CN116068944A - Vacuum induction smelting process detection and control system - Google Patents
Vacuum induction smelting process detection and control system Download PDFInfo
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Abstract
The invention discloses a vacuum induction smelting process detection and control system, which is used for carrying out evaluation calculation on furnace body parameters, further extracting risk parameters in the furnace body parameters, checking risk parameter data, carrying out calculation classification on checked risk parameters, calculating parameter supplementing quantity, comparing calculated parameter values with corresponding parameter safety ranges to extract parameter safety supplementing quantity, supplementing corresponding parameters by a furnace body control module through the parameter safety supplementing quantity, extracting parameters of which the calculated parameter values are not in the corresponding parameter safety ranges by a risk parameter extraction unit, and carrying out evaluation calculation on the risk values and alarm by a risk evaluation module, thereby effectively improving the control precision in the processing process, carrying out quick alarm on the condition that the safety production of the furnace body cannot be ensured, and effectively improving the production quality of vacuum induction smelting workpieces.
Description
Technical Field
The invention relates to the field of program control systems, in particular to a vacuum induction melting process detection and control system.
Background
The precision casting is a casting method relative to the traditional casting technology, the precision casting in the prior art can obtain relatively accurate shape and higher casting precision, the temperature and the vacuum degree in the vacuum furnace are regulated by a program, but the furnace body has a certain volume, and the temperature or the vacuum degree measured by a temperature sensor or a vacuum degree sensor is only a bit, so that the real environment in the furnace body cannot be predicted, abnormal data in the furnace body cannot be rapidly judged, and meanwhile, in the use process, the actual parameters of the furnace body may deviate from the setting parameters of the furnace body, so that the non-uniformity of data is generated in the control process, the parameter control is inaccurate, and the production quality of a vacuum induction smelted workpiece is affected;
for example, in chinese patent publication No. CN211823798U, a superalloy vacuum melting temperature control system is disclosed, comprising a height plate, a height rod, a height table, a glass cover, a mold shell crucible, an induction coil, and a sealing cover; the height rod is inserted into the hole of the height disc; a height table is arranged above the height rod; the mould shell crucible is arranged in the middle of the height platform; the height position of the alloy in the vacuum furnace induction coil is accurately changed through a special height disc, so that the molten steel temperature of different kinds of alloys is accurately controlled;
In another example, in chinese patent with publication No. CN103231017B, a precision casting device for complex thin-wall castings of superalloy comprises a vacuum melting chamber, a casting chamber, a gate valve, an induction melting device, a main/auxiliary vacuum system, a control system, a vibration mechanism, a mold shell transmission system and an inflation/deflation system, wherein: a gate valve is arranged between the vacuum melting chamber and the casting chamber; the vacuum smelting chamber is connected with the main vacuum system, the casting chamber and the auxiliary vacuum system through respective pipelines and valves, and is provided with a vacuum instrument and an inflation and deflation system; the induction smelting device is used for smelting alloy; the mould shell transmission system and the vibration mechanism are arranged at the bottoms of the vacuum melting chamber and the casting chamber. According to the invention, casting pouring and solidification are realized in a vibration mode, the material utilization efficiency is effectively improved, the problem of insufficient filling and feeding in the pouring of complex thin-wall castings is solved, and particularly, the filling and feeding capacity of the thin-wall castings with the wall thickness smaller than 3mm is greatly improved;
all of the above patents exist: the invention provides a detection and control system for a vacuum induction smelting process, which aims to solve the problems that actual parameters of a furnace body can deviate from setting parameters of the furnace body in the use process, so that data are generated in the control process in a non-uniform manner, thereby causing inaccurate parameter control and influencing the production quality of a vacuum induction smelted workpiece.
Disclosure of Invention
The invention mainly aims to provide a vacuum induction melting process detection and control system which can effectively solve the problems in the background technology: the furnace body has certain volume, and temperature sensor or vacuum sensor measuring just is temperature and vacuum of a bit, leads to like this can't foreseeing the inside real environment of furnace body, can't carry out quick judgement to the inside unusual data of furnace body, and in the use, furnace body actual parameter can have the deviation with furnace body setting parameter setting simultaneously, causes the non-unification of data in the control process like this to lead to parameter control inaccuracy, influence the technical problem of the production quality of vacuum induction smelting's work piece.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the vacuum induction smelting process detection and control system comprises a smelting data monitoring module, a furnace body parameter acquisition module, a smelting data calculation module, a furnace body parameter checking module, a smelting data calculation module, a smelting data evaluation module, a furnace body parameter evaluation module, an evaluation data fusion module, an evaluation data checking module, a risk evaluation module and a furnace body control module;
The smelting data monitoring module is used for collecting and monitoring smelting data in the smelting furnace in the smelting process;
the furnace body parameter acquisition module is used for acquiring and monitoring the setting and actual parameters of the smelting furnace in the smelting process;
the smelting data calculation module is used for carrying out real-time comparison calculation on the collected smelting data in the smelting process and finding out abnormal smelting data;
the furnace body parameter checking module is used for checking and calculating the acquired setting and actual parameters of the smelting furnace in the smelting process;
the smelting data evaluation module is used for substituting the collected smelting data into a smelting evaluation strategy in the smelting process to evaluate the smelting process of the smelting, so as to obtain smelting evaluation data;
the furnace body parameter evaluation module is used for substituting the acquired furnace body parameters into a furnace body evaluation strategy in the smelting process to evaluate the furnace body smelting process so as to obtain furnace body evaluation data;
the evaluation data fusion module is used for fusing the evaluation data of the smelting object obtained by the smelting data evaluation module and the furnace body evaluation data obtained by the furnace body parameter evaluation module to find out abnormal values in the evaluation data;
The evaluation data checking module is used for checking the abnormal value obtained by the evaluation data;
the risk evaluation module is used for substituting the abnormal value into a risk parameter evaluation strategy and calculating a risk value generated by the abnormal data;
the furnace body control module is used for extracting the abnormal value and substituting the abnormal value into a furnace body control strategy, calculating the furnace body adjusting parameter and further controlling the abnormal value.
The invention is further improved in that the smelting data monitoring module comprises a smelting temperature monitoring unit, a smelting vacuum degree monitoring unit, a smelting time monitoring unit and a smelting object monitoring unit;
the smelting temperature monitoring unit is used for monitoring smelting temperature data of each position in the smelting furnace to obtain a smelting temperature sequence;
the smelting vacuum degree monitoring unit is used for monitoring vacuum degree data of each position in the smelting furnace to obtain a vacuum degree sequence;
the smelting time monitoring unit is used for monitoring the smelting time of the smelting object in real time;
the smelt monitoring unit is used for monitoring the area of scum in the smelt in real time.
The invention is further improved in that the smelting data calculation module comprises a smelting data comparison unit and an abnormal data identification unit, wherein the smelting data comparison unit is used for comparing the data monitored by the smelting data monitoring module with a set corresponding data threshold value, and the abnormal data identification unit is used for identifying and extracting the data which are obtained by the comparison of the smelting data comparison unit and are not in the range of the data threshold value.
The invention is further improved in that the furnace body parameter acquisition module comprises a furnace body real-time parameter acquisition unit and a furnace body setting parameter acquisition unit, wherein the furnace body real-time parameter acquisition unit is used for acquiring real-time parameter values of a furnace body, and the furnace body setting parameter acquisition unit is used for acquiring setting parameter values of the furnace body.
The invention further improves that the evaluation data fusion module comprises an evaluation data classification unit and an evaluation data calculation unit, wherein the evaluation data classification unit is used for classifying the abnormal data obtained in the evaluation process according to the types of the abnormal data, and the evaluation data calculation unit is used for calculating the evaluation abnormal data to obtain risk parameters in the evaluation abnormal data.
The invention is further improved in that the evaluation data checking module comprises a parameter compensation calculating unit and a risk parameter extracting unit, wherein the output end of the evaluation data calculating unit is connected with the risk parameter extracting unit, the output end of the parameter compensation calculating unit is connected with the furnace body control module, the risk parameter extracting unit is connected with the risk evaluation module, the parameter compensation calculating unit is used for calculating compensation parameters of abnormal data, and the risk parameter extracting unit is used for extracting risk parameters in the abnormal data.
The invention is further improved in that the control system comprises a process control strategy method, and the process control strategy method comprises the following specific steps:
s1: setting a qualified value of parameters in the furnace body, and acquiring and monitoring the internal data of the furnace body and the smelting data of the smelting materials by a smelting data monitoring moduleThe data includes smelting temperature
Wherein the smelting material in the furnace body is uniformly divided into n layers, j is the number of layers corresponding to the collection point in the furnace body, and the vacuum degree is +.>
Smelting time t and dross area in the smelt +.>
Meanwhile, the furnace body parameter acquisition module monitors the furnace body setting parameters and control data in real time, wherein the furnace body setting parameters comprise the furnace body radius
Setting a temperature value +.>
And setting a vacuum value +.>
The control data comprise real-time heating power values by means of the heater +.>
Calculating the average value of the temperature inside the furnace body +.>
Average vacuum degree in furnace body +.>
The scum area in the furnace body accounts for +.>
；
S2: the smelting data calculation module compares the internal data of the furnace body, the smelting data of the smelting materials with a set qualified value, and identifies and extracts abnormal data in the acquisition process, and the judging method of the abnormal data comprises the following steps: comparing the calculated temperature average value data, the average vacuum degree and the proportion of the scum area to the area in the furnace body with a set qualified value, obtaining a data type which is not in a qualified value, judging the data of the data type which is not in the qualified value as abnormal data, evaluating the abnormal data, judging the type of the abnormal data, and searching the type of furnace body parameters related to the type of the abnormal data;
S3: the method comprises the steps of extracting data of furnace body parameter types related to abnormal data types, checking and comparing the data with set parameters acquired by a furnace body set parameter acquisition unit, evaluating and calculating the furnace body parameters, extracting risk parameters in the furnace body parameters, and checking risk parameter data;
s4: the method comprises the steps of carrying out calculation and classification on risk parameters after checking, calculating parameter supplementing quantity, comparing calculated parameter values with corresponding parameter safety ranges to extract parameter safety supplementing quantity, supplementing corresponding parameters through the parameter safety supplementing quantity by a furnace body control module, extracting parameters of which the calculated parameter values are not in the corresponding parameter safety ranges by a risk parameter extraction unit, and carrying out evaluation on the risk parameters by a risk evaluation module to calculate risk values and alarm.
The invention is further improved in that the smelt evaluation strategy comprises the following concrete contents: comparing the calculated temperature average value, the average vacuum degree and the ratio of the scum area in the furnace body with a set temperature, a set vacuum degree threshold and a set scum area threshold, finding out the data parameters of which the calculated temperature average value, the calculated average vacuum degree and the calculated ratio of the scum area in the furnace body are not in the threshold range, extracting the data parameters, setting the data parameters as abnormal data, obtaining the ratio of the temperature average value, the calculated average vacuum degree and the calculated ratio of the scum area in the furnace body to the internal area of the furnace body, introducing the abnormal data into a BP neural network established in advance, searching furnace body parameters related to the abnormal data in the smelting furnace body data, introducing the abnormal data into a smelting evaluation formula, and evaluating the smelting evaluation value, wherein the calculating formula of the smelting evaluation value is as follows: 
Wherein->
For the average temperature>
For average vacuum>
The scum area in the furnace body accounts for the area in the furnace body>
Is the temperature duty ratio +.>
Is the duty ratio of vacuum degree, +.>
Is the slag area ratio coefficient +.>
Maximum and minimum values corresponding to the time-safe temperature range, +.>
Maximum and minimum values corresponding to the time safety vacuum range, < >>
Maximum and minimum values corresponding to the ratio of the dross area to the internal area of the furnace, < ->
Is that
The value closest to the average value of the temperature inside the furnace body,/->
Is->
Is closest to the value of the average vacuum, +.>
Is->
The closest value to the ratio of the dross area to the internal area of the furnace, wherein
Comparing the calculated value with a safety range threshold value of the evaluation value of the smelting material set before to obtain whether the evaluation value is in a safety range, if so, calculating +.>
If the data is within the safe range, the data is not required to be safe, only the data is required to be monitored, the S2-S4 operation is not required, and if the data is calculated to be +.>
If the data is not in the safety range, the data safety is required, and the S2-S4 operation is required.
The invention is further improved in that the furnace body evaluation strategy comprises the following specific contents: extracting furnace body parameters related to abnormal data, checking and comparing the furnace body parameters with the acquired set parameters, evaluating and calculating the furnace body parameters, extracting risk parameters in the furnace body parameters, wherein the set range of the risk parameters is a parameter which is five percent different from the acquired set parameters, acquiring the risk parameters again after 2 seconds, comparing the average value of the risk parameters with the set parameters to check the risk parameter data, searching the types of abnormal values in the risk parameters, and resetting the parameters by a controller.
The invention is further improved in that the furnace body control strategy comprises the following specific contents: for a pair of
Is arranged in descending order, the furnace body control module controls parameters in descending order, and the air extraction equipment is controlledFix->
Vacuumizing the furnace body in time to ensure the vacuum degree in the furnace body, if the vacuum degree is calculated to be +.>
After the gas is pumped out by the time pumping equipment, the calculated vacuum degree can not reach the safe vacuum degree, the vacuum degree parameter is set as a risk parameter, and the temperature in the furnace body is controlled to be within the stipulation of +.>
The temperature in the furnace body is regulated to be within a safe temperature range in time, and the minimum heating power required by the heating module is calculated, < + >>
To get->
Wherein->
For the heating module, minimum heating power is required, +.>
Is the specific heat capacity of the alloy->
For the mass of the alloy, < >>
For thermal conductivity, if the maximum heating power of the heating module is lower than + ->
Setting the temperature parameter as risk parameter, calculating the scum area at +.>
The amount of change in time is set to be the scum area if it is unchangedThe risk parameter extraction unit extracts the risk parameters, the risk assessment module calculates the risk values in such a way that the number of the risk parameters is extracted and the risk values are +. >
Extracting parameter values of risk parameters after time, and calculating the ratio of the distance value from the parameter values to the minimum value of the safety range to the distance value of the safety range value, wherein the calculation process of the risk values is that the calculated calculation ratio of each parameter is substituted into a risk value calculation formula->
Wherein->
And (3) for the calculation proportion of the ith parameter, n is the number of terms, extracting the calculated c and the parameter number of terms, and giving an alarm. />
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a method for collecting smelting temperature, vacuum degree, smelting time and scum area in a smelting object through a data collector arranged in an array, so as to accurately calculate the temperature average value in a furnace body, the average vacuum degree in the furnace body and the proportion of the scum area in the furnace body to the internal area of the furnace body, simultaneously compare the calculated temperature average value data, the average vacuum degree and the proportion of the scum area to the internal area of the furnace body with a set qualified value, obtain a data type which is not in a qualified value, judge the data of the data type which is not in the qualified value as abnormal data, evaluate the abnormal data, judge the type of the abnormal data, search the type of furnace body parameters related to the abnormal data, extract the data of the type of the furnace body parameters related to the abnormal data, and check and compare the set parameters acquired by a furnace body setting parameter acquisition unit, the method comprises the steps of evaluating and calculating furnace body parameters, extracting risk parameters in the furnace body parameters, checking risk parameter data, calculating and classifying the checked risk parameters, calculating parameter supplementing quantity, comparing calculated parameter values with corresponding parameter safety ranges to extract the parameter safety supplementing quantity, supplementing corresponding parameters through the parameter safety supplementing quantity by a furnace body control module, extracting parameters of which the calculated parameter values are not in the corresponding parameter safety ranges by a risk parameter extracting unit, evaluating and calculating the risk values and alarming the risk parameters by a risk evaluation module, effectively improving control precision in the processing process, rapidly alarming the condition that safety production of the furnace body cannot be guaranteed, and effectively improving production quality of vacuum induction smelted workpieces.
Drawings
FIG. 1 is a schematic diagram of a vacuum induction melting process detection and control system according to the present invention.
FIG. 2 is a schematic diagram of the connection between a melting data monitoring module and a melting data evaluation module of a vacuum induction melting process detection and control system according to the present invention.
FIG. 3 is a schematic diagram showing the connection between a furnace body parameter acquisition module and a furnace body parameter evaluation module of a vacuum induction melting process detection and control system according to the present invention.
FIG. 4 is a schematic diagram showing the connection of an evaluation data fusion module, a furnace body control module and a risk evaluation module of a vacuum induction melting process detection and control system.
FIG. 5 is a schematic flow chart of a process control strategy method of a vacuum induction melting process detection and control system of the present invention.
Detailed Description
In order that the technical means, the creation characteristics, the achievement of the objects and the effects of the present invention may be easily understood, it should be noted that in the description of the present invention, the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements to be referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "a", "an", "the" and "the" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The invention is further described below in conjunction with the detailed description.
Examples
The embodiment proposes that the smelting temperature, the vacuum degree, the smelting time and the scum area in the smelting material are acquired through the data acquisition devices arranged in an array so as to accurately calculate the temperature average value in the furnace body, the average vacuum degree in the furnace body and the proportion of the scum area in the furnace body to the internal area of the furnace body, meanwhile, the calculated temperature average value data, the average vacuum degree and the proportion of the scum area to the internal area of the furnace body are compared with the set qualified value to acquire the data type which is not in the qualified value, the data of the data type which is not in the qualified value is judged as abnormal data, the abnormal data is evaluated, the type of the abnormal data is judged, the type of the furnace body parameter related to the abnormal data is searched, the type of the furnace body parameter related to the abnormal data is extracted, the set parameter acquired by the furnace body setting parameter acquisition unit is checked and compared, evaluating and calculating furnace body parameters, further extracting risk parameters in the furnace body parameters, checking risk parameter data, calculating and classifying checked risk parameters, calculating parameter supplementing quantity, comparing calculated parameter values with corresponding parameter safety ranges to extract parameter safety supplementing quantity, supplementing corresponding parameters through the parameter safety supplementing quantity by a furnace body control module, extracting parameters of which the calculated parameter values are not in the corresponding parameter safety ranges by a risk parameter extracting unit, evaluating and calculating risk values and alarming the risk parameters by a risk evaluating module, effectively improving control precision in the processing process, rapidly alarming the condition of not guaranteeing safe production of the furnace body, effectively improving production quality of vacuum induction smelted workpieces, as shown in figures 1-5, the vacuum induction smelting process detection and control system comprises a smelting data monitoring module, a furnace body parameter acquisition module, a smelting data calculation module, a furnace body parameter checking module, a smelting data calculation module, a smelting data evaluation module, a furnace body parameter evaluation module, an evaluation data fusion module, an evaluation data checking module, a risk evaluation module and a furnace body control module;
The smelting data monitoring module is used for collecting and monitoring smelting data in the smelting furnace in the smelting process;
the furnace body parameter acquisition module is used for acquiring and monitoring the setting and actual parameters of the smelting furnace in the smelting process;
the smelting data calculation module is used for carrying out real-time comparison calculation on the collected smelting data in the smelting process and finding out abnormal smelting data;
the furnace body parameter checking module is used for checking and calculating the acquired setting and actual parameters of the smelting furnace in the smelting process;
the smelting data evaluation module is used for substituting the collected smelting data into a smelting evaluation strategy in the smelting process to evaluate the smelting process of the smelting, so as to obtain smelting evaluation data;
the furnace body parameter evaluation module is used for substituting the acquired furnace body parameters into a furnace body evaluation strategy in the smelting process to evaluate the furnace body smelting process so as to obtain furnace body evaluation data;
the evaluation data fusion module is used for fusing the evaluation data of the smelting object obtained by the smelting data evaluation module and the furnace body evaluation data obtained by the furnace body parameter evaluation module to find out an abnormal value in the evaluation data;
the evaluation data checking module is used for checking the data of the abnormal value obtained by the evaluation data;
The risk evaluation module is used for substituting the abnormal value into a risk parameter evaluation strategy and calculating a risk value generated by the abnormal data;
the furnace body control module is used for extracting the abnormal value and substituting the abnormal value into a furnace body control strategy, calculating the furnace body adjusting parameter and further controlling the abnormal value.
In this embodiment, the smelting data monitoring module includes a smelting temperature monitoring unit, a smelting vacuum degree monitoring unit, a smelting time monitoring unit, and a smelt monitoring unit;
the smelting temperature monitoring unit is used for monitoring smelting temperature data of each position in the smelting furnace to obtain a smelting temperature sequence;
the smelting vacuum degree monitoring unit is used for monitoring vacuum degree data of each position in the smelting furnace to obtain a vacuum degree sequence;
the smelting time monitoring unit is used for monitoring the smelting time of the smelting object in real time;
the smelt monitoring unit is used for monitoring the area of scum in the smelt in real time.
In this embodiment, the smelting data calculation module includes a smelting data comparison unit and an abnormal data identification unit, the smelting data comparison unit is used for comparing the data monitored by the smelting data monitoring module with a set corresponding data threshold, and the abnormal data identification unit is used for identifying and extracting the data which is obtained by the comparison of the smelting data comparison unit and is not within the range of the data threshold.
In this embodiment, the furnace body parameter acquisition module includes a furnace body real-time parameter acquisition unit and a furnace body setting parameter acquisition unit, the furnace body real-time parameter acquisition unit is used for acquiring real-time parameter values of the furnace body, and the furnace body setting parameter acquisition unit is used for acquiring setting parameter values of the furnace body.
In this embodiment, the evaluation data fusion module includes an evaluation data classification unit and an evaluation data calculation unit, the evaluation data classification unit is used for classifying the abnormal data obtained in the evaluation process according to the type of the abnormal data, and the evaluation data calculation unit is used for calculating the evaluation abnormal data to obtain the risk parameters in the evaluation abnormal data.
In this embodiment, the evaluation data checking module includes a parameter compensation calculating unit and a risk parameter extracting unit, an output end of the evaluation data calculating unit is connected with the risk parameter extracting unit, an output end of the parameter compensation calculating unit is connected with the furnace body control module, the risk parameter extracting unit is connected with the risk evaluation module, the parameter compensation calculating unit is used for calculating compensation parameters of abnormal data, and the risk parameter extracting unit is used for extracting risk parameters in the abnormal data.
In this embodiment, the control system includes a process control strategy method, which includes the following specific steps:
s1: setting up qualified values of parameters in the furnace body, and acquiring and monitoring internal furnace body data and smelting data of the smelting materials by a smelting data monitoring module, wherein the acquired data comprise smelting temperature
Wherein the smelting material in the furnace body is uniformly divided into n layers, j is the number of layers corresponding to the collection point in the furnace body, and the vacuum degree is +.>
Smelting time t and dross area in the smelt +.>
Meanwhile, the furnace body parameter acquisition module monitors the furnace body setting parameters and control data in real time, wherein the furnace body setting parameters comprise the furnace body radius
Setting a temperature value +.>
And setting a vacuum value +.>
The control data comprise real-time heating power values by means of the heater +.>
Calculating the average value of the temperature inside the furnace body +.>
Average vacuum degree in furnace body +.>
The scum area in the furnace body accounts for +.>
；
S2: the smelting data calculation module compares the internal data of the furnace body, the smelting data of the smelting materials with a set qualified value, and identifies and extracts abnormal data in the acquisition process, and the judging method of the abnormal data comprises the following steps: comparing the calculated temperature average value data, the average vacuum degree and the proportion of the scum area to the area in the furnace body with a set qualified value, obtaining a data type which is not in a qualified value, judging the data of the data type which is not in the qualified value as abnormal data, evaluating the abnormal data, judging the type of the abnormal data, and searching the type of furnace body parameters related to the type of the abnormal data;
S3: the method comprises the steps of extracting data of furnace body parameter types related to abnormal data types, checking and comparing the data with set parameters acquired by a furnace body set parameter acquisition unit, evaluating and calculating the furnace body parameters, extracting risk parameters in the furnace body parameters, and checking risk parameter data;
s4: the method comprises the steps of carrying out calculation and classification on risk parameters after checking, calculating parameter supplementing quantity, comparing calculated parameter values with corresponding parameter safety ranges to extract parameter safety supplementing quantity, supplementing corresponding parameters through the parameter safety supplementing quantity by a furnace body control module, extracting parameters of which the calculated parameter values are not in the corresponding parameter safety ranges by a risk parameter extraction unit, and carrying out evaluation on the risk parameters by a risk evaluation module to calculate risk values and alarm.
In this embodiment, the smelt evaluation strategy includes the following specific contents: comparing the calculated temperature average value, the average vacuum degree and the ratio of the dross area in the furnace body to the set temperature, vacuum degree threshold and dross area threshold, finding out the data parameters of the temperature average value, the average vacuum degree and the ratio of the dross area in the furnace body to the inside area not in the threshold, extracting the data parameters, setting the data parameters as abnormal data, obtaining the ratio of the temperature average value, the average vacuum degree and the dross area in the furnace body to the inside area, and introducing the abnormal data into the data of the smelting furnace body by using the BP neural network established in advance according to the difference between the calculated temperature average value, the average vacuum degree and the ratio of the dross area in the furnace body to the set temperature, the vacuum degree threshold and the dross area threshold Searching furnace body parameters related to abnormal data, introducing the abnormal data into a smelting evaluation formula, performing smelting evaluation, and calculating a smelting evaluation value, wherein the smelting evaluation value calculation formula is as follows:
wherein->
For the average temperature>
For average vacuum>
The scum area in the furnace body accounts for the area in the furnace body>
Is the temperature duty ratio +.>
Is the duty ratio of vacuum degree, +.>
Is the slag area ratio coefficient +.>
Maximum and minimum values corresponding to the time-safe temperature range, +.>
Maximum and minimum values corresponding to the time safety vacuum range, < >>
Maximum and minimum values corresponding to the ratio of the dross area to the internal area of the furnace, < ->
Is->
Is closest to the interior of the furnace bodyValue of temperature average,/">
Is->
Is closest to the value of the average vacuum, +.>
Is->
The value closest to the ratio of the dross area to the internal area of the furnace, wherein +.>
Comparing the calculated value with a safety range threshold value of the previously set smelt evaluation value to obtain whether the evaluation value is in a safety range, if so
If the data is within the safe range, the data is not required to be safe, only the monitoring is required, the S2-S4 operation is not required, and if the data is calculated +. >
If the data is not in the safety range, the data safety is required, and the S2-S4 operation is required.
In this embodiment, the furnace body evaluation policy includes the following specific contents: extracting furnace body parameters related to abnormal data, checking and comparing the furnace body parameters with the acquired set parameters, evaluating and calculating the furnace body parameters, extracting risk parameters in the furnace body parameters, wherein the set range of the risk parameters is a parameter which is five percent different from the acquired set parameters, acquiring the risk parameters again after 2 seconds, comparing the average value of the risk parameters with the set parameters to check the risk parameter data, searching the types of abnormal values in the risk parameters, and resetting the parameters by a controller.
In this embodiment, the furnace body control strategy includes the following specific contents: for a pair of
Is arranged in descending order, the furnace control module controls the parameters in descending order, and the air extraction equipment is controlled to prescribe +.>
Vacuumizing the furnace body in time to ensure the vacuum degree in the furnace body, if the vacuum degree is calculated to be +.>
After the gas is pumped out by the time pumping equipment, the calculated vacuum degree can not reach the safe vacuum degree, the vacuum degree parameter is set as a risk parameter, and the temperature in the furnace body is controlled to be within the stipulation of +. >
The temperature in the furnace body is regulated to be within a safe temperature range in time, and the minimum heating power required by the heating module is calculated, < + >>
To get->
Wherein->
For the heating module, minimum heating power is required, +.>
Is the specific heat capacity of the alloy->
For the mass of the alloy, < >>
For thermal conductivity, if the maximum heating power of the heating module is lower than + ->
The temperature parameter is set as a risk parameter,calculating the scum area at +.>
If the amount of change in time is unchanged, setting the scum area as a risk parameter, extracting the risk parameter by a risk parameter extracting unit, calculating a risk value by a risk evaluation module, wherein the risk value is calculated by extracting the number of the risk parameters and comparing ++>
Extracting parameter values of risk parameters after time, and calculating the ratio of the distance value from the parameter values to the minimum value of the safety range to the distance value of the safety range value, wherein the calculation process of the risk values is that the calculated calculation ratio of each parameter is substituted into a risk value calculation formula->
Wherein->
And (3) for the calculation proportion of the ith parameter, n is the number of terms, extracting the calculated c and the parameter number of terms, and giving an alarm.
The implementation can be realized through the embodiment: the method comprises the steps of collecting smelting temperature, vacuum degree, smelting time and scum area in a smelted object through a data collector arranged in an array, accurately calculating the temperature average value inside a furnace body, the average vacuum degree inside the furnace body, the scum area inside the furnace body and the proportion of the scum area in the furnace body to the area in the furnace body, comparing the calculated temperature average value data, the average vacuum degree and the proportion of the scum area to the area in the furnace body with set qualified values, obtaining data types which are not in qualified values, judging the data of the data types which are not in qualified values as abnormal data, evaluating the abnormal data, judging the types of the abnormal data, searching the furnace body parameter types related to the abnormal data types, carrying out data extraction on the furnace body parameter types related to the abnormal data types, carrying out check comparison on the setting parameters acquired by a furnace body setting parameter acquisition unit, carrying out evaluation calculation on the furnace body parameters, further carrying out extraction on risk parameters in the furnace body parameters, checking the risk parameter data, carrying out calculation classification on the risk parameters after the check, carrying out calculation on the risk supplement quantity calculation on the parameters, comparing the calculated parameter values with corresponding parameter safety ranges, carrying out the comparison on the calculated parameter safety values, carrying out the safety supplement on the safety parameters, carrying out the safety evaluation on the quality of the processed products by a corresponding to the safety control module, and carrying out the safety control of the quality of the processed products can not be fast in the corresponding to the safety assessment, and the quality of the processed products can not be guaranteed.
It is important to note that the construction and arrangement of the present application as shown in a variety of different exemplary embodiments is illustrative only. Although only a few embodiments have been described in detail in this disclosure, those skilled in the art who review this disclosure will readily appreciate that many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters (e.g., temperature, pressure, etc.), mounting arrangements, use of materials, colors, orientations, etc.) without materially departing from the novel teachings and advantages of the subject matter described in this application. For example, elements shown as integrally formed may be constructed of multiple parts or elements, the position of elements may be reversed or otherwise varied, and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of present invention. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. In the claims, any means-plus-function clause is intended to cover the structures described herein as performing the recited function and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present inventions. Therefore, the invention is not limited to the specific embodiments, but extends to various modifications that nevertheless fall within the scope of the appended claims.
Furthermore, in an effort to provide a concise description of the exemplary embodiments, all features of an actual implementation may not be described (i.e., those not associated with the best mode presently contemplated for carrying out the invention, or those not associated with practicing the invention).
It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. A vacuum induction melting process detection and control system is characterized in that: the system comprises a smelting data monitoring module, a furnace body parameter acquisition module, a smelting data calculation module, a furnace body parameter checking module, a smelting data calculation module, a smelting data evaluation module, a furnace body parameter evaluation module, an evaluation data fusion module, an evaluation data checking module, a risk evaluation module and a furnace body control module;
The smelting data monitoring module is used for collecting and monitoring smelting data in the smelting furnace in the smelting process;
the furnace body parameter acquisition module is used for acquiring and monitoring the setting and actual parameters of the smelting furnace in the smelting process;
the smelting data calculation module is used for carrying out real-time comparison calculation on the collected smelting data in the smelting process and finding out abnormal smelting data;
the furnace body parameter checking module is used for checking and calculating the acquired setting and actual parameters of the smelting furnace in the smelting process;
the smelting data evaluation module is used for substituting the collected smelting data into a smelting evaluation strategy in the smelting process to evaluate the smelting process of the smelting, so as to obtain smelting evaluation data;
the furnace body parameter evaluation module is used for substituting the acquired furnace body parameters into a furnace body evaluation strategy in the smelting process to evaluate the furnace body smelting process so as to obtain furnace body evaluation data;
the evaluation data fusion module is used for fusing the evaluation data of the smelting object obtained by the smelting data evaluation module and the furnace body evaluation data obtained by the furnace body parameter evaluation module to find out abnormal values in the evaluation data;
The evaluation data checking module is used for checking the abnormal value obtained by the evaluation data;
the risk evaluation module is used for substituting the abnormal value into a risk parameter evaluation strategy and calculating a risk value generated by the abnormal data;
the furnace body control module is used for extracting the abnormal value and substituting the abnormal value into a furnace body control strategy, calculating the furnace body adjusting parameter and further controlling the abnormal value.
2. The vacuum induction melting process detection and control system of claim 1 wherein: the smelting data monitoring module comprises a smelting temperature monitoring unit, a smelting vacuum degree monitoring unit, a smelting time monitoring unit and a smelting object monitoring unit;
the smelting temperature monitoring unit is used for monitoring smelting temperature data of each position in the smelting furnace to obtain a smelting temperature sequence;
the smelting vacuum degree monitoring unit is used for monitoring vacuum degree data of each position in the smelting furnace to obtain a vacuum degree sequence;
the smelting time monitoring unit is used for monitoring the smelting time of the smelting object in real time;
the smelt monitoring unit is used for monitoring the area of scum in the smelt in real time.
3. The vacuum induction melting process detection and control system of claim 2 wherein: the smelting data calculation module comprises a smelting data comparison unit and an abnormal data identification unit, wherein the smelting data comparison unit is used for comparing data monitored by the smelting data monitoring module with a set corresponding data threshold value, and the abnormal data identification unit is used for identifying and extracting data which are obtained by comparison of the smelting data comparison unit and are not in the range of the data threshold value.
4. A vacuum induction melting process detection and control system as claimed in claim 3 wherein: the furnace body parameter acquisition module comprises a furnace body real-time parameter acquisition unit and a furnace body setting parameter acquisition unit, wherein the furnace body real-time parameter acquisition unit is used for acquiring real-time parameter values of a furnace body, and the furnace body setting parameter acquisition unit is used for acquiring setting parameter values of the furnace body.
5. The vacuum induction melting process detection and control system of claim 4 wherein: the evaluation data fusion module comprises an evaluation data classification unit and an evaluation data calculation unit, wherein the evaluation data classification unit is used for classifying abnormal data obtained in the evaluation process according to the types of the abnormal data, and the evaluation data calculation unit is used for calculating the evaluation abnormal data to obtain risk parameters in the evaluation abnormal data.
6. The vacuum induction melting process detection and control system of claim 5 wherein: the evaluation data checking module comprises a parameter compensation calculating unit and a risk parameter extracting unit, wherein the output end of the evaluation data calculating unit is connected with the risk parameter extracting unit, the output end of the parameter compensation calculating unit is connected with the furnace body control module, the risk parameter extracting unit is connected with the risk evaluation module, the parameter compensation calculating unit is used for calculating compensation parameters of abnormal data, and the risk parameter extracting unit is used for extracting risk parameters in the abnormal data.
7. The vacuum induction melting process detection and control system of claim 6 wherein: the control system comprises a processing control strategy method, wherein the processing control strategy method comprises the following specific steps of:
s1: setting a qualified value of parameters in a furnace body, acquiring and monitoring the internal data of the furnace body and smelting data of a smelted object by a smelting data monitoring module, wherein the acquired data comprise smelting temperature, vacuum degree, smelting time and scum area in the smelted object, and simultaneously, real-time monitoring the setting parameters of the furnace body and control data by a furnace body parameter acquisition module, wherein the setting parameters of the furnace body comprise the radius of the furnace body, the setting temperature value and the setting vacuum value, the control data comprise real-time heating power values through a heater, calculating the average value of the temperature in the furnace body, the average vacuum degree in the furnace body and the proportion of the scum area in the furnace body to the internal area of the furnace body;
S2: the smelting data calculation module compares the internal data of the furnace body, the smelting data of the smelting materials with a set qualified value, and identifies and extracts abnormal data in the acquisition process, and the judging method of the abnormal data comprises the following steps: comparing the calculated temperature average value data, the average vacuum degree and the proportion of the scum area to the area in the furnace body with a set qualified value, obtaining a data type which is not in a qualified value, judging the data of the data type which is not in the qualified value as abnormal data, evaluating the abnormal data, judging the type of the abnormal data, and searching the type of furnace body parameters related to the type of the abnormal data;
s3: the method comprises the steps of extracting data of furnace body parameter types related to abnormal data types, checking and comparing the data with set parameters acquired by a furnace body set parameter acquisition unit, evaluating and calculating the furnace body parameters, extracting risk parameters in the furnace body parameters, and checking risk parameter data;
s4: the method comprises the steps of carrying out calculation and classification on risk parameters after checking, calculating parameter supplementing quantity, comparing calculated parameter values with corresponding parameter safety ranges to extract parameter safety supplementing quantity, supplementing corresponding parameters through the parameter safety supplementing quantity by a furnace body control module, extracting parameters of which the calculated parameter values are not in the corresponding parameter safety ranges by a risk parameter extraction unit, and carrying out evaluation on the risk parameters by a risk evaluation module to calculate risk values and alarm.
8. The vacuum induction melting process detection and control system of claim 7 wherein: the smelt evaluation strategy comprises the following concrete contents: comparing the calculated temperature average value, the average vacuum degree and the ratio of the scum area in the furnace body with the set temperature, vacuum degree threshold and scum area threshold, finding out the data parameters of which the calculated temperature average value, the average vacuum degree and the ratio of the scum area in the furnace body are not in the threshold range, extracting and setting the data parameters as abnormal data, obtaining the ratio of the temperature average value, the average vacuum degree and the scum area in the furnace body, and the difference value between the calculated temperature average value, the average vacuum degree and the scum area in the furnace body, the calculated temperature average value, the calculated vacuum degree and the scum area threshold, introducing the abnormal data into a BP neural network established in advance to search the furnace body parameters related to the abnormal data in the furnace body data, introducing the abnormal data into a smelt evaluation formula, evaluating the smelt, calculating the smelt evaluation value, and if the calculated smelt evaluation value is in the safety range, indicating that the data safety is not needed, and if the calculated smelt evaluation value is not in the safety range, the operation of S2-S4 is not needed.
9. The vacuum induction melting process detection and control system of claim 8 wherein: the furnace body evaluation strategy comprises the following specific contents: extracting furnace body parameters related to abnormal data, checking and comparing the furnace body parameters with the acquired set parameters, evaluating and calculating the furnace body parameters, extracting risk parameters in the furnace body parameters, wherein the set range of the risk parameters is a parameter which is five percent different from the acquired set parameters, acquiring the risk parameters again after 2 seconds, comparing the average value of the risk parameters with the set parameters to check the risk parameter data, searching the types of abnormal values in the risk parameters, and resetting the parameters by a controller.
10. The vacuum induction melting process detection and control system of claim 9 wherein: the furnace body control strategy comprises the following specific contents: the method comprises the steps that a furnace body control module controls parameters according to descending order, vacuumizing is carried out on the inside of a furnace body in a set time through control of an air pumping device to ensure the vacuum degree of the inside of the furnace body, if the calculated vacuum degree cannot reach the safety vacuum degree after air pumped by the air pumping device in the set time is obtained through calculation, the vacuum degree parameter is set as a risk parameter, the temperature in the inside of the furnace body is controlled through control of the heating power of the heating module, the temperature in the furnace body is regulated to be within a safety temperature range in the set time, the minimum heating power required by the heating module is calculated, if the maximum heating power of the heating module is lower than the minimum heating power required by the heating module, the temperature parameter is set as a risk parameter, the change amount of the scum area in the set time is calculated, if the scum area is unchanged, the scum area is set as a risk parameter, the risk parameter is extracted by a risk parameter extracting unit, the risk evaluation module calculates a risk value, the calculation mode of the risk parameter is extracted by the number of the risk parameter, the parameter value of the risk value after the set time is extracted, the parameter value of the risk value is calculated, the distance value of the minimum value of the risk value is calculated to the safety range is calculated, the distance value of the minimum value of the safety range is calculated, the value is calculated, and the risk value is calculated, and the ratio is calculated, and the value is calculated.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116698917A (en) * | 2023-08-08 | 2023-09-05 | 江苏美特林科特殊合金股份有限公司 | Nondestructive testing method and system for coating defects |
| CN116823233A (en) * | 2023-08-30 | 2023-09-29 | 青岛巨商汇网络科技有限公司 | User data processing method and system based on full-period operation and maintenance |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107991002A (en) * | 2018-01-23 | 2018-05-04 | 深圳力士智造科技有限公司 | A kind of temp measuring system and temperature measurement of vacuum furnace compensation method for vacuum drying oven |
| CN109612266A (en) * | 2018-12-28 | 2019-04-12 | 株洲晨昕中高频设备有限公司 | A kind of vacuum furnace automatic control system |
| CN110153526A (en) * | 2019-07-10 | 2019-08-23 | 无锡应达工业有限公司 | A kind of vacuum brazing furnace hot-zone efficiently controlling temperature system |
| CN212057782U (en) * | 2020-04-15 | 2020-12-01 | 松下制冷（大连）有限公司 | Energy-saving control system applied to vacuum boiler |
| CN115306700A (en) * | 2022-08-16 | 2022-11-08 | 江苏汉华热管理科技有限公司 | Carbide furnace is vacuum pump operating mode on-line monitoring system for exhaust emission |
| CN115718459A (en) * | 2023-01-09 | 2023-02-28 | 南京瑞为新材料科技有限公司 | Control system and method of heat sink vacuum furnace |
-
2023
- 2023-03-06 CN CN202310202024.4A patent/CN116068944B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107991002A (en) * | 2018-01-23 | 2018-05-04 | 深圳力士智造科技有限公司 | A kind of temp measuring system and temperature measurement of vacuum furnace compensation method for vacuum drying oven |
| CN109612266A (en) * | 2018-12-28 | 2019-04-12 | 株洲晨昕中高频设备有限公司 | A kind of vacuum furnace automatic control system |
| CN110153526A (en) * | 2019-07-10 | 2019-08-23 | 无锡应达工业有限公司 | A kind of vacuum brazing furnace hot-zone efficiently controlling temperature system |
| CN212057782U (en) * | 2020-04-15 | 2020-12-01 | 松下制冷（大连）有限公司 | Energy-saving control system applied to vacuum boiler |
| CN115306700A (en) * | 2022-08-16 | 2022-11-08 | 江苏汉华热管理科技有限公司 | Carbide furnace is vacuum pump operating mode on-line monitoring system for exhaust emission |
| CN115718459A (en) * | 2023-01-09 | 2023-02-28 | 南京瑞为新材料科技有限公司 | Control system and method of heat sink vacuum furnace |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116698917A (en) * | 2023-08-08 | 2023-09-05 | 江苏美特林科特殊合金股份有限公司 | Nondestructive testing method and system for coating defects |
| CN116698917B (en) * | 2023-08-08 | 2023-10-20 | 江苏美特林科特殊合金股份有限公司 | Nondestructive testing method and system for coating defects |
| CN116823233A (en) * | 2023-08-30 | 2023-09-29 | 青岛巨商汇网络科技有限公司 | User data processing method and system based on full-period operation and maintenance |
| CN116823233B (en) * | 2023-08-30 | 2023-11-07 | 青岛巨商汇网络科技有限公司 | User data processing method and system based on full-period operation and maintenance |
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