Automatic shearing system and method for steel grating

Abstract

The invention discloses an automatic shearing system and method for a steel grating, and relates to the technical field of automatic shearing, and the method comprises the steps: collecting image data of the steel grating through a sensor, and carrying out the preprocessing; constructing a steel grating quality evaluation model based on the 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 execution mechanism of the shearing equipment according to the parameter adjustment scheme; starting a shearing tool to shear 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 the monitoring data. The position parameter, the angle parameter, the operation parameter and the shearing force parameter of the shearing tool can be dynamically adjusted according to the specific quality condition of the steel grating, the intelligent parameter adjustment effectively improves the shearing precision, the rejection rate is reduced, and the consistency and the reliability of the product quality are improved.

Description

Technical Field

[0001] The invention relates to the technical field of automated shearing, and in particular to an automated shearing system and method for steel gratings. Background Art

[0002] In the field of modern industrial manufacturing, steel grating, as a basic material widely used in many industries such as construction, chemical industry, and electric power, has always attracted much attention for its high efficiency and precision in 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 used in steel grating production. Early automated shearing equipment was mainly based on simple mechanical automation principles. The motion trajectory and parameters of the shearing tool were controlled by preset mechanical programs. Limit switches and mechanical transmission devices were used to make the shearing tool shear the steel grating at a fixed path and speed. This method is very effective in It has improved production efficiency to a certain extent and reduced the labor intensity of manual operation. Most of the existing automated shearing systems use relatively simple automated control logic. These systems have poor adaptability when faced with differences in steel grating materials, changes in surface conditions and complex processing requirements. When the raw material batches of the steel grating change, resulting in changes in its hardness, surface texture and other characteristics, the existing automated shearing system is difficult to automatically adjust the shearing parameters to ensure the stability of the shearing quality. In terms of quality assessment of steel gratings, traditional automated systems can often only perform some basic appearance defect detection and are unable to deeply analyze the internal structural characteristics and overall quality status of the steel gratings, making it difficult to achieve accurate optimization of the shearing parameters.

[0003] However, the common solutions currently available have many shortcomings, including: due to the limitations of the parameter adjustment scheme, the existing technology cannot achieve precise control of parameters such as tool position, angle, operating parameters and shear force when controlling the actuator of the shearing equipment, and cannot flexibly adjust the shearing process according to the actual quality of the steel grating. As a result, when facing steel gratings of different quality grades and different material properties, the adaptability is poor and the shearing effect is unstable, making it difficult to meet the increasingly diversified and high-precision market demands. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing automatic shearing system and method for steel grating, the present invention is proposed.

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

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides an automated shearing method for steel gratings, which includes collecting image data of steel gratings using sensors and performing preprocessing; 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 a control signal to each actuator of the shearing equipment according to the parameter adjustment scheme; starting a shearing tool to perform a shearing operation on the steel grating according to the optimized path and parameters; and optimizing the steel grating quality assessment model in real time according to the shearing results and monitoring data.

[0009] As a preferred scheme of the automated shearing method for steel gratings described in the present invention, wherein: the sensors include area array industrial cameras, linear array industrial cameras and 3D industrial cameras; the image data include data related to the appearance of the steel gratings, data related to the surface features of the steel gratings, data related to the dimensions of the steel gratings, steel grating identification 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 clamp device; the monitoring data includes tool status data, steel grating status data and equipment operation status data during the shearing process.

[0010] As a preferred scheme of the automated shearing method for steel gratings described in the present invention, the specific steps of constructing a steel grating quality assessment model are as follows: using sensors to collect real-time image data and historical image data of steel gratings and preprocessing them; constructing a steel grating quality assessment model based on a recurrent neural network; inputting historical image data into the steel grating quality assessment model for training; using a deep learning algorithm to further analyze and evaluate the real-time image data; outputting a parameter adjustment scheme based on the analysis results; sending a control signal to each actuator of the shearing equipment according to the parameter adjustment scheme; starting the shearing tool to perform shearing operations on the steel grating according to the optimized path and parameters; and optimizing the parameters of the steel grating quality assessment model in real time according to the shearing results and monitoring data.

[0011] As a preferred solution of the automatic shearing method of the steel grating of the present invention, the specific formula of the quality analysis result of the steel grating is as follows:

[0012]

[0013] Among them, y k is the quality analysis result of steel grating; x i is the characteristic value of the i-th type of steel grating collected by the sensor; ω ik is the weight corresponding to the input data and the kth neuron in the output layer; b k is the bias of the kth neuron in the output layer.

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

[0015]

[0016] Among them, x i is the characteristic value of the i-th type of steel grating; z i is the characteristic data of the i-th type of steel grating; a ik is the coefficient of the k-th feature evaluation corresponding to the i-th type of data.

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

[0018] As a preferred scheme of the automatic cutting method of steel grating described in the present invention, the standard threshold value needs to be determined according to the design requirements and standard specifications of the steel grating, data analysis and model training, and the standard threshold value changes according to the application scenario of the steel grating.

[0019] In the second aspect, in order to further solve the above-mentioned technical problems, the embodiment of the present invention provides an automatic shearing system for steel gratings, which includes: a data collection module, used to collect image data of steel gratings and perform preprocessing; a model construction module, used to construct a steel grating quality assessment model and analyze and evaluate the image data; a solution generation module, used to generate a parameter adjustment solution based on the analysis results and send it to the actuator of the shearing equipment; a shearing operation module, used to start the shearing tool to perform shearing operations on the steel gratings according to the optimized path and parameters; a model optimization module, used to optimize the steel grating quality assessment model in real time according to the shearing results and monitoring data.

[0020] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the automated shearing method for steel gratings as described in the first aspect of the present invention is implemented.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the automated shearing method for steel gratings as described in the first aspect of the present invention is implemented.

[0022] The beneficial effects of the present invention are as follows: the quality assessment model constructed based on the recurrent neural network of the present invention has powerful learning and analysis capabilities. This deep learning-based model can adapt to the assessment needs of steel gratings of different types and different quality standards, and provide more accurate and comprehensive quality analysis results. The parameter adjustment scheme output according to the quality analysis results is highly targeted and adaptable. It can dynamically adjust the position parameters, angle parameters, operating parameters and shear force parameters of the shearing tool according to the specific quality conditions of the steel grating. This intelligent parameter adjustment effectively improves the shearing accuracy, reduces the scrap rate, and improves the consistency and reliability of product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0024] Figure 1 This is a flow chart for implementing the present invention in Example 1. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0028] Example 1

[0029] Reference Figure 1 , which is the first embodiment of the present invention, provides a method for automatic shearing of steel gratings, comprising the following steps:

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

[0031] Preferably, Figure 1 The implementation process of the present invention is shown. First, the image data of the steel grating is collected and preprocessed by using sensors. Then, a steel grating quality assessment model is constructed based on a recurrent neural network and the image data is analyzed and evaluated. Then, a parameter adjustment plan is output based on the analysis result and a control signal is sent to each actuator of the shearing equipment according to the parameter adjustment plan. Subsequently, the shearing tool 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 according to 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 of the steel grating, data related to the surface characteristics of the steel grating, data related to the size of the steel grating, the identification of the steel grating and other characteristic data.

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

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

[0036] Furthermore, the specific steps of constructing the steel grating quality assessment model are as follows: using sensors to collect real-time image data and historical image data of the steel grating and perform preprocessing.

[0037] A steel grating quality assessment model is constructed based on recurrent neural network.

[0038] The 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 plans based on the analysis results.

[0041] The control signals are sent to various actuators of the shearing equipment according to the parameter adjustment scheme.

[0042] Start the shearing tool according to the optimized path and parameters to perform shearing operations on the steel grating.

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

[0044] Preferably, through the powerful feature learning and analysis capabilities of the recurrent neural network, it is possible to deeply mine the implicit features in the image data and accurately identify the quality status of the steel grating, including subtle surface defects and potential problems in the internal structure. This makes the subsequent output parameter adjustment plan more accurate and targeted, effectively improving the quality of the sheared products and reducing the scrap rate.

[0045] S3: Output a parameter adjustment plan based on the analysis results and send a control signal to each actuator of the shearing device according to the parameter adjustment plan.

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

[0047]

[0048] Among them, y k is the quality analysis result of steel grating; x i is the characteristic value of the i-th type of steel grating collected by the sensor; ω ik is the weight corresponding to the input data and the kth neuron in the output layer; b k is the bias of the kth 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] Among them, x iis the characteristic value of the i-th type of steel grating; z i is the characteristic data of the i-th type of steel grating; a ik is the coefficient of the k-th feature evaluation corresponding to the i-th type of data.

[0052] Specifically, the specific meaning of the characteristic value of the i-th type of steel grating is as follows:

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

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

[0055] Furthermore, 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.

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

[0057] Preferably, the dynamically output parameter adjustment scheme combined with the precise control of each actuator of the shearing equipment can adjust the shearing process in real time and accurately according to the real-time quality status of the steel grating, which improves the shearing accuracy and efficiency, reduces the 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: Real-time optimization of the steel grating quality assessment model 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 raw material changes, equipment wear, and production process adjustments, which enables the system to always maintain the best working state, further improving the stability of the production process and the long-term reliability of product quality, and enhancing the adaptability of the system.

[0060] The present embodiment also provides an automated shearing system for steel gratings, comprising: a data collection module for collecting and preprocessing image data of steel gratings; a model building module for building a steel grating quality assessment model and analyzing and evaluating the image data; a solution generation module for generating a parameter adjustment solution based on the analysis results and sending it to an actuator of a shearing device; a shearing operation module for starting a shearing tool to perform a shearing operation on the steel grating according to an optimized path and parameters; and a model optimization module for optimizing the steel grating quality assessment model in real time according to shearing results and monitoring data.

[0061] This embodiment also provides a computer device, which is applicable to a method for automated cutting of steel gratings, and includes: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a method for automated cutting of steel gratings as proposed in the above embodiment.

[0062] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0063] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a method for automatically cutting a steel grating as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (Static Random Access Memory, referred to as SRAM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), an erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), a programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), a read-only memory (Read-Only Memory, referred to as ROM), a magnetic memory, a flash memory, a disk or an optical disk.

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

[0065] Example 2

[0066] Referring to Tables 1 to 3, the second embodiment of the present invention is shown. This embodiment is different from the first embodiment in that, in order to verify its beneficial effects, operating data and related instructions of the present invention in an actual environment are provided.

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

[0068] Table 1 Detailed data table of 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] As shown in Table 3, it is a comparison table of the accuracy of steel grating quality assessment and shear parameter adjustment effect between the prior art and our invention in this example.

[0073] Table 3 Evaluation and accuracy comparison of steel grating automated shearing technology

[0074]

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

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for automatic shearing of steel grating, characterized in that: include: Use sensors to collect image data of steel gratings and perform preprocessing; Construct a steel grating quality assessment model based on a recurrent neural network and analyze and evaluate image data; Outputting a parameter adjustment plan based on the analysis results and sending control signals to various actuators of the shearing device according to the parameter adjustment plan; Start the shearing tool to shear the steel grating according to the optimized path and parameters; The steel grating quality assessment model is optimized in real time according to the shearing results and monitoring data.

2. The automatic shearing method for steel grating according to claim 1, characterized in that: The sensors include area array industrial cameras, linear array industrial cameras and 3D industrial cameras; The image data includes data related to the appearance of the steel grating, data related to the surface characteristics of the steel grating, data related to the size of the steel grating, the identification of the steel grating and other characteristic data; The parameter adjustment scheme includes shearing tool position parameters, shearing tool angle parameters, shearing tool operation parameters and shearing force parameters; The actuator includes a tool drive motor, a transmission device, a tool position adjustment device and a clamp device; The monitoring data includes tool status data, steel grating status data and equipment operation status data during the shearing process.

3. The automatic shearing method for steel grating according to claim 2, characterized in that: The specific steps of constructing the steel grating quality assessment model are as follows: Use sensors to collect real-time image data and historical image data of steel gratings and perform preprocessing; Construct a steel grating quality assessment model based on recurrent neural network; Input historical image data into the steel grating quality assessment model for training; Use deep learning algorithms to further analyze and evaluate real-time image data; Output parameter adjustment plan based on analysis results; Sending control signals to various actuators of the shearing equipment according to the parameter adjustment scheme; Start the shearing tool to shear the steel grating according to the optimized path and parameters; The steel grating quality assessment model parameters are optimized in real time according to the shearing results and monitoring data.

4. The automatic shearing method for steel grating according to claim 3, characterized in that: The specific formula for the steel grating quality analysis result is as follows: Among them, y k is the quality analysis result of steel grating; x i is the characteristic value of the i-th type of steel grating collected by the sensor; ω ik is the weight corresponding to the input data and the kth neuron in the output layer; b k is the bias of the kth neuron in the output layer.

5. The automatic shearing method for steel grating according to claim 4, characterized in that: The specific formula of the characteristic value of the i-th type of steel grating is as follows: Among them, x i is the characteristic value of the i-th type of steel grating; z i is the characteristic data of the i-th type of steel grating; a ik is the coefficient of the k-th feature evaluation corresponding to the i-th type of data.

6. The automatic shearing method for steel grating according to claim 5, characterized in that: The specific meaning of the characteristic value of the i-th type of the steel grating is as follows: The characteristic value x of the i-th type of steel grating i When it is greater than the standard threshold, it means that the selected steel grating type i data and its corresponding coefficient a ik After comprehensive evaluation, the steel grating meets good standards in this feature; The characteristic value x of the i-th type of steel grating i When it is less than the standard threshold, it means the selected steel grating type i data and its corresponding coefficient a ik After comprehensive evaluation, the steel grating does not meet the good range in this feature.

7. The automatic shearing method for steel grating according to claim 6, characterized in that: 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.

8. An automated steel grating shearing system, based on an automated steel grating shearing method according to any one of claims 1 to 7, characterized in that: include, Data collection module, used to collect image data of steel grating and perform preprocessing; Model building module, used to build a steel grating quality assessment model and analyze and evaluate image data; A scheme generation module, used for generating a parameter adjustment scheme based on the analysis results and sending the scheme to the actuator of the shearing device; A shearing operation module is used to start the shearing tool to shear the steel grating according to the optimized path and parameters; Model optimization module, used to optimize the steel grating quality assessment model in real time based on shearing results and monitoring data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the automatic cutting method of steel grating described in any one of claims 1 to 7 are implemented.

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 an automated steel grating cutting method as described in any one of claims 1 to 7 are implemented.

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