Method and system for detecting strength of copper-clad steel wire

Abstract

The invention provides a copper-clad steel wire strength detection method and system, and the method comprises the steps: randomly selecting a certain number of copper-clad steel wires from a production line as samples, carrying out the unique marking of each sample, recording the initial length and diameter of each sample through a high-precision measuring tool, and calculating the original sectional area; fixing the sample on a tensile testing machine, setting a loading rate, starting the testing machine, gradually increasing the tensile force until the wire rod is broken, recording the force and corresponding displacement data, and then calculating stress, strain and the like. High-efficiency identification of surface defects and internal structure changes of the copper-clad steel wire is realized, a high-definition camera is matched with an advanced image processing technology, a large amount of sample information can be captured and analyzed in a short time, and a problem area is rapidly positioned; and meanwhile, the self-adaptive control system automatically adjusts key parameters such as the loading rate and the pre-tightening force according to material characteristics, so that the consistency of test conditions is ensured, and result deviation caused by human factors is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of copper-clad steel wires, and in particular to a copper-clad steel wire strength detection method and system. Background Art

[0002] In modern industrial production, copper-clad steel wire is widely used in power transmission, communication cables and other fields due to its excellent conductivity and mechanical strength. However, in order to ensure the quality and reliability of these materials, there must be a set of accurate and efficient detection methods and systems to evaluate their mechanical properties. Traditional detection methods often rely on physical experiments such as tensile tests and bending tests under fixed parameter settings. Although they can provide basic performance indicators, they lack sufficient flexibility and accuracy when facing different batches or types of materials. In addition, traditional methods are difficult to effectively identify surface defects and internal structural changes, which may lead to potential quality problems not being discovered in time, thereby affecting the safety and life of the final product.

[0003] Therefore, in order to solve the above problems, a copper clad steel wire strength detection method and system are now developed. Summary of the invention

[0004] In order to overcome the shortcomings of existing devices that lack sufficient flexibility and accuracy when dealing with different batches or types of materials, in addition, traditional methods are difficult to effectively identify surface defects and internal structural changes, which may lead to potential quality problems not being discovered in time, thereby affecting the safety of use and life of the final product, the present invention provides a copper clad steel wire strength detection method and system.

[0005] The technical solution of the present invention is: a copper-clad steel wire strength detection method, comprising:

[0006] Step 1: Randomly select a certain number of copper-clad steel wires from the production line as samples, uniquely mark each sample, and use high-precision measuring tools to record its initial length and diameter, and calculate the original cross-sectional area;

[0007] Step 2: Fix the sample on the tensile testing machine, set the loading rate, start the testing machine, gradually increase the tensile force until the wire breaks, and record the force and corresponding displacement data at the same time. Then calculate the stress and strain, draw the stress-strain curve according to the measured data points, and determine the elastic modulus, yield strength, and tensile strength parameters. For samples with obvious cracks or defects, use the J integral method to evaluate the fracture toughness;

[0008] Step 3: Adjust the bending test equipment to the specified radius to ensure that the bending angle meets the standard requirements. Repeat the bending operation for another group of samples, observe and record whether there are cracks or other damage. Select the undamaged area, measure the surface hardness value with a hardness tester, and obtain the hardness value under other hardness scales through the conversion formula;

[0009] Step 4: Introduce a machine vision system to automatically identify surface defects or internal structural changes of the sample, capture images with a high-definition camera, and analyze the images in combination with a deep learning algorithm to quickly locate the problem area;

[0010] Step 5: Apply the exponential smoothing method to preprocess the experimental data, and then use the nonlinear least squares method to fit the stress-strain curve to improve the model accuracy. Then collect historical test data sets to train the model, establish control charts to monitor the changing trends of key quality characteristics in the production process, promptly warn of abnormal situations, calculate the process capability index, and evaluate the stability of the production line.

[0011] As a preferred embodiment of the present invention, the formula for calculating the original cross-sectional area is: Where d is the diameter of the steel wire;

[0012] The formula for calculating stress is: Where F is the value of the force on the steel wire;

[0013] The formula for calculating strain is: Where ΔL is the corresponding displacement data, and L0 is the length of the steel wire.

[0014] As a preferred embodiment of the present invention, it also includes: using drawing software to draw a stress-strain curve according to the measured data points, finding a straight line segment on the stress-strain curve, calculating the slope by linear regression as the elastic modulus, and at the same time relying on the 0.2% offset method to find the yield strength of the sample during the test, and recording the stress value at the maximum load and marking it as the tensile strength.

[0015] As a preferred embodiment of the present invention, the fracture toughness evaluation formula is: J = ∫ Γ (t·n)ds;

[0016] Among them, t is the stress vector outside the sample fracture surface, n is the unit normal vector on the path Γ, and ds is the length of the fracture surface on the sample path. The calculated J integral value can then be compared with the standard fracture toughness of the material.

[0017] As a preferred embodiment of the present invention, the method of locating the problem area is as follows:

[0018] Step 1: According to the size and characteristics of the copper-clad steel wire, select a high-definition camera with suitable resolution and frame rate, install lighting equipment to ensure that the camera has sufficient optical zoom capability and good lighting conditions, build a stable shooting platform, use an automated conveyor belt to enable the sample to be shot from multiple angles while moving, and equip it with ultrasonic and X-ray sensors simultaneously;

[0019] Step 2: Use the programming interface to control the camera to collect high-resolution images of the sample, record relevant metadata, and optimize the original image using contrast adjustment and noise filtering techniques. For the initially collected image data, the system will autonomously monitor and annotate defect areas to form a high-quality training data set.

[0020] Step 3: Input the labeled images into the training environment, optimize the model performance through cross-validation iteration, and deploy the trained model to the edge computing device. Once a defect is detected, an alarm is immediately triggered to notify relevant personnel and generate a detailed report including the defect location, type and severity.

[0021] A copper-clad steel wire strength detection system, comprising:

[0022] Automated testing equipment, including but not limited to tensile testing machines and bending testing devices, which can precisely control the loading speed and accurately record relevant parameters;

[0023] A data processing unit, which is responsible for collecting data from various test devices and performing real-time analysis and processing on the data;

[0024] A control unit, which is used to coordinate the workflow of each component to achieve automated operation of the entire detection process;

[0025] A software platform, wherein the software platform is configured to provide a user interface for operators to input instructions, view progress and results, and a built-in algorithm supports deep mining and visual presentation of data;

[0026] The quality traceability unit is used to record the inspection information of each batch of products and establish a long-term quality file to facilitate subsequent query and management.

[0027] As a preferred embodiment of the present invention, it also includes an adaptive control system, which is used to automatically control and host the entire system. The operator starts it through the software platform, so that the entire system enters an automated operation state. The adaptive control system includes: a perception layer, a control layer and an execution layer. The perception layer includes a force sensor, a displacement sensor and a strain gauge, which are used to monitor the physical state of the material during the stretching process in real time. The core of the control layer is an embedded controller, which is responsible for executing the algorithm and sending instructions to the actuator. The execution layer is connected to the motor and hydraulic system in the automated testing equipment, automatically sets the test parameters according to preset values, and checks the real-time data and results.

[0028] As a preferred embodiment of the present invention, the adaptive control system also includes a data model library and an adaptive algorithm. The data model library is established by an operator and covers a database of various metal material properties. Each entry contains material name, elastic modulus, yield strength, and tensile strength information. At the same time, a corresponding prediction model is trained for each material. For unknown materials, an exploratory tensile test is carried out before the formal test. The material type is preliminarily determined by analyzing the obtained stress-strain curve, and the closest match is retrieved from the database; the adaptive algorithm uses fuzzy logic to deal with uncertainty and nonlinear problems, dynamically adjusts the loading rate according to the current test progress, and uses a PID controller to achieve precise control of the preload force, ensuring that the fixture applies appropriate initial pressure to avoid sample slippage or local stress concentration, and introduces a closed-loop feedback mechanism to continuously compare the difference between the actual measurement value and the target setting value, and timely correct the control strategy to ensure that the test process is stable and reliable.

[0029] By adopting the above technical solution, the present invention has the following advantages:

[0030] 1. The present invention realizes efficient identification of surface defects and internal structural changes of copper-clad steel wires through machine vision systems and deep learning algorithms. High-definition cameras combined with advanced image processing technology can capture and analyze a large amount of sample information in a short time and quickly locate problem areas. At the same time, the adaptive control system automatically adjusts key parameters such as loading rate and preload according to material properties, ensuring the consistency of test conditions and avoiding result deviations caused by human factors.

[0031] 2. The present invention can easily manage the entire testing process through the user interface and obtain instant feedback. More importantly, the built-in quality traceability unit records the detailed testing information of each batch of products and establishes a long-term quality file. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The present invention is a flow chart of the steps of the copper-clad steel wire strength detection method.

[0033] Figure 2 Flowchart of steps for locating problem areas for the present invention.

[0034] Figure 3 It is a structural schematic diagram of the copper-clad steel wire strength detection system of the present invention.

[0035] Figure 4 It is a schematic diagram of the structure of the adaptive control system of the present invention. DETAILED DESCRIPTION

[0036] Reference to an embodiment herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0037] A copper clad steel wire strength testing method, such as Figure 1 As shown, including:

[0038] Step 1: Randomly select a certain number of copper-clad steel wires from the production line as samples, uniquely mark each sample, and use high-precision measuring tools to record its initial length and diameter, and calculate the original cross-sectional area;

[0039] Step 2: Fix the sample on the tensile testing machine, set the loading rate, start the testing machine, gradually increase the tensile force until the wire breaks, and record the force and corresponding displacement data at the same time. Then calculate the stress and strain, draw the stress-strain curve according to the measured data points, and determine the elastic modulus, yield strength, and tensile strength parameters. For samples with obvious cracks or defects, use the J integral method to evaluate the fracture toughness;

[0040] Step 3: Adjust the bending test equipment to the specified radius to ensure that the bending angle meets the standard requirements. Repeat the bending operation for another group of samples, observe and record whether there are cracks or other damage. Select the undamaged area, measure the surface hardness value with a hardness tester, and obtain the hardness value under other hardness scales through the conversion formula;

[0041] Step 4: Introduce a machine vision system to automatically identify surface defects or internal structural changes of the sample, capture images with a high-definition camera, and analyze the images in combination with a deep learning algorithm to quickly locate the problem area;

[0042] Step 5: Apply the exponential smoothing method to preprocess the experimental data, and then use the nonlinear least squares method to fit the stress-strain curve to improve the model accuracy. Then collect historical test data sets to train the model, establish control charts to monitor the changing trends of key quality characteristics in the production process, promptly warn of abnormal situations, calculate the process capability index, and evaluate the stability of the production line.

[0043] The formula for calculating the original cross-sectional area is: Where d is the diameter of the steel wire;

[0044] The formula for calculating stress is: Where F is the value of the force on the steel wire;

[0045] The formula for calculating strain is: Where ΔL is the corresponding displacement data, and L0 is the length of the steel wire.

[0046] It should be noted that the stress-strain curve is drawn according to the measured data points using drawing software, and the straight line segment is found on the stress-strain curve. The slope is calculated by linear regression, which is the elastic modulus. At the same time, the yield strength of the sample during the test is found by the 0.2% offset method, and the stress value at the maximum load is recorded and marked as the tensile strength.

[0047] The fracture toughness evaluation formula is: J = ∫ Γ (t·n)ds;

[0048] Among them, t is the stress vector outside the sample fracture surface, n is the unit normal vector on the path Γ, and ds is the length of the fracture surface on the sample path. The calculated J integral value can then be compared with the standard fracture toughness of the material.

[0049] like Figure 2 As shown, the way to locate the problem area is as follows:

[0050] Step 1: According to the size and characteristics of the copper-clad steel wire, select a high-definition camera with suitable resolution and frame rate, install lighting equipment to ensure that the camera has sufficient optical zoom capability and good lighting conditions, build a stable shooting platform, use an automated conveyor belt to enable the sample to be shot from multiple angles while moving, and equip it with ultrasonic and X-ray sensors simultaneously;

[0051] Step 2: Use the programming interface to control the camera to collect high-resolution images of the sample, record relevant metadata, and optimize the original image using contrast adjustment and noise filtering techniques. For the initially collected image data, the system will autonomously monitor and annotate defect areas to form a high-quality training data set.

[0052] Step 3: Input the labeled images into the training environment, optimize the model performance through cross-validation iteration, and deploy the trained model to the edge computing device. Once a defect is detected, an alarm is immediately triggered to notify relevant personnel and generate a detailed report including the defect location, type and severity.

[0053] A copper clad steel wire strength detection system, such as Figure 3 As shown, including:

[0054] Automated testing equipment, including but not limited to tensile testing machines and bending testing devices, which can precisely control the loading speed and accurately record relevant parameters;

[0055] Data processing unit, which is responsible for collecting data from various test equipment and performing real-time analysis and processing on them;

[0056] Control unit, which is used to coordinate the workflow of each component to realize the automated operation of the entire testing process;

[0057] Software platform, which is configured to provide a user interface for operators to input commands, view progress and results, and built-in algorithms to support deep mining and visualization of data;

[0058] Quality traceability unit: The quality traceability unit is used to record the inspection information of each batch of products and establish long-term quality files to facilitate subsequent query and management.

[0059] like Figure 4 As shown, it also includes an adaptive control system, which is used to automatically control and host the entire system. The operator starts it through the software platform, so that the entire system enters an automated operation state. The adaptive control system includes: a perception layer, a control layer, and an execution layer. The perception layer includes force sensors, displacement sensors, and strain gauges, which are used to monitor the physical state of the material during the stretching process in real time. The core of the control layer is an embedded controller, which is responsible for executing algorithms and sending instructions to the actuators. The execution layer connects to the motor and hydraulic system in the automated testing equipment, automatically sets test parameters according to preset values, and views real-time data and results.

[0060] like Figure 4As shown in the figure, the adaptive control system also includes a data model library and an adaptive algorithm. The data model library is established by the operator and covers a database of various metal material properties. Each entry contains the material name, elastic modulus, yield strength, and tensile strength information. At the same time, a corresponding prediction model is trained for each material. For unknown materials, an exploratory tensile test is carried out before the formal test. The material type is preliminarily determined by analyzing the obtained stress-strain curve, and the closest match is retrieved from the database; the adaptive algorithm uses fuzzy logic to deal with uncertainty and nonlinear problems, dynamically adjusts the loading rate according to the current test progress, and uses a PID controller to achieve precise control of the preload force, ensuring that the fixture applies appropriate initial pressure to avoid sample slippage or local stress concentration. A closed-loop feedback mechanism is introduced to continuously compare the difference between the actual measurement value and the target setting value, and to promptly correct the control strategy to ensure that the test process is stable and reliable.

[0061] It should be noted that the specific usage is:

[0062] Connect all sensors, controllers and actuators, calibrate the equipment to ensure that they work properly, enter the characteristic data of common materials into the system database, and set default test parameters. The user selects the material to be tested or lets the system identify it by itself and defines the test objectives to be achieved;

[0063] According to the selected material properties, the PID controller calculates and applies the appropriate preload force, gradually increases the load at the predetermined loading rate, and closely monitors the changes in stress and strain. Once an abnormal trend (such as a sudden increase or decrease in stress) is found, the adaptive algorithm is immediately triggered to re-evaluate and adjust the loading rate and preload force, and all data generated during the test are recorded throughout the process;

[0064] After the test, the system automatically generates a detailed report, including stress-strain curves, key performance indicators, etc., and adds the test data to the historical database for future parameter optimization and model training.

[0065] The above embodiments are provided for persons familiar with the art to implement or use the present invention. Personnel familiar with the art can make various modifications or changes to the above embodiments without departing from the concept of the present invention. Therefore, the protection scope of the present invention is not limited to the above embodiments, but should be the maximum scope of the innovative features mentioned in the claims.

Claims

1. A copper-clad steel wire strength detection method, characterized in that: Included are: Step 1: Randomly select a certain number of copper-clad steel wires from the production line as samples, uniquely mark each sample, and use high-precision measuring tools to record its initial length and diameter, and calculate the original cross-sectional area; Step 2: Fix the sample on the tensile testing machine, set the loading rate, start the testing machine, gradually increase the tensile force until the wire breaks, and record the force and corresponding displacement data at the same time. Then calculate the stress and strain, draw the stress-strain curve according to the measured data points, and determine the elastic modulus, yield strength, and tensile strength parameters. For samples with obvious cracks or defects, use the J integral method to evaluate the fracture toughness; Step 3: Adjust the bending test equipment to the specified radius to ensure that the bending angle meets the standard requirements. Repeat the bending operation for another group of samples, observe and record whether there are cracks or other damage. Select the undamaged area, measure the surface hardness value with a hardness tester, and obtain the hardness value under other hardness scales through the conversion formula; Step 4: Introduce a machine vision system to automatically identify surface defects or internal structural changes of the sample, capture images with a high-definition camera, and analyze the images in combination with a deep learning algorithm to quickly locate the problem area; Step 5: Apply the exponential smoothing method to preprocess the experimental data, and then use the nonlinear least squares method to fit the stress-strain curve to improve the model accuracy. Then collect historical test data sets to train the model, establish control charts to monitor the changing trends of key quality characteristics in the production process, promptly warn of abnormal situations, calculate the process capability index, and evaluate the stability of the production line.

2. A copper-clad steel wire strength detection method according to claim 1, characterized in that: The formula for calculating the original cross-sectional area is: Where d is the diameter of the steel wire; The formula for calculating stress is: Where F is the value of the force on the steel wire; The formula for calculating strain is: Where ΔL is the corresponding displacement data, and L0 is the length of the steel wire.

3. A copper-clad steel wire strength detection method according to claim 1, characterized in that: It also includes: using drawing software to draw a stress-strain curve based on the measured data points, finding the straight line segment on the stress-strain curve, calculating the slope as the elastic modulus through linear regression, and relying on the 0.2% offset method to find the yield strength of the sample during the test, and recording the stress value at the maximum load and marking it as the tensile strength.

4. A copper-clad steel wire strength detection method according to claim 1, characterized in that: The fracture toughness evaluation formula is: J = ∫r ( t·n)ds; Among them, t is the stress vector outside the sample fracture surface, n is the unit normal vector on the path Γ, and ds is the length of the fracture surface on the sample path. The calculated J integral value can then be compared with the standard fracture toughness of the material.

5. A copper-clad steel wire strength detection method according to claim 1, characterized in that: Here's how to locate the problem area: Step 1: According to the size and characteristics of the copper-clad steel wire, select a high-definition camera with suitable resolution and frame rate, install lighting equipment to ensure that the camera has sufficient optical zoom capability and good lighting conditions, build a stable shooting platform, use an automated conveyor belt to enable the sample to be shot from multiple angles while moving, and equip it with ultrasonic and X-ray sensors simultaneously; Step 2: Use the programming interface to control the camera to collect high-resolution images of the sample, record relevant metadata, and optimize the original image using contrast adjustment and noise filtering techniques. For the initially collected image data, the system will autonomously monitor and annotate defect areas to form a high-quality training data set. Step 3: Input the labeled images into the training environment, continuously optimize the model performance through cross-validation, and deploy the trained model to the edge computing device. Once a defect is detected, an alarm is immediately triggered to notify relevant personnel and generate a detailed report including the defect location, type and severity.

6. A copper-clad steel wire strength detection system, characterized in that: Included are: Automated testing equipment, including but not limited to tensile testing machines and bending testing devices, which can precisely control the loading speed and accurately record relevant parameters; A data processing unit, which is responsible for collecting data from various test devices and performing real-time analysis and processing on the data; A control unit, which is used to coordinate the workflow of each component to achieve automated operation of the entire detection process; A software platform, wherein the software platform is configured to provide a user interface for operators to input instructions, view progress and results, and a built-in algorithm supports deep mining and visual presentation of data; The quality traceability unit is used to record the inspection information of each batch of products and establish a long-term quality file to facilitate subsequent query and management.

7. A copper-clad steel wire strength detection system according to claim 6, characterized in that: It also includes an adaptive control system, which is used to automatically control and host the entire system. The operator starts it through the software platform, so that the entire system enters an automated operation state. The adaptive control system includes: a perception layer, a control layer and an execution layer. The perception layer includes a force sensor, a displacement sensor and a strain gauge, which are used to monitor the physical state of the material during the stretching process in real time. The core of the control layer is an embedded controller, which is responsible for executing the algorithm and sending instructions to the actuator. The execution layer is connected to the motor and hydraulic system in the automated testing equipment, automatically sets the test parameters according to preset values, and views real-time data and results.

8. A copper-clad steel wire strength detection system according to claim 7, characterized in that: The adaptive control system also includes a data model library and an adaptive algorithm. The data model library is established by the operator and covers a database of various metal material properties. Each entry contains material name, elastic modulus, yield strength, and tensile strength information. At the same time, a corresponding prediction model is trained for each material. For unknown materials, an exploratory tensile test is carried out before the formal test. The material type is preliminarily determined by analyzing the obtained stress-strain curve, and the closest match is retrieved from the database; the adaptive algorithm uses fuzzy logic to deal with uncertainty and nonlinear problems, dynamically adjusts the loading rate according to the current test progress, and uses a PID controller to achieve precise control of the preload force, ensuring that the fixture applies appropriate initial pressure to avoid sample slippage or local stress concentration. A closed-loop feedback mechanism is introduced to continuously compare the difference between the actual measurement value and the target setting value, and the control strategy is corrected in time to ensure that the test process is stable and reliable.

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