Drawing machine optimization method and system based on big data analysis

Abstract

The invention belongs to the technical field of wire drawing machines, and particularly relates to a wire drawing machine optimization method and system based on big data analysis, and dynamic monitoring and automatic adjustment are achieved by collecting real-time image data and technological parameters of the microcosmic wear form of the surface of a mold. The method comprises the following steps: firstly, extracting morphological characteristics of image data, generating wear quantization parameters, and calculating a three-dimensional shape change value; and then, establishing a dynamic association relationship between the process parameters and the morphology change values, calculating a real-time drift compensation amount according to the dynamic association relationship, and superposing the compensation amount to the process parameter reference values to form a correction instruction to be sent to a control system. In addition, the continuous and stable operation of the production process is ensured by comparing the difference between the diameter of the wire drawing finished product and the standard value, reversely correcting the parameter weight in the incidence relation and updating the reference value. According to the method, the consistency and the quality stability of finished products are improved, the downtime and the material waste are reduced, and the production efficiency and the economic benefits are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of wire drawing machines, and particularly relates to a wire drawing machine optimization method and system based on big data analysis. BACKGROUND

[0002] In the wire drawing process, the micro-wear on the surface of the die is one of the important factors affecting product quality and production efficiency. The existing technology usually adopts the method of regular inspection and replacement of the die to cope with the influence of die wear. Although this method can ensure the continuity of production and the quality of products to a certain extent, it cannot respond to the changes in the micro-wear on the surface of the die in real time. Due to the random variation of the micro-wear pattern on the surface of the wire drawing die, the process parameters (such as speed, pressure, etc.) drift, which further affects the quality stability of the wire drawing finished product.

[0003] For example, in the actual production process, the operator may need to manually adjust the process parameters based on experience or periodic detection results. This method is not only inefficient, but also difficult to control accurately, which can easily cause resource waste and cost increase. SUMMARY

[0004] The purpose of the present application is to provide a wire drawing machine optimization method and system based on big data analysis, which effectively solves the problem of process parameter drift caused by the random variation of the micro-wear pattern on the surface of the wire drawing die. By monitoring the wear condition of the die surface in real time and automatically adjusting the relevant process parameters, the consistency and quality stability of the wire drawing finished product can be significantly improved, and unnecessary downtime and material waste can be reduced.

[0005] To achieve the above purpose, the present application adopts the following technical scheme: a wire drawing machine optimization method based on big data analysis, comprising the following steps: Collecting real-time image data of the micro-wear pattern on the surface of the die during the wire drawing process and corresponding process parameters; Extracting morphological features from the real-time image data to generate quantized parameters of the wear pattern, and calculating the three-dimensional topographic change value of the die surface wear based on the quantized parameters; According to the three-dimensional topographic change value, a dynamic correlation between the process parameters and the three-dimensional topographic change value is established, and a real-time drift compensation amount of the process parameters is calculated based on the dynamic correlation; Adding the real-time drift compensation amount to the reference value of the process parameters to generate a corrected process parameter instruction, sending the corrected process parameter instruction to the control end of the wire drawing machine, and adjusting the wire drawing process parameters of the die; By adjusting the difference value between the diameter of the wire drawing finished product and the preset standard value, the parameter weight in the dynamic correlation is corrected in reverse, and the reference value of the process parameters is updated.

[0006] Preferably, the real-time image data and corresponding process parameters of the microscopic wear morphology of the mold surface during the wire drawing process are collected, including: during the operation of the wire drawing machine, installing a camera above the mold to capture the microscopic wear morphology of the mold surface; preprocessing the image obtained by the camera, including grayscale and noise filtering, to enhance the contrast of the wear characteristics; based on the preprocessed image data, using an edge detection method to determine the boundary of the wear area on the mold surface; and synchronously recording the coordinate information of the wear area boundary and the corresponding process parameters to form an associated data set.

[0007] Preferably, the morphological feature extraction of the real-time image data to generate quantitative parameters of the wear morphology includes: calculating the area of ​​the wear area based on the coordinate information of the boundary of the wear area; using the area of ​​the wear area in combination with the image brightness value obtained by the camera to calculate the average brightness to reflect the overall brightness characteristics of the wear morphology; defining quantitative parameters based on the area and average brightness of the wear area, which are used to describe the quantitative parameters of the degree of wear on the mold surface.

[0008] Preferably, the three-dimensional morphological change value of the mold surface wear is calculated based on the quantitative parameters, including: first calculating the average depth of the wear area on the mold surface according to the quantitative parameters; using the average depth and the area of ​​the wear area to calculate the wear volume to describe the overall three-dimensional morphological change of the wear area; based on the wear volume and the quantitative parameters, calculating the three-dimensional morphological change rate of wear, the change rate is used to measure the change of wear morphology over time or degree of use; and associating the three-dimensional morphological change rate with the process parameters recorded during the wire drawing process to form a data set.

[0009] Preferably, a dynamic correlation between the process parameters and the three-dimensional morphology change values ​​is established based on the three-dimensional morphology change values, including: first determining the degree of influence of the speed and pressure in the process parameters on the wear three-dimensional morphology change based on the three-dimensional morphology change rate, and defining an influence factor for quantifying the effect of the process parameters on the wear morphology change; calculating an adjustment coefficient using the influence factor and the three-dimensional morphology change rate; establishing a dynamic correlation between the process parameters and the three-dimensional morphology change values ​​based on the adjustment coefficient and the speed and pressure values ​​recorded in the historical data set; applying the dynamic correlation to a real-time monitoring system, and forming a data set by continuously updating the three-dimensional morphology change rate and the corresponding process parameters for continuously optimizing the working state of the wire drawing machine.

[0010] Preferably, the real-time drift compensation amount of the process parameters is calculated according to the dynamic correlation relationship, including: first calculating the deviation value between the current process parameters and the ideal state according to the dynamic correlation relationship; using the deviation value and the three-dimensional morphology change rate to calculate the basic amount of real-time drift compensation, which is used to quantify the degree of process parameter drift caused by wear; based on the basic amount, combined with the speed and pressure in actual operation, determining the specific compensation amount for each process parameter; applying the compensation amount to adjust the actual value of the process parameter to form a new adjustment instruction, and sending the adjustment instruction to the wire drawing machine control system to achieve real-time correction of the process parameters.

[0011] Preferably, the real-time drift compensation amount is superimposed on the baseline value of the process parameter to generate a corrected process parameter instruction, including: first determining the baseline value of the current process parameter based on the compensation amount, and calculating the corrected speed value and pressure value using the compensation amount and the baseline value; based on the corrected speed value and pressure value, generating a new process parameter combination instruction for guiding the wire drawing machine to adjust parameters; sending the process parameter combination instruction to the wire drawing machine control system, and recording the adjustment results.

[0012] Preferably, the corrected process parameter instruction is sent to the wire drawing machine control end to adjust the wire drawing process parameters of the mold, including: first parsing the corrected speed value and pressure value according to the process parameter combination instruction; converting the parsed speed value and pressure value into a control signal for matching the input signal format required by the wire drawing machine control system; using the control signal, sending these signals to the wire drawing machine control end through the communication interface to form an adjustment command; after receiving the adjustment command, the wire drawing machine control system updates the current working parameters according to the adjustment command, realizes real-time adjustment of the mold wire drawing process parameters, and records the actual adjustment results.

[0013] Preferably, the parameter weights in the dynamic association relationship are reversely corrected by adjusting the difference between the diameter of the finished wire drawing product and the preset standard value, and the baseline value of the process parameter is updated, including: calculating the difference value based on the difference between the actual diameter of the finished wire drawing product and the preset standard value, which is used to quantify the deviation between the actual production result and the expected target; adjusting the parameter weights in the dynamic association relationship by using the difference value and the dynamic association relationship; recalculating the baseline value based on the updated parameter weight and the speed and pressure values ​​in the corrected process parameter instruction; applying the newly calculated baseline value to the subsequent process parameter adjustment process to form an updated data set, so as to continuously monitor and optimize the working status of the wire drawing machine.

[0014] On the other hand, the present invention provides an optimization system for a wire drawing machine based on big data analysis, comprising: Real-time data acquisition module, used to collect real-time image data of the microscopic wear morphology of the die surface during the wire drawing process and the corresponding process parameters; a wear morphology quantitative analysis module, configured to extract morphological features from the real-time image data, generate quantitative parameters of the wear morphology, and calculate a three-dimensional morphological change value of the mold surface wear based on the quantitative parameters; a dynamic correlation and compensation calculation module, configured to establish a dynamic correlation between the process parameters and the three-dimensional shape change values ​​according to the three-dimensional shape change values, and calculate a real-time drift compensation amount for the process parameters according to the dynamic correlation; a control instruction sending module, configured to execute superposition of the real-time drift compensation amount to the reference value of the process parameter, generate a corrected process parameter instruction, send the corrected process parameter instruction to the wire drawing machine control end, and adjust the wire drawing process parameters of the die; The parameter updating module is used to execute the reverse correction of the parameter weights in the dynamic association relationship based on the difference between the adjusted finished wire drawing product diameter and the preset standard value, and update the reference value of the process parameter.

[0015] Technical effects and advantages of the present invention: The present invention proposes a wire drawing machine optimization method and system based on big data analysis, which has the following advantages over the prior art: The present invention realizes dynamic monitoring of mold surface wear and automatic adjustment of process parameters by collecting real-time image data of the microscopic wear morphology of the mold surface during the wire drawing process and the corresponding process parameters, and processing and analyzing these data.

[0016] By extracting morphological features from real-time image data, quantitative parameters of the wear morphology are generated, and based on this, the three-dimensional morphological change value of the mold surface wear is calculated; then, a dynamic correlation relationship is established between the process parameters and the three-dimensional morphological change value, and this relationship is used to calculate the real-time drift compensation amount of the process parameters. Finally, these compensation amounts are superimposed on the baseline value of the process parameters to form a corrected process parameter instruction that is sent to the wire drawing machine control system.

[0017] In addition, this method also reversely corrects the parameter weights in the dynamic correlation relationship by comparing the difference between the adjusted finished wire drawing diameter and the preset standard value, thereby updating the baseline value of the process parameter to ensure that the production process can run continuously and stably. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the wire drawing machine optimization method based on big data analysis of the present invention; Figure 2 This is a block diagram of the optimization system of the wire drawing machine based on big data analysis of the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. The specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work belong to the scope of protection of the present application.

[0020] The present application provides a method for optimizing a wire drawing machine based on big data analysis, which effectively solves the problem of process parameter drift caused by the random changes in the microscopic wear patterns of the wire drawing die surface. By monitoring the die surface wear in real time and automatically adjusting the relevant process parameters, not only can the consistency and quality stability of the wire drawing products be significantly improved, but also unnecessary downtime and material waste can be reduced. Figure 1

[0021] Preferably, a method for optimizing a wire drawing machine based on big data analysis, comprising the following steps: Step 1: Collect real-time image data of the microscopic wear patterns of the die surface during the wire drawing process and the corresponding process parameters, specifically including: During the operation of the wire drawing machine, a camera is installed directly above the die to ensure that the microscopic wear patterns of the die surface can be captured; Using the principle of optical imaging, the image obtained by the camera is preprocessed, including grayscale and noise filtering, and the formula is: I_new=(I_old-N) / G, where I_old represents the original image brightness value, N represents the noise value, and G is the conversion coefficient for converting the image from color to grayscale to enhance the contrast of the wear features; the formula aims to remove noise in the image and convert color images to grayscale images. By subtracting the noise and dividing by G, a clearer and higher-contrast grayscale image can be obtained, which helps to highlight the wear features.

[0022] According to the preprocessed image data, an edge detection method is used to determine the boundary of the wear area of the die surface, and the calculation formula is: E=sqrt((dI / dx)^2+(dI / dy)^2), where E represents the edge intensity, dI / dx and dI / dy represent the brightness change rate along the x-axis and y-axis directions, respectively, and a larger E value indicates that there is a clear edge or boundary at this point.

[0023] Finally, the coordinate information of the wear area boundary and the corresponding process parameters (such as speed V and pressure P) are recorded synchronously to form a correlation data set D={E,V,P}, so as to analyze the relationship between the die surface wear and the process parameters in the subsequent analysis. ​

[0024] Step 2: Extracting morphological features from the real-time image data to generate quantitative parameters of the wear morphology, specifically including: Based on the coordinate information E of the wear region boundary, calculate the area S of the wear region using the formula S = ∑(x_i - x_(i-1)) * (y_i - y_(i-1)), where x_i and y_i represent the coordinates of the i-th point on the boundary. This formula is used to calculate the area of ​​the polygon (i.e., the wear region). By traversing each point (x_i, y_i) on the boundary and calculating the area of ​​a small rectangle based on the coordinate difference between two adjacent points, the total area S is calculated by adding up the areas of all these small rectangles.

[0025] Using the area S of the wear area and the image brightness value I_new obtained by the camera, the average brightness L_avg=(∑I_new) / A is calculated, where A represents the total area of ​​the image, to reflect the overall brightness characteristics of the wear morphology; by summing the brightness values ​​of all pixels in the entire wear area and dividing it by the total area A, an average value representing the brightness characteristics of the wear area can be obtained.

[0026] Based on the wear area S and the average brightness L_avg, a quantitative parameter Q = S * L_avg is defined to quantitatively describe the degree of mold surface wear. This metric reflects the combined influence of the wear area size and its brightness characteristics. Q provides a single value to quantify the degree of wear, facilitating subsequent correlation analysis with other process parameters.

[0027] The quantitative parameter Q is associated with process parameters (such as speed V and pressure P) to form a data set D_1 = {Q, V, P}, which facilitates subsequent analysis of the impact of the wear morphology on the wire drawing process. By correlating the quantitative parameter Q with key process parameters in actual operation (such as speed V and pressure P), a direct link between the wear state and the production process can be established.

[0028] Step 3: Calculating the three-dimensional topography change value of the mold surface wear based on the quantitative parameters, specifically including: Based on the quantitative parameter Q, we first calculate the average depth D_avg of the wear area on the mold surface. We use the formula D_avg = (∑d_i) / N, where d_i represents the depth of the i-th point within the wear area and N is the number of points within the wear area. This formula calculates the average depth within the wear area. The average depth D_avg is calculated by summing the depth values ​​d_i at all measured points and dividing by the total number of points, N. This helps us understand the overall impact of wear on the mold surface.

[0029] The wear volume V_wear = D_avg * S is calculated using the average depth D_avg and the wear area S to describe the overall three-dimensional morphological changes of the wear area, which helps quantify the material loss caused by wear to the mold.

[0030] Based on the wear volume V_wear and the quantitative parameter Q, the three-dimensional wear topography change rate R_change = V_wear / Q is further calculated. This rate of change measures how the wear morphology changes over time or with usage. The three-dimensional topography change rate provides a method for comparing the proportional relationship between the wear volume and the quantitative parameter at different wear levels. This helps identify the trend and rate of wear development.

[0031] The three-dimensional morphology change rate R_change is associated with the process parameters recorded during the wire drawing process (such as speed V and pressure P) to form a data set D_2 = {R_change, V, P}, so as to analyze the need for process parameter adjustment due to the change in the wear three-dimensional morphology.

[0032] Step 4: establishing a dynamic correlation between the process parameters and the three-dimensional shape change value according to the three-dimensional shape change value, specifically including: Based on the 3D topography change rate R_change, we first determine the extent to which the process parameters, speed V and pressure P, affect the 3D wear topography. We define an influence factor (IF) = R_change / (V*P) to quantify the effect of these process parameters on the wear morphology. The influence factor is calculated by dividing the 3D topography change rate R_change by the product of the current speed V and pressure P. This helps us understand the rate of wear progression under given process conditions and identify which process conditions need to be adjusted to reduce wear.

[0033] The adjustment coefficient AC = IF * R_change is calculated using the influencing factor IF and the three-dimensional morphology change rate R_change. The adjustment coefficient AC is used to dynamically adjust the process parameters based on wear changes. This coefficient reflects the amount of process parameter adjustment required based on the current wear situation, helping to dynamically adjust the speed and pressure during the production process.

[0034] Based on the adjustment coefficient AC, combined with the velocity V and pressure P values ​​recorded in the historical data set, a dynamic correlation relationship DR=V+P-AC is established between the process parameters (V, P) and the three-dimensional morphology change value, where DR represents the result value of the dynamic correlation relationship, thereby reflecting how the process parameters should be adjusted accordingly under different wear conditions; this relationship is intended to dynamically adjust the process parameters so that they can adapt to the ever-changing wear conditions and maintain production efficiency and quality.

[0035] The dynamic correlation relationship DR is applied to the real-time monitoring system, and the three-dimensional morphology change rate R_change and the corresponding process parameters are continuously updated to form a data set D_3={DR, R_change, V, P}, so as to continuously optimize the working state of the wire drawing machine.

[0036] Step 5: Calculating the real-time drift compensation of the process parameters according to the dynamic correlation relationship, specifically including: Based on the dynamic relationship DR, we first calculate the deviation between the current process parameters and the ideal state. The deviation value DV is defined as DR - DR_ideal, where DR_ideal represents the dynamic relationship value under the ideal, wear-free state. By comparing the current dynamic relationship DR with the ideal, wear-free relationship value DR_ideal, we can quantify the degree to which the current process parameters deviate from the ideal state. This deviation value DV reflects the process parameter changes caused by mold wear.

[0037] The deviation value DV and the three-dimensional topography change rate R_change are used to calculate the basic quantity BC for real-time drift compensation: DV / R_change. The basic quantity BC is used to quantify the degree of process parameter drift caused by wear. This formula helps determine the specific degree of process parameter drift caused by wear, thereby providing guidance on how to compensate.

[0038] Based on the basic value BC, combined with the speed V and pressure P in actual operation, the specific compensation amount for each process parameter is further determined. The compensation amount CP_V = BC*V and CP_P = BC*P, corresponding to the compensation amount of speed and pressure respectively;

[0039] The compensation amounts CP_V and CP_P are applied to adjust the actual values ​​of the process parameters to form a new adjustment instruction AI={V+CP_V,P+CP_P}, and the adjustment instruction is sent to the wire drawing machine control system to achieve real-time correction of the process parameters.

[0040] Step 6: Adding the real-time drift compensation amount to the reference value of the process parameter to generate a corrected process parameter instruction, specifically including: According to the process parameter combination instruction PI, the corrected speed value V_new and pressure value P_new in the instruction are first parsed; by parsing the instruction, the specific process parameter values ​​that need to be adjusted can be clarified to ensure that the subsequent control signal generation and sending can be accurate.

[0041] Convert the V_new and P_new obtained by the analysis into control signals CS_V=k_V*V_new and CS_P=k_P*P_new, where k_V and k_P are conversion coefficients corresponding to speed and pressure respectively, which are used to match the input signal format required by the wire drawing machine control system; The control signals CS_V and CS_P are sent to the wire drawing machine control terminal via a communication interface, forming an adjustment command AC = {CS_V, CS_P}. This ensures that the adjustment command is correctly received and executed by the wire drawing machine control system. Through a reliable communication mechanism, a seamless transition from data processing to actual equipment control is achieved, ensuring that adjustment instructions are executed promptly and accurately.

[0042] After receiving the adjustment command AC, the wire drawing machine control system updates the current operating parameters according to the command, achieving real-time adjustment of the die drawing process parameters and recording the actual adjustment result RA = {V_current, P_current}, where V_current and P_current represent the actual speed and pressure values ​​after adjustment, respectively. This step ensures closed-loop feedback of the adjustment process, facilitating subsequent analysis and optimization.

[0043] Step 7: Send the corrected process parameter instructions to the wire drawing machine control terminal to adjust the wire drawing process parameters of the die, including: Based on the compensation values ​​CP_V and CP_P, the baseline values ​​for the current process parameters are first determined. Let the velocity baseline be V_0 and the pressure baseline be P_0. The baseline values ​​(V_0, P_0) represent the initial process parameters set under wear-free or ideal conditions. By comparing these baseline values ​​with the real-time drift compensation values ​​(CP_V, CP_P), the specific values ​​that need to be adjusted can be calculated.

[0044] The compensation value CP_V and the reference value V_0 are used to calculate the corrected speed value V_new = V_0 + CP_V. Similarly, the compensation value CP_P and the reference value P_0 are used to calculate the corrected pressure value P_new = P_0 + CP_P. These values ​​are used to calculate the new values ​​of speed and pressure after compensation. This step achieves precise adjustment of process parameters by superimposing the compensation value on the reference value.

[0045] Based on the corrected speed value V_new and pressure value P_new, a new process parameter combination instruction PI = {V_new, P_new} is generated. This instruction is used to guide the wire drawing machine to adjust its parameters. The process parameter combination instruction PI includes the new compensated speed value V_new and new pressure value P_new. This instruction is directly transmitted to the control system, instructing it to operate according to the new parameters.

[0046] The process parameter combination instruction PI is sent to the wire drawing machine control system, and the adjustment result AR={V_new-V_0, P_new-P_0} is recorded to facilitate subsequent analysis of the impact of the real-time drift compensation on actual production.

[0047] Step 8: Reversely correct the parameter weights in the dynamic correlation relationship based on the difference between the adjusted finished wire drawing diameter and the preset standard value, and update the reference value of the process parameter, specifically including: Based on the difference between the actual diameter D_real of the finished wire drawing and the preset standard value D_std, the difference value ΔD = D_real - D_std is calculated. This step is used to quantify the deviation between the actual production results and the expected target; Using the difference value ΔD and the dynamic association relationship DR, adjust the parameter weight W_new in the dynamic association relationship. Use the formula W_new = W_old + K * ΔD, where W_old represents the original parameter weight and K is the adjustment coefficient. This reflects the impact of the difference on the parameter weight and helps the system dynamically adjust the parameter weight according to the actual situation. Based on the updated parameter weight W_new, combined with the speed V_new and pressure P_new in the corrected process parameter instruction PI, the reference values ​​B_V=V_new / W_new and B_P=P_new / W_new are recalculated to ensure that the new reference values ​​can adapt to the production process requirements under the current wear conditions; by dividing the corrected speed and pressure values ​​by the updated parameter weights, new reference values ​​that adapt to the current wear conditions can be obtained.

[0048] The newly calculated reference values ​​B_V and B_P are applied to the subsequent process parameter adjustment process to form an updated data set D_4 = {B_V, B_P, ΔD}, so as to continuously monitor and optimize the working state of the wire drawing machine.

[0049] This approach not only increases the level of automation in production but also significantly enhances the system's responsiveness and stability, ensuring optimal production even in the presence of wear. By adjusting and recording in real time, changes in process parameters can be dynamically monitored, further improving production stability and product quality.

[0050] On the other hand, the present invention proposes an optimization system for wire drawing machines based on big data analysis, such as Figure 2 Shown, including: Real-time data acquisition module, used to collect real-time image data of the microscopic wear morphology of the die surface during the wire drawing process and the corresponding process parameters; a wear morphology quantitative analysis module, configured to extract morphological features from the real-time image data, generate quantitative parameters of the wear morphology, and calculate a three-dimensional morphological change value of the mold surface wear based on the quantitative parameters; a dynamic correlation and compensation calculation module, configured to establish a dynamic correlation between the process parameters and the three-dimensional shape change values ​​according to the three-dimensional shape change values, and calculate a real-time drift compensation amount for the process parameters according to the dynamic correlation; a control instruction sending module, configured to execute superposition of the real-time drift compensation amount to the reference value of the process parameter, generate a corrected process parameter instruction, send the corrected process parameter instruction to the wire drawing machine control end, and adjust the wire drawing process parameters of the die; The parameter updating module is used to execute the reverse correction of the parameter weights in the dynamic association relationship based on the difference between the adjusted finished wire drawing product diameter and the preset standard value, and update the reference value of the process parameter.

[0051] In addition, when executed, the above modules are also used to implement other steps of the above-mentioned wire drawing machine optimization method based on big data analysis, which are as follows: Consider a large copper processing plant that has introduced a new wire drawing machine for producing 3.5mm diameter copper wire. The plant produces approximately 50 tons of copper wire daily. Continuous production leads to severe die wear, impacting product quality and consistency. Using the aforementioned wire drawing machine optimization method based on big data analysis, the plant can improve production efficiency and product consistency.

[0052] On-site configuration and data collection: Equipment parameter settings: drawing speed setting value V0=6m / s, mold pressure setting value P0=18MPa.

[0053] A high-resolution industrial camera (resolution of 2048 × 2048 pixels) was used, installed 5 cm above the mold, and the image acquisition frequency was set to once per minute.

[0054] Initial state: There is no obvious wear on the mold surface, the standard value of the copper wire diameter produced is D_std = 3.5mm, and the initial weight of the dynamic association relationship is W_old = 1.

[0055] Day 1 Run & build basic dataset: Step 1: Image preprocessing and edge detection The camera captures the current mold surface image and processes it: Grayscale and noise reduction, formula: I_new=(I_old-N) / G; Example calculation: Assume that the original brightness of a pixel point I_old = 180, the noise estimation value N = 10, and G = 2, then I_new = (180-10) / 2 = 85.

[0056] Edge detection calculation, formula: E=sqrt((dI / dx)^2+(dI / dy)^2); Example calculation: If the brightness change in the x direction is dI / dx=20, and the brightness change in the y direction is dI / dy=15, then E=sqrt(20^2+15^2)=sqrt(625)=25.

[0057] Generate a related dataset D={E,V,P}; Record the edge strength, velocity, and pressure values ​​at each time point, for example: D={(25,6,18),(26,6,18),...,(27,6,18)} Step 2: Quantitative analysis of wear morphology Take one set of data as an example: The calculation formula for the wear area S is: S=∑(x_i-x_{i-1})*(y_i-y_{i-1}); Assume that the boundary coordinates are: (0,0), (2,0), (2,1), (0,1), then S=(2-0)*(0-0)+(2-2)*(1-0)+(0-2)*(1-1)+(0-0)*(0-1)=0+0+0+0=2; Calculation of average brightness L_avg, formula: L_avg=(∑I_new) / A; If the total brightness of the image is 12000, and the total area A = 2048 × 2048 = 4,194,304, then L_avg=12000 / 4194304=0.00286.

[0058] Define the wear quantification index Q=S*L_avg; Calculation result: Q=2*0.00286=0.00572; Form a new data set D1={Q,V,P}, such as (0.00572,6,18).

[0059] Step 3: Calculation of 3D shape change value Calculation of the average depth D_avg, assuming that the depth values ​​of the points in the wear area are measured as follows: 0.01mm, 0.012mm, 0.011mm, ... a total of 10 points, the formula is: D_avg = (∑d_i) / N; D_avg=(0.01+0.012+...+0.011) / 10=0.011mm.

[0060] The calculation formula for wear volume V_wear is: V_wear=D_avg*S=0.011mm*2mm²=0.022mm³.

[0061] The calculation formula of the three-dimensional shape change rate R_change is: R_change=V_wear / Q=0.022 / 0.00572=3.845mm³ / unit; Construct the dataset D2={R_change,V,P}.

[0062] Step 4: Establish dynamic association The calculation formula of impact factor IF is: IF=R_change / (V*P)=3.845 / (6*18)=3.845 / 108=0.0356; The calculation formula for the adjustment coefficient AC is: AC=IF*R_change=0.0356*3.845=0.137.

[0063] The calculation formula for the dynamic correlation relationship DR is: DR=V+P-AC=6+18-0.137=23.863.

[0064] Construct the dataset D3={DR,R_change,V,P}.

[0065] Step 5: Calculate the drift compensation Calculation of the deviation value DV (ideal DR_ideal=24), formula: DV=DR-DR_ideal=23.863-24=-0.137.

[0066] The calculation formula for the basic compensation amount BC is: BC=DV / R_change=-0.137 / 3.845=-0.0356.

[0067] Calculation of process parameter compensation CP_V and CP_P: CP_V=BC*V=-0.0356*6=-0.214; CP_P=BC*P=-0.0356*18=-0.641; Update instruction AI={V+CP_V,P+CP_P}={6-0.214,18-0.641}={5.786,17.359}.

[0068] Step 6: Control signal sending and execution Input the new speed value V_new=5.786 and pressure value P_new=17.359 into the control system; Control signal conversion formula: CS_V=k_V*V_new(k_V=0.8mA / m·s -1 )→CS_V=0.8*5.786=4.629mA.

[0069] CS_P=k_P*P_new(k_P=0.5V / MPa)→CS_P=0.5*17.359=8.68V.

[0070] The control signal is sent to the PLC controller via the RS485 communication interface to update the actual operating parameters.

[0071] Step 7: Complete closed-loop feedback The actual measured finished wire diameter D_real=3.505mm; Difference value ΔD = D_real - D_std = 3.505 - 3.5 = 0.005 mm; Parameter weight update formula: W_new=W_old+K*ΔD (K=0.2); W_new=1+0.2*0.005=1.001.

[0072] New benchmark calculation: B_V=V_new / W_new=5.786 / 1.001=5.780m / s; B_P=P_new / W_new=17.359 / 1.001=17.342MPa; Update dataset D4={B_V,B_P,ΔD}.

[0073] index Initial value Final Value Range of change Drawing speed 6m / s 5.786m / s ↓3.56% Drawing pressure 18MPa 17.359MPa ↓3.56% Finished product diameter error 0mm +0.005mm Good control Mold life 8-hour replacement 12-hour replacement ↑50% After the system ran continuously for a week, statistics showed that the fluctuation range of copper wire diameter was reduced from ±0.02mm to ±0.008mm, the frequency of mold replacement was reduced by about 40%, and the average energy consumption decreased by 3.2%.

[0074] This real-time wear monitoring and process parameter adaptive adjustment method based on big data analysis has effectively improved product quality stability and equipment operation efficiency in actual production, demonstrating its good engineering application value.

[0075] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. 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 wire drawing machine optimization method based on big data analysis, characterized in that: The following steps are involved: Collect real-time image data of the microscopic wear morphology of the die surface during the wire drawing process and the corresponding process parameters; Extracting morphological features from the real-time image data to generate quantitative parameters of the wear morphology, and calculating a three-dimensional morphological change value of the mold surface wear based on the quantitative parameters; establishing a dynamic correlation between the process parameter and the three-dimensional shape change value according to the three-dimensional shape change value, and calculating a real-time drift compensation amount of the process parameter according to the dynamic correlation relationship; Adding the real-time drift compensation amount to the reference value of the process parameter to generate a corrected process parameter instruction, sending the corrected process parameter instruction to the wire drawing machine control end to adjust the wire drawing process parameters of the die; The parameter weights in the dynamic association relationship are reversely corrected by the difference between the adjusted finished wire drawing product diameter and the preset standard value, and the reference value of the process parameter is updated.

2. According to claim 1, a wire drawing machine optimization method based on big data analysis is characterized in that: The real-time image data of the microscopic wear morphology of the die surface and the corresponding process parameters collected during the wire drawing process include: During the operation of the wire drawing machine, a camera is installed above the die to capture the microscopic wear morphology of the die surface; Preprocessing the image acquired by the camera, including grayscale conversion and noise filtering, to enhance the contrast of wear features; Determining the boundary of the wear area on the mold surface using an edge detection method based on the preprocessed image data; The coordinate information of the boundary of the wear area and the corresponding process parameters are synchronously recorded to form a related data set.

3. According to claim 2, a wire drawing machine optimization method based on big data analysis is characterized in that: The step of extracting morphological features from the real-time image data to generate quantitative parameters of the wear morphology includes: Calculating the area of ​​the wear region according to the coordinate information of the boundary of the wear region; The average brightness is calculated using the area of ​​the wear region and the brightness value of the image acquired by the camera to reflect the overall brightness characteristics of the wear morphology; Based on the area and average brightness of the worn area, a quantitative parameter is defined to describe the quantitative parameter of the wear degree of the mold surface.

4. According to claim 3, a wire drawing machine optimization method based on big data analysis is characterized in that: Calculating a three-dimensional topographic change value of the mold surface wear based on the quantitative parameters includes: First, the average depth of the wear area on the mold surface is calculated based on the quantitative parameters; Calculating the wear volume using the average depth and the area of ​​the wear region to describe the overall three-dimensional morphological changes of the wear region; Calculating a rate of change of the three-dimensional morphology of wear based on the wear volume and the quantitative parameters, where the rate of change is used to measure changes in the wear morphology over time or degree of use; The three-dimensional morphology change rate is associated with the process parameters recorded during the wire drawing process to form a data set.

5. A wire drawing machine optimization method based on big data analysis according to claim 4, characterized in that: Establishing a dynamic correlation between the process parameter and the three-dimensional shape change value according to the three-dimensional shape change value includes: Based on the three-dimensional morphology change rate, the influence of the speed and pressure in the process parameters on the wear three-dimensional morphology change is first determined, and an influence factor is defined to quantify the effect of the process parameters on the wear morphology change; Calculating an adjustment coefficient using the influencing factor and the three-dimensional morphology change rate; Based on the adjustment coefficient, combined with the speed and pressure values ​​recorded in the historical data set, a dynamic correlation relationship between the process parameters and the three-dimensional morphology change value is established; The dynamic correlation relationship is applied to a real-time monitoring system, and a data set is formed by continuously updating the three-dimensional morphology change rate and the corresponding process parameters, which is used to continuously optimize the working state of the wire drawing machine.

6. A wire drawing machine optimization method based on big data analysis according to claim 5, characterized in that: Calculating the real-time drift compensation amount of the process parameter according to the dynamic correlation relationship includes: According to the dynamic correlation relationship, the deviation between the current process parameters and the ideal state is first calculated; Calculating a basic amount of real-time drift compensation using the deviation value and the three-dimensional topography change rate, so as to quantify the degree of process parameter drift caused by wear; Based on the basic amount and in combination with the speed and pressure in actual operation, a specific compensation amount for each process parameter is determined; The compensation amount is applied to adjust the actual value of the process parameter to form a new adjustment instruction, and the adjustment instruction is sent to the wire drawing machine control system to achieve real-time correction of the process parameter.

7. A wire drawing machine optimization method based on big data analysis according to claim 6, characterized in that: Adding the real-time drift compensation amount to the reference value of the process parameter to generate a corrected process parameter instruction includes: According to the compensation amount, firstly, a reference value of the current process parameter is determined, and the corrected speed value and pressure value are calculated using the compensation amount and the reference value; Based on the corrected speed value and pressure value, a new process parameter combination instruction is generated to guide the wire drawing machine to adjust the parameters; The process parameter combination instruction is sent to the wire drawing machine control system, and the adjustment result is recorded.

8. A wire drawing machine optimization method based on big data analysis according to claim 7, characterized in that: The method of sending the corrected process parameter instruction to the wire drawing machine control terminal to adjust the wire drawing process parameters of the die includes: According to the process parameter combination instruction, firstly, the corrected speed value and pressure value are parsed; Convert the parsed speed and pressure values ​​into control signals to match the input signal format required by the wire drawing machine control system; Using the control signals, these signals are sent to the wire drawing machine control terminal through the communication interface to form adjustment commands; After receiving the adjustment command, the wire drawing machine control system updates the current working parameters according to the adjustment command, realizes the real-time adjustment of the die wire drawing process parameters, and records the actual adjustment results.

9. A wire drawing machine optimization method based on big data analysis according to claim 8, characterized in that: Reversely correcting the parameter weights in the dynamic association relationship based on the difference between the adjusted finished wire drawing diameter and the preset standard value, and updating the reference value of the process parameter, including: According to the difference between the actual diameter of the finished wire drawing product and the preset standard value, the difference value is calculated to quantify the deviation between the actual production result and the expected target; Using the difference value and the dynamic association relationship, adjusting the parameter weights in the dynamic association relationship; Based on the updated parameter weights, the reference values ​​are recalculated in combination with the speed and pressure values ​​in the revised process parameter instructions; The newly calculated benchmark values ​​are applied to subsequent process parameter adjustments to form an updated data set for continuous monitoring and optimization of the wire drawing machine's operating status.

10. A wire drawing machine optimization system based on big data analysis for implementing the method according to any one of claims 1 to 9, characterized in that: include: Real-time data acquisition module, used to collect real-time image data of the microscopic wear morphology of the die surface during the wire drawing process and the corresponding process parameters; a wear morphology quantitative analysis module, configured to extract morphological features from the real-time image data, generate quantitative parameters of the wear morphology, and calculate a three-dimensional morphological change value of the mold surface wear based on the quantitative parameters; a dynamic correlation and compensation calculation module, configured to establish a dynamic correlation between the process parameters and the three-dimensional shape change values ​​according to the three-dimensional shape change values, and calculate a real-time drift compensation amount for the process parameters according to the dynamic correlation; a control instruction sending module, configured to execute superposition of the real-time drift compensation amount to the reference value of the process parameter, generate a corrected process parameter instruction, send the corrected process parameter instruction to the wire drawing machine control end, and adjust the wire drawing process parameters of the die; The parameter updating module is used to execute the reverse correction of the parameter weights in the dynamic association relationship based on the difference between the adjusted finished wire drawing product diameter and the preset standard value, and update the reference value of the process parameter.

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