Then, the steel bar is fed into the cutting port, and the rotating cutting tool cuts the steel bar. In this process, it is necessary to control the cutting speed of the steel bar. A relatively fast cutting speed may cause the cut of the steel bar to be uneven and have burrs, while a relatively slow cutting speed will result in a slower processing speed of the steel bar. Therefore, how to determine a reasonable cutting speed has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the present invention is to provide a steel bar processing control system based on digital production to solve the following technical problems:

[0005] The mechanical cutting method has the characteristics of high efficiency and precision. It can quickly and accurately cut steel bars according to a predetermined length through professional cutting equipment. Specifically, after starting the cutting equipment, the cutting tool starts to rotate under the drive of power. Then, the steel bar is fed into the cutting port, and the rotating cutting tool cuts the steel bar. In this process, it is necessary to control the cutting speed of the steel bar. A relatively fast cutting speed may cause the cut of the steel bar to be uneven and have burrs, while a relatively slow cutting speed will result in a slower processing speed of the steel bar.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A steel bar processing control system based on digital production, comprising:

[0008] Initial module: Set the service life interval [0, t] of the cutting tool, where t represents the design life of the cutting tool, and set a number of time nodes at a preset service life interval within the service life interval;

[0009] Set the cutting speed interval [v1, v2] of the steel bar, select a number of cutting speeds as initial speeds at a preset cutting speed interval within the cutting speed interval, and sort the initial speeds in descending order to obtain an initial sorting;

[0010] Calibration module: Obtain the usage duration Ta corresponding to time node a, obtain the tool with a usage duration of Ta, denoted as tool DTa, obtain the initial speed V1 at the first position in the initial sorting, maintain the cutting speed at the initial speed V1, and cut the steel bar with the tool DTa;

[0011] After the steel bar cutting is completed, collect the image of the front of the steel bar cut, obtain the density p of the burrs at the steel bar cut based on the image, then the cutting score P = η / p, generate a coordinate point (V1, P) in the coordinate system, where η is a preset correction coefficient;

[0012] Remove the initial speed V1 at the first position from the initial sorting to obtain a new initial sorting, repeat the above steps to generate new coordinate points until the number of coordinate points reaches the preset number, fit the coordinate points to obtain a fitting curve f(x), where x represents the cutting speed;

[0013] Substitute the preset cutting score threshold Pys into the fitting curve f(x) to obtain the first speed, and denote the maximum first speed as the theoretical speed VTa of the tool DTa;

[0014] Control module: Obtain the usage duration of the current tool, denoted as the actual duration T, calculate the time difference ΔTi = |T - Ti|, obtain the theoretical speed VDY corresponding to the minimum time difference, maintain the cutting speed at the theoretical speed VDY, and cut the steel bar with the current tool.

[0015] As a further solution of the present invention: In the calibration module, when the theoretical speed VTa < v1, send a warning message for reporting.

[0016] As a further solution of the present invention: In the control module, during the process of obtaining the theoretical speed VDY, when there are two time differences that are the same and are the minimum time difference, calculate the average value of the corresponding theoretical speeds as the theoretical speed VDY.

[0017] As a further solution of the present invention: In the calibration module, during the process of cutting the steel bar, keep the rotation speed of the tool unchanged.

[0018] As a further solution of the present invention: In the calibration module, the process of obtaining the density p of the burrs at the steel bar cut based on the image specifically includes:

[0019] Establish a database, and the database stores images with the burr density already marked;

[0020] A burr density recognition model is established based on a deep learning model, and the burr density recognition model is trained and verified through the database. The image is input into the verified burr density recognition model, and the density p of the burrs at the steel bar cut is output.

[0021] As a further solution of the present invention: in the calibration module, the images with the marked burr density stored in the database are obtained manually.

[0022] As a further solution of the present invention: in the calibration module, in the process of obtaining the theoretical speed VTa, the following steps are further included:

[0023] Obtain n tools with a service life of Ta, and respectively obtain the corresponding theoretical speeds. Calculate the mean value VPJ of the theoretical speeds of the n tools with a service life of Ta as the theoretical speed VTa, where n represents the preset number of tools.

[0024] As a further solution of the present invention: in the process of calculating the mean value VPJ, when the difference between a certain theoretical speed and the mean value VPJ is greater than or equal to the preset difference threshold, remove this theoretical speed and calculate the mean value VPJ again.

[0025] Advantages of the present invention: In this solution, first, the usage duration range of the tool and the cutting speed range of the steel bar are set, and several nodes and speeds are selected, providing an organized and planned data basis for subsequent experiments. By defining the usage duration range [0, t] of the tool and subdividing it into multiple time nodes, the entire process from the brand-new tool to gradual wear can be comprehensively covered, enabling a detailed study of the characteristics of the tool at different wear stages. For the cutting speed range [v1, v2] of the steel bar, several speeds are selected at a preset interval and sorted in descending order as the initial speeds, which allows for a systematic exploration of the influence of different speeds on the cut quality subsequently. Since the cutting speed is a key factor affecting the cut quality, this ordered speed selection method helps establish a clear relationship between the speed and the cut quality, laying a foundation for ultimately establishing an accurate model. After that, relevant information is obtained after the tool cuts the steel bar at a specific speed for a specific usage duration, thereby establishing a direct connection between the tool usage duration, cutting speed, and cut quality, and quantifying the abstract cutting effect. It should be noted that as the tool usage time increases, its wear degree changes. When cutting at different speeds, the change in the cut quality due to tool wear is reflected in the density of burrs. By converting these data into coordinate points and fitting a curve, the relationship between the cutting speed and the cut quality at different tool wear stages can be clearly seen, providing a basis for determining the appropriate cutting speed according to the actual tool usage duration subsequently. Finally, the current tool usage duration is obtained and the time difference from the ideal duration is calculated, and then the theoretical speed is determined and the cutting is carried out at this speed. This realizes the dynamic optimization of the cutting process. In actual production, the tool usage duration is in a dynamic change. By obtaining the current tool usage duration in real time and calculating the time difference from the ideal duration, the corresponding theoretical speed VDY can be found. Since the relationship model between the tool usage duration, cutting speed, and cut quality has been established previously, the cutting speed determined in this way can adapt to the current wear state of the tool, ensuring that the cut quality does not decline due to tool wear and avoiding unreasonably reducing the cutting speed due to excessive pursuit of quality, thus achieving a good balance between quality and efficiency in the entire steel bar cutting process while ensuring the cut quality of the steel bar and avoiding a decrease in cutting efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The present invention will be further described below with reference to the accompanying drawings.

[0027] Figure 1 is a schematic flowchart of a steel bar processing control system based on digital production according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0029] Please refer to Figure 1 As shown, the present invention is a steel bar processing control system based on digital production, including:

[0030] Initial module: Set the service life interval [0, t] of the tool, where t represents the design life of the tool, and set several time nodes at a preset service life interval within the service life interval;

[0031] Set the cutting speed interval [v1, v2] of the steel bar, select several cutting speeds as the initial speeds at a preset cutting speed interval within the cutting speed interval, and sort the initial speeds in descending order to obtain the initial sorting;

[0032] Calibration module: Obtain the service life Ta corresponding to the time node a, obtain the tool with the service life Ta, denoted as tool DTa, obtain the initial speed V1 at the first place in the initial sorting, keep the cutting speed as the initial speed V1, and cut the steel bar with the tool DTa;

[0033] After the steel bar cutting is completed, collect the image of the front of the steel bar cut, obtain the density p of the burrs at the steel bar cut based on the image, then the cutting score P = η / p, and generate a coordinate point (V1, P) in the coordinate system, where η is a preset correction coefficient;

[0034] Remove the initial speed V1 at the first place from the initial sorting to obtain a new initial sorting, repeat the above steps to generate new coordinate points until the number of coordinate points reaches the preset number, and fit the coordinate points to obtain the fitting curve f(x), where x represents the cutting speed;

[0035] Substitute the preset cutting score threshold Pys into the fitting curve f(x) to obtain the first speed, and denote the maximum first speed as the theoretical speed VTa of the tool DTa;

[0036] Control module: Obtain the service life of the current tool, denoted as the actual time T, calculate the time difference ΔTi = |T - Ti|, obtain the theoretical speed VDY corresponding to the minimum time difference, keep the cutting speed as the theoretical speed VDY, and cut the steel bar with the current tool.

[0037] It should be noted that first, the usage duration range of the tool and the cutting speed range of the steel bars are set, and several nodes and speeds are selected, thus providing an organized and planned data basis for subsequent experiments. By defining the usage duration range of the tool as [0, t] and subdividing it into multiple time nodes, the entire process of the tool from being brand new to gradually wearing can be comprehensively covered, enabling a detailed study of the characteristics of the tool at different wear stages. For the cutting speed range of the steel bars [v1, v2], several speeds are selected at a preset interval and sorted in descending order as the initial speeds. This allows for a systematic exploration of the influence of different speeds on the cut quality subsequently, because the cutting speed is a key factor affecting the cut quality. This ordered speed selection method helps to establish a clear relationship between the speed and the cut quality, laying a foundation for finally establishing an accurate model. After that, relevant information about the tool cutting the steel bars at a specific speed for a specific usage duration is obtained, thereby establishing a direct connection among the tool usage duration, cutting speed, and cut quality, and quantifying the abstract cutting effect. It is worth noting that as the tool usage time increases, its wear degree changes. When cutting at different speeds, the change in the cut quality due to tool wear is reflected in the density of burrs. By converting this data into coordinate points and fitting a curve, the relationship between the cutting speed and the cut quality at different tool wear stages can be clearly seen, providing a basis for determining the appropriate cutting speed according to the actual tool usage duration subsequently. Finally, the current tool usage duration is obtained and the time difference from the ideal duration is calculated, and then the theoretical speed is determined and the cutting is carried out at this speed. In this way, the dynamic optimization of the cutting process is achieved. In actual production, the tool usage duration is in a dynamic change. By obtaining the current tool usage duration in real time and calculating the time difference from the ideal duration, the corresponding theoretical speed VDY can be found. Since the relationship model among the tool usage duration, cutting speed, and cut quality has been established previously, the cutting speed determined in this way can adapt to the current wear state of the tool, ensuring that the cut quality does not decline due to tool wear and avoiding unreasonably reducing the cutting speed in pursuit of quality, thus achieving a good balance between quality and efficiency in the entire steel bar cutting process.

[0038] In another preferred embodiment of the present invention, in the calibration module, when the theoretical speed VTa < v1, a warning message is sent for reporting.

[0039] In another preferred embodiment of the present invention, in the control module, during the process of obtaining the theoretical speed VDY, when there are two time differences that are the same and are the minimum time difference, the average value of the corresponding theoretical speeds is calculated as the theoretical speed VDY.

[0040] In another preferred embodiment of the present invention, during the process of cutting the steel bars in the calibration module, the rotation speed of the cutting tool is kept constant.

[0041] It should be noted that different rotation speeds will also affect the quality of the cut. Therefore, during the actual calibration process, the rotation speed of the cutting tool needs to be kept constant.

[0042] In another preferred embodiment of the present invention, in the calibration module, the process of obtaining the density p of burrs at the cut of the steel bar based on the image specifically includes:

[0043] Establish a database, and store images with marked burr densities in the database;

[0044] Based on a deep learning model, establish a burr density recognition model, train and verify the burr density recognition model through the database, input the image into the verified burr density recognition model, and output the density p of burrs at the cut of the steel bar.

[0045] It is worth noting that by utilizing the powerful feature extraction and pattern recognition capabilities of deep learning, the burr density can be evaluated more accurately and efficiently. Traditional manual evaluation of burr density may be affected by subjective factors and has low efficiency. By establishing a dedicated database and storing images with marked burr densities, rich learning materials are provided for the deep learning model. The burr density recognition model trained and verified based on these data can quickly and accurately identify the burr density in new images;

[0046] It is worth noting that during the process of collecting the front image of the cut of the steel bar, the position of collecting the image should be kept consistent to reduce the influence of the change in image information caused by the position difference on the evaluation of burr density; if the collection position is not fixed, the images at different positions may have significant differences in burr morphology and distribution due to factors such as the shape of the steel bar and the stress distribution around the cut, which will increase the difficulty of evaluating burr density and easily introduce errors.

[0047] In another preferred embodiment of the present invention, in the calibration module, the images with marked burr densities stored in the database are obtained manually.

[0048] In another preferred embodiment of the present invention, in the calibration module, during the process of obtaining the theoretical speed VTa, the following steps are further included:

[0049] Obtain n cutting tools with a service life of Ta, and respectively obtain the corresponding theoretical speeds, calculate the average value VPJ of the theoretical speeds of the n cutting tools with a service life of Ta, and use it as the theoretical speed VTa, where n represents the preset number of cutting tools.

[0050] It is understandable that taking the average of the data of multiple tools can effectively reduce the errors caused by the characteristics of a single tool. Even if different tools have the same usage duration, due to factors such as manufacturing processes and slight material differences, their actual performance may vary. For example, there may be differences in aspects such as the sharpness of the cutting edge and the balance of the tool. By obtaining the theoretical speeds of n tools and calculating the average, the influence of these individual differences on the result can be weakened, making the obtained theoretical speed VTa more accurately reflect the general performance level of the tool at this usage duration.

[0051] In another preferred embodiment of the present invention, during the process of calculating the average value VPJ, when the difference between a certain theoretical speed and the average value VPJ is greater than or equal to a preset difference threshold, this theoretical speed is removed, and the average value VPJ is recalculated.

[0052] It should be noted that during the actual production process, due to various accidental factors, some theoretical speed data that deviate significantly from the normal range may be generated. For example, the tool may encounter a sudden external impact during the cutting process, or there may be local abnormal hardness unevenness in the material itself, which may cause the theoretical speed of the tool to be very different from the normal expectation. By setting a difference threshold and removing other data with too large a difference from it, the interference of these abnormal data on the overall average calculation can be avoided, making the finally obtained average value VPJ more accurately reflect the theoretical speed level of the tool under normal circumstances.

[0053] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A steel bar processing control system based on digital production, characterized in that, Including: Initial module: Set the usage duration range [0, t] of the tool, where t represents the designed life of the tool, and set several time nodes at preset usage duration intervals within the usage duration range; Set the cutting speed range [v1, v2] of the steel bar, select several cutting speeds as initial speeds at preset cutting speed intervals within the cutting speed range, sort the initial speeds in descending order to obtain an initial sorting; Calibration module: Obtain the usage duration Ta corresponding to the time node a, obtain the tool with the usage duration Ta, denoted as tool DTa, obtain the first initial speed V1 in the initial sorting, keep the cutting speed as the initial speed V1, and cut the steel bar with the tool DTa; After the steel bar cutting is completed, collect the image of the front of the steel bar cut, obtain the density p of the burrs at the steel bar cut based on the image, then the cutting score P = η / p, generate a coordinate point (V1, P) in the coordinate system, where η is a preset correction coefficient; Remove the first initial speed V1 from the initial sorting to obtain a new initial sorting, repeat the above steps to generate new coordinate points until the number of coordinate points reaches a preset number, fit the coordinate points to obtain a fitting curve f(x), where x represents the cutting speed; Substitute the preset cutting score threshold Pys into the fitting curve f(x) to obtain a first speed, and denote the maximum first speed as the theoretical speed VTa of the tool DTa; Control module: Obtain the actual usage duration of the current tool, denoted as the actual duration T, calculate the time difference ΔTi = |T - Ti|, obtain the theoretical speed VDY corresponding to the minimum time difference, keep the cutting speed as the theoretical speed VDY, and cut the steel bar with the current tool; 2. The steel bar processing control system based on digital production according to claim 1, wherein In the calibration module, when the theoretical speed VTa < v1, send a warning message for reporting; 3. A steel bar processing control system based on digital production according to claim 1, characterized in that, In the control module, during the process of obtaining the theoretical speed VDY, when there are two time differences that are the same and are the minimum time difference, calculate the average value of the corresponding theoretical speeds as the theoretical speed VDY; 4. A steel bar processing control system based on digital production according to claim 1, characterized in that, In the calibration module, during the process of cutting the steel bar, keep the rotation speed of the tool unchanged; 5. A steel bar processing control system based on digital production according to claim 1, characterized in that, In the calibration module, the process of obtaining the density p of the burrs at the steel bar cut based on the image specifically includes: Establish a database, and store images with marked burr densities in the database; Establish a burr density recognition model based on a deep learning model, train and verify the burr density recognition model through the database, input the image into the verified burr density recognition model, and output the density p of the burrs at the steel bar cut; 6. The steel bar processing control system based on digital production according to claim 5, wherein, In the calibration module, the images with marked burr densities stored in the database are obtained manually; 7. A steel bar processing control system based on digital production according to claim 1, characterized in that, In the calibration module, during the process of obtaining the theoretical speed VTa, the following steps are also included: Obtain n tools with a usage duration of Ta, and respectively obtain the corresponding theoretical speeds. Calculate the average value VPJ of the theoretical speeds corresponding to the n tools with a usage duration of Ta, and use it as the theoretical speed VTa, where n represents the preset number of tools.

8. A steel bar processing control system based on digital production according to claim 7, characterized in that, During the process of calculating the average value VPJ, when the difference between a certain theoretical speed and the average value VPJ is greater than or equal to the preset difference threshold, remove this theoretical speed and calculate the average value VPJ again.

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