Method and System for Online Removal of Oxide Scale from Wire Rod Based on Laser Technology

Abstract

The present invention relates to the field of laser processing technology, and discloses a method and system for online removal of iron oxide scale from wire rods based on laser technology. The method comprises the following steps: establishing a relationship between detachment stress and adhesion force, determining the relationship between different process parameters and the effect of iron oxide scale removal, and determining initial laser process parameters in combination with the current wire rod material properties and the oxide scale performance; dynamically adjusting the initial laser process parameters based on the thickness distribution of the oxide layer on the wire rod surface; laser processing the iron oxide scale on the wire rod surface using a rotating laser head; and detecting the effect of iron oxide scale removal on the wire rod surface using image recognition technology, and dynamically optimizing model parameters based on the detection results. The present invention can remove iron oxide scale and stains on the surface of wire rods in a continuous production process at one time, solving the pain points of the prior art, which are the inability to completely remove iron oxide scale from the wire rod surface and the inability to efficiently process iron oxide scale on the workpiece surface online.

Description

Technical Field

[0001] The present invention relates to the field of laser processing technology, and in particular to a method and system for online removal of wire rod iron oxide scale based on laser technology. Background Art

[0002] In the metallurgical industry, surface cleaning is a key step in improving product quality and production efficiency during deep metal surface processing. Traditional surface cleaning methods primarily include pickling and mechanical stripping, but both methods have significant drawbacks and limitations, particularly in terms of environmental protection, cost control, and process efficiency.

[0003] The pickling process is the most common method for cleaning metal surfaces. The workpiece is immersed in a pickling tank and cleaned by the chemical reaction between the acid and the oxide layer or oil on the metal surface. Although pickling can effectively remove the oxide layer on the metal surface, the process carries a large number of environmental pollution risks. The replacement of the acid, the recycling of the acid, and the treatment of wastewater all require complex supporting facilities and high investment costs. At the same time, the acid emissions and pollutants generated during the pickling process are difficult to treat, and it is impossible to achieve completely zero emissions, which violates the current increasingly stringent environmental protection requirements. What is more serious is that the acid is highly toxic. Long-term exposure may lead to occupational diseases and pose a threat to the personal safety of workers.

[0004] Mechanical stripping primarily removes the oxide layer from the workpiece surface through machining. While this method can fairly thoroughly remove surface impurities, it results in significant metal loss, reducing the yield rate. Furthermore, the machining process inevitably involves the use of machining fluids, which not only pollutes the environment but also increases production costs. Furthermore, mechanical stripping presents significant challenges for machining precision workpieces and is difficult to adapt to high-precision, demanding surface treatments.

[0005] As a relatively advanced surface cleaning method, laser cleaning technology has gradually been used to remove oxides from metal surfaces due to its high energy density, cleanliness and high efficiency. Laser cleaning technology irradiates the metal surface with a high-energy laser beam, instantly evaporating or exciting the surface oxides or contaminants to achieve the purpose of cleaning. Compared with pickling and mechanical stripping, laser cleaning has the advantages of low energy consumption, no chemical pollution, and no need to contact the workpiece surface. However, current laser cleaning technology still faces certain limitations in practical applications. In particular, when multiple cleanings are required to completely remove the iron oxide scale, repeated cleaning will lead to low efficiency and increase production time and cost. In addition, how to efficiently remove the iron oxide scale on the workpiece surface during continuous deep processing production is still a technical problem that needs to be solved urgently.

[0006] Therefore, the present invention proposes a method and system for online removal of wire rod iron oxide scale based on laser technology. Summary of the Invention

[0007] In response to the problems in the related art, the present invention proposes a method and system for online removal of wire rod iron oxide scale based on laser technology to overcome the above-mentioned technical problems existing in the existing related art.

[0008] To this end, the specific technical solutions adopted in the present invention are as follows:

[0009] According to one aspect of the present invention, a method for online removal of wire rod scale based on laser technology is provided, comprising the following steps:

[0010] S1. Based on the temperature and displacement distribution caused by laser heating, the relationship between detachment stress and adhesion force is established, and the relationship between different process parameters and scale removal effect is determined. In addition, the initial laser process parameters are determined based on the current wire rod material properties and scale performance.

[0011] S2. Using the oxide layer thickness identification model combined with terahertz technology, the thickness distribution of the oxide layer on the wire rod surface is identified, and the initial laser process parameters are dynamically adjusted according to the thickness distribution of the oxide layer on the wire rod surface to obtain the optimal laser process parameters based on the thickness distribution;

[0012] S3. Based on the optimal laser process parameters, the iron oxide scale on the surface of the wire rod is laser treated using a rotating laser head, and the metal powder is collected using negative pressure technology. The removal effect of the iron oxide scale on the surface of the wire rod is detected using image recognition technology, and the model parameters are dynamically optimized based on the detection results.

[0013] Furthermore, the relationship between the detachment stress and the adhesion force is established based on the temperature distribution and displacement distribution caused by laser heating, the relationship between different process parameters and the iron oxide scale removal effect is determined, and the initial laser process parameters are determined in combination with the current wire rod material properties and oxide scale performance, including the following steps:

[0014] S11. Establish a temperature distribution model caused by laser heating based on laser power, material properties, and heat conduction characteristics; establish a displacement and stress distribution model caused by laser heating based on the temperature gradient caused by laser heating and the thermal expansion characteristics of the material;

[0015] S12, determining the detachment stress between the iron oxide scale and the substrate based on the thermal stress, calculating the adhesion between the iron oxide scale and the substrate, and determining a critical value between the detachment stress and the adhesion based on the calculated results of the adhesion and the detachment stress;

[0016] S13. Use finite element analysis to determine the relationship between laser parameters, material parameters and the cleaning and removal effect of iron oxide scale, and determine the initial laser process parameters based on the current wire rod material properties and iron oxide scale performance.

[0017] Furthermore, the expression of the temperature distribution model caused by laser heating is:

[0018]

[0019] The expression of the displacement distribution model caused by laser is:

[0020]

[0021]

[0022] In the formula, T represents temperature, t represents time, represents the Laplace operator of temperature, represents the thermal diffusion coefficient of the material, Q(r) represents the distribution of laser power at position r, ρ is the density of the material, c represents the specific heat capacity of the material, represents thermal stress, represents the temperature change, E represents the elastic modulus, represents the thermal expansion coefficient of the material, u(x,y,z) represents the distribution of displacement in space, and L represents the length of the path.

[0023] Furthermore, the formula for calculating the adhesion between the iron oxide scale and the substrate is:

[0024]

[0025] Where F represents the adhesion between the iron oxide scale and the substrate, It represents the adhesion strength between the iron oxide scale and the substrate, and A represents the contact area between the iron oxide scale and the substrate.

[0026] Furthermore, the method of using finite element analysis to determine the relationship between laser parameters, material parameters and the cleaning and removal effect of iron oxide scale, and combining the current wire rod material properties and oxide scale performance to determine the initial laser process parameters includes the following steps:

[0027] S131. Based on the temperature distribution model, displacement and stress distribution model, and in combination with the critical value between the detachment stress and the adhesion force, simulate the effect of different laser process parameters on the iron oxide scale removal effect, and determine the basic laser process parameters based on the simulation results;

[0028] S132. Obtain the property data of the wire rod material to be processed, the property data of the iron oxide scale and the laser parameter data, optimize the process conditions of laser power and scanning speed in the basic laser process parameters through finite element simulation, and determine the initial laser process parameters based on the optimization results.

[0029] Furthermore, the method of using the oxide layer thickness identification model in combination with terahertz technology to identify the thickness distribution of the oxide layer on the wire rod surface, and dynamically adjusting the initial laser process parameters according to the thickness distribution of the oxide layer on the wire rod surface to obtain the optimal laser process parameters based on the thickness distribution includes the following steps:

[0030] S21. Based on a pre-built oxide layer thickness identification model, identifying the thickness distribution of the oxide layer on the surface of the wire rod to be processed, and obtaining a first distribution result of the oxide layer thickness;

[0031] S22, using terahertz technology to identify the thickness distribution of the oxide layer on the surface of the wire rod to be processed, and obtain a second distribution result of the oxide layer thickness;

[0032] S23, determining the thickness distribution of the oxide layer on the surface of the wire rod to be processed based on the first distribution result and the second distribution result of the oxide layer thickness;

[0033] S24. According to the thickness distribution of the oxide layer on the surface of the wire rod to be processed, the initial laser process parameters at different positions on the wire rod surface are dynamically adjusted to obtain the optimal laser process parameters based on the thickness of the oxide layer.

[0034] Furthermore, the identification of the thickness distribution of the oxide layer on the surface of the wire rod to be processed based on the pre-built oxide layer thickness identification model to obtain a first distribution result of the oxide layer thickness includes the following steps:

[0035] S211, obtaining a surface image of the wire rod to be processed and performing preprocessing, analyzing brightness differences in the surface image, extracting grayscale values of different areas on the oxide layer surface, calculating local brightness distribution in the surface image, obtaining average brightness of the local area through a sliding window method, and calculating the global brightness distribution to generate a grayscale histogram;

[0036] S212. Use the local binary pattern method to extract the texture features of the surface image, and use the edge detection method to identify the boundary between the oxide layer and the substrate; based on the pre-constructed oxide layer thickness recognition model, output the oxide layer thickness corresponding to the texture features of the surface image, and obtain the first distribution results of the oxide layer thickness at different positions on the wire rod surface.

[0037] Furthermore, the method of using terahertz technology to identify the thickness distribution of the oxide layer on the surface of the wire rod to be processed and obtaining a second distribution result of the oxide layer thickness includes the following steps:

[0038] S221, using an ultrashort pulse terahertz wave to illuminate the surface of the wire rod to be processed, measuring the reflection, transmission, or absorption of the terahertz wave by the oxide layer, and recording the amplitude, phase, and time delay information of the terahertz wave;

[0039] S222. Calculate the reflection and transmission time difference of the terahertz wave at different oxide layer thicknesses through time domain analysis, convert the time domain signal into a frequency domain signal using Fourier transform, extract the spectral characteristics of different frequencies, and calculate the absorption and reflectivity of the oxide layer for the terahertz wave of a specific frequency to determine its thickness information;

[0040] S223. Based on the transmission or reflection time difference of the terahertz wave and the refractive index of the oxide layer, the thickness of the oxide layer is calculated, and a mathematical model is used to invert the thickness distribution of the oxide layer to obtain a second distribution result of the oxide layer thickness at different positions on the wire rod surface.

[0041] Furthermore, the determining of the thickness distribution of the oxide layer on the surface of the wire rod to be processed based on the first distribution result and the second distribution result of the oxide layer thickness includes the following steps:

[0042] S231, obtaining a first distribution result and a second distribution result of the oxide layer thickness at different positions on the wire rod surface, and performing data alignment and preprocessing; establishing a spatial correspondence between the first distribution result data and the second distribution result data based on a meshing method for the wire rod surface, and spatially aligning the thickness distribution data of the first distribution result with the thickness distribution data of the second distribution result using a feature matching method;

[0043] S232. Use the weighted average method in combination with a preset weight coefficient to fuse the first distribution result data and the second distribution result data to obtain a final thickness value, and generate an oxide layer thickness distribution map at different positions on the wire rod surface based on the final thickness value.

[0044] According to another aspect of the present invention, a system for online removal of wire rod iron scale based on laser technology is provided, comprising an initial laser process parameter determination module, a laser process parameter optimization module, and an iron scale online removal module;

[0045] The initial laser process parameter determination module is used to establish the relationship between the detachment stress and the adhesion force based on the temperature distribution and displacement distribution caused by laser heating, determine the relationship between different process parameters and the iron oxide scale removal effect, and determine the initial laser process parameters in combination with the current wire rod material properties and iron oxide scale performance;

[0046] The laser process parameter optimization module is used to identify the thickness distribution of the oxide layer on the wire rod surface by using the oxide layer thickness identification model combined with terahertz technology, and dynamically adjust the initial laser process parameters according to the thickness distribution of the oxide layer on the wire rod surface to obtain the optimal laser process parameters based on the thickness distribution;

[0047] The online iron oxide scale removal module is used to laser treat the iron oxide scale on the surface of the wire rod using a rotating laser head based on optimal laser process parameters, and collect metal powder through negative pressure technology; use image recognition technology to detect the removal effect of the iron oxide scale on the wire rod surface, and dynamically optimize the model parameters based on the detection results.

[0048] The beneficial effects of the present invention are:

[0049] 1) This invention leverages the high energy density and precise control of lasers to rapidly heat iron oxide scale to its shedding temperature, achieving rapid scale removal. This allows for the simultaneous removal of scale and stains from wire rod surfaces during continuous production, resolving the existing challenges of incomplete wire rod surface removal and the inability to efficiently remove scale from workpiece surfaces online. Compared to traditional methods, laser removal requires no chemicals or extensive mechanical force, resulting in highly efficient removal without damaging the substrate.

[0050] 2) The present invention combines precise adjustment of laser power and scanning speed to accurately treat iron oxide scales of different thicknesses and properties. By dynamically adjusting process parameters, it can ensure that the iron oxide scale removal process always remains in the optimal state, avoiding overheating or damage to the wire rod surface.

[0051] 3) By integrating an advanced oxide layer thickness identification model and terahertz technology, the present invention monitors the thickness distribution of the oxide layer on the wire rod surface in real time and dynamically adjusts the laser process parameters based on actual conditions. This real-time feedback mechanism ensures that the removal effect of iron oxide scale during laser processing is always at an optimal level.

[0052] 4) By combining the wire rod material properties, oxide scale performance and laser technology, the present invention can adapt to wire rod materials of different types and sizes. Whether it is a thin oxide layer or a thick oxide layer, the ideal removal effect can be achieved by flexibly adjusting the process parameters.

[0053] 5) The present invention combines image recognition technology with negative pressure collection technology to detect the removal effect in real time and perform dynamic optimization, ensuring the thoroughness and uniformity of iron oxide scale removal, further improving the automation and intelligence level of the process. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in 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 paying any creative work.

[0055] Figure 1The figure is a flow chart of a method for online removal of wire rod iron oxide scale based on laser technology according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention. They are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. By referring to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0057] According to an embodiment of the present invention, a method and system for online removal of wire rod scale based on laser technology are provided.

[0058] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a method for online removal of wire rod iron oxide scale based on laser technology is provided, comprising the following steps:

[0059] S1. Based on the temperature and displacement distribution caused by laser heating, the relationship between detachment stress and adhesion force is established, and the relationship between different process parameters and scale removal effect is determined. In addition, the initial laser process parameters are determined based on the current wire rod material properties and scale performance.

[0060] The process of establishing the relationship between the detachment stress and the adhesion force based on the temperature distribution and displacement distribution caused by laser heating, determining the relationship between different process parameters and the iron oxide scale removal effect, and determining the initial laser process parameters in combination with the current wire rod material properties and iron oxide scale performance includes the following steps:

[0061] S11. Establish a temperature distribution model caused by laser heating based on laser power, material properties, and heat conduction characteristics; establish a displacement and stress distribution model caused by laser heating based on the temperature gradient caused by laser heating and the thermal expansion characteristics of the material;

[0062] Specifically, when a laser heats a metal surface, the laser energy is absorbed by the material and converted into heat energy, causing the surface temperature of the material to rise, which in turn affects the temperature distribution of the material. The temperature distribution model needs to comprehensively consider the laser power, the thermal conductivity, density, specific heat capacity and other physical properties of the material. The following are the basic steps to establish a temperature distribution model:

[0063] 1) Temperature distribution equation: Based on the heat conduction equation and laser power distribution, the temperature distribution of laser heating can be described by a two-dimensional heat conduction equation. That is, the expression of the temperature distribution model caused by laser heating is:

[0064]

[0065] In the formula, T represents temperature, t represents time, The Laplace operator representing temperature (representing heat diffusion), represents the thermal diffusivity of the material (determined by thermal conductivity, density and specific heat capacity), Q(r) represents the distribution of laser power at position r, ρ is the density of the material, and c represents the specific heat capacity of the material;

[0066] 2) Laser power distribution Q(r): The distribution of laser power is usually determined by the shape of the laser spot. For a Gaussian laser beam, the laser power follows a Gaussian distribution in the radial direction:

[0067]

[0068] Where Q0 represents the maximum value of the laser power, r represents the radial distance from the center of the laser beam, and k represents the width of the spot;

[0069] 3) Steady-state temperature distribution: For steady state (ignoring time changes), the temperature distribution equation is simplified to:

[0070]

[0071] This equation can be solved numerically (e.g., finite difference method or finite element method) to obtain the temperature distribution caused by laser heating;

[0072] Laser heating causes thermal expansion of the material. The presence of temperature gradients causes different regions of the material to expand to different degrees, leading to the formation of internal stress, which in turn causes displacement. Based on the relationship between thermal stress and the thermal expansion characteristics of the material, a displacement and stress distribution model can be established, including the following basic steps:

[0073] 1) Thermal stress model: Thermal stress is generated by temperature gradient and thermal expansion of the material. The distribution of thermal stress can be described by the following thermal stress formula, that is, the expression of the displacement distribution model caused by laser is:

[0074]

[0075] Where, represents thermal stress, represents the temperature change, E represents the elastic modulus, Indicates the thermal expansion coefficient of the material;

[0076] 2) Displacement Model: Thermal stress will cause the material to deform, which manifests as displacement. The distribution of displacement can be calculated using the equations of elasticity, assuming that the material obeys linear elastic behavior during heating. The displacement model can be expressed as follows:

[0077]

[0078] Where, represents the divergence of stress, and f represents the external force density (e.g., volume force caused by laser heating). This equation allows the relationship between stress and displacement to be calculated.

[0079] 3) Displacement caused by thermal expansion: Based on the temperature gradient and the thermal expansion characteristics of the material, the displacement can be calculated using the following formula:

[0080]

[0081] Where u(x,y,z) represents the distribution of displacement in space, and L represents the length of the path;

[0082] 4) Finite element analysis of stress and displacement: Based on temperature distribution, thermal stress and thermal expansion characteristics, displacement and stress can be numerically calculated using finite element analysis (FEA) to obtain accurate temperature, stress and displacement distributions.

[0083] S12. Determine the detachment stress between the iron oxide scale and the substrate based on the thermal stress, calculate the adhesion between the iron oxide scale and the substrate, and determine a critical value between the detachment stress and the adhesion based on the calculated results of the adhesion and detachment stress; specifically, including:

[0084] 1) Determine the separation stress between the iron oxide scale and the substrate based on thermal stress:

[0085] During laser heating, the temperature gradient creates a thermal expansion difference between the substrate and the iron oxide scale, causing thermal stress that in turn affects the adhesion between the iron oxide scale and the substrate. Breakaway stress refers to the minimum stress required for the iron oxide scale to begin separating from the substrate under external influences. By calculating the magnitude of the thermal stress, the breakaway stress between the iron oxide scale and the substrate can be further inferred.

[0086] Assuming that the thermal expansion coefficients of the iron oxide scale and the substrate are different, when the iron oxide scale is heated by the laser, the temperature difference between the iron oxide scale and the substrate will generate thermal stress. The separation stress refers to the minimum stress that needs to be overcome when the iron oxide scale and the substrate begin to separate. Assuming that the adhesion of the iron oxide scale increases with the increase of thermal stress, when the thermal stress reaches a certain value, the iron oxide scale will begin to separate from the substrate surface. The separation stress can be calculated by subtracting the difference between the adhesion force between the iron oxide scale and the substrate from the stress caused by thermal expansion.

[0087] 2) Calculated based on the adhesion between the iron oxide scale and the substrate:

[0088] The adhesion between the iron oxide scale and the substrate refers to the strength of the adhesion between the two, which determines whether the iron oxide scale can be detached during the laser cleaning process. The adhesion can be calculated using the following formula:

[0089]

[0090] Where F represents the adhesion between the iron oxide scale and the substrate, Indicates the adhesion strength between the iron oxide scale and the substrate. Adhesion strength is part of the material properties and is usually determined experimentally. A represents the contact area between the iron oxide scale and the substrate, which can be estimated by scanning the oxide layer surface or by a model.

[0091] 3) Determine the critical value between detachment stress and adhesion force:

[0092] The critical value refers to the critical point between the adhesion force between the iron oxide scale and the substrate and the thermal stress. When the thermal stress is greater than or equal to the adhesion force, the iron oxide scale will begin to separate from the substrate, forming a critical condition for separation stress.

[0093] S13. Using finite element analysis to determine the relationship between laser parameters, material parameters, and scale cleaning and removal effects, and combining the current wire rod material properties and scale performance to determine initial laser process parameters;

[0094] Specifically, the use of finite element analysis to determine the relationship between laser parameters, material parameters and the cleaning and removal effect of iron oxide scale, and combining the current wire rod material properties and oxide scale performance to determine the initial laser process parameters includes the following steps:

[0095] S131. Based on the temperature distribution model, displacement and stress distribution model, and in combination with the critical value between the detachment stress and the adhesion force, simulate the effect of different laser process parameters on the iron oxide scale removal effect, and determine the basic laser process parameters based on the simulation results;

[0096] In order to optimize the laser cleaning process parameters and effectively remove the iron oxide scale, based on the temperature distribution model, displacement and stress distribution model, combined with the critical value between the detachment stress and adhesion force, the effect of different laser process parameters (such as laser power, scanning speed, etc.) on the iron oxide scale removal effect can be simulated. The following is an overview of the steps:

[0097] 1) Overview of simulation steps

[0098] 1.1) Determine the range of laser process parameters: First, set a set of laser process parameter ranges. These parameters usually include:

[0099] Laser power: affects energy transfer efficiency, thereby affecting heating rate and thermal stress;

[0100] Scanning speed: determines the speed at which the laser beam moves on the workpiece surface, thus affecting the heating time;

[0101] Laser beam diameter (spot size): affects the distribution of heat, directly affecting the temperature distribution and stress distribution;

[0102] Pulse frequency (for pulsed lasers): affects the repetition rate of laser pulses, thereby affecting the energy input per unit time;

[0103] These process parameters will affect the metal surface temperature distribution, thermal expansion, stress distribution and the detachment of iron oxide scale, so it is necessary to simulate the removal effect under different process conditions;

[0104] 1.2) Use the temperature distribution model to calculate the temperature distribution under different laser process parameters: Based on parameters such as laser power, scanning speed, and spot size, the temperature distribution of the metal surface after laser heating is calculated using the temperature distribution equation (heat conduction equation). By changing different laser parameters, different temperature distributions are simulated, thereby obtaining the temperature field under different laser process conditions;

[0105] 1.3) Calculating Displacement and Stress Distribution Based on Temperature Distribution: Using thermal stress equations, we calculate the thermal stress and displacement generated under different laser process conditions. Different temperature distributions lead to different thermal expansion effects, resulting in different displacement and stress fields. By simulating these stresses and displacements, we can estimate the adhesion and detachment stress between the iron oxide scale and the substrate under different laser parameters.

[0106] 1.4) Calculate the detachment stress and adhesion force: Based on the relationship between thermal stress and material thermal expansion, combined with the adhesion between the iron oxide scale and the substrate, calculate the magnitude of the detachment stress under different temperature distribution conditions. If the thermal stress is greater than the adhesion force, the iron oxide scale will begin to detach;

[0107] 1.5) Comparison of scale removal performance under different laser process conditions: Based on the magnitude of the release stress and the change in adhesion force, the scale removal performance under each laser process condition was determined. If the release stress reaches or exceeds the adhesion force, the scale can be removed. This simulation process can evaluate the impact of different laser process parameters on scale removal performance.

[0108] 2) Determine basic laser process parameters based on simulation results

[0109] 2.1) Analysis of simulation results: The simulation results will show the effect of iron oxide scale removal under different laser process parameters, including:

[0110] Removal efficiency: that is, the speed and degree of removal of iron oxide scale under different laser process parameters;

[0111] Degree of thermal damage: Excessive laser power or low scanning speed may cause overheating and thermal damage to the metal substrate, thus affecting the quality of the final product;

[0112] Surface quality: It is necessary to ensure that there is no obvious damage or deformation on the workpiece surface after laser cleaning;

[0113] Based on these simulation results, we analyzed the effects of different process parameters on the removal of iron oxide scale and found the most suitable laser process parameters to avoid excessive thermal stress that could damage the substrate.

[0114] 2.2) Optimize basic laser process parameters: By comparing the simulation results, optimize the optimal laser power, scanning speed and other process parameters. Generally speaking, the basic laser process parameters should meet the following conditions:

[0115] Reasonable matching of laser power and scanning speed ensures that the temperature distribution can achieve the conditions required for stress release while avoiding excessive heat causing damage to the substrate;

[0116] The laser beam diameter and spot size should ensure uniform heat distribution to improve removal efficiency and avoid surface overheating;

[0117] Ultimately, the basic laser process parameters can be further optimized using finite element analysis (FEA) or other numerical methods to ensure that the oxide scale can be removed efficiently and evenly without damaging the workpiece.

[0118] S132. Obtaining property data of the wire rod material to be processed, property data of the iron oxide scale, and laser parameter data, optimizing the process conditions of laser power and scanning speed in the basic laser process parameters through finite element simulation (such as ABAQUS, ANSYS, etc.), and determining initial laser process parameters based on the optimization results (i.e., selecting a laser power and scanning speed that can efficiently remove the iron oxide scale, avoid overheating damage, and ensure surface quality based on the simulation results);

[0119] The physical properties of the wire rod substrate include: density, specific heat capacity, thermal conductivity, elastic modulus, wire rod substrate thickness and thermal expansion coefficient;

[0120] The physical properties of iron oxide scale include density, specific heat capacity, thermal conductivity, elastic modulus, iron oxide scale thickness and thermal expansion coefficient;

[0121] S2. Using the oxide layer thickness identification model combined with terahertz technology, the thickness distribution of the oxide layer on the wire rod surface is identified, and the initial laser process parameters are dynamically adjusted according to the thickness distribution of the oxide layer on the wire rod surface to obtain the optimal laser process parameters based on the thickness distribution;

[0122] The method of using the oxide layer thickness identification model in combination with terahertz technology to identify the thickness distribution of the oxide layer on the wire rod surface, and dynamically adjusting the initial laser process parameters according to the thickness distribution of the oxide layer on the wire rod surface to obtain the optimal laser process parameters based on the thickness distribution includes the following steps:

[0123] S21. Based on a pre-built oxide layer thickness identification model, identifying the thickness distribution of the oxide layer on the surface of the wire rod to be processed, and obtaining a first distribution result of the oxide layer thickness;

[0124] Specifically, the method of identifying the thickness distribution of the oxide layer on the surface of the wire rod to be processed based on the pre-built oxide layer thickness identification model and obtaining a first distribution result of the oxide layer thickness includes the following steps:

[0125] S211, obtaining a surface image of the wire rod to be processed and performing preprocessing, analyzing brightness differences in the surface image, extracting grayscale values of different areas on the oxide layer surface, calculating local brightness distribution in the surface image, obtaining average brightness of the local area through a sliding window method, and calculating global brightness distribution to generate a grayscale histogram; specifically including:

[0126] 1) Obtain an image of the surface of the wire rod to be processed: Use a high-resolution image acquisition device (such as a digital camera, microscope, or scanning electron microscope) to capture an image of the wire rod surface. The image should be as clear as possible, showing details of the scale. Ensure that the image is clear and clearly shows a clear difference between the scale and the base material.

[0127] 2) Image preprocessing:

[0128] Denoising: After image acquisition, it is usually necessary to remove noise from the image to improve the accuracy of subsequent analysis. Common noise removal methods include Gaussian blurring and median filtering. These methods can effectively eliminate subtle noise or background noise.

[0129] Grayscale processing: Convert the image from color mode to grayscale mode to simplify subsequent brightness analysis. Grayscale images only consider brightness information, not color information, which is very helpful for analyzing the distribution of oxide layers and brightness differences;

[0130] Contrast Enhancement: Use histogram equalization to enhance the contrast of the image. This step can make the brightness difference more obvious, especially at the boundary between the iron oxide area and the substrate, improving the image's legibility.

[0131] 3) Brightness difference analysis:

[0132] Calculation of local brightness distribution: To analyze image brightness differences, a sliding window method can be used. The image is divided into multiple small blocks, and each block is subjected to local brightness analysis using a sliding window technique. The brightness of each window area can be calculated by calculating the average grayscale value of all pixels within that area. This method can be used to determine the local brightness distribution of different image regions, thereby understanding the oxide layer thickness, reflectivity, and other characteristics of different surface regions.

[0133] Global brightness distribution statistics: Global brightness distribution refers to the brightness distribution of the entire image. The histogram of global brightness distribution can be obtained by calculating the grayscale values of all pixels in the image and counting the frequency of each grayscale value.

[0134] 4) Generate a grayscale histogram: A grayscale histogram is a commonly used image analysis tool used to show the distribution of different grayscale values in an image. By counting the number of pixels at each grayscale value in an image, a graph can be plotted showing the grayscale value and its corresponding number of pixels.

[0135] Brightness distribution: From the grayscale histogram, you can intuitively see the distribution of higher and lower brightness areas in the image. For example, if the iron oxide area has higher brightness, there will be more pixels on the right side of the histogram (closer to the grayscale value of 255); if the substrate area is darker, there will be more pixels on the left side of the histogram (closer to the grayscale value of 0);

[0136] Analyze image brightness differences: By comparing the grayscale values of different areas, you can analyze the brightness differences between the iron oxide and the substrate. High-brightness areas usually correspond to the iron oxide with higher reflectivity, while low-brightness areas may correspond to a darker substrate or areas with thinner iron oxide.

[0137] 5) Extract the grayscale values of different areas on the oxide layer surface:

[0138] Identifying oxide layer areas: By setting grayscale thresholds or using image segmentation techniques, oxide layer areas can be identified in the image. These areas typically have varying brightness levels, perhaps brighter than the substrate, or due to varying thicknesses of the oxide layer.

[0139] Statistical grayscale value of the oxide layer area: Extracting the grayscale value of the oxide layer area can further calculate the average brightness of the area and analyze it. In this way, the thickness and reflective properties of the oxide layer, as well as the uniformity of the oxide layer, can be quantified;

[0140] S212, extracting texture features of the surface image using a local binary pattern method, and identifying the boundary between the oxide layer and the substrate using an edge detection method; outputting the oxide layer thickness corresponding to the texture features of the surface image based on a pre-built oxide layer thickness recognition model, and obtaining a first distribution result of the oxide layer thickness at different positions on the wire rod surface; specifically comprising:

[0141] 1) Extracting Texture Features Using Local Binary Patterns: Local Binary Patterns (LBP) is a commonly used texture analysis method that extracts local texture features by comparing the grayscale values of each pixel in an image with its neighboring pixels. It is highly robust to texture changes and well-adapted to lighting variations. The specific steps are as follows:

[0142] 1.1) Grayscale image conversion: Convert the surface image to a grayscale image (if the image is a color image) for texture analysis;

[0143] 1.2) Calculate LBP features: For each pixel in the image, calculate the local binary pattern by comparing the pixel value with the pixel value of its surrounding neighborhood. Generally speaking, the neighborhood around each pixel is a 3x3 area, that is, 8 neighboring pixels;

[0144] 1.3) LBP Texture Mapping: Calculate the LBP texture features of the entire image. These features are usually represented by a histogram, which counts the frequency of each LBP value. This histogram can reflect the texture pattern of the image.

[0145] 1.4) LBP feature matrix: An LBP feature matrix can be obtained to describe the texture features of the surface image. Texture features can help in the subsequent analysis of the oxide layer thickness in different areas;

[0146] Compare the neighboring pixels of each pixel from left to right and from top to bottom. If the value of the neighboring pixel is greater than the central pixel, it is assigned a value of 1, otherwise it is 0.

[0147] Convert the obtained binary number into decimal to obtain the LBP value of the pixel;

[0148] 2) Identify the boundary between the oxide layer and the substrate using edge detection: To accurately extract the oxide layer thickness, it is necessary to first identify the boundary between the oxide layer and the substrate. Edge detection is a common image processing method, and commonly used edge detection algorithms include Canny edge detection and Sobel operator.

[0149] 2.1) Grayscale Image Edge Detection: In the grayscale image, apply an edge detection method to identify the boundary between the oxide layer and the substrate. For example, you can use Canny edge detection, which detects edges by calculating the gradient in the image.

[0150] Canny edge detection algorithm steps:

[0151] Gaussian filtering: First use Gaussian filter to remove noise;

[0152] Calculate gradient: Calculate the gradient of the image through the Sobel operator to obtain the intensity and direction of the edge;

[0153] Non-maximum suppression: refine edges and remove false edges;

[0154] Dual threshold detection: determine the true edge by setting high and low thresholds;

[0155] 2.2) Edge Extraction: After edge detection, the boundary between the oxide layer and the substrate is clearly identified. In the edge detection image, the edge of the oxide layer usually appears as a high-contrast area.

[0156] 2.3) Extracting boundary information: Combine the edge detection results with the original image to accurately determine the boundary position of the oxide layer;

[0157] 3) Output oxide layer thickness based on oxide layer thickness identification model:

[0158] 3.1) Constructing an oxide layer thickness recognition model: Based on the texture features (such as LBP features) and edge detection results in the image, an oxide layer thickness recognition model can be constructed. This model can be based on machine learning (such as support vector machines, decision trees, random forests, etc.) or estimation based on physical models. When training the model, a set of sample data with calibrated oxide layer thickness is required to learn the relationship between texture features and oxide layer thickness.

[0159] 3.2) Model Input: The input features of the model are texture features (such as LBP histogram) and edge information extracted from the image; the model predicts the oxide layer thickness at each location based on these inputs;

[0160] 3.3) Oxide layer thickness output: Using the trained oxide layer thickness model and the texture features extracted from the image, the oxide layer thickness value at each location is output. The oxide layer thickness at each location corresponds to the texture features.

[0161] 4) Obtain the first distribution results of the oxide layer thickness at different positions on the wire rod surface:

[0162] 4.1) Thickness distribution map generation: Based on the oxide layer thickness data output by the model, a thickness distribution map can be generated to show the oxide layer thickness at different locations on the wire rod surface. The distribution map can represent the oxide layer thickness by color coding, thereby intuitively showing the uniformity and thickness variation of the oxide layer;

[0163] 4.2) Analysis and Visualization: Analyze the oxide layer thickness distribution and identify areas with thinner or thicker oxide layers. This can help optimize the subsequent laser cleaning process. The oxide layer thickness distribution can be visualized using heat maps, contour maps, or 3D plots.

[0164] 4.3) Statistics of oxide layer thickness: Based on the thickness distribution diagram, the statistical data of oxide layer thickness can be calculated, such as average thickness, maximum thickness, minimum thickness, etc.

[0165] S22, using terahertz technology to identify the thickness distribution of the oxide layer on the surface of the wire rod to be processed, and obtain a second distribution result of the oxide layer thickness;

[0166] Specifically, the method of using terahertz technology to identify the thickness distribution of the oxide layer on the surface of the wire rod to be processed and obtaining a second distribution result of the oxide layer thickness includes the following steps:

[0167] S221, using an ultrashort pulse terahertz wave to illuminate the surface of the wire rod to be processed, measuring the reflection, transmission, or absorption of the terahertz wave by the oxide layer, and recording the amplitude, phase, and time delay information of the terahertz wave;

[0168] Using ultrashort pulsed terahertz waves to illuminate the surface of the wire rod being processed and measuring the reflection, transmission, or absorption of the terahertz wave by the oxide layer is a highly efficient surface inspection and surface property analysis technology. By recording the amplitude, phase, and time delay information of the terahertz wave, the physical properties, thickness, and uniformity of the oxide layer can be determined. The detailed steps are as follows:

[0169] 1) Principle of ultrashort pulse terahertz wave irradiation

[0170] Generation of terahertz waves: Ultrashort pulsed terahertz waves are typically generated by laser excitation of materials or using a terahertz pulse generator (such as a photoconductive antenna). These terahertz waves typically have very short timescales (picosecond or femtosecond levels), resulting in very high temporal resolution, making them suitable for rapid dynamic analysis of surfaces and materials.

[0171] Terahertz wave properties: Terahertz waves typically have a frequency range of 0.1 THz to 10 THz. Electromagnetic waves in this frequency range have strong penetrating properties and can penetrate many materials, such as non-metals, composite materials, and insulating materials. Therefore, using terahertz waves is very suitable for surface and oxide layer analysis. Especially when inspecting metal surfaces, terahertz waves provide important information on the reflection of metals and the absorption and transmission of oxide layers.

[0172] Ultrashort pulses: Ultrashort pulsed terahertz waves typically have high peak power and short pulse width, which can provide sufficient temporal resolution to detect detailed changes in the oxide layer;

[0173] 2) Interaction between the irradiated terahertz wave and the wire rod surface

[0174] Irradiation method: The terahertz wave is irradiated onto the surface of the wire rod to be processed. The interaction between the terahertz wave and the oxide layer is mainly carried out through reflection, transmission and absorption:

[0175] Reflection: When the terahertz wave hits the surface, part of the wave will be reflected back to the sensor, which depends on the electromagnetic properties of the surface and the oxide layer (such as conductivity, thickness, etc.);

[0176] Transmission: If the oxide layer is thin, some of the terahertz wave may continue to propagate through the oxide layer and enter the substrate. In this case, the properties of the oxide layer can be analyzed by measuring the intensity and time delay of the transmitted wave.

[0177] Absorption: The oxide layer absorbs terahertz waves, especially within a specific frequency range. The absorption peak of the oxide layer for terahertz waves may be related to the thickness and composition of the oxide layer.

[0178] Influence of the oxide layer: The influence of the oxide layer on terahertz waves is mainly reflected in its physical properties such as thickness, dielectric constant, and conductivity. Changes in the oxide layer will lead to changes in the reflectivity, transmittance, and absorption characteristics of terahertz waves;

[0179] 3) Recording the amplitude, phase, and time delay of terahertz waves

[0180] Amplitude measurement: Using a terahertz wave detector (such as a photoconductive antenna or a terahertz time-domain spectrometer) to measure the amplitude of the terahertz wave can reveal the reflection, transmission, and absorption properties of the oxide layer.

[0181] Reflection amplitude: records the amplitude of the reflected terahertz wave, reflecting the properties of the interface between the oxide layer and the substrate;

[0182] Transmission amplitude: records the amplitude of the terahertz wave passing through the oxide layer, which can reflect the physical properties of the oxide layer such as thickness, density and dielectric constant;

[0183] Phase information: By analyzing the phase of the irradiated, reflected, and transmitted terahertz waves, the thickness, surface uniformity, and dielectric properties of the oxide layer can be determined. The thicker the oxide layer, the greater the phase delay of the terahertz wave. Using interferometry or Fourier transform methods, the phase change of the terahertz wave after passing through the oxide layer can be accurately calculated.

[0184] Time delay information: This records the time delay from terahertz wave irradiation to reflection / transmission. This time delay is related to the thickness and dielectric constant of the oxide layer. A thicker oxide layer will cause the terahertz wave to have a longer delay when passing through it. Time domain spectroscopy (TDS) is used to measure this time delay. By extracting the propagation time and delay of the oxide layer from the reflected signal, the oxide layer thickness can be inferred.

[0185] 4) Analyze the reflection, transmission and absorption characteristics of the oxide layer

[0186] Reflection characteristic analysis: The amplitude of the reflected signal can be used to estimate the thickness of the oxide layer and the quality of the interface. Thinner oxide layers generally result in lower reflected signals, while thicker or uneven oxide layers increase the reflected signal. The conductivity and other electromagnetic properties of the oxide layer can also be inferred based on the amplitude and phase changes of the reflected signal.

[0187] Transmission characteristics analysis: The intensity and phase changes of the transmission signal can provide information about the thickness of the oxide layer. Thinner oxide layers transmit more terahertz waves, while thicker oxide layers may absorb or reflect more waves. Based on the time delay and phase of the transmission signal, the actual thickness of the oxide layer can be calculated.

[0188] Absorption characteristics analysis: Absorption characteristics can be analyzed by measuring the attenuation of terahertz waves. Thicker oxide layers typically result in more energy absorbed by the terahertz wave during penetration. Absorption characteristics are closely related to the composition of the oxide layer. By studying changes in absorption, information about the chemical composition and thickness of the oxide layer can be revealed.

[0189] S222. Calculate the reflection and transmission time difference of the terahertz wave at different oxide layer thicknesses through time domain analysis, convert the time domain signal into a frequency domain signal using Fourier transform, extract the spectral characteristics of different frequencies, and calculate the absorption and reflectivity of the oxide layer for the terahertz wave of a specific frequency to determine its thickness information;

[0190] To calculate the reflection and transmission time difference of terahertz waves at different oxide layer thicknesses through time domain analysis, convert the time domain signal into a frequency domain signal using Fourier transform, extract the spectral characteristics of different frequencies, and then calculate the absorption and reflectivity of the oxide layer for terahertz waves of specific frequencies. Finally, determine the thickness of the oxide layer, you can follow the following steps:

[0191] 1) Time domain analysis of the reflection and transmission time difference of terahertz waves

[0192] 1.1) Terahertz wave irradiation, reflection, and transmission: When a terahertz wave strikes the surface of the wire rod being processed, part of the wave is reflected back to the sensor, while another part may penetrate the oxide layer and enter the substrate. The oxide layer's thickness, dielectric constant, and conductivity affect the time-domain performance of the reflected and transmitted waves.

[0193] 1.2) Measuring reflection and transmission signals:

[0194] Reflected signal: The arrival time of the reflected wave can be directly measured, and the amplitude and phase of the reflected signal will change with the thickness of the oxide layer;

[0195] Transmission signal: Similarly, the time delay and intensity of the transmission signal are also affected by the thickness of the oxide layer. A thicker oxide layer usually leads to a longer time delay of the transmission signal and may cause the transmission signal to attenuate.

[0196] 1.3) Calculating the time difference: By measuring the arrival times of the reflected and transmitted waves, the time difference between them can be calculated. This time difference is closely related to the thickness of the oxide layer. The thicker the oxide layer, the longer the wave propagation time (especially the signal passing through the oxide layer), resulting in a time delay. The calculation formula can be derived from the speed of light and the dielectric constant of the oxide layer.

[0197] 2) Fourier transform converts time domain signals into frequency domain signals

[0198] 2.1) Fourier Transform Principle: Time-domain signals can be converted into frequency-domain signals through Fourier transform. Fourier transform can reveal the response of terahertz waves at different frequencies, helping us analyze the impact of oxide layers on terahertz waves of different frequencies.

[0199] 2.2) Converting Time Domain Signals to Frequency Domain Signals: The frequency domain signal obtained through Fourier transform contains information about each frequency component. For terahertz waves, the frequency range is generally 0.1THz to 10THz. By analyzing the response at different frequencies, we can understand the absorption and reflection characteristics of the oxide layer at different frequency bands.

[0200] 2.3) Spectral Analysis: The frequency domain signal contains the amplitude and phase information of the terahertz wave at various frequencies, helping us identify the impact of the oxide layer on specific frequencies. Every material (including the oxide layer) responds differently to different frequencies, and analyzing the spectral signature provides a deeper understanding of the oxide layer's properties.

[0201] 3) Extract spectral features of different frequencies

[0202] 3.1) Spectral Feature Extraction: For the frequency domain signal obtained through Fourier transform, the amplitude spectrum (Magnitude Spectrum) and phase spectrum (Phase Spectrum) at different frequencies are extracted. The amplitude spectrum represents the intensity of the signal at each frequency, while the phase spectrum provides information on the relative phase of the frequency components. These two features can reflect the absorption, reflection, or transmission characteristics of the oxide layer at different frequencies:

[0203] Amplitude spectrum: The absorption and reflection of terahertz waves of different frequencies by the oxide layer usually show significant differences within a specific frequency range. Therefore, the amplitude spectrum can reveal characteristics such as the thickness and uniformity of the oxide layer.

[0204] Phase spectrum: Phase changes can further reveal changes in the dielectric constant of the material. Thicker oxide layers usually cause larger phase changes.

[0205] 3.2) Frequency characteristic analysis:

[0206] High-frequency part: The thin layer or surface unevenness of the oxide layer usually has a significant impact on the reflection of high-frequency terahertz waves. By analyzing the response of the high-frequency part, we can understand the microstructure of the oxide layer;

[0207] Low-frequency part: When the oxide layer is thicker, it absorbs low-frequency terahertz waves more strongly. Therefore, the low-frequency response is also important for estimating the thickness of the oxide layer.

[0208] 4) Calculate the absorption and reflectivity of the oxide layer to terahertz waves of a specific frequency

[0209] Reflectivity: Reflectivity indicates the proportion of the terahertz wave that is reflected back when it hits the surface. Reflectivity R(f) can usually be obtained by measuring the ratio of the amplitude of the reflected signal to the amplitude of the incident signal, that is, , where Sref(f) represents the frequency domain representation of the reflected signal, and Sinc(f) represents the frequency domain representation of the incident signal;

[0210] Absorptivity: Absorptivity indicates the fraction of terahertz waves absorbed by the oxide layer. Absorptivity A(f) can generally be calculated using the following formula: A(f) = 1-R(f) - T(f), where T(f) represents the transmittance, which indicates the fraction of the wave that passes through the oxide layer. Absorptivity reflects the absorption characteristics of the oxide layer, and the position and magnitude of the absorption peak are related to the thickness of the oxide layer and its dielectric constant.

[0211] Frequency dependence of absorption and reflection: By analyzing the absorption and reflection characteristics at different frequencies, the thickness of the oxide layer can be identified. Typically, the absorption and reflection characteristics of the oxide layer at specific frequencies will change with thickness, so these changes can be used to estimate the thickness of the oxide layer.

[0212] 5) Determine the thickness of the oxide layer

[0213] 5.1) Using Model Fitting: Compare the spectral signature obtained from the Fourier transform with the spectral data of an oxide layer sample of known thickness. Inversion algorithms or machine learning models can be used to extract oxide layer thickness information from the frequency domain data.

[0214] 5.2) Thickness Calculation Formula: By analyzing the changing trends of reflectivity, transmittance, and absorptivity, combined with the oxide layer's response to specific frequencies, the following model can be used to estimate the oxide layer thickness:

[0215] d=f(Δt,A(f),R(f),T(f))

[0216] Where d is the thickness of the oxide layer, Δt is the time delay, and A(f), R(f), and T(f) are the functions of absorptivity, reflectivity, and transmittance at different frequencies, respectively.

[0217] 5.3) Thickness distribution: Through the above process, we can finally obtain the distribution map of the oxide layer thickness at different locations on the surface, which helps to further analyze the uniformity and quality of the oxide layer;

[0218] S223. Calculating the thickness of the oxide layer based on the transmission or reflection time difference of the terahertz wave and the refractive index of the oxide layer, and inverting the thickness distribution of the oxide layer using a mathematical model to obtain a second distribution result of the oxide layer thickness at different positions on the wire rod surface;

[0219] In order to calculate the thickness of the oxide layer based on the transmission or reflection time difference of the terahertz wave and the refractive index of the oxide layer, and to invert the thickness distribution of the oxide layer using a mathematical model, we can follow the following steps:

[0220] 1) Calculate oxide layer thickness based on time difference

[0221] 1.1) Time difference between reflection and transmission:

[0222] When a terahertz wave strikes the surface of a wire rod, part of the wave is reflected by the oxide layer, while the rest passes through it. If the thickness of the oxide layer is d, the refractive index is n, and the time delay of the wave propagating through the oxide layer is taken into account, the thickness of the oxide layer can be inferred from the time difference between reflection and transmission.

[0223] The arrival time difference between the reflected wave and the transmitted wave is closely related to the thickness of the oxide layer. When the terahertz wave passes through the oxide layer, the wave speed is affected by the change in refractive index;

[0224] Time delay formula: When the terahertz wave passes through the oxide layer, the time delay Δt can be calculated by the following formula: , where d is the thickness of the oxide layer, cg is the speed of light in a vacuum, and n is the refractive index of the oxide layer;

[0225] By measuring the time difference Δt between the reflected wave or the transmitted wave and the refractive index n of the oxide layer, the thickness of the oxide layer can be calculated;

[0226] 2) Effect of refractive index on wave propagation

[0227] Influence of refractive index: The refractive index of the oxide layer directly affects the propagation speed of terahertz waves in the oxide layer. The refractive index is closely related to the electromagnetic properties of the medium and can usually be determined experimentally or obtained using known material property data.

[0228] Wave velocity calculation: In the oxide layer, the propagation speed of the terahertz wave is the ratio of the speed of light to the refractive index of the oxide layer;

[0229] Refractive index inversion: If the refractive index of the oxide layer is unknown, the refractive index value can be inverted by comparing the wave speed at different frequencies, or by analyzing the effect of the oxide layer on terahertz waves of different frequencies;

[0230] 3) Mathematical model inversion of oxide layer thickness distribution

[0231] 3.1) Establishing a Mathematical Model: Based on the time difference between reflection and transmission, an inversion model can be established to calculate the distribution of oxide layer thickness based on the experimentally measured time delay. This model can use an inverse problem-solving approach to infer the oxide layer thickness at various locations on the wire rod surface using known time difference data and refractive index data.

[0232] Inversion model: By establishing an inversion algorithm, such as the least squares method or optimization algorithm (such as gradient descent, Newton method, etc.), the optimal solution for the oxide layer thickness is obtained;

[0233] 3.2) Distribution inversion: Using this inversion model, the oxide layer thickness at each location can be estimated based on time-difference data obtained at multiple locations (e.g., via a sensor array or scan), ultimately yielding the oxide layer thickness distribution.

[0234] 3.3) Selection of optimization algorithm:

[0235] Least Squares Method: Based on the calculation of the difference between reflection and transmission time, the least squares method can be used to obtain the best estimate of the thickness distribution. The least squares method inverts the thickness of the oxide layer by minimizing the error between the predicted value and the actual measured value.

[0236] Finite Element Method (FEM): For more complex geometries or uneven oxide layer distribution, the finite element method can be used to simulate the interaction between the oxide layer and the terahertz wave and perform inverse calculations;

[0237] 4) Distribution results of oxide layer thickness

[0238] 4.1) Generate a Thickness Distribution Map: Based on the inverted oxide layer thickness (estimated thickness at different locations), a surface oxide layer thickness distribution map can be generated. This data can be visualized using graphical tools (such as heat maps and contour plots) to show the differences in oxide layer thickness at various locations on the wire rod surface.

[0239] 4.2) Further Analysis: Analyze the thickness uniformity of the oxide layer: By observing the distribution diagram, check whether the oxide layer is uniform and whether there are locally thicker or thinner areas. Optimize subsequent processes: Based on the thickness distribution results, the subsequent laser cleaning process, surface treatment process, etc. can be optimized;

[0240] 4.3) Statistical analysis:

[0241] Average thickness: calculate the average thickness of the oxide layer;

[0242] Max / Min Thickness: Find the maximum and minimum thickness of the oxide layer;

[0243] Standard deviation: evaluates the uniformity of oxide layer thickness;

[0244] 5) Verification and application

[0245] 5.1) Experimental Verification: The inverted oxide layer thickness distribution can be verified by other methods, such as mechanical measurement, scanning electron microscopy (SEM), or other surface inspection methods to verify the accuracy of the inversion results;

[0246] 5.2) Practical Application: This thickness distribution result can provide a basis for subsequent laser cleaning, coating, or other surface treatment processes, and optimize process parameters to achieve uniform surface quality.

[0247] S23, determining the thickness distribution of the oxide layer on the surface of the wire rod to be processed based on the first distribution result and the second distribution result of the oxide layer thickness;

[0248] Specifically, determining the thickness distribution of the oxide layer on the surface of the wire rod to be processed based on the first distribution result and the second distribution result of the oxide layer thickness includes the following steps:

[0249] S231, obtaining a first distribution result and a second distribution result of the oxide layer thickness at different positions on the wire rod surface, and performing data alignment and preprocessing; establishing a spatial correspondence between the first distribution result data and the second distribution result data based on a meshing method for the wire rod surface, and spatially aligning the thickness distribution data of the first distribution result with the thickness distribution data of the second distribution result using a feature matching method;

[0250] S232, using a weighted average method in combination with a preset weight coefficient to fuse the first distribution result data and the second distribution result data to obtain a final thickness value, and generating a distribution map of oxide layer thickness at different positions on the wire rod surface based on the final thickness value;

[0251] Specifically, in this embodiment, the weight of the oxide layer thickness identification model: This model can adapt to more complex surface structures and can provide real-time feedback on the effect of oxide layer removal. Therefore, it has greater advantages in the case of complex surfaces and irregular oxide layers. In this case, it is given a higher weight, that is, the weight coefficient of the first distribution result data is 0.8;

[0252] Weight of terahertz technology: Terahertz technology has higher accuracy when the thickness is relatively uniform and the oxide layer is thin, but errors may occur for complex surfaces or thicker oxide layers. Therefore, the weight of terahertz wave is slightly lower, that is, the weight coefficient of the second distribution result data is 0.2;

[0253] In addition, after data fusion, the error between the fusion result and the original first distribution result and the second distribution result can be calculated to analyze the deviation between the two. If the error exceeds the allowable range, the fusion weight is adjusted or the measurement data is recalibrated.

[0254] S24, dynamically adjusting initial laser process parameters at different positions on the surface of the wire rod according to the thickness distribution of the oxide layer on the surface of the wire rod to be processed, to obtain optimal laser process parameters based on the thickness of the oxide layer;

[0255] In order to dynamically adjust the initial laser process parameters at different positions on the wire rod surface according to the thickness distribution of the oxide layer on the wire rod surface to be processed, and ultimately obtain the optimal laser process parameters based on the oxide layer thickness, the following steps can be followed:

[0256] 1) Analyze the characteristics of oxide layer thickness distribution

[0257] Oxide layer uniformity: Check the uniformity of the oxide layer on the surface. Thinner oxide layers may require stronger laser power or longer laser exposure time, while thicker oxide layers may require lower power or shorter laser exposure time. Changes in the oxide layer not only affect the cleaning effect, but also affect the absorption, reflection, and scattering characteristics of the laser.

[0258] Identification of high-thickness and low-thickness areas: Identify areas with thinner and thicker oxide layers. These areas may have different effects on the laser's absorption characteristics, requiring adjustment of laser power, pulse duration, scanning speed, and other process parameters based on the thickness of the oxide layer.

[0259] 2) Adjust laser process parameters according to the thickness distribution of the oxide layer

[0260] 2.1) Establish a model for the relationship between laser process parameters and oxide layer thickness: There is a specific relationship between the process parameters used in the laser cleaning process (such as laser power, pulse width, scan speed, pulse frequency, etc.) and the thickness of the oxide layer. This relationship between oxide layer thickness and laser parameters can be learned by establishing a mathematical model or using a machine learning algorithm.

[0261] 2.2) Adjust process parameters: For areas with thinner oxide layers, increase the laser power, extend the pulse width, or slow down the scanning speed to ensure effective oxide layer removal. For areas with thicker oxide layers, reduce the laser power, shorten the pulse width, or increase the scanning speed to prevent over-ablation or over-cleaning.

[0262] 2.3) Spatial Adjustment of Process Parameters: Dynamically adjust laser process parameters at different locations based on the distribution of oxide layer thickness at different locations. This process can be achieved through a control system, in which the laser equipment can adjust parameters such as laser power, pulse width, and scanning speed based on real-time oxide layer thickness data;

[0263] 3) Optimize laser process parameters

[0264] Optimization of laser power: For thin oxide layers, using lower laser power can avoid over-cleaning and reduce damage to the substrate. For thick oxide layers, using higher laser power can ensure that the oxide layer is sufficiently removed, but avoid damaging the substrate.

[0265] Optimization of pulse width: In areas with thinner oxide layers, shorter pulse widths can avoid excessive heat accumulation and reduce the impact of thermal effects on the substrate. In areas with thicker oxide layers, longer pulse widths may help remove the oxide layer more effectively because thicker oxide layers absorb more energy.

[0266] Optimization of scanning speed: In areas with thinner oxide layers, higher scanning speeds may be appropriate because thin oxide layers require shorter laser irradiation times. In areas with thicker oxide layers, lower scanning speeds may be appropriate because thicker oxide layers require longer laser irradiation times to be effectively removed.

[0267] Other parameter adjustments: The pulse frequency can be adjusted according to the thickness of the oxide layer. In thinner oxide layer areas, a lower pulse frequency can be used; while in thicker oxide layer areas, a higher pulse frequency can be used to enhance the removal effect;

[0268] S3. Based on the optimal laser process parameters, a rotating laser head is used to laser treat the iron oxide scale on the wire rod surface, and metal powder is collected using negative pressure technology. Image recognition technology is used to detect the removal effect of the iron oxide scale on the wire rod surface, and model parameters are dynamically optimized based on the detection results.

[0269] Specifically, in this embodiment, multiple rotating laser heads are arranged in three rows around the circumference of the wire rod (rotating around the centerline of the wire rod). Each rotating laser head is connected to a laser generator and contains multiple laser heads arranged circumferentially. These multiple rotating laser heads are each fixed to a bracket and equipped with a protective cover. The angle between the laser beam generated by the rotating laser head around the wire rod and the wire rod is 60-80 degrees. The width of the laser beam around the wire rod matches the circumferential width of the wire rod surface, enabling the laser beam to achieve full coverage of the wire rod circumference. The rotating laser head contains multiple laser beams, which, through its own rotation, can remove and clean iron oxide scale from the wire rod surface at multiple angles and multiple times, improving the efficiency and quality of iron oxide scale cleaning.

[0270] Specifically, when cleaning the iron oxide scale on the wire rod, the exhaust fan, electrode dust removal and other equipment are combined to achieve negative pressure conditions in the laser cleaning machine area to prevent the cleaned metal powder from overflowing and polluting the environment, and the metal powder is recycled and reused so that the particles will no longer return to the clean surface.

[0271] Specifically, image recognition technology is used to detect the removal effect of iron oxide scale on the wire rod surface, and the model parameters are dynamically optimized based on the detection results, including:

[0272] Image recognition technology principle:

[0273] Use image recognition technology (such as computer vision-based technology) to monitor the effectiveness of scale removal on the wire rod surface in real time. Install high-resolution cameras, laser scanners, or infrared imaging equipment to comprehensively inspect the wire rod surface. Image recognition technology can analyze the degree of scale removal, uniformity, and residual content. Common image processing techniques include edge detection, texture analysis, and color contrast analysis.

[0274] Detection removal effect:

[0275] Removal rate: Image recognition technology can calculate the scale removal rate by comparing it with the image before treatment. If the removal rate is low, it may be necessary to increase the laser power or reduce the scanning speed;

[0276] Uniformity detection: Image recognition can also detect the uniformity of removal, ensuring that no untreated areas are left on the surface and ensuring consistency of the treatment effect;

[0277] Surface damage: Image recognition can also detect whether the laser cleaning process has caused damage to the substrate surface, such as excessive ablation or other physical damage. This can avoid unnecessary damage to the wire rod during the process;

[0278] Image processing result feedback: The image recognition system generates feedback data by real-time detection of the removal effect, indicating which areas require further adjustment of laser parameters. The processed image results are transmitted to the central control system;

[0279] Data feedback and optimization model: The removal effect data (such as removal rate, uniformity, and surface damage) obtained through image recognition will be fed back to the central control system. Based on these real-time detection results, the parameters of the oxide layer thickness recognition model will be dynamically adjusted.

[0280] According to another embodiment of the present invention, a system for online removal of wire rod iron oxide scale based on laser technology is provided, comprising an initial laser process parameter determination module, a laser process parameter optimization module, and an iron oxide scale online removal module;

[0281] The initial laser process parameter determination module is used to establish the relationship between the detachment stress and the adhesion force based on the temperature distribution and displacement distribution caused by laser heating, determine the relationship between different process parameters and the iron oxide scale removal effect, and determine the initial laser process parameters in combination with the current wire rod material properties and iron oxide scale performance;

[0282] The laser process parameter optimization module is used to identify the thickness distribution of the oxide layer on the wire rod surface by using the oxide layer thickness identification model combined with terahertz technology, and dynamically adjust the initial laser process parameters according to the thickness distribution of the oxide layer on the wire rod surface to obtain the optimal laser process parameters based on the thickness distribution;

[0283] The online iron oxide scale removal module is used to laser treat the iron oxide scale on the surface of the wire rod using a rotating laser head based on optimal laser process parameters, and collect metal powder through negative pressure technology; use image recognition technology to detect the removal effect of the iron oxide scale on the wire rod surface, and dynamically optimize the model parameters based on the detection results.

[0284] In summary, with the help of the above-mentioned technical solutions of the present invention, the high energy density and precise control of the laser can quickly heat the iron oxide scale to the shedding temperature, achieving rapid removal of the iron oxide scale. It can remove the iron oxide scale and stains on the surface of the wire rod in a continuous production process at one time, solving the pain points of the existing technology that cannot completely remove the iron oxide scale on the wire rod surface and cannot efficiently process the iron oxide scale on the workpiece surface online. Compared with traditional methods, the laser removal process does not require chemicals or large amounts of mechanical force, has high removal efficiency, and does not damage the substrate.

[0285] In addition, the present invention combines the precise adjustment of laser power and scanning speed to accurately process iron oxide scales of different thicknesses and properties. By dynamically adjusting the process parameters, it can ensure that the iron oxide scale removal process always remains in the optimal state, avoiding overheating or damage to the wire rod surface.

[0286] In addition, the present invention integrates an advanced oxide layer thickness identification model and terahertz technology to monitor the thickness distribution of the oxide layer on the surface of the wire rod in real time, and dynamically adjusts the laser process parameters according to actual conditions. This real-time feedback mechanism ensures that the removal effect of iron oxide scale during laser processing is always at the optimal level.

[0287] In addition, the present invention can adapt to wire rod materials of different types and sizes by combining the properties of wire rod materials, oxide scale performance and laser technology. Whether it is a thin oxide layer or a thick oxide layer, the ideal removal effect can be achieved by flexibly adjusting the process parameters.

[0288] In addition, the present invention combines image recognition technology with negative pressure collection technology, which can detect the removal effect in real time and perform dynamic optimization to ensure the thoroughness and uniformity of iron oxide scale removal, further improving the automation and intelligence level of the process.

[0289] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for online removal of wire rod iron scale based on laser technology, characterized in that: The following steps are involved: S1. Based on the temperature and displacement distribution caused by laser heating, the relationship between detachment stress and adhesion force is established, and the relationship between different process parameters and scale removal effect is determined. In addition, the initial laser process parameters are determined based on the current wire rod material properties and scale performance. S2. Using the oxide layer thickness identification model combined with terahertz technology, the thickness distribution of the oxide layer on the wire rod surface is identified, and the initial laser process parameters are dynamically adjusted according to the thickness distribution of the oxide layer on the wire rod surface to obtain the optimal laser process parameters based on the thickness distribution; S3. Based on the optimal laser process parameters, a rotating laser head is used to laser treat the iron oxide scale on the wire rod surface, and metal powder is collected using negative pressure technology. Image recognition technology is used to detect the removal effect of the iron oxide scale on the wire rod surface, and model parameters are dynamically optimized based on the detection results. Among them, S1 includes: S11. Establish a temperature distribution model caused by laser heating based on laser power, material properties, and heat conduction characteristics; establish a displacement and stress distribution model caused by laser heating based on the temperature gradient caused by laser heating and the thermal expansion characteristics of the material; S12, determining the detachment stress between the iron oxide scale and the substrate based on the thermal stress, calculating the adhesion between the iron oxide scale and the substrate, and determining a critical value between the detachment stress and the adhesion based on the calculated results of the adhesion and the detachment stress; S13. Using finite element analysis to determine the relationship between laser parameters, material parameters, and scale cleaning and removal effects, and combining the current wire rod material properties and scale performance to determine initial laser process parameters; S2 includes: S21. Based on a pre-built oxide layer thickness identification model, identifying the thickness distribution of the oxide layer on the surface of the wire rod to be processed, and obtaining a first distribution result of the oxide layer thickness; S22, using terahertz technology to identify the thickness distribution of the oxide layer on the surface of the wire rod to be processed, and obtain a second distribution result of the oxide layer thickness; S23, determining the thickness distribution of the oxide layer on the surface of the wire rod to be processed based on the first distribution result and the second distribution result of the oxide layer thickness; S24. According to the thickness distribution of the oxide layer on the surface of the wire rod to be processed, the initial laser process parameters at different positions on the wire rod surface are dynamically adjusted to obtain the optimal laser process parameters based on the thickness of the oxide layer.

2. The method for online removal of wire rod iron scale based on laser technology according to claim 1, characterized in that: The expression of the temperature distribution model caused by laser heating is: The expression of the displacement distribution model caused by laser is: In the formula, T represents temperature, t represents time, represents the Laplace operator of temperature, α represents the thermal diffusivity of the material, Q(r) represents the distribution of laser power at position r, ρ represents the density of the material, c represents the specific heat capacity of the material, σ represents the thermal stress, ΔT represents the temperature change, E represents the elastic modulus, λ represents the thermal expansion coefficient of the material, u(x, y, z) represents the distribution of displacement in space, and L represents the length of the path.

3. The method for online removal of wire rod iron scale based on laser technology according to claim 1, characterized in that: The formula for calculating the adhesion between iron oxide scale and substrate is: F=τ·A Where F represents the adhesion force between the iron oxide scale and the substrate, τ represents the adhesion strength between the iron oxide scale and the substrate, and A represents the contact area between the iron oxide scale and the substrate.

4. The method for online removal of wire rod iron scale based on laser technology according to claim 1, characterized in that: The method of using finite element analysis to determine the relationship between laser parameters, material parameters, and the effect of cleaning and removing iron oxide scale, and combining the current wire rod material properties and oxide scale performance to determine the initial laser process parameters includes the following steps: S131. Based on the temperature distribution model, displacement and stress distribution model, and in combination with the critical value between the detachment stress and the adhesion force, simulate the effect of different laser process parameters on the iron oxide scale removal effect, and determine the basic laser process parameters based on the simulation results; S132. Obtain the property data of the wire rod material to be processed, the property data of the iron oxide scale and the laser parameter data, optimize the process conditions of laser power and scanning speed in the basic laser process parameters through finite element simulation, and determine the initial laser process parameters based on the optimization results.

5. The method for online removal of wire rod iron scale based on laser technology according to claim 1, characterized in that: The method of identifying the thickness distribution of the oxide layer on the surface of the wire rod to be processed based on the pre-built oxide layer thickness identification model and obtaining a first distribution result of the oxide layer thickness comprises the following steps: S211, obtaining a surface image of the wire rod to be processed and performing preprocessing, analyzing brightness differences in the surface image, extracting grayscale values of different areas on the oxide layer surface, calculating local brightness distribution in the surface image, obtaining average brightness of the local area through a sliding window method, and calculating the global brightness distribution to generate a grayscale histogram; S212. Use the local binary pattern method to extract the texture features of the surface image, and use the edge detection method to identify the boundary between the oxide layer and the substrate; based on the pre-constructed oxide layer thickness recognition model, output the oxide layer thickness corresponding to the texture features of the surface image, and obtain the first distribution results of the oxide layer thickness at different positions on the wire rod surface.

6. The method for online removal of wire rod iron scale based on laser technology according to claim 1, characterized in that: The method of using terahertz technology to identify the thickness distribution of the oxide layer on the surface of the wire rod to be processed and obtaining a second distribution result of the oxide layer thickness comprises the following steps: S221, using an ultrashort pulse terahertz wave to illuminate the surface of the wire rod to be processed, measuring the reflection, transmission, or absorption of the terahertz wave by the oxide layer, and recording the amplitude, phase, and time delay information of the terahertz wave; S222. Calculate the reflection and transmission time difference of the terahertz wave at different oxide layer thicknesses through time domain analysis, convert the time domain signal into a frequency domain signal using Fourier transform, extract the spectral characteristics of different frequencies, and calculate the absorption and reflectivity of the oxide layer for the terahertz wave of a specific frequency to determine its thickness information; S223. Based on the transmission or reflection time difference of the terahertz wave and the refractive index of the oxide layer, the thickness of the oxide layer is calculated, and a mathematical model is used to invert the thickness distribution of the oxide layer to obtain a second distribution result of the oxide layer thickness at different positions on the wire rod surface.

7. The method for online removal of wire rod iron scale based on laser technology according to claim 1, characterized in that: Determining the thickness distribution of the oxide layer on the surface of the wire rod to be processed based on the first distribution result and the second distribution result of the oxide layer thickness comprises the following steps: S231, obtaining a first distribution result and a second distribution result of the oxide layer thickness at different positions on the wire rod surface, and performing data alignment and preprocessing; establishing a spatial correspondence between the first distribution result data and the second distribution result data based on a meshing method for the wire rod surface, and spatially aligning the thickness distribution data of the first distribution result with the thickness distribution data of the second distribution result using a feature matching method; S232. Use the weighted average method in combination with a preset weight coefficient to fuse the first distribution result data and the second distribution result data to obtain a final thickness value, and generate an oxide layer thickness distribution map at different positions on the wire rod surface based on the final thickness value.

8. A system for online removal of wire rod iron scale based on laser technology, used to implement the steps of the method for online removal of wire rod iron scale based on laser technology according to any one of claims 1 to 7, characterized in that: It includes the initial laser process parameter determination module, the laser process parameter optimization module and the iron oxide scale online removal module; The initial laser process parameter determination module is used to establish the relationship between the detachment stress and the adhesion force based on the temperature distribution and displacement distribution caused by laser heating, determine the relationship between different process parameters and the iron oxide scale removal effect, and determine the initial laser process parameters in combination with the current wire rod material properties and iron oxide scale performance; The laser process parameter optimization module is used to identify the thickness distribution of the oxide layer on the wire rod surface by using the oxide layer thickness identification model combined with terahertz technology, and dynamically adjust the initial laser process parameters according to the thickness distribution of the oxide layer on the wire rod surface to obtain the optimal laser process parameters based on the thickness distribution; The online iron oxide scale removal module is used to laser treat the iron oxide scale on the surface of the wire rod using a rotating laser head based on optimal laser process parameters, and collect metal powder through negative pressure technology; use image recognition technology to detect the removal effect of the iron oxide scale on the wire rod surface, and dynamically optimize the model parameters based on the detection results.

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