A method, system and device for detecting a protective gas layer in copper alloy smelting

Abstract

The present invention relates to the technical field of copper-containing alloy manufacturing, and in particular to a method, system, and equipment for detecting a protective gas layer for copper alloy smelting. The method comprises: dividing the smelting process into multiple continuous process stages based on the characteristics of smelting temperature changes and process requirements, and setting a target protective gas composition ratio range for each stage; constructing a layered detection network to collect gradient distribution data of the axial and radial gas protective layers in each continuous process stage in real time; calculating the deviation between the actual gas composition ratio and the composition ratio range in each continuous process stage based on the gradient distribution data, and dynamically adjusting the deviation threshold according to the smelting temperature change to generate a gas ratio correction parameter; correlating and analyzing the gas ratio correction parameter with a historical smelting quality database to output a multi-dimensional detection report. Through the present invention, the problems of single-dimensional detection of the protective gas layer for copper alloy smelting, uneven oxidation control, and gas waste caused by delayed response and reliance on experience are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of copper-containing alloy manufacturing, and in particular to a method, system and equipment for detecting a protective gas layer in copper alloy smelting. Background Art

[0002] During the copper alloy smelting process, the protective gas layer inhibits metal oxidation by isolating oxygen and regulating heat exchange. The core requires precise control of the gas composition ratio and maintaining uniform axial and radial distribution.

[0003] Traditional detection technologies have several limitations: First, single-point static detection methods (such as thermocouples or oxygen probes) can only obtain local data and cannot reflect the global gradient distribution of the gas layer. Furthermore, the high-temperature environment leads to short lifespans and high maintenance costs for detection equipment. Second, offline sampling and analysis rely on laboratory instruments, resulting in long detection cycles, destabilizing the gas layer, and making it difficult to guide real-time process adjustments. Finally, experience-driven control relies on the subjective judgment of operators and lacks quantitative standards, which can easily lead to control inaccuracies due to individual differences. These technical shortcomings lead to problems such as uneven oxidation control, excessive gas consumption, and process fluctuations. These can easily lead to localized oxidation inclusions in castings, low gas utilization, and unstable batch quality, severely restricting the industrial production level of high-precision copper alloy smelting.

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

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

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for detecting a copper alloy smelting protective gas layer, the method comprising:

[0008] Based on the melting temperature variation characteristics and process requirements, the melting process is divided into multiple continuous process stages, and the target shielding gas composition ratio range is set for each stage;

[0009] Constructing a layered detection network to collect real-time gradient distribution data of the axial and radial gas protection layers in each of the continuous process stages;

[0010] Based on the gradient distribution data, calculating the deviation between the actual gas composition ratio and the composition ratio range in each of the continuous process stages, and dynamically adjusting the deviation threshold according to the change in melting temperature to generate a gas ratio correction parameter;

[0011] The gas ratio correction parameter is correlated with the historical smelting quality database for analysis, and a multi-dimensional detection report is output.

[0012] Furthermore, calculating the deviation between the actual gas composition ratio in each of the continuous process stages and the composition ratio range includes:

[0013] For each of the continuous process stages, extracting a preset ratio range of each component in the target protective gas and a current ratio value detected in real time;

[0014] The deviation between the actual ratio of each gas component and the target range is quantified as a percentage, and the individual deviation is calculated;

[0015] Dynamically adjust the correction coefficient of the single deviation according to the difference between the current melting temperature and the stage target temperature, wherein the correction coefficient in the over-temperature state increases with the increase of the temperature difference, and decreases in the under-temperature state;

[0016] Based on the correction coefficient, the corrected individual deviation amounts are weighted and synthesized to generate a comprehensive deviation index that represents the overall deviation degree.

[0017] Furthermore, the deviation threshold is dynamically adjusted according to the change of the melting temperature, including:

[0018] Establishing the deviation threshold for each of the consecutive process stages based on the comprehensive deviation distribution of qualified process stages in the smelting history;

[0019] Real-time monitoring of the temperature change rate of the smelting process, and when the temperature change rate exceeds a preset safety range, compressing the deviation threshold value at a fixed ratio;

[0020] Analyze the deviation data of a fixed smelt, and if the deviation threshold is triggered continuously, iteratively optimize the threshold parameter according to the gradient step size;

[0021] An adaptive correlation relationship is established between the adjusted deviation threshold and the key quality index of copper alloy smelting, thereby forming an adaptive adjustment of the deviation threshold.

[0022] Furthermore, a correction instruction including gas type, adjustment direction and flow rate change range is generated, including:

[0023] generating a ranked list of gas types requiring priority adjustment based on the absolute value of the individual deviations of the gas components and the adaptive association relationship;

[0024] For each gas component in the gas type sorted list, if the actual ratio is lower than the lower limit of the target range, it is marked as a positive flow adjustment; if it is higher than the upper limit of the target range, it is marked as a negative flow adjustment;

[0025] When multiple gases need to be adjusted simultaneously, adjustment instructions are generated in sequence according to the gas type sorting list, and the total pressure of the gas layer is maintained stable;

[0026] The adjustment instruction is compared with the target range, and the gas ratio correction parameter is generated after the over-limit instruction is deleted.

[0027] Furthermore, real-time collection of gradient distribution data of the axial and radial gas protection layers in each of the continuous process stages includes:

[0028] The gas absorption characteristics at different axial depths are scanned layer by layer along the height direction of the furnace to generate an axial gradient distribution data set;

[0029] The non-uniform characteristics of radial gas composition distribution are captured in the circumferential direction of the horizontal cross section of the molten pool to generate a radial gradient distribution data set;

[0030] performing spatiotemporal alignment on the axial gradient distribution dataset and the radial gradient distribution dataset, and performing temperature compensation correction on the gas concentration in the dataset based on the temperature in the smelting furnace;

[0031] Identify mutation areas in the axial and radial data based on the gradient changes of the data, and mark potential protection failure areas based on the component ratio range;

[0032] The time-space aligned gradient data and the labeling results are mapped into a three-dimensional distribution map, which is stored in the gas detection database of the hierarchical detection network.

[0033] Furthermore, the mutation areas in the axial and radial data are identified based on the gradient changes of the data, including:

[0034] For each detection point in the axial gradient distribution data set and the radial gradient distribution data set, calculating the gas concentration change rate between the detection point and the adjacent points, the gas concentration change rate including the axial gradient change rate and the radial gradient change rate;

[0035] According to the change of the melting temperature, setting a gradient change rate allowable threshold value for each of the continuous process stages;

[0036] Marking all detection points where the gradient change rate exceeds the allowable threshold, and screening consecutive exceeding-limit points to form a mutation region boundary;

[0037] Comparing the gas component ratio of the mutation area with the component ratio range, and marking it as a potential protection failure area if at least one gas component exceeds the limit;

[0038] Based on the three-dimensional spatial coordinates of the mutation area and the result of the temperature compensation correction, a gradient risk hotspot report is generated and stored in the gas detection database.

[0039] Furthermore, the gas ratio correction parameter is correlated with the historical smelting quality database for analysis, including:

[0040] Retrieving a historical case data set having similar process characteristics from a historical database based on the continuous process stages and the gas ratio correction parameter;

[0041] Extracting the quality index change data after gas ratio correction in the historical case data set and calculating the average influence coefficient of the gas ratio correction parameter;

[0042] Inputting the average influence coefficient, the deviation threshold and the melting temperature change data into a time series prediction model to predict the fluctuation trend of the corrected quality index;

[0043] Based on the fluctuation trend of the quality indicators, a multi-dimensional inspection report is generated, which includes target gas ratio fine-tuning suggestions, process temperature compensation values ​​and equipment maintenance prompts.

[0044] Furthermore, calculating the average influence coefficient of the gas ratio correction parameter includes:

[0045] From the historical case data set, oxide film thickness, porosity, and mechanical properties were selected as core quality evaluation dimensions;

[0046] For each of the core quality evaluation dimensions, calculate the absolute change and change rate after the gas ratio correction in the historical cases;

[0047] Assigning differentiated weights to quality indicator changes at different continuous process stages according to the continuous process stages and the deviation threshold;

[0048] Based on the absolute change amount and the differentiation weight, respectively calculate the average impact intensity of each quality indicator in the historical cases to generate a single indicator impact coefficient;

[0049] Perform a weighted average on the single indicator influence coefficients to generate an average influence coefficient.

[0050] A detection system for a copper alloy smelting protective gas layer, the system comprising:

[0051] The target setting module divides the smelting process into multiple continuous process stages based on the smelting temperature variation characteristics and process requirements, and sets the target shielding gas composition ratio range for each stage;

[0052] The data detection module builds a layered detection network to collect the gradient distribution data of the axial and radial gas protection layers in each continuous process stage in real time;

[0053] The deviation protection module calculates the deviation 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 melting temperature to generate the gas ratio correction parameter;

[0054] The correlation analysis module correlates the gas ratio correction parameters with the historical smelting quality database and outputs a multi-dimensional inspection report.

[0055] A device for detecting a protective gas layer for copper alloy smelting is used to implement a method for detecting a protective gas layer for copper alloy smelting.

[0056] The technical solution of the present invention can achieve the following technical effects:

[0057] Real-time detection and dynamic proportion control of the entire gas layer gradient are achieved, improving oxidation inhibition accuracy, process response efficiency and gas utilization rate, while taking into account both melt quality and energy consumption control through multi-objective collaborative optimization.

[0058] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 The figure is a flow chart of a method for detecting a protective gas layer during copper alloy smelting;

[0061] Figure 2 Schematic diagram of the process for calculating gas composition deviation;

[0062] Figure 3 Schematic diagram of the process for collecting axial and radial gradient distribution data;

[0063] Figure 4 Schematic diagram of the flow chart for gas ratio correction parameter correlation analysis. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0066] Embodiment 1;

[0067] like Figure 1 As shown, the present application provides a method for detecting a copper alloy smelting protective gas layer, the method comprising:

[0068] S10: Based on the melting temperature variation characteristics and process requirements, the melting process is divided into multiple continuous process stages, and the target shielding gas composition ratio range is set for each stage;

[0069] S20: Build a layered detection network to collect real-time gradient distribution data of the axial and radial gas shielding layers at each continuous process stage;

[0070] S30: Based on the gradient distribution data, the deviation between the actual gas composition ratio and the composition ratio range in each continuous process stage is calculated, and the deviation threshold is dynamically adjusted according to the change of the melting temperature to generate a gas ratio correction parameter;

[0071] S40: Correlate and analyze the gas ratio correction parameters with the historical smelting quality database and output a multi-dimensional inspection report.

[0072] Specifically, during the smelting process, the smelting process is first divided into multiple continuous process stages according to the changing characteristics of the smelting temperature and process requirements. Each continuous process stage has different process requirements, and a target protective gas composition ratio range is set for each stage. For example, in the initial stage of smelting, a lower protective gas concentration may be required to maintain the molten pool temperature, while a higher gas concentration may be required in the later stage to prevent excessive oxidation; a layered detection network is constructed to monitor the distribution of the gas protective layer during the smelting process in real time. The layered detection network is responsible for collecting gas gradient distribution data at different positions of the furnace, especially in the axial and radial directions, which helps to fully understand the distribution status of the gas layer; based on the collected gradient distribution data, the deviation of the actual gas composition ratio of each continuous process stage from the target composition ratio range is calculated. The deviation represents the difference between the current actual gas composition ratio and the preset target range. Distance, dynamically adjust the deviation threshold according to the characteristics of temperature changes during the smelting process. If the smelting temperature exceeds the target value, the correction coefficient of the threshold adjustment will increase as the temperature difference increases, making the correction of the gas composition more stringent. Conversely, if the temperature is low, the correction coefficient will decrease. The gas ratio correction parameters are correlated with the data in the historical smelting quality database. That is, the historical data is compared and analyzed with the deviation of the current smelting process, the gas ratio correction parameters and other related factors. Through analysis, a multi-dimensional detection report is generated containing target gas ratio fine-tuning suggestions, process temperature compensation values, equipment maintenance prompts, etc. For example, the successful case data set in the historical database can be used to predict the impact of a certain correction parameter on the quality index, and then put forward optimization suggestions. The report not only includes suggestions for gas ratio adjustment, but also includes smelting temperature optimization and equipment maintenance suggestions.

[0073] Through the technical solution of the present invention, real-time detection and dynamic proportion control of the full-area gradient of the gas layer are realized, thereby improving the oxidation inhibition accuracy, process response efficiency and gas utilization rate. At the same time, multi-objective collaborative optimization is used to take into account both melt quality and energy consumption control.

[0074] Further, if Figure 2 As shown, the deviation between the actual gas composition ratio and the composition ratio range in each continuous process stage is calculated, including:

[0075] For each continuous process stage, the preset ratio range of each component in the target protective gas and the current ratio value detected in real time are extracted;

[0076] The deviation between the actual ratio of each gas component and the target range is quantified as a percentage, and the individual deviation is calculated;

[0077] According to the difference between the current melting temperature and the stage target temperature, the correction coefficient of the single deviation is dynamically adjusted. The correction coefficient in the over-temperature state increases with the increase of the temperature difference, while it decreases in the under-temperature state.

[0078] Based on the correction coefficient, the corrected individual deviations are weighted and synthesized to generate a comprehensive deviation index that represents the overall degree of deviation.

[0079] As a preferred embodiment of the above, for each continuous process stage, during the smelting process, the preset ratio range of each component in the target protective gas is first extracted. The preset ratio range is set according to the requirements of the smelting process and the gas characteristics, and will vary according to the process requirements of different stages. At the same time, the current component ratio value of the gas in each stage is detected in real time, and the current gas ratio data is obtained by means of online sensors and the like; for each gas component, the degree of deviation between its actual ratio and the target component ratio range is calculated. Specifically, the deviation amount of each gas component can be obtained by quantifying the difference between the actual gas ratio and the target ratio range as a percentage. For example, if the target argon ratio is 70% and the actual ratio is 68%, the deviation amount of argon can be calculated to measure the degree of deviation of the component; according to the current The difference between the melting temperature and the target stage temperature is used to dynamically adjust the correction coefficient of the single deviation of each gas component. In the over-temperature state, the correction coefficient should increase as the temperature difference increases; in the under-temperature state, the correction coefficient decreases. This is to more accurately adjust the gas ratio when the temperature fluctuates greatly. Based on the above correction coefficient, the single deviation is weighted and synthesized to generate a comprehensive deviation index to characterize the overall deviation degree. The weighted synthesis is based on the importance of each gas component in the melting process. For example, some gas components have a greater impact and need to be given a higher weight. For example, if argon is the main shielding gas, its deviation may have a greater weight than other gas components. Finally, a comprehensive deviation index is calculated to evaluate the current state of the melting shielding gas layer as a whole.

[0080] Furthermore, the deviation threshold is dynamically adjusted according to the change of melting temperature, including:

[0081] Based on the comprehensive deviation distribution of qualified process stages in the smelting history, the deviation threshold of each consecutive process stage is established;

[0082] Real-time monitoring of the temperature change rate of the smelting process. When the temperature change rate exceeds the preset safety range, the deviation threshold is compressed at a fixed ratio.

[0083] Analyze the deviation data of a fixed smelt. If the deviation threshold is triggered continuously, iteratively optimize the threshold parameters according to the gradient step size.

[0084] An adaptive correlation is established between the adjusted deviation threshold and the key quality indicators of copper alloy smelting to form an adaptive adjustment of the deviation threshold.

[0085] As a preferred embodiment of the above embodiment, a deviation threshold is established for each continuous process stage based on the comprehensive deviation distribution of qualified process stages in the smelting history. The deviation threshold is derived based on the analysis results of historical smelting data and represents the qualified deviation range for each process stage. The verified qualified stages in the historical data are used to determine the optimal deviation threshold to ensure that the gas composition during the smelting process is within an appropriate range. For example, the historical smelting data is analyzed to calculate the deviation distribution under different temperature, time and other conditions, and the deviation threshold for each process stage is determined using statistical methods (such as mean and standard deviation). The temperature change rate of the smelting process is monitored in real time. If the temperature change rate exceeds a preset safety range, the current deviation threshold is compressed at a fixed ratio. Excessively rapid temperature change rate may indicate an unstable smelting process. It is necessary to strengthen the control of the gas composition ratio by reducing the range of the deviation threshold to avoid excessive deviation. For example, the safety range of the temperature change rate is set to ±5°C per minute. If the temperature change rate exceeds this range, the tolerance of the deviation threshold is automatically reduced, thereby more strictly controlling the gas composition ratio; the deviation data of a fixed number of melting processes is analyzed. If the deviation triggers the threshold continuously (that is, the deviation exceeds the threshold repeatedly), the threshold parameters are iteratively optimized according to the gradient step size. The optimization process will gradually adjust the threshold based on the changing trend of the deviation during the melting process to achieve more precise control. For example, after every 10 melting processes, it is automatically checked whether there are multiple cases of exceeding the deviation threshold. If so, a preset iterative step size (such as ±1% adjustment each time) will be used to gradually optimize the deviation threshold to make it more consistent with the actual smelting process; the adjusted deviation threshold will be adaptively associated with the key quality indicators of copper alloy smelting (such as alloy composition, surface quality, etc.). By establishing this association, the production process can be dynamically adjusted according to changes in the deviation threshold to ensure that the quality of the final product always meets the standards. For example, the deviation threshold may be associated with the final composition of the alloy or the quality control parameters in the smelting process (such as porosity, hardness, etc.). When the threshold changes, the corresponding production process will also be adjusted to optimize the final copper alloy quality.

[0086] Furthermore, a correction instruction including gas type, adjustment direction and flow rate change range is generated, including:

[0087] Generate a prioritized list of gas types that require adjustment based on the absolute value of the individual deviations of each gas component and the adaptive correlation relationship;

[0088] For each gas component in the gas type sorting list, if the actual ratio is lower than the lower limit of the target range, it is marked as a positive flow adjustment; if it is higher than the upper limit of the target range, it is marked as a negative flow adjustment;

[0089] When multiple gases need to be adjusted at the same time, the adjustment instructions are generated in sequence according to the gas type sort list, and the total pressure of the gas layer is kept stable;

[0090] Compare the adjustment command with the target range, delete the out-of-limit command and generate the gas ratio correction parameter.

[0091] As a preferred embodiment of the above embodiment, a ranked list of gas types requiring priority adjustment is generated based on the absolute value of the individual deviations of each gas component and the adaptive association. The absolute value of the deviation reflects the difference between the actual component and the target range. Components with larger absolute values ​​are adjusted first, so that the gas composition during the smelting process is adjusted to the target range as quickly as possible. The adaptive association helps determine which gas components have a greater impact on the final quality, further optimizing the adjustment order. For example, if the deviation of argon is greater than the deviations of oxygen and nitrogen, and argon has a greater impact on the smelting quality, argon will be adjusted first and ranked first in the ranked list. The adjustment direction is marked based on 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 a positive flow adjustment, indicating that the flow rate of the 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 a negative flow adjustment, indicating that the flow rate of the gas needs to be reduced. For example, if the actual ratio of oxygen is 4%, If the target range is 5%-7%, the oxygen flow rate is adjusted in the positive direction, that is, the oxygen flow rate is increased. When multiple gases need to be adjusted simultaneously, adjustment instructions are generated in sequence according to the gas type sort list. The adjustment instructions will specify the adjustment direction and flow rate change range for each gas to ensure the correction of the gas ratio. At the same time, during the adjustment process, the total pressure of the gas layer needs to be maintained stable to avoid instability in the melting environment due to excessive flow rate changes. For example, the highest priority gas (such as argon) is adjusted first, and then the second priority gas (such as oxygen). Each adjustment is controlled to keep the total gas flow rate within a stable range to ensure that the total gas layer pressure does not fluctuate violently during the melting process. The generated adjustment instructions are compared with the target range. If an adjustment instruction exceeds the tolerance of the target range (for example, the gas flow rate change range is too large), the over-limit instruction is deleted and the final gas ratio correction parameters are generated. The correction parameters will be used during the melting process to dynamically adjust the composition of the protective gas layer to ensure that each gas component remains within the target range.

[0092] Further, if Figure 3 As shown, the gradient distribution data of the axial and radial gas protection layers in each continuous process stage are collected in real time, including:

[0093] The gas absorption characteristics at different axial depths are scanned layer by layer along the height direction of the furnace to generate an axial gradient distribution data set;

[0094] The non-uniform characteristics of radial gas composition distribution are captured in the circumferential direction of the horizontal cross section of the molten pool to generate a radial gradient distribution data set;

[0095] The axial gradient distribution dataset and the radial gradient distribution dataset are aligned in time and space, and the gas concentration in the dataset is temperature compensated based on the temperature in the melting furnace;

[0096] Identify mutation areas in axial and radial data based on data gradient changes, and mark potential protection failure areas based on component ratio ranges;

[0097] The spatiotemporally aligned gradient data and labeling results are mapped into a three-dimensional distribution map and stored in the gas detection database of the hierarchical detection network.

[0098] As a preferred embodiment of the above, during the smelting process, gas absorption characteristics at different axial depths are layered scanned along the height of the furnace. Gas sensors or infrared absorption spectroscopy are used to collect gas composition and absorption characteristics at different heights. Different axial depths may reflect different gas concentrations and distributions. In the horizontal cross-section of the molten pool, sensors are used to capture the circumferential radial gas composition distribution, with particular attention paid to the unevenness of the gas distribution. By scanning the gas composition in all directions around the molten pool, a dataset reflecting the changes in gas concentration at different locations within the molten pool (i.e., a radial gradient distribution dataset) can be generated. The collected axial gradient distribution dataset and radial gradient distribution dataset are then temporally and spatially aligned to ensure that the data are from the same time point and match the spatial location during the smelting process. At the same time, these gas concentration data are temperature compensated based on the temperature data within the smelting furnace. Temperature changes may affect gas absorption characteristics, so temperature compensation is required to ensure data accuracy and that data acquired at different temperatures have the same reference baseline to avoid gas concentration errors caused by temperature fluctuations. Based on the temporally and spatially aligned gradient data, abrupt regions in the axial and radial data are analyzed and identified. Abrupt regions refer to areas where the gas composition distribution suddenly changes. Areas with sudden changes are usually indicators of gas protection layer failure. Combined with the set target composition ratio range, these potential protection failure areas are marked, so that abnormalities in the gas protection layer during the smelting process can be discovered in a timely manner and corresponding corrective measures can be taken. For example, the spatial gradient changes of the gas composition are calculated through a data processing algorithm. When the gradient change exceeds a preset threshold, it is marked as a potential protection failure area. The marked area may indicate insufficient gas flow, uneven gas composition, or excessively high smelting temperature. The gradient data after time and space alignment and the marked potential protection failure area results are mapped into a three-dimensional distribution map. The three-dimensional map can comprehensively display the distribution of the gas layer in space during the smelting process, as well as possible failure areas. The generated three-dimensional map will be stored in the gas detection database of the layered detection network for subsequent analysis and reference.

[0099] Furthermore, the mutation areas in the axial and radial data are identified based on the gradient changes of the data, including:

[0100] For each detection point in the axial gradient distribution data set and the radial gradient distribution data set, the gas concentration change rate between the detection point and the adjacent points is calculated. The gas concentration change rate includes the axial gradient change rate and the radial gradient change rate.

[0101] According to the change of melting temperature, the allowed threshold value of gradient change rate is set for each continuous process stage;

[0102] Mark all detection points where the gradient change rate exceeds the allowed threshold, and filter consecutive exceeding-limit points to form the boundary of the mutation area;

[0103] Compare the gas composition ratio in the mutation area with the composition ratio range. If at least one gas component exceeds the limit, it is marked as a potential protection failure area.

[0104] Based on the three-dimensional spatial coordinates of the mutation area and the results of temperature compensation correction, a gradient risk hotspot report is generated and stored in the gas detection database.

[0105] As a preferred embodiment of the above embodiment, for each detection point in the axial gradient distribution data set and the radial gradient distribution data set, the gas concentration change rate between the point and the adjacent point is calculated. The gas concentration change rate reflects the degree of change of the gas composition 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 in the horizontal section of the molten pool, reflecting the difference in gas distribution in the circumferential direction of the molten pool. The concentration change rate is calculated by dividing the gas concentration difference between adjacent detection points by the spatial distance. In this way, the rate of change of gas concentration in space can be measured; according to the change of melting temperature, the allowable threshold value of gradient change rate is set for each continuous process stage. Different process stages may have different temperature ranges and gas composition requirements, so the tolerance of gas concentration change rate is different. When the temperature changes greatly, a looser gradient change rate tolerance range may be required; when the temperature is relatively stable, the gas concentration change rate should be controlled within a smaller range. For example, in the early stage of melting, when the temperature fluctuates greatly, the allowable gas concentration change rate may be higher; and in the later stage of melting, 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 value. These points may indicate a sharp change in gas composition at a certain position, which may be an indication of failure or instability of the gas protection layer. These out-of-limit points are screened out and continuously The boundary of the mutation area is formed by the over-limit point; the gas component ratios of the identified mutation area are compared to check whether at least one gas component exceeds the set component ratio range. If the ratio of a gas component exceeds the upper or lower limit of the target range, the area is marked as a potential protection failure area; based on the three-dimensional spatial coordinates of the mutation area and the results of temperature compensation correction, a gradient risk hotspot report is generated. The report will include information such as the spatial position of the mutation area, gas component ratio, and temperature correction results, which is used to identify and warn of gas protection failure areas that may occur during the smelting process. The report will include three-dimensional coordinate information (such as X, Y, and Z coordinates) of each mutation area. These coordinates reflect the specific position of the mutation area in the smelting furnace. In addition, the report will also include corrected data after temperature compensation to ensure the accuracy of the evaluation results. Finally, these data will be stored in the gas detection database for subsequent analysis and reference.

[0106] Further, if Figure 4As shown, the gas ratio correction parameters are correlated with the historical smelting quality database for analysis, including:

[0107] Based on the continuous process stages and gas ratio correction parameters, a historical case data set with similar process characteristics is retrieved from the historical database;

[0108] Extract the quality index change data after gas ratio correction in the historical case data set and calculate the average influence coefficient of the gas ratio correction parameter;

[0109] The average influence coefficient, deviation threshold and melting temperature change data are input into the time series prediction model to predict the fluctuation trend of the corrected quality indicators;

[0110] Based on the fluctuation trend of quality indicators, a multi-dimensional inspection report is generated, which includes suggestions for fine-tuning the target gas ratio, process temperature compensation values, and equipment maintenance prompts.

[0111] As a preferred embodiment of the above, according to the continuous process stage and the gas ratio correction parameter, a historical case data set with similar process characteristics is retrieved from the historical smelting quality database. The historical case data set should have process conditions similar to the current smelting process stage and the gas ratio correction parameter. By searching the historical data, it can help predict the potential impact of the correction parameter on the quality index; from the historical case data set, the quality index change data after the gas ratio correction is extracted. These quality indexes may include the alloy composition, surface quality, porosity, etc. after smelting. After extracting the data, the influence coefficient of the gas ratio correction parameter on the quality index is calculated. Usually, the average influence coefficient of each correction parameter is calculated by a statistical method (such as regression analysis). For example, the quality data related to the gas ratio adjustment is extracted, the alloy composition change of the smelting product after the correction is calculated, and the average influence coefficient of each gas composition adjustment on the quality of the final product is further calculated. For example, increasing the argon concentration may reduce the porosity, and adjusting the nitrogen concentration may affect the hardness of the alloy; the average influence coefficient is calculated. The response coefficient, deviation threshold, and melting temperature change data are input into a time series prediction model. Based on the trends in historical data, the fluctuation trends of quality indicators after correcting for changes in gas ratio and melting temperature can be predicted. By considering historical quality fluctuations and the impact of correction parameters, the changing trends of quality indicators during the melting process can be predicted, allowing potential problems to be discovered in advance. For example, using machine learning or regression analysis, a time series model is trained based on historical data to predict the fluctuations of quality indicators (such as alloy composition and surface quality) over time after gas ratio correction. By inputting the current deviation, gas correction parameters, and temperature change data, the model can output the quality fluctuation trends during the future melting process. Based on the fluctuation trends of quality indicators, a multi-dimensional inspection report is generated, which includes target gas ratio fine-tuning suggestions, process temperature compensation values, and equipment maintenance prompts. The report will provide specific suggestions for the current melting process, including how to fine-tune the gas ratio, temperature compensation scheme, and equipment maintenance and optimization suggestions. The data in the report will help operators adjust the process to ensure the quality of the final product.

[0112] Furthermore, the average influence coefficient of the gas ratio correction parameter is calculated, including:

[0113] From the historical case data set, oxide film thickness, porosity, and mechanical properties were selected as core quality evaluation dimensions;

[0114] For each core quality evaluation dimension, calculate the absolute change and change rate after the gas ratio correction in historical cases;

[0115] According to the continuous process stage and deviation threshold, different weights are assigned to the changes in quality indicators in different continuous process stages;

[0116] Based on the absolute change and differentiation weight, the average impact intensity of each quality indicator in historical cases is calculated to generate the single indicator impact coefficient;

[0117] Perform weighted averaging on the single indicator impact coefficients to generate the average impact coefficient.

[0118] As a preferred embodiment of the above, oxide film thickness, porosity and mechanical property indicators are selected from the historical case data set as core quality evaluation dimensions. These indicators are important parameters for measuring quality changes during the copper alloy smelting 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 smelting quality. For example, oxide film thickness reflects the degree of metal surface oxidation during the smelting process; porosity directly affects the density and strength of the material; mechanical properties (such as tensile strength, hardness, etc.) determine the performance of the copper alloy. By selecting these indicators, the impact of gas ratio correction on smelting quality can be comprehensively evaluated; for each core quality evaluation dimension, the absolute change and change rate after executing gas ratio correction in historical cases are calculated. The absolute change refers to the difference in quality indicators before and after 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, the gas ratio correction is performed for different process stages. Differentiated weights are assigned to the changes in quality indicators of different stages. Different process stages may have different impacts on quality. Therefore, different weights need to be assigned to the changes in quality indicators of each stage according to actual conditions. For example, in the initial smelting stage, the gas composition may have a greater impact on the oxide film thickness, while in the later stages, more attention may be paid to the changes in mechanical properties. Based on the absolute change and the differentiated weight, the average impact intensity of each quality indicator in the historical cases is calculated respectively, and a single indicator impact coefficient is generated. The single indicator impact coefficient reflects the average impact intensity of the corrected gas ratio of each core quality evaluation dimension in the historical cases. It is usually calculated by the weighted average method. For example, for the oxide film thickness, the weighted average impact intensity of the oxide film thickness change in all historical cases is calculated, and the single indicator impact coefficient of the oxide film thickness is obtained. All single indicator impact coefficients are weighted averaged to generate the final average impact coefficient. The weight of the weighted average is usually allocated based on the contribution of each quality indicator to the final quality in the smelting process. The final average impact coefficient can fully reflect the comprehensive impact of the gas ratio correction on the smelting quality.

[0119] Embodiment 2;

[0120] Based on the same inventive concept as the method for detecting a copper alloy smelting protective gas layer in the aforementioned embodiment, the present invention further provides a system for detecting a copper alloy smelting protective gas layer, the system comprising:

[0121] The target setting module divides the smelting process into multiple continuous process stages based on the smelting temperature variation characteristics and process requirements, and sets the target shielding gas composition ratio range for each stage;

[0122] The data detection module builds a layered detection network to collect the gradient distribution data of the axial and radial gas protection layers in each continuous process stage in real time;

[0123] The deviation protection module calculates the deviation 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 melting temperature to generate the gas ratio correction parameter;

[0124] The correlation analysis module correlates the gas ratio correction parameters with the historical smelting quality database and outputs a multi-dimensional inspection report.

[0125] The above-mentioned adjustment system in the present invention can be effectively implemented, and the technical effects that can be achieved are as described in the above-mentioned embodiments, which will not be repeated here.

[0126] Embodiment 3;

[0127] Based on the same inventive concept as the method for detecting a copper alloy smelting protective gas layer in the aforementioned embodiment, the present invention also provides a device for detecting a copper alloy smelting protective gas layer, which is used to implement a method for detecting a copper alloy smelting protective gas layer.

[0128] The above-mentioned equipment in the present invention can effectively realize a method for detecting a copper alloy smelting protective gas layer, and the technical effects that can be achieved are as described in the above-mentioned embodiments and will not be repeated here.

[0129] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application as defined herein and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.

Claims

1. A method for detecting a protective gas layer during copper alloy smelting, characterized in that: The method comprises: Based on the melting temperature variation characteristics and process requirements, the melting process is divided into multiple continuous process stages, and the target shielding gas composition ratio range is set for each stage; Constructing a layered detection network to collect real-time gradient distribution data of the axial and radial gas protection layers in each of the continuous process stages; Based on the gradient distribution data, calculating the deviation between the actual gas composition ratio and the composition ratio range in each of the continuous process stages, and dynamically adjusting the deviation threshold according to the change in melting temperature to generate a gas ratio correction parameter; The gas ratio correction parameter is correlated with the historical smelting quality database for analysis, and a multi-dimensional detection report is output.

2. The method for detecting the copper alloy smelting protective gas layer according to claim 1, characterized in that: Calculating the deviation between the actual gas composition ratio in each of the continuous process stages and the composition ratio range includes: For each of the continuous process stages, extracting a preset ratio range of each component in the target protective gas and a current ratio value detected in real time; The deviation between the actual ratio of each gas component and the target range is quantified as a percentage, and the individual deviation is calculated; Dynamically adjust the correction coefficient of the single deviation according to the difference between the current melting temperature and the stage target temperature, wherein 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 individual deviation amounts are weighted and synthesized to generate a comprehensive deviation index that represents the overall deviation degree.

3. The method for detecting the copper alloy smelting protective gas layer according to claim 2, characterized in that: Dynamically adjust the deviation threshold according to the change of melting temperature, including: Establishing the deviation threshold for each of the consecutive process stages based on the comprehensive deviation distribution of qualified process stages in the smelting history; Real-time monitoring of the temperature change rate of the smelting process, and when the temperature change rate exceeds a preset safety range, compressing the deviation threshold value at a fixed ratio; Analyze the deviation data of a fixed smelt, and if the deviation threshold is triggered continuously, iteratively optimize the threshold parameter according to the gradient step size; An adaptive correlation relationship is established between the adjusted deviation threshold and the key quality index of copper alloy smelting, thereby forming an adaptive adjustment of the deviation threshold.

4. The method for detecting the copper alloy smelting protective gas layer according to claim 3, characterized in that: Generate correction instructions including gas type, adjustment direction and flow rate change range, including: generating a ranked list of gas types requiring priority adjustment based on the absolute value of the individual deviations of the gas components and the adaptive association relationship; For each gas component in the gas type sorted list, if the actual ratio is lower than the lower limit of the target range, it is marked as a positive flow adjustment; 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 gas type sorting list, and the total pressure of the gas layer is maintained stable; The adjustment instruction is compared with the target range, and the gas ratio correction parameter is generated after the over-limit instruction is deleted.

5. The method for detecting the copper alloy smelting protective gas layer according to claim 1, characterized in that: Real-time collection of gradient distribution data of the axial and radial gas shield layers in each of the continuous process stages, including: The gas absorption characteristics at different axial depths are scanned layer by layer along the height direction of the furnace to generate an axial gradient distribution data set; The non-uniform characteristics of radial gas composition distribution are captured in the circumferential direction of the horizontal cross section of the molten pool to generate a radial gradient distribution data set; performing spatiotemporal alignment on the axial gradient distribution dataset and the radial gradient distribution dataset, and performing temperature compensation correction on the gas concentration in the dataset based on the temperature in the smelting furnace; Identify mutation areas in the axial and radial data based on the gradient changes of the data, and mark potential protection failure areas based on the component ratio range; The time-space aligned gradient data and the labeling results are mapped into a three-dimensional distribution map, which is stored in the gas detection database of the hierarchical detection network.

6. The method for detecting the copper alloy smelting protective gas layer according to claim 5, characterized in that: Identify the abrupt regions in the axial and radial data based on the gradient changes of the data, including: For each detection point in the axial gradient distribution data set and the radial gradient distribution data set, calculating the gas concentration change rate between the detection point and the adjacent points, the gas concentration change rate including the axial gradient change rate and the radial gradient change rate; According to the change of the melting temperature, setting a gradient change rate allowable threshold value for each of the continuous process stages; Marking all detection points where the gradient change rate exceeds the allowable threshold, and screening consecutive exceeding-limit points to form a mutation region boundary; Comparing the gas component ratio of the mutation area with the component ratio range, and marking it as a potential protection failure area if at least one gas component exceeds the limit; Based on the three-dimensional spatial coordinates of the mutation area and the result of the temperature compensation correction, a gradient risk hotspot report is generated and stored in the gas detection database.

7. The method for detecting the copper alloy smelting protective gas layer according to claim 1, characterized in that: Correlating and analyzing the gas ratio correction parameter with a historical smelting quality database includes: Retrieving a historical case data set having similar process characteristics from a historical database based on the continuous process stages and the gas ratio correction parameter; Extracting the quality index change data after gas ratio correction in the historical case data set and calculating the average influence coefficient of the gas ratio correction parameter; Inputting the average influence coefficient, the deviation threshold and the melting temperature change data into a time series prediction model to predict the fluctuation trend of the corrected quality index; Based on the fluctuation trend of the quality indicators, a multi-dimensional inspection report is generated, which includes target gas ratio fine-tuning suggestions, process temperature compensation values ​​and equipment maintenance prompts.

8. The method for detecting the copper alloy smelting protective gas layer according to claim 7, characterized in that: Calculating an average influence coefficient of the gas ratio correction parameter, including: From the historical case data set, oxide film thickness, porosity, and mechanical properties were selected as core quality evaluation dimensions; For each of the core quality evaluation dimensions, calculate the absolute change and change rate after the gas ratio correction in the historical cases; Assigning differentiated weights to quality indicator changes at different continuous process stages according to the continuous process stages and the deviation threshold; Based on the absolute change amount and the differentiation weight, respectively calculate the average impact intensity of each quality indicator in the historical cases to generate a single indicator impact coefficient; Perform a weighted average on the single indicator influence coefficients to generate an average influence coefficient.

9. A detection system for a protective gas layer in copper alloy smelting, characterized in that: The system comprises: The target setting module divides the smelting process into multiple continuous process stages based on the smelting temperature variation characteristics and process requirements, and sets the target shielding gas composition ratio range for each stage; The data detection module builds a layered detection network to collect 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 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 melting temperature to generate the gas ratio correction parameter; The correlation analysis module correlates the gas ratio correction parameters with the historical smelting quality database and outputs a multi-dimensional inspection report.

10. A detection device for a protective gas layer in copper alloy smelting, characterized in that: Used to implement a method for detecting a copper alloy smelting protective gas layer as described in any one of claims 1-8.

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