An automatic steel grating shearing system and method

Abstract

The application discloses an automatic steel grating shearing system and method, and relates to the technical field of automatic shearing, which comprises the following steps: collecting image data of the steel grating by using a sensor and performing pretreatment; constructing a steel grating quality evaluation model based on a recurrent neural network and analyzing and evaluating the image data; outputting a parameter adjustment scheme based on the analysis result and sending a control signal to each actuator of a shearing device according to the parameter adjustment scheme; starting a shearing cutter to perform shearing operation on the steel grating according to the optimized path and parameters; and optimizing the steel grating quality evaluation model in real time according to the shearing result and monitoring data. The application can dynamically adjust the position parameters, angle parameters, operation parameters and shearing force parameters of the shearing cutter according to the specific quality condition of the steel grating, and the intelligent parameter adjustment effectively improves the shearing precision, reduces the waste rate and improves the consistency and reliability of the product quality.

Description

Technical Field

[0001] This invention relates to the field of automated shearing technology, and in particular to an automated shearing system and method for steel grating. Background Technology

[0002] In modern industrial manufacturing, steel grating, as a basic material widely used in construction, chemical, power, and many other industries, has always been a focus of attention due to the high efficiency and precision of its production and processing. The shearing process of steel grating, as a key link in the entire production process, plays a decisive role in the quality and adaptability of the final product. With the continuous evolution of automation technology, automated shearing systems have gradually been applied in steel grating production. Early automated shearing equipment was mainly based on simple mechanical automation principles, controlling the movement trajectory and parameters of the shearing blades through preset mechanical programs. Limit switches and mechanical transmission devices were used to ensure that the shearing blades performed shearing operations on the steel grating according to a fixed path and speed. This method... While improving production efficiency to some extent and reducing the labor intensity of manual operation, most existing automated shearing systems adopt relatively simple automated control logic. These systems are poorly adaptable to the differences in steel grating materials, changes in surface condition, and complex processing requirements. When the raw material batches of steel grating change, resulting in changes in its hardness, surface texture, and other characteristics, existing automated shearing systems cannot automatically adjust shearing parameters to ensure the stability of shearing quality. In terms of quality assessment of steel grating, traditional automated systems can often only perform some basic appearance defect detection and cannot deeply analyze the internal structural characteristics and overall quality of the steel grating, thus making it difficult to achieve precise optimization of shearing parameters.

[0003] However, the current common solutions have many drawbacks, including: when controlling the actuator of the shearing equipment, the existing technology cannot achieve precise control of parameters such as the position, angle, operating parameters and shearing force due to the limitations of the parameter adjustment scheme. It cannot flexibly adjust the shearing process according to the actual quality of the steel grating, resulting in poor adaptability when facing steel gratings of different quality grades and material properties, unstable shearing effect, and difficulty in meeting the increasingly diversified and high-precision market demands. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the problems existing in the current automated steel grating shearing system and method, the present invention is proposed.

[0006] Therefore, the purpose of this invention is to provide an automated steel grating shearing system and method, which is applicable to solving the problem that the existing technology, when controlling the actuator of the shearing equipment, cannot achieve precise control of parameters such as the position, angle, operating parameters, and shearing force due to the limitations of the parameter adjustment scheme. It also cannot flexibly adjust the shearing process according to the actual quality of the steel grating, resulting in poor adaptability and unstable shearing effect when facing steel gratings of different quality grades and material properties, making it difficult to meet the increasingly diversified and high-precision market demands.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide an automated steel grating shearing method, which includes collecting image data of the steel grating using sensors and preprocessing it; constructing a steel grating quality assessment model based on a recurrent neural network and analyzing and evaluating the image data; outputting a parameter adjustment scheme based on the analysis results and sending control signals to each actuator of the shearing device according to the parameter adjustment scheme; starting the shearing blade to perform shearing operation on the steel grating according to the optimized path and parameters; and optimizing the steel grating quality assessment model in real time based on the shearing results and monitoring data.

[0009] In a preferred embodiment of the automated steel grating shearing method of the present invention, the sensors include an area array industrial camera, a line array industrial camera, and a 3D industrial camera; the image data includes steel grating appearance morphology data, steel grating surface feature data, steel grating size data, steel grating markings, and other feature data; the parameter adjustment scheme includes shearing tool position parameters, shearing tool angle parameters, shearing tool operating parameters, and shearing force parameters; the actuator includes a tool drive motor, a transmission device, a tool position adjustment device, and a clamping device; and the monitoring data includes tool status data, steel grating status data, and equipment operating status data during the shearing process.

[0010] As a preferred embodiment of the automated steel grating shearing method of the present invention, the specific steps for constructing the steel grating quality assessment model are as follows: Real-time and historical image data of the steel grating are collected using sensors and preprocessed; a steel grating quality assessment model is constructed based on a recurrent neural network; historical image data is input into the steel grating quality assessment model for training; real-time image data is further analyzed and evaluated using a deep learning algorithm; a parameter adjustment scheme is output based on the analysis results; control signals are sent to each actuator of the shearing equipment according to the parameter adjustment scheme; the shearing blade is started to shear the steel grating according to the optimized path and parameters; and the parameters of the steel grating quality assessment model are optimized in real time based on the shearing results and monitoring data.

[0011] As a preferred embodiment of the automated shearing method for steel grating described in this invention, the specific formula for the quality analysis result of the steel grating is as follows:

[0012]

[0013] Among them, y k The results of the steel grating quality analysis; x i ω represents the feature value of the i-th type of steel grating collected by the sensor; ik b represents the weights corresponding to the input data and the k-th neuron in the output layer; k This is the bias of the k-th neuron in the output layer.

[0014] As a preferred embodiment of the automated steel grating shearing method of the present invention, the specific formula for the characteristic value of the i-th type of steel grating is as follows:

[0015]

[0016] Where, x i z is the characteristic value of the i-th type of steel grating; i Here are the feature data for the i-th type of steel grating; a ik For the i-th type of data, the coefficients for evaluating the k-th feature are given.

[0017] As a preferred embodiment of the automated shearing method for steel grating described in this invention, the specific meaning of the characteristic value of the i-th type of steel grating is as follows: the characteristic value x of the i-th type of steel grating i When the value is greater than the standard threshold, it indicates the selected i-th type of steel grating data and its corresponding coefficient a. ik After comprehensive evaluation, the steel grating meets the good standard in this aspect; the characteristic value x of the i-th type of steel grating i If the value is less than the standard threshold, it indicates the selected i-th type of steel grating data and its corresponding coefficient a. ik After comprehensive evaluation, steel grating does not meet the good range in this aspect.

[0018] As a preferred embodiment of the automated shearing method for steel grating described in this invention, the determination of the standard threshold needs to be based on the design requirements and standard specifications of the steel grating, data analysis, and model training, and the standard threshold varies according to the application scenario of the steel grating.

[0019] Secondly, to further address the aforementioned technical problems, the present invention provides an automated steel grating shearing system, comprising: a data collection module for collecting and preprocessing image data of the steel grating; a model building module for constructing a steel grating quality assessment model and analyzing and evaluating the image data; a scheme generation module for generating parameter adjustment schemes based on the analysis results and sending them to the actuator of the shearing equipment; a shearing operation module for starting the shearing blade to perform shearing operations on the steel grating according to the optimized path and parameters; and a model optimization module for optimizing the steel grating quality assessment model in real time based on the shearing results and monitoring data.

[0020] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements any step of the automated steel grating shearing method as described in the first aspect of the present invention.

[0021] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the automated steel grating shearing method as described in the first aspect of the present invention.

[0022] The beneficial effects of this invention are as follows: The quality assessment model built based on recurrent neural networks has powerful learning and analysis capabilities. This deep learning-based model can adapt to the evaluation needs of steel gratings of different types and quality standards, providing more accurate and comprehensive quality analysis results. The parameter adjustment scheme output based on the quality analysis results is highly targeted and adaptable. It can dynamically adjust the position parameters, angle parameters, running parameters, and shearing force parameters of the shearing blade according to the specific quality condition of the steel grating. This intelligent parameter adjustment effectively improves the shearing accuracy, reduces the scrap rate, and enhances the consistency and reliability of product quality. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0024] Figure 1 This is a flowchart illustrating the implementation of the present invention in Example 1. Detailed Implementation

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0026] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0028] Example 1

[0029] Reference Figure 1 This is the first embodiment of the present invention, which provides an automated steel grating shearing method, including the following steps:

[0030] S1: Use sensors to collect image data of the steel grating and perform preprocessing.

[0031] Preferred, such as Figure 1 The following is the implementation process of the present invention. First, image data of the steel grating is collected using sensors and preprocessed. Then, a steel grating quality assessment model is constructed based on a recurrent neural network, and the image data is analyzed and evaluated. Based on the analysis results, a parameter adjustment scheme is output, and control signals are sent to each actuator of the shearing equipment according to the parameter adjustment scheme. Subsequently, the shearing blade is started to shear the steel grating according to the optimized path and parameters. Finally, the steel grating quality assessment model is optimized in real time based on the shearing results and monitoring data.

[0032] Furthermore, the sensors include area array industrial cameras, line array industrial cameras, and 3D industrial cameras.

[0033] Furthermore, the image data includes data related to the appearance and shape of the steel grating, data related to the surface features of the steel grating, data related to the dimensions of the steel grating, steel grating markings, and other feature data.

[0034] Specifically, preprocessing ensures data quality and consistency by performing real-time preprocessing on image data at data nodes, including data cleaning, enhancement, and normalization.

[0035] S2: Construct a steel grating quality assessment model based on a recurrent neural network and analyze and evaluate the image data.

[0036] Furthermore, the specific steps for constructing a steel grating quality assessment model are as follows: real-time and historical image data of the steel grating are collected using sensors and preprocessed.

[0037] A quality assessment model for steel gratings was constructed based on a recurrent neural network.

[0038] Historical image data is input into the steel grating quality assessment model for training.

[0039] Deep learning algorithms are used to further analyze and evaluate real-time image data.

[0040] Output parameter adjustment schemes based on analysis results.

[0041] Control signals are sent to each actuator of the shearing equipment according to the parameter adjustment scheme.

[0042] The shearing blade is activated to cut the steel grating according to the optimized path and parameters.

[0043] The parameters of the steel grating quality assessment model are optimized in real time based on shearing results and monitoring data.

[0044] Preferably, the powerful feature learning and analysis capabilities of recurrent neural networks can deeply mine the hidden features in image data and accurately identify the quality status of steel gratings, including minor surface defects and potential internal structural problems. This makes the subsequent parameter adjustment schemes more accurate and targeted, effectively improving the quality of sheared products and reducing the scrap rate.

[0045] S3: Based on the analysis results, output parameter adjustment schemes and send control signals to each actuator of the shearing equipment according to the parameter adjustment schemes.

[0046] Preferably, the specific formula for the quality analysis results of steel grating is as follows:

[0047]

[0048] Among them, y k The results of the steel grating quality analysis; x i ω represents the feature value of the i-th type of steel grating collected by the sensor; ik b represents the weights corresponding to the input data and the k-th neuron in the output layer; k This is the bias of the k-th neuron in the output layer.

[0049] Furthermore, the specific formula for the characteristic value of the i-th type of steel grating is as follows:

[0050]

[0051] Where, x iz is the characteristic value of the i-th type of steel grating; i Here are the feature data for the i-th type of steel grating; a ik For the i-th type of data, the coefficients for evaluating the k-th feature are given.

[0052] Specifically, the meanings of the characteristic values ​​for the i-th type of steel grating are as follows:

[0053] The characteristic value x of the i-th type of steel grating i When the value is greater than the standard threshold, it indicates the selected i-th type of steel grating data and its corresponding coefficient a. ik After comprehensive evaluation, the steel grating meets the good standard in this aspect.

[0054] The characteristic value x of the i-th type of steel grating i If the value is less than the standard threshold, it indicates the selected i-th type of steel grating data and its corresponding coefficient a. ik After comprehensive evaluation, steel grating does not meet the good range in this aspect.

[0055] Furthermore, the determination of the standard threshold needs to be based on the design requirements and standards of the steel grating, data analysis, and model training, and the standard threshold varies depending on the application scenario of the steel grating.

[0056] S4: Start the shearing tool to shear the steel grating according to the optimized path and parameters.

[0057] The preferred approach is to combine dynamic output parameter adjustment with precise control of each actuator of the shearing equipment. This allows for real-time and precise adjustment of the shearing process based on the real-time quality of the steel grating. This improves the accuracy and efficiency of shearing, reduces problems such as tool wear and poor shearing effect caused by inappropriate parameters, and also reduces the scrap rate and improves the consistency of product quality.

[0058] S5: Optimize the steel grating quality assessment model in real time based on shearing results and monitoring data.

[0059] Preferably, by optimizing the quality assessment model in real time, the system can continuously adapt to various dynamic factors in the production process, such as changes in raw materials, equipment wear and tear, and adjustments to production processes. This keeps the system in its optimal working state, further improving the stability of the production process and the long-term reliability of product quality, and enhancing the system's adaptability.

[0060] This embodiment also provides an automated steel grating shearing system, including: a data collection module for collecting and preprocessing image data of the steel grating; a model building module for constructing a steel grating quality assessment model and analyzing and evaluating the image data; a scheme generation module for generating parameter adjustment schemes based on the analysis results and sending them to the actuator of the shearing equipment; a shearing operation module for starting the shearing blade to perform shearing operations on the steel grating according to the optimized path and parameters; and a model optimization module for optimizing the steel grating quality assessment model in real time based on the shearing results and monitoring data.

[0061] This embodiment also provides a computer device applicable to an automated steel grating shearing method, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the automated steel grating shearing method proposed in the above embodiment.

[0062] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0063] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements an automated steel grating shearing method as proposed in the above embodiments. 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.

[0064] In summary, the quality assessment model built on recurrent neural networks in this invention has powerful learning and analytical capabilities. This deep learning-based model can adapt to the assessment needs of steel gratings of different types and quality standards, providing more accurate and comprehensive quality analysis results. The parameter adjustment scheme output based on the quality analysis results is highly targeted and adaptable. It can dynamically adjust the position parameters, angle parameters, running parameters, and shearing force parameters of the shearing blade according to the specific quality condition of the steel grating. This intelligent parameter adjustment effectively improves the shearing accuracy, reduces the scrap rate, and enhances the consistency and reliability of product quality.

[0065] Example 2

[0066] Referring to Tables 1 to 3, this is the second embodiment of the present invention. The difference between this embodiment and the first embodiment is that, in order to verify its beneficial effects, operational data and related descriptions of the present invention in a real environment are provided.

[0067] Tables 1 and 2 show the detailed data on steel grating quality assessment and shearing process parameter adjustment data collected in this example, which provide a data foundation for subsequent optimization of the steel grating quality assessment model.

[0068] Table 1. Detailed Data Table for Steel Grating Quality Assessment

[0069]

[0070] Table 2. Shearing Process Parameter Adjustment Data Table

[0071] Steel grating number Tool position adjustment (mm) Shear force adjustment (%) Tool speed adjustment (%) 1 0.5 15 －10 2 0.3 12 －8 3 0.6 18 －12 ... ... ... ... n 0.4 13 －9

[0072] Table 3 shows a comparison between the prior art and our invention in terms of the accuracy of steel grating quality assessment and the effect of shear parameter adjustment in this example.

[0073] Table 3 Evaluation and Accuracy Comparison of Automated Shearing Technology for Steel Grating

[0074]

[0075] As can be seen from the table above, the present invention has significant advantages over the prior art in improving the quality and accuracy of automated steel grating shearing, which strongly supports the innovation and effectiveness of the present invention from a data perspective.

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An automated shearing method for steel grating, characterized in that: include: Image data of the steel grating is collected using sensors and preprocessed. The image data includes data related to the appearance and shape of the steel grating, data related to the surface features of the steel grating, data related to the dimensions of the steel grating, steel grating markings, and other feature data; A steel grating quality assessment model was constructed based on a recurrent neural network, and image data was analyzed and evaluated. The specific steps for constructing the steel grating quality assessment model are as follows: Real-time and historical image data of the steel grating are collected using sensors and preprocessed. A steel grating quality assessment model was constructed based on a recurrent neural network. Historical image data is input into the steel grating quality assessment model for training; Deep learning algorithms are used to further analyze and evaluate real-time image data; Output parameter adjustment scheme based on analysis results; According to the parameter adjustment plan, control signals are sent to each actuator of the shearing equipment; The shearing tool is started to shear the steel grating according to the optimized path and parameters; The parameters of the steel grating quality assessment model are optimized in real time based on the shearing results and monitoring data. Based on the analysis results, a parameter adjustment scheme is output, and control signals are sent to each actuator of the shearing equipment according to the parameter adjustment scheme; The specific formula for the quality analysis results of steel grating is as follows: ； in, The results of the steel grating quality analysis; For the steel grating collected by the sensor Eigenvalues ​​of various types; For input data and output layer 1 The weights corresponding to each neuron; For the output layer Bias of each neuron; The steel grating The specific formulas for the eigenvalues ​​of this type are as follows: ； in, For steel grating Eigenvalues ​​of various types; For steel grating Types of feature data; For the first The first type of data corresponds to the second Coefficients for each characteristic evaluation aspect; The shearing tool is activated to perform a shearing operation on the steel grating based on the optimized path and parameters; The steel grating quality assessment model is optimized in real time based on shearing results and monitoring data.

2. The automated steel grating shearing method as described in claim 1, characterized in that: The sensors include area array industrial cameras, line array industrial cameras, and 3D industrial cameras. The parameter adjustment scheme includes shearing tool position parameters, shearing tool angle parameters, shearing tool running parameters, and shearing force parameters; The actuator includes a tool drive motor, a transmission device, a tool position adjustment device, and a clamping device; The monitoring data includes the status data of the cutting tool during the shearing process, the status data of the steel grating, and the operating status data of the equipment.

3. The automated steel grating shearing method as described in claim 1, characterized in that: The steel grating The specific meanings of the eigenvalues ​​of this type are as follows: Steel grating Eigenvalues ​​of type If it exceeds the standard threshold, it indicates that the selected steel grating is of the [missing value]. Types of data and their corresponding coefficients After comprehensive evaluation, the steel grating meets the good standard in this aspect; Steel grating Eigenvalues ​​of type If it is less than the standard threshold, it indicates that the selected steel grating is of the [missing value]. Types of data and their corresponding coefficients After comprehensive evaluation, steel grating does not meet the good range in this aspect.

4. The automated steel grating shearing method as described in claim 3, characterized in that: The determination of the standard threshold needs to be based on the design requirements and standards of the steel grating, data analysis, and model training, and the standard threshold varies depending on the application scenario of the steel grating.

5. An automated steel grating shearing system, based on the automated steel grating shearing method according to any one of claims 1 to 4, characterized in that: include, The data collection module is used to collect image data of the steel grating and perform preprocessing. The model building module is used to build a steel grating quality assessment model and analyze and evaluate image data; The scheme generation module is used to generate parameter adjustment schemes based on the analysis results and send them to the actuator of the shearing equipment. The shearing operation module is used to start the shearing blade to shear the steel grating according to the optimized path and parameters. The model optimization module is used to optimize the steel grating quality assessment model in real time based on shearing results and monitoring data.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the automated steel grating shearing method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the automated steel grating shearing method according to any one of claims 1 to 4.

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