A method and system for detecting the strength of a copper-clad steel wire

Abstract

The application provides a copper-clad steel wire strength detection method and system, which comprises the following steps: randomly selecting a certain number of copper-clad steel wires from a production line as samples, uniquely marking each sample, recording the initial length and diameter of each sample by using a high-precision measuring tool, and calculating the original cross-sectional area; fixing the samples on a tensile testing machine, setting the loading rate, starting the testing machine, gradually increasing the tension until the wire breaks, recording the force and corresponding displacement data, and then calculating the stress and strain. The application realizes efficient identification of surface defects and internal structure changes of copper-clad steel wires. The high-definition camera cooperates with advanced image processing technology to capture and analyze a large amount of sample information in a short time, quickly locate the problem area, and automatically adjust the key parameters such as loading rate and pre-tightening force according to the material characteristics through the self-adaptive control system, thereby ensuring the consistency of the test conditions and avoiding the result deviation caused by human factors.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of copper-clad steel wire, and particularly relates to a copper-clad steel wire strength detection method and system. BACKGROUND

[0002] In modern industrial production, copper-clad steel wire is widely used in power transmission, communication cables and other fields due to its excellent electrical conductivity and mechanical strength. However, in order to ensure the quality and reliability of these materials, a precise and efficient detection method and system must be used to evaluate their mechanical properties. Traditional detection methods often rely on physical experiments such as tensile testing and bending testing under fixed parameter settings. Although these methods can provide basic performance indicators, they 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 that are not discovered in time, thereby affecting the safety and service life of the final product.

[0003] Therefore, in view of the above problems, a copper-clad steel wire strength detection method and system are developed. SUMMARY

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

[0005] The technical scheme of the present application 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 the initial length and diameter, and calculate the original cross-sectional area;

[0007] Step 2: fix the sample on a tensile testing machine, set the loading rate, start the testing machine, gradually increase the tension until the wire breaks, and record the force and corresponding displacement data, then calculate the stress and strain, and draw a stress-strain curve according to the measured data points, from which the elastic modulus, yield strength, and tensile strength parameters are determined. For samples with obvious cracks or defects, the J-integral method is used to evaluate the fracture toughness;

[0008] Step 3: Adjust the bending test equipment to the specified radius, ensure that the bending angle meets the standard requirements, and perform multiple repeated bending operations on another group of samples. Observe and record whether cracks or other damage occur. Select an undamaged area and use a hardness tester to measure the surface hardness value. Convert the formula to obtain the hardness value under other hardness scales.

[0009] Step 4: Introduce a machine vision system to automatically identify sample surface defects or internal structural changes. Capture images using a high-definition camera and analyze the images using deep learning algorithms to quickly locate problem areas.

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

[0011] As a preferred embodiment of the present application, 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 force value of 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 application, 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 through linear regression, which is the elastic modulus, and finding the yield strength during the sample test process by relying on the 0.2% offset method. Record the stress value at the maximum load as the tensile strength.

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

[0016] Where t is the stress vector outside the sample fracture surface, n is the unit normal vector on the path Γ, and ds is the fracture surface length on the sample path. Then compare the calculated J integral value with the standard fracture toughness of the material.

[0017] As a preferred embodiment of the present application, the method for locating problem areas is as follows:

[0018] Step 1: According to the size and characteristics of copper-clad steel wire, select a high-definition camera with appropriate 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 automated conveyor belts to complete multi-angle shooting during sample movement, and synchronize with ultrasonic and X-ray sensors;

[0019] Step 2: Control the camera to collect high-resolution images of the sample through a programming interface, record relevant metadata, apply contrast adjustment and noise filtering techniques to optimize the original images, and for the preliminary collected image data, the system automatically monitors and labels the defect area to form a high-quality training data set;

[0020] Step 3: Input the labeled images into the training environment, continuously iterate and optimize model performance through cross-validation, deploy the trained model to edge computing devices, trigger an alarm notification to relevant personnel as soon as a defect is detected, and generate a detailed report including defect location, type, and severity.

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

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

[0023] Data processing unit, responsible for collecting data from various test equipment and performing real-time analysis and processing;

[0024] Control unit, used to coordinate the workflow of various components and realize automatic operation of the entire detection process;

[0025] Software platform, configured to provide a user interface for operators to input instructions, view progress and results, and support deep data mining and visual presentation of built-in algorithms;

[0026] Quality traceability unit, used to record the detection information of each batch of products and establish a long-term quality archive for subsequent query and management.

[0027] As a preferred embodiment of the present application, an adaptive control system is further included for automatic control of the entire system, which is started by the software platform to make the entire system enter an automatic operation state, the adaptive control system comprises a perception layer, a control layer and an execution layer, the perception layer comprises force sensors, displacement sensors and strain gauges for real-time monitoring of the physical state of the material during stretching, the control layer is an embedded controller responsible for executing algorithms and sending instructions to the execution mechanism, and the execution layer is connected to the motors and hydraulic systems in the automatic test equipment, and automatically sets test parameters according to preset values and checks real-time data and results.

[0028] As a preferred embodiment of the present application, the adaptive control system further comprises 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 characteristics, each entry contains material name, elastic modulus, yield strength and tensile strength information, and a corresponding prediction model is trained for each material, for unknown materials, exploratory tensile tests are performed before formal testing, the material type is preliminarily judged by analyzing the obtained stress-strain curve, and the closest matching item is retrieved from the database; the adaptive algorithm uses fuzzy logic to handle uncertainty and non-linear problems, dynamically adjusts the loading rate according to the current test progress, uses a PID controller to realize accurate control of the pre-tightening force, ensures that the clamp applies appropriate initial pressure, avoids sample slippage or local stress concentration, introduces a closed-loop feedback mechanism, continuously compares the difference between the actual measured value and the target set value, and timely corrects the control strategy to ensure stable and reliable testing process.

[0029] By adopting the above technical scheme, the present application has the following advantages:

[0030] 1. The present application realizes efficient recognition of surface defects and internal structure changes of copper-clad steel wire through a machine vision system and a deep learning algorithm, a high-definition camera cooperates with advanced image processing technology to capture and analyze a large amount of sample information in a short time, quickly locates the problem area, and the adaptive control system automatically adjusts the loading rate, pre-tightening force and other key parameters according to the material characteristics to ensure the consistency of test conditions and avoid result deviation caused by human factors.

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

[0032] Figure 1 The flowchart of the steps of the copper-clad steel wire strength detection method of the present application.

[0033] Figure 2 Flow chart for positioning problem area of the present application.

[0034] Figure 3 Structural schematic diagram of the copper-clad steel wire strength detection system of the present application.

[0035] Figure 4 Structural schematic diagram of the adaptive control system of the present application. DETAILED DESCRIPTION

[0036] Reference herein to an embodiment means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase that in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combinable with other embodiments.

[0037] A copper-clad steel wire strength detection method, as shown in Figure 1 , comprises the following steps:

[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 the 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 tension until the wire breaks, and record the force and corresponding displacement data, 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, ensure that the bending angle meets the standard requirements, and perform repeated bending operations on another group of samples, observe and record whether cracks or other damage occur, select the undamaged area, measure the surface hardness value using a hardness tester, and obtain the hardness value under other hardness scales through a conversion formula;

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

[0042] Step 5: Apply exponential smoothing to the experimental data, then use nonlinear least squares to fit the stress-strain curve to improve model accuracy, then collect historical test data sets to train the model, establish control charts to monitor the trend of key quality characteristics in the production process, and timely alert abnormal situations, calculate 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 using the measured data points using drawing software, and the slope of the linear segment on the stress-strain curve is calculated by linear regression, which is the elastic modulus. At the same time, the yield strength in the sample testing process is found by relying on the 0.2% offset method, and the stress value at the maximum load is recorded as the tensile strength.

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

[0048] Where t is the stress vector outside the sample fracture surface, n is the unit normal vector on the path Γ, and ds is the fracture surface length on the sample path. Then compare the calculated J integral value with the standard fracture toughness of the material.

[0049] As shown in Figure 2 The positioning 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 appropriate 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 complete multi-angle shooting during sample movement, and simultaneously equip with ultrasonic and X-ray sensors;

[0051] Step 2: Control the camera to collect high-resolution images of the sample through the programming interface, while recording relevant metadata, apply contrast adjustment and noise filtering techniques to optimize the original images, and for the preliminary collected image data, the system automatically monitors and labels the defect area to form a high-quality training data set;

[0052] Step 3: The labeled images are input into the training environment, the model performance is continuously iterated and optimized through cross-validation, the trained model is deployed on the edge computing device, an alarm is triggered as soon as a defect is detected to notify relevant personnel, and a detailed report is generated, including the defect location, type, and severity.

[0053] A copper-clad steel wire strength detection system, as shown in Figure 3 , comprising:

[0054] An automated test device, including but not limited to a tensile testing machine and a bending testing device, which can accurately control the loading speed and accurately record relevant parameters;

[0055] A data processing unit responsible for collecting data from various test devices and performing real-time analysis and processing;

[0056] A control unit for coordinating the workflow of various components and achieving automatic operation of the entire detection process;

[0057] A software platform configured to provide a user interface for operators to input instructions, view progress and results, and built-in algorithms to support deep mining and visual presentation of data;

[0058] A quality traceability unit for recording detection information of each batch of products and establishing a long-term quality archive for subsequent query and management.

[0059] As shown in Figure 4 , it also includes an adaptive control system for automatic control and hosting of the entire system. The operator starts the system through the software platform, and the system enters an automatic 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 for real-time monitoring of the physical state of the material during stretching. The control layer is an embedded controller that executes algorithms and sends instructions to the execution mechanism. The execution layer connects to the motors and hydraulic systems in the automated test device. It automatically sets test parameters based on preset values and views real-time data and results.

[0060] As shown in Figure 4As shown, 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 characteristics. Each entry contains information about the material name, elastic modulus, yield strength, and tensile strength. For each material, a corresponding prediction model is trained. For unknown materials, exploratory tensile tests are performed before formal testing. By analyzing the obtained stress-strain curves, the material type is preliminarily judged, and the closest matching item is retrieved from the database. The adaptive algorithm uses fuzzy logic to handle uncertainty and non-linear problems. According to the current test progress, the loading rate is dynamically adjusted. A PID controller is used to precisely control the pre-tightening force, ensuring that the clamp applies appropriate initial pressure, avoiding 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 set value, and timely correct the control strategy to ensure stable and reliable testing process.

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

[0062] Connect all sensors, controllers, and actuators, calibrate the equipment, ensure its normal operation, enter the characteristic data of commonly used materials into the system database, and set the default test parameters. The user selects the material to be tested or lets the system identify it automatically, and defines the desired test target.

[0063] According to the selected material characteristics, the PID controller calculates and applies appropriate pre-tightening force, gradually increases the load at a predetermined loading rate, closely monitors the stress and strain changes during the process, and immediately triggers the adaptive algorithm to re-evaluate and adjust the loading rate and pre-tightening force if abnormal trends (such as sudden stress increase or decrease) are found. All data generated during the test are recorded throughout the process.

[0064] After the test is completed, the system automatically generates a detailed report including the stress-strain curve, key performance indicators, etc. The data of this test is added to the historical database for future parameter optimization and model training.

[0065] The above embodiments are provided to those skilled in the art to implement or use the present application. Those skilled in the art can make various modifications or changes to the above embodiments without departing from the spirit of the present application. Therefore, the protection scope of the present application should not be limited by the above embodiments, but should be the maximum scope of the innovative features mentioned in the claims.

Claims

1. A method for detecting the strength of a copper-clad steel wire, characterized in that: Includes: 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 to the tensile testing machine, set the loading rate, start the tester, and gradually increase the tensile force until the wire breaks. Simultaneously record the force and corresponding displacement data. Then calculate the stress and strain. Based on the measured data points, draw a stress-strain curve to 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 on another set of samples several times, observing and recording whether there are cracks or other damage. Select an undamaged area and measure the surface hardness value using a hardness tester. Use the conversion formula to obtain the hardness value under other hardness scales. Step 4: Introduce a machine vision system to automatically identify surface defects or internal structural changes in the sample. Use a high-definition camera to capture images and analyze them using a deep learning algorithm to quickly locate problem areas. Step 5: Apply exponential smoothing to preprocess the experimental data, then use nonlinear least squares to fit the stress-strain curve to improve model accuracy. Then, collect historical test data sets to train the model, establish control charts to monitor the changing trends of key quality characteristics during the production process, promptly warn of abnormal conditions, calculate the process capability index, and evaluate the stability of the production line. Here's how to locate the problem area: Step 1: Based on the size and characteristics of the copper-clad steel wire, select a high-definition camera with appropriate resolution and frame rate. Install lighting equipment to ensure the camera has sufficient optical zoom capability and good lighting conditions. Build a stable shooting platform and use an automated conveyor belt to enable multi-angle shooting of the sample while it is moving. Also, equip it with ultrasonic and X-ray sensors. Step 2: The camera is controlled through a programming interface to capture high-resolution images of the sample, while recording relevant metadata. Contrast adjustment and noise filtering techniques are applied to optimize the original image. For the initially collected image data, the system autonomously monitors and annotates defective areas to form a high-quality training dataset. 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.

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: ,in is the corresponding displacement data, is the length of the steel wire.

3. A copper-clad steel wire strength detection method according to claim 2, 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 3, characterized in that: The fracture toughness evaluation formula is: ; Where t is the stress vector outside the sample fracture surface, n is the unit normal vector on the path, and 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 4, characterized in that: Includes: 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 configured to provide a user interface for operators to input instructions, view progress and results, and built-in algorithms to support deep mining and visualization 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.

6. A copper-clad steel wire strength detection method according to claim 5, 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 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 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.

7. A copper-clad steel wire strength detection method according to claim 6, 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, exploratory tensile tests are carried out before formal testing. 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 to ensure 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 set value, and timely correct the control strategy to ensure that the test process is stable and reliable.

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