Method, system and equipment for detecting copper alloy smelting protective gas layer

Abstract

The invention relates to the technical field of copper-containing alloy manufacturing, in particular to a method, system and equipment for detecting a copper alloy smelting protective gas layer, and the method comprises the following steps: dividing a smelting process into a plurality of continuous process stages based on smelting temperature change characteristics and process requirements, and setting a component proportion range of target protective gas for each stage; constructing a layered detection network, and collecting gradient distribution data of axial and radial gas protection layers in each continuous process stage in real time; based on the gradient distribution data, the deviation degree between the actual gas component proportion and the component proportion range in each continuous process stage is calculated, the deviation degree threshold value is dynamically adjusted according to the smelting temperature change, and gas proportion correction parameters are generated; and performing correlation analysis on the gas proportion correction parameter and a historical smelting quality database, and outputting a multi-dimensional detection report. According to the invention, the problems of non-uniform oxidation control, gas waste and the like caused by single detection dimension, response lag and experience dependence of the copper alloy smelting protective gas layer are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of copper alloy manufacturing, and particularly to a detection method, system and equipment for a protective gas layer in copper alloy melting. Background Art

[0002] During the copper alloy melting process, the protective gas layer inhibits metal oxidation by isolating oxygen and regulating heat exchange. The core is to precisely control the gas composition ratio and maintain the uniformity of the axial and radial distributions.

[0003] There are some limitations in traditional detection technologies: First, single-point static detection methods (such as thermocouples or oxygen probes) can only obtain local data, unable to reflect the global gradient distribution of the gas layer, and the high-temperature environment leads to short service life of the detection equipment and high maintenance costs; Second, off-line sampling analysis relies on laboratory instruments, with a long detection cycle, destroying the stability of the gas layer, and it is difficult to guide real-time process adjustment; Finally, experience-driven regulation relies on the subjective judgment of operators, lacking quantitative standards, and it is easy to cause control inaccuracies due to individual differences. These technical defects cause problems such as uneven oxidation control, excessive gas consumption, and process fluctuations, easily resulting in local oxidation inclusions in castings, low gas utilization rate, and unstable batch quality, seriously restricting the industrial production level of high-precision copper alloy melting.

[0004] The information disclosed in this background art section is only intended to deepen the understanding of the overall background art of the present disclosure, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a detection method, system and equipment for a protective gas layer in copper alloy melting, which can effectively solve the problems in the background art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows: A detection method for a protective gas layer in copper alloy melting, the method comprising: Based on the melting temperature change characteristics and process requirements, dividing the melting process into multiple continuous process stages, and setting a range of composition ratios of the target protective gas for each stage; Constructing a hierarchical detection network to collect gradient distribution data of the axial and radial gas protection layers in each of the continuous process stages in real time; Based on the gradient distribution data, calculating the deviation degree between the actual gas composition ratio and the range of the composition ratio in each of the continuous process stages, and dynamically adjusting the deviation degree threshold according to the melting temperature change to generate a gas ratio correction parameter; Associating and analyzing the gas ratio correction parameter with the historical melting quality database, and outputting a multi-dimensional detection report.

[0007] Further, calculating the deviation degree between the actual gas composition ratio of each of the continuous process stages and the composition ratio range includes: For each of the continuous process stages, extracting the preset ratio range of each component in the target protective gas and the current ratio value detected in real time; Quantifying the deviation degree between the actual ratio of each gas component and the target range as a percentage, and calculating the single-item deviation amount; According to the difference between the current smelting temperature and the stage target temperature, dynamically adjusting the correction coefficient of the single-item deviation amount, where the correction coefficient in the over-temperature state increases with the increase of the temperature difference, and decreases in the under-temperature state; Based on the correction coefficient, performing weighted synthesis on the corrected single-item deviation amount to generate a comprehensive deviation degree index characterizing the overall deviation degree.

[0008] Further, dynamically adjusting the deviation degree threshold according to the change of the smelting temperature includes: Based on the comprehensive deviation degree distribution of the qualified process stages in the smelting history, establishing the deviation degree threshold for each of the continuous process stages; Real-time monitoring the temperature change rate of the smelting, and when the temperature change rate exceeds the preset safety range, compressing the deviation degree threshold at a fixed ratio; Analyzing the deviation degree data of a fixed number of smelting times, and if the trigger phenomenon of the deviation degree threshold continuously appears, iteratively optimizing the threshold parameters by a gradient step size; Establishing an adaptive association relationship between the adjusted deviation degree threshold and the key quality indicators of copper alloy smelting to form the adaptive adjustment of the deviation degree threshold.

[0009] Further, generating a correction instruction including the gas type, adjustment direction, and flow rate change amplitude includes: According to the absolute value of the single-item deviation amount of each gas component and the adaptive association relationship, generating a sorted list of gas types to be preferentially adjusted; For each gas component in the sorted list of gas types, if the actual ratio is lower than the lower limit of the target range, it is marked as positive flow adjustment, and if it is higher than the upper limit of the target range, it is marked as negative flow adjustment; When multiple gases need to be adjusted simultaneously, generating adjustment instructions in sequence according to the sorted list of gas types and maintaining the stability of the total gas layer pressure; Comparing the adjustment instruction with the target range, and deleting the over-limit instructions to generate the gas ratio correction parameter.

[0010] Further, real-time collecting the gradient distribution data of the axial and radial gas protection layers in each of the continuous process stages includes: Scan the gas absorption characteristics at different axial depths in layers along the height direction of the furnace to generate an axial gradient distribution dataset; Capture the non-uniformity characteristics of the radial gas composition distribution in the circumferential direction of the molten pool horizontal cross-section to generate a radial gradient distribution dataset; Perform spatio-temporal alignment on the axial gradient distribution dataset and the radial gradient distribution dataset, and perform temperature compensation and correction on the gas concentration in the dataset based on the temperature in the smelting furnace; Identify the mutation regions in the axial and radial data according to the gradient changes of the data, and combine the composition ratio range to mark the potential protection failure regions; Map the gradient data after spatio-temporal alignment and the marking results into a three-dimensional distribution map and store it in the gas detection database of the hierarchical detection network.

[0011] Further, identifying the mutation regions in the axial and radial data according to the gradient changes of the data includes: For each detection point in the axial gradient distribution dataset and the radial gradient distribution dataset, calculate the gas concentration change rate between it and the adjacent point, and the gas concentration change rate includes the axial gradient change rate and the radial gradient change rate; According to the change of the smelting temperature, set the allowable threshold of the gradient change rate for each continuous process stage; Mark all the detection points whose gradient change rate exceeds the allowable threshold, and screen the continuously over-limit points to form the boundary of the mutation region; Compare the gas composition ratio in the mutation region with the composition ratio range. If at least one gas component exceeds the limit, mark it as a potential protection failure region; Generate a gradient risk hot spot report based on the three-dimensional space coordinates of the mutation region and the result of the temperature compensation correction, and store it in the gas detection database.

[0012] Further, the correlation analysis of the gas ratio correction parameter and the historical smelting quality database includes: Based on the continuous process stage and the gas ratio correction parameter, retrieve the historical case dataset with similar process characteristics from the historical database; Extract the quality index change data after gas ratio correction in the historical case dataset and calculate the average influence coefficient of the gas ratio correction parameter; Input the average influence coefficient, the deviation threshold and the smelting temperature change data into a time series prediction model to predict the fluctuation trend of the corrected quality index; Generate a multi-dimensional detection report including suggestions for fine-tuning the target gas ratio, process temperature compensation value and equipment maintenance tips according to the quality index fluctuation trend.

[0013] Further, calculating the average influence coefficient of the gas ratio correction parameter includes: Selecting the oxide film thickness, porosity, and mechanical property indexes from the historical case dataset as the core quality evaluation dimensions; For each of the core quality evaluation dimensions, calculating the absolute change amount and the change rate after performing the gas ratio correction in the historical cases; According to the continuous process stages and the deviation threshold, assigning different weights to the changes in the quality indexes of different continuous process stages; Based on the absolute change amount and the different weights, calculating the average influence intensity of each quality index in the historical cases respectively to generate a single-index influence coefficient; Performing a weighted average on the single-index influence coefficients to generate an average influence coefficient.

[0014] A detection system for the protective gas layer in copper alloy melting, the system includes: A target setting module, based on the melting temperature change characteristics and process requirements, dividing the melting process into multiple continuous process stages and setting the composition ratio range of the target protective gas for each stage; A data detection module, constructing a hierarchical detection network to collect the gradient distribution data of the axial and radial gas protection layers in each continuous process stage in real time; A deviation protection module, based on the gradient distribution data, calculating the deviation degree of the actual gas composition ratio from the composition ratio range in each continuous process stage, and dynamically adjusting the deviation threshold according to the melting temperature change to generate a gas ratio correction parameter; An association analysis module, associating and analyzing the gas ratio correction parameter with the historical melting quality database and outputting a multi-dimensional detection report.

[0015] A detection device for the protective gas layer in copper alloy melting, used to implement a detection method for the protective gas layer in copper alloy melting.

[0016] Through the technical solution of the present invention, the following technical effects can be achieved: Realize the full-range gradient real-time detection and dynamic ratio regulation of the gas layer, improve the oxidation inhibition accuracy, process response efficiency and gas utilization rate, and at the same time take into account the melt quality and energy consumption control through multi-objective collaborative optimization.

[0017] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic flow chart of a detection method for a copper alloy melting protective gas layer; Figure 2 It is a schematic flow chart for calculating the deviation degree of gas components; Figure 3 It is a schematic flow chart for collecting axial and radial gradient distribution data; Figure 4 It is a schematic flow chart for correlation analysis of gas ratio correction parameters. Specific Embodiments

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0022] Embodiment 1; As Figure 1 shown, the present application provides a detection method for a copper alloy melting protective gas layer, and the method includes: S10: Based on the melting temperature change characteristics and process requirements, divide the melting process into multiple continuous process stages, and set the composition ratio range of the target protective gas for each stage; S20: Construct a hierarchical detection network to collect the gradient distribution data of the axial and radial gas protection layers in each continuous process stage in real time; S30: Based on the gradient distribution data, calculate the deviation degree between the actual gas composition ratio and the composition ratio range in each continuous process stage, and dynamically adjust the deviation threshold according to the melting temperature change to generate a gas ratio correction parameter; S40: Correlate and analyze the gas ratio correction parameter with the historical melting quality database, and output a multi-dimensional detection report.

[0023] Specifically, during the smelting process, first, according to the variation characteristics of the smelting temperature and process requirements, the smelting process is divided into multiple consecutive process stages. Each consecutive process stage has different process requirements, and a range of component ratios of the target protective gas is set for each stage. For example, in the initial stage of smelting, a lower concentration of the protective gas may be required to maintain the temperature of the molten pool, while in the later stage, a higher gas concentration may be needed to prevent excessive oxidation. A hierarchical detection network is constructed to monitor the distribution of the gas protection layer during the smelting process in real time. The hierarchical detection network is responsible for collecting data on the gas gradient distribution at different positions in the furnace, especially in the axial and radial directions, which helps to comprehensively understand the distribution of the gas layer. Based on the collected gradient distribution data, the deviation degree between the actual gas component ratio and the target component ratio range in each consecutive process stage is calculated. The deviation degree represents the gap between the current actual gas component ratio and the preset target range. According to the characteristics of the temperature change during the smelting process, the deviation threshold is dynamically adjusted. If the smelting temperature exceeds the target value, the correction coefficient for the threshold adjustment will increase with the increase in the temperature difference, making the correction of the gas components more stringent. Conversely, if the temperature is lower, the correction coefficient will decrease. The gas ratio correction parameters are correlated and analyzed with the data in the historical smelting quality database, that is, the historical data is compared and analyzed with the deviation degree of the current smelting process, the gas ratio correction parameters, and other relevant factors. Through the analysis, a multi-dimensional detection report is generated, including suggestions for fine-tuning the target gas ratio, process temperature compensation values, equipment maintenance tips, etc. For example, the dataset of successful cases in the historical database can be used to predict the impact degree of a certain correction parameter on the quality index, and then optimization suggestions can be put forward. The report not only includes suggestions for adjusting the gas ratio but also includes optimization of the smelting temperature and equipment maintenance suggestions.

[0024] Through the technical solution of the present invention, real-time detection of the global gradient of the gas layer and dynamic ratio regulation are achieved, improving the oxidation inhibition accuracy, process response efficiency, and gas utilization rate. At the same time, through multi-objective collaborative optimization, both the melt quality and energy consumption control are taken into account.

[0025] Furthermore, as Figure 2 shown, calculating the deviation degree between the actual gas component ratio and the component ratio range in each consecutive process stage includes: For each consecutive process stage, extract the preset ratio range of each component in the target protective gas and the current ratio value detected in real time; Quantify the deviation degree between the actual ratio and the target range of each gas component as a percentage to calculate the single-item deviation amount; According to the difference between the current smelting temperature and the stage target temperature, dynamically adjust the correction coefficient of the single-item deviation amount, where the correction coefficient in the over-temperature state increases with the increase in the temperature difference, and decreases in the under-temperature state; Based on the correction coefficient, the corrected single-item deviation is weighted and synthesized to generate a comprehensive deviation index representing the overall deviation degree.

[0026] As an optimization of the above embodiment, for each continuous process stage, during the melting process, first, the preset proportion range of each component in the target protective gas is extracted. The preset proportion range is set according to the requirements of the melting process and the gas characteristics, and it will vary according to the process requirements of different stages. At the same time, the current component proportion value of the gas in each stage is detected in real time, and the proportion data of the current gas is obtained through means such as on-line sensors. For each gas component, calculate the deviation degree between its actual proportion and the target component proportion range. Specifically, the difference between the actual gas proportion and the target proportion range can be quantified as a percentage to obtain the deviation amount of each gas component. For example, if the target argon proportion is 70% and the actual proportion is 68%, the deviation amount of argon can be calculated to measure the deviation degree of this component. According to the difference between the current melting temperature and the target stage temperature, dynamically adjust the correction coefficient of the single-item deviation of each gas component. In the over-temperature state, as the temperature difference increases, the correction coefficient should increase; while in the under-temperature state, the correction coefficient decreases. This is to more precisely adjust the gas proportion in the case of large temperature fluctuations. Based on the above correction coefficient, the single-item deviations are weighted and synthesized to generate a comprehensive deviation index representing the overall deviation degree. The weighted synthesis is based on the importance of each gas component during the melting process. For example, the influence of some gas components is greater and higher weights need to be assigned. For example, if argon is the main protective gas, its deviation amount may have a greater weight than other gas components. Finally, a comprehensive deviation index is calculated through calculation to comprehensively evaluate the state of the current melting protective gas layer.

[0027] Furthermore, dynamically adjusting the deviation threshold according to the change of the melting temperature includes: Based on the comprehensive deviation distribution of qualified process stages in the melting history, establish the deviation threshold for each continuous process stage; Real-time monitor the temperature change rate of the melting. When the temperature change rate exceeds the preset safety range, compress the deviation threshold at a fixed ratio; Analyze the deviation data of a fixed number of melts. If the triggering phenomenon of the deviation threshold continuously appears, iteratively optimize the threshold parameters according to the gradient step size; Establish an adaptive association relationship between the adjusted deviation threshold and the key quality index of copper alloy melting to form an adaptive adjustment of the deviation threshold.

[0028] As a preference of the above embodiments, based on the comprehensive deviation distribution of qualified process stages in the smelting history, a deviation threshold for each continuous process stage is established. The deviation threshold is obtained based on the analysis result of historical smelting data and represents the qualified deviation range in each process stage. In historical data, the verified qualified stages will be used to determine the optimal deviation threshold to ensure that the gas components are within an appropriate range during the smelting process. For example, by analyzing historical smelting data, calculate the deviation distribution under different conditions such as temperature and time, and determine the deviation threshold for each process stage through statistical methods (such as mean and standard deviation); monitor the temperature change rate of the smelting in real time. If it is detected that the temperature change rate exceeds the preset safety range, compress the current deviation threshold at a fixed ratio. An overly fast temperature change rate may indicate an unstable smelting process, and it is necessary to strengthen the control of the gas component ratio by reducing the deviation threshold range to avoid excessive deviation. For example, set the safety range of the temperature change rate to ±5°C per minute. If the temperature change rate exceeds this range, automatically reduce the tolerance of the deviation threshold, thereby more strictly controlling the gas component ratio; analyze the deviation data in a fixed number of smelting processes. If the phenomenon of the deviation triggering the threshold continuously occurs (i.e., the deviation exceeding the threshold occurs repeatedly), optimize the threshold parameters by iterating in gradient step sizes. The optimization process will gradually adjust the threshold based on the change trend of the deviation during the smelting process to achieve more refined control. For example, after every 10 smelting processes are completed, automatically check whether there are multiple cases where the deviation exceeds the threshold. If so, gradually optimize the deviation threshold using the preset iteration step size (such as adjusting ±1% each time) to make it more in line with the actual smelting process; establish an adaptive correlation between the adjusted deviation threshold and the key quality indicators of copper alloy smelting (such as alloy composition, surface quality, etc.). By establishing this correlation, the production process can be dynamically adjusted according to the change of the deviation threshold to ensure that the quality of the final product always meets the standards. For example, the deviation threshold may be correlated with the final composition of the alloy or the quality control parameters during the smelting process (such as porosity, hardness, etc.). When the threshold changes, the corresponding production process will also be adjusted to optimize the quality of the final copper alloy.

[0029] Furthermore, generate a correction instruction including gas type, adjustment direction, and flow rate change amplitude, including: Generate a sorted list of gas types to be preferentially adjusted according to the absolute value of the individual deviation of each gas component and the adaptive correlation; For each gas component in the sorted list of gas types, if the actual ratio is lower than the lower limit of the target range, mark it as positive flow adjustment, and if it is higher than the upper limit of the target range, mark it as negative flow adjustment; When multiple gases need to be adjusted simultaneously, generate adjustment instructions in sequence according to the sorted list of gas types and maintain the stability of the total gas layer pressure; Compare the adjustment instruction with the target range, and generate a gas ratio correction parameter after deleting the out-of-limit instructions.

[0030] As an optimization of the above embodiment, generate a sorted list of gas types to be adjusted preferentially according to the absolute value of the single deviation of each gas component and the adaptation correlation relationship. The absolute value of the deviation reflects the difference between the actual component and the target range. The component with a larger absolute value is adjusted preferentially to quickly adjust the gas components in the smelting process to the target range. The adaptation correlation relationship helps to determine which gas components have a greater impact on the final quality and further optimize the adjustment order. For example, if the deviation of argon is greater than that of oxygen and nitrogen, and argon has a greater impact on the smelting quality, then argon will be adjusted preferentially and ranked at the top of the sorted list; Mark the adjustment direction according to the relationship between the actual ratio of each gas component and the target range. Specifically, if the actual ratio of the gas component is lower than the lower limit of the target range, it is marked as positive flow adjustment, indicating that the flow rate of this gas needs to be increased. If the actual ratio of the gas component is higher than the upper limit of the target range, it is marked as negative flow adjustment, indicating that the flow rate of this gas needs to be decreased. For example, if the actual ratio of oxygen is 4% while the target range is 5%-7%, then the adjustment direction of oxygen is positive flow adjustment, that is, to increase the flow rate of oxygen; When multiple gases need to be adjusted simultaneously, generate adjustment instructions in sequence according to the sorted list of gas types. The adjustment instructions will specify the adjustment direction and the amplitude of the flow rate change for each gas to ensure the correction of the gas ratio. At the same time, during the adjustment process, it is necessary to maintain the stability of the total pressure of the gas layer to avoid the instability of the smelting environment due to excessive flow rate changes. For example, first adjust the gas with the highest priority (such as argon), and then adjust the second-priority gas (such as oxygen). Each time an adjustment is made, control the change in the total gas flow rate within a stable range to ensure that the total pressure of the gas layer in the smelting process does not fluctuate violently; Compare the generated adjustment instructions with the target range. If a certain adjustment instruction exceeds the tolerance of the target range (for example, the amplitude of the gas flow rate change is too large), then delete the out-of-limit instruction and generate the final gas ratio correction parameter. The correction parameter will be used in the smelting process to dynamically adjust the composition of the protective gas layer to ensure that each gas component is maintained within the target range.

[0031] Furthermore, as Figure 3 shown, real-time collect the gradient distribution data of the axial and radial gas protection layers in each continuous process stage, including: Scan the gas absorption characteristics at different axial depths in layers along the height direction of the furnace to generate an axial gradient distribution data set; Capture the non-uniformity characteristics of the radial gas component distribution in the circumferential direction of the molten pool horizontal section to generate a radial gradient distribution data set; Spatially and temporally align the axial gradient distribution dataset and the radial gradient distribution dataset, and perform temperature compensation and correction on the gas concentration in the dataset based on the temperature in the smelting furnace; Identify the mutation regions in the axial and radial data according to the gradient change of the data, and combine the component ratio range to mark the potential protection failure regions; Map the gradient data after spatio-temporal alignment and the marking results into a three-dimensional distribution map and store it in the gas detection database of the hierarchical detection network.

[0032] As a preference of the above embodiment, during the smelting process, the gas absorption characteristics at different axial depths are scanned layer by layer along the height direction of the furnace. Technologies such as gas sensors or infrared absorption spectroscopy are used to collect the gas components and absorption characteristics at different height positions. Different axial depths may reflect different gas concentrations and distribution situations; at the horizontal cross-section of the molten pool, the circumferential radial gas component distribution is captured by sensors, especially paying attention to the non-uniformity characteristics of the gas distribution. By scanning the gas components in all directions around the molten pool, a dataset reflecting the gas concentration changes at different positions inside the molten pool (i.e., the radial gradient distribution dataset) can be generated; spatially and temporally align the above-mentioned collected axial gradient distribution dataset and the radial gradient distribution dataset. Spatio-temporal alignment is to ensure that the data comes from the same time point and matches the spatial position during the smelting process. At the same time, based on the temperature data in the smelting furnace, temperature compensation and correction are performed on these gas concentration data. Temperature changes may affect the gas absorption characteristics. Therefore, temperature compensation is required to ensure the accuracy of the data and that the data obtained at different temperatures has the same reference benchmark, avoiding errors in gas concentration caused by temperature fluctuations; according to the gradient data after spatio-temporal alignment, analyze and identify the mutation regions in the axial and radial data. The mutation region refers to the region where the gas component distribution suddenly changes, which is usually an indication of the failure of the gas protection layer. Combine the set target component ratio range to mark these potential protection failure regions, so that abnormal gas protection layers during the smelting process can be detected in time and corresponding correction measures can be taken. For example, through a data processing algorithm, calculate the gradient change of the gas components in space. When the gradient change exceeds the preset threshold, mark it as a potential protection failure region. The marked region may indicate situations such as insufficient gas flow, uneven gas components, or too high smelting temperature; map the gradient data after spatio-temporal alignment and the results of the marked potential protection failure regions into a three-dimensional distribution map. This three-dimensional map can comprehensively display the distribution of the gas layer in space during the smelting process and the possible failure regions. The generated three-dimensional map will be stored in the gas detection database of the hierarchical detection network for subsequent analysis and reference.

[0033] Furthermore, identifying the mutation regions in the axial and radial data according to the gradient change of the data includes: For each detection point in the axial gradient distribution dataset and the radial gradient distribution dataset, calculate the gas concentration change rate between it and its adjacent points. The gas concentration change rate includes the axial gradient change rate and the radial gradient change rate; Set the allowable threshold of the gradient change rate for each continuous process stage according to the change of the smelting temperature; Mark all detection points whose gradient change rates exceed the allowable threshold, and screen out the continuous over-limit points to form the boundary of the mutation region; Compare the gas component ratio in the mutation region with the component ratio range. If at least one gas component exceeds the limit, mark it as a potential protection failure region; Generate a gradient risk hotspot report based on the three-dimensional space coordinates of the mutation region and the result of temperature compensation correction, and store it in the gas detection database.

[0034] Preferably, for each detection point in the axial gradient distribution dataset and the radial gradient distribution dataset, calculate the gas concentration change rate between this point and its adjacent points. The gas concentration change rate reflects the degree of change of gas components in space, including calculating the axial gradient change rate of the gas concentration change at different height positions in the smelting furnace, and calculating the radial gradient change rate of the gas concentration change at different azimuth points on the horizontal cross-section of the molten pool, which reflects the distribution difference of gas in the circumferential direction of the molten pool. Calculate the concentration change rate by dividing the gas concentration difference between adjacent detection points by the spatial distance. In this way, the change rate of gas concentration in space can be measured; according to the change of smelting temperature, set the allowable threshold of the gradient change rate for each continuous process stage. Different process stages may have different temperature ranges and gas component requirements, so their tolerances for the change rate of gas concentration are different. When the temperature changes greatly, a relatively loose tolerance range for the gradient change rate may be required; while when the temperature is relatively stable, the change rate of gas concentration should be controlled within a smaller range. For example, in the initial stage of smelting, when the temperature fluctuates greatly, the allowable gas concentration change rate may be higher; while in the later stage of smelting, when the temperature is relatively stable, the allowable concentration change rate is lower; mark all detection points where the gas concentration change rate exceeds the allowable threshold. These points may indicate a sharp change in gas components at a certain position, and may be an indication of the failure or instability of the gas protection layer. Screen out these over-limit points, and form the boundary of the mutation region through continuous over-limit points; compare the gas component ratios in the identified mutation region to check whether at least one gas component exceeds the set component ratio range. If the ratio of a certain gas component exceeds the upper or lower limit of the target range, mark this region as a potential protection failure region; based on the three-dimensional spatial coordinates of the mutation region and the result of temperature compensation and correction, generate a gradient risk hot spot report. The report will include information such as the spatial position of the mutation region, the gas component ratio, and the temperature correction result, which is used to identify and warn of possible gas protection failure regions during the smelting process. The report will include the three-dimensional coordinate information (such as X, Y, Z coordinates) of each mutation region, and these coordinates reflect the specific position of the mutation region in the smelting furnace. In addition, the report will also include the corrected data after temperature compensation to ensure the accuracy of the evaluation results. Finally, store these data in the gas detection database for subsequent analysis and reference.

[0035] Furthermore, as Figure 4 shown, associate the gas ratio correction parameter with the historical smelting quality database for analysis, including: Based on the continuous process stage and the gas ratio correction parameter, retrieve the historical case dataset with similar process characteristics from the historical database; Extract the changed data of the quality index after gas ratio correction in the historical case dataset and calculate the average influence coefficient of the gas ratio correction parameter; Input the average influence coefficient, deviation threshold, and melting temperature change data into a time series prediction model to predict the fluctuation trend of the corrected quality index; Generate a multi-dimensional inspection report including fine-tuning suggestions for the target gas ratio, process temperature compensation value, and equipment maintenance tips based on the quality index fluctuation trend.

[0036] As an optimization of the above embodiment, according to the continuous process stage and gas ratio correction parameters, retrieve a historical case data set with similar process characteristics from the historical melting quality database. The historical case data set should have process conditions similar to the current melting process stage and gas ratio correction parameters. By looking up historical data, it can help predict the potential impact of the correction parameters on the quality index; from the historical case data set, extract the quality index change data after the gas ratio correction. These quality indexes may include alloy composition, surface quality, porosity, etc. after melting. After extracting the data, calculate the influence coefficient of the gas ratio correction parameter on the quality index. Usually, the average influence coefficient of each correction parameter is calculated through statistical methods (such as regression analysis). For example, extract the quality data related to the gas ratio adjustment, calculate the change in the alloy composition of the melted product after correction, and further calculate the average influence coefficient of each gas component adjustment on the final product quality. For example, increasing the argon concentration may reduce the porosity, and adjusting the nitrogen concentration may affect the hardness of the alloy; input the average influence coefficient, deviation threshold, and melting temperature change data into a time series prediction model. Based on the trend of historical data, it is possible to predict the fluctuation trend of the quality index after correcting the gas ratio and melting temperature change. By considering the historical quality fluctuations and the influence of the correction parameters, predict the change trend of the quality index during the melting process to discover potential problems in advance. For example, use methods such as machine learning or regression analysis to train a time series model based on historical data to predict the fluctuation of the quality index (such as alloy composition, surface quality) over time after correcting the gas ratio. Input the current deviation, gas correction parameters, and temperature change data, and the model can output the quality fluctuation trend during the future melting process; generate a multi-dimensional inspection report including fine-tuning suggestions for the target gas ratio, process temperature compensation value, and equipment maintenance tips based on the quality index fluctuation trend. The report will provide specific suggestions for the current melting process, including how to fine-tune the gas ratio, temperature compensation plan, and equipment maintenance and optimization suggestions. The data in the report will help the operator adjust the process to ensure the quality of the final product.

[0037] Furthermore, calculating the average influence coefficient of the gas ratio correction parameter includes: Select the oxide film thickness, porosity, and mechanical property indexes from the historical case data set as the core quality evaluation dimensions; For each core quality evaluation dimension, calculate the absolute change and change rate after the execution gas ratio correction in historical cases; According to the continuous process stage and deviation threshold, assign differential weights to the changes in quality indicators for different continuous process stages; Based on the absolute change and differential weights, calculate the average influence intensity of each quality indicator in historical cases respectively, and generate a single-indicator influence coefficient; Perform a weighted average on the single-indicator influence coefficients to generate an average influence coefficient.

[0038] As a preference of the above embodiment, select the oxide film thickness, porosity, and mechanical property indicators from the historical case dataset as the core quality evaluation dimensions. These indicators are important parameters for measuring the quality change in the copper alloy melting process. Indicators such as oxide film thickness, porosity, and mechanical properties directly affect the final quality of the alloy. Selecting these dimensions can accurately reflect the impact of gas ratio correction on melting quality. For example, the oxide film thickness reflects the degree of metal surface oxidation during the melting process; the porosity directly affects the density and strength of the material; the mechanical properties (such as tensile strength, hardness, etc.) determine the service performance of the copper alloy. By selecting these indicators, the impact of gas ratio correction on melting quality can be comprehensively evaluated. For each core quality evaluation dimension, calculate the absolute change and change rate after the execution gas ratio correction in historical cases. The absolute change refers to the difference in quality indicators before and after the gas ratio correction, and the change rate is the ratio between the absolute change and the original quality indicator. Through these data, the impact of gas ratio adjustment on each quality indicator can be quantified. According to the continuous process stage and the deviation threshold, assign differential weights to the changes in quality indicators for different process stages. Different process stages may have different impacts on quality, so it is necessary to assign different weights to the quality indicator changes in each stage according to the actual situation. For example, in the initial melting stage, the gas composition may have a greater impact on the oxide film thickness, while in the later stage, more attention may be paid to the changes in mechanical properties. Based on the absolute change and the differential weights, calculate the average influence intensity of each quality indicator in historical cases respectively, and generate a single-indicator influence coefficient. The single-indicator influence coefficient reflects the average influence intensity of the corrected gas ratio in each core quality evaluation dimension in historical cases, usually calculated by the weighted average method. For example, for the oxide film thickness, calculate the weighted average influence intensity of the oxide film thickness changes in all historical cases and obtain the single-indicator influence coefficient of the oxide film thickness. Perform a weighted average on all single-indicator influence coefficients to generate the final average influence coefficient. The weights for the weighted average are usually assigned based on the contribution degree of each quality indicator to the final quality in the melting process. The final average influence coefficient can comprehensively reflect the comprehensive impact of gas ratio correction on melting quality.

[0039] Embodiment 2; Based on the same inventive concept as the method for detecting a protective gas layer in copper alloy melting in the foregoing embodiments, the present invention also provides a detection system for a protective gas layer in copper alloy melting. The system includes: A target setting module, which divides the melting process into multiple consecutive process stages based on the characteristics of melting temperature change and process requirements, and sets the composition ratio range of the target protective gas for each stage; A data detection module, which constructs a hierarchical detection network and collects the gradient distribution data of the axial and radial gas protection layers in each consecutive process stage in real time; A deviation protection module, which calculates the deviation degree between the actual gas composition ratio and the composition ratio range in each consecutive process stage based on the gradient distribution data, and dynamically adjusts the deviation threshold according to the change of melting temperature to generate a gas ratio correction parameter; An association analysis module, which associates and analyzes the gas ratio correction parameter with the historical melting quality database and outputs a multi-dimensional detection report.

[0040] The above adjustment system in the present invention can be effectively realized, and the technical effects that can be achieved are as described in the foregoing embodiments, which will not be elaborated here.

[0041] Embodiment III; Based on the same inventive concept as the method for detecting a protective gas layer in copper alloy melting in the foregoing embodiments, the present invention also provides a detection device for a protective gas layer in copper alloy melting, which is used to implement a method for detecting a protective gas layer in copper alloy melting.

[0042] The above device in the present invention can effectively implement a method for detecting a protective gas layer in copper alloy melting, and the technical effects that can be achieved are as described in the foregoing embodiments, which will not be elaborated here.

[0043] Although the present application has been described in conjunction with specific features and their embodiments, it is obvious that various modifications and combinations can be made without departing from the spirit and scope of the present application. Accordingly, the present specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for detecting a protective gas layer in copper alloy smelting, characterized in that, The method includes: Based on the characteristics of the melting temperature change and process requirements, the melting process is divided into multiple consecutive process stages, and a composition ratio range of the target protective gas is set for each stage; A hierarchical detection network is constructed to collect the gradient distribution data of the axial and radial gas protection layers in each of the consecutive process stages in real time; Based on the gradient distribution data, the deviation degree between the actual gas composition ratio and the composition ratio range in each of the consecutive process stages is calculated, and the deviation threshold is dynamically adjusted according to the melting temperature change to generate a gas ratio correction parameter; The gas ratio correction parameter is associated and analyzed with the historical melting quality database, and a multi-dimensional detection report is output.

2. The detection method of the copper alloy melting protective gas layer according to claim 1, characterized in that, Calculating the deviation degree between the actual gas composition ratio and the composition ratio range in each of the consecutive process stages includes: For each of the consecutive process stages, the preset ratio range of each component in the target protective gas and the current ratio value detected in real time are extracted; The deviation degree between the actual ratio and the target range of each gas component is quantified as a percentage to calculate the single-item deviation amount; According to the difference between the current melting temperature and the stage target temperature, the correction coefficient of the single-item deviation amount is dynamically adjusted, where the correction coefficient in the over-temperature state increases with the increase of the temperature difference, and decreases in the under-temperature state; Based on the correction coefficient, the corrected single-item deviation amounts are weighted and synthesized to generate a comprehensive deviation degree index representing the overall deviation degree.

3. The detection method of the copper alloy melting protective gas layer according to claim 2, wherein Dynamically adjusting the deviation threshold according to the melting temperature change includes: Based on the comprehensive deviation degree distribution of qualified process stages in the melting history, the deviation thresholds for each of the consecutive process stages are established; The temperature change rate of the melting is monitored in real time. When the temperature change rate exceeds the preset safety range, the deviation threshold is compressed by a fixed ratio; The deviation degree data of a fixed number of meltings are analyzed. If the triggering phenomenon of the deviation threshold continuously appears, the threshold parameters are iteratively optimized according to the gradient step size; An adaptive association relationship is established between the adjusted deviation threshold and the key quality indicators of copper alloy melting to form the adaptive adjustment of the deviation threshold.

4. The detection method of the copper alloy melting protective gas layer according to claim 3, characterized in that, Generating a correction instruction including the gas type, adjustment direction and flow rate change amplitude, includes: According to the absolute value of the single-item deviation amount of each gas component and the adaptive association relationship, a sorted list of gas types to be preferentially adjusted is generated; For each gas component in the sorted list of gas types, if the actual ratio is lower than the lower limit of the target range, it is marked as a positive flow adjustment, and if it is higher than the upper limit of the target range, it is marked as a negative flow adjustment; When multiple gases need to be adjusted simultaneously, adjustment instructions are generated in sequence according to the sorted list of gas types, and the total pressure of the gas layer is maintained stable; The adjustment instructions are compared with the target range, and the gas ratio correction parameter is generated after deleting the over-limit instructions.

5. The detection method of the copper alloy melting protective gas layer according to claim 1, characterized in that, Collecting the gradient distribution data of the axial and radial gas protection layers in each of the consecutive process stages in real time includes: Scanning the gas absorption characteristics at different axial depths in layers along the height direction of the furnace to generate an axial gradient distribution data set; Capturing the non-uniformity characteristics of the radial gas composition distribution in the circumferential direction of the molten pool horizontal section to generate a radial gradient distribution data set; Perform spatio-temporal alignment on the axial gradient distribution dataset and the radial gradient distribution dataset, and perform temperature compensation and correction on the gas concentration in the dataset based on the temperature in the smelting furnace; Identify the mutation regions in the axial and radial data according to the gradient change of the data, and combine the component ratio range to mark the potential protection failure regions; Map the gradient data after spatio-temporal alignment and the marking results into a three-dimensional distribution map and store it in the gas detection database of the hierarchical detection network.

6. The detection method of the copper alloy melting protective gas layer according to claim 5, characterized in that, Identify the mutation regions in the axial and radial data according to the gradient change of the data, including: For each detection point in the axial gradient distribution dataset and the radial gradient distribution dataset, calculate the gas concentration change rate between it and the adjacent point, and the gas concentration change rate includes the axial gradient change rate and the radial gradient change rate; Set the allowable threshold of the gradient change rate for each of the continuous process stages according to the change of the smelting temperature; Mark all the detection points whose gradient change rate exceeds the allowable threshold, and screen the continuously over-limit points to form the boundary of the mutation region; Compare the gas component ratio in the mutation region with the component ratio range, and if at least one gas component exceeds the limit, mark it as a potential protection failure region; Generate a gradient risk hot spot report based on the three-dimensional space coordinates of the mutation region and the result of the temperature compensation and correction, and store it in the gas detection database.

7. The detection method of the copper alloy melting protective gas layer according to claim 1, characterized in that Perform correlation analysis on the gas ratio correction parameter and the historical smelting quality database, including: Based on the continuous process stage and the gas ratio correction parameter, retrieve the historical case dataset with similar process characteristics from the historical database; Extract the quality index change data after the gas ratio correction in the historical case dataset and calculate the average influence coefficient of the gas ratio correction parameter; Input the average influence coefficient, the deviation threshold and the smelting temperature change data into a time series prediction model to predict the fluctuation trend of the corrected quality index; Generate a multi-dimensional detection report including suggestions for fine-tuning the target gas ratio, the process temperature compensation value and equipment maintenance tips according to the quality index fluctuation trend.

8. The detection method of the copper alloy melting protective gas layer according to claim 7, characterized in that Calculate the average influence coefficient of the gas ratio correction parameter, including: Select the oxide film thickness, porosity and mechanical property index from the historical case dataset as the core quality evaluation dimensions; For each of the core quality evaluation dimensions, calculate the absolute change amount and the change rate after the gas ratio correction in the historical cases; According to the continuous process stage and the deviation threshold, assign different weights to the quality index changes in different continuous process stages; Based on the absolute change amount and the different weights, calculate the average influence intensity of each quality index in the historical cases respectively to generate a single-index influence coefficient; Perform weighted average on the single-index influence coefficients to generate an average influence coefficient.

9. A detection system for the protective gas layer in copper alloy melting, characterized in that, The system includes: A target setting module, which divides the smelting process into multiple continuous process stages based on the smelting temperature change characteristics and process requirements, and sets the component ratio range of the target protective gas for each stage; A data detection module, which constructs a hierarchical detection network and collects the gradient distribution data of the axial and radial gas protection layers in each continuous process stage in real time; The deviation protection module calculates the deviation degree between the actual gas composition ratio and the composition ratio range in each continuous process stage based on the gradient distribution data, and dynamically adjusts the deviation threshold according to the change of the melting temperature to generate a gas ratio correction parameter; The correlation analysis module performs a correlation analysis on the gas ratio correction parameter and the historical melting quality database, and outputs a multi-dimensional detection report.

10. A detection device for a copper alloy melting protective gas layer, characterized in that, It is used to implement a detection method for the protective gas layer of copper alloy melting as described in claims 1-8.

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