WO2021098234A1 - 一种基于原位采集信息调控热处理的方法及应用 - Google Patents

一种基于原位采集信息调控热处理的方法及应用 Download PDF

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Publication number
WO2021098234A1
WO2021098234A1 PCT/CN2020/101214 CN2020101214W WO2021098234A1 WO 2021098234 A1 WO2021098234 A1 WO 2021098234A1 CN 2020101214 W CN2020101214 W CN 2020101214W WO 2021098234 A1 WO2021098234 A1 WO 2021098234A1
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heat treatment
information
time
data
situ
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PCT/CN2020/101214
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English (en)
French (fr)
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李红英
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中南大学
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Priority to US17/778,435 priority Critical patent/US20230002851A1/en
Priority to JP2022529614A priority patent/JP2023502716A/ja
Publication of WO2021098234A1 publication Critical patent/WO2021098234A1/zh

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    • C—CHEMISTRY; METALLURGY
    • C21—METALLURGY OF IRON
    • C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
    • C21D11/00—Process control or regulation for heat treatments
    • C—CHEMISTRY; METALLURGY
    • C21—METALLURGY OF IRON
    • C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
    • C21D1/00—General methods or devices for heat treatment, e.g. annealing, hardening, quenching or tempering
    • C21D1/26—Methods of annealing
    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22F—CHANGING THE PHYSICAL STRUCTURE OF NON-FERROUS METALS AND NON-FERROUS ALLOYS
    • C22F1/00—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working
    • C22F1/04—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working of aluminium or alloys based thereon
    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22F—CHANGING THE PHYSICAL STRUCTURE OF NON-FERROUS METALS AND NON-FERROUS ALLOYS
    • C22F1/00—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working
    • C22F1/04—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working of aluminium or alloys based thereon
    • C22F1/053—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working of aluminium or alloys based thereon of alloys with zinc as the next major constituent
    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22F—CHANGING THE PHYSICAL STRUCTURE OF NON-FERROUS METALS AND NON-FERROUS ALLOYS
    • C22F1/00—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working
    • C22F1/04—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working of aluminium or alloys based thereon
    • C22F1/057—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working of aluminium or alloys based thereon of alloys with copper as the next major constituent
    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22F—CHANGING THE PHYSICAL STRUCTURE OF NON-FERROUS METALS AND NON-FERROUS ALLOYS
    • C22F1/00—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working
    • C22F1/06—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working of magnesium or alloys based thereon
    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22F—CHANGING THE PHYSICAL STRUCTURE OF NON-FERROUS METALS AND NON-FERROUS ALLOYS
    • C22F1/00—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working
    • C22F1/16—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working of other metals or alloys based thereon
    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22F—CHANGING THE PHYSICAL STRUCTURE OF NON-FERROUS METALS AND NON-FERROUS ALLOYS
    • C22F1/00—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working
    • C22F1/16—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working of other metals or alloys based thereon
    • C22F1/165—Changing the physical structure of non-ferrous metals or alloys by heat treatment or by hot or cold working of other metals or alloys based thereon of zinc or cadmium or alloys based thereon

Definitions

  • the invention relates to a method and application for regulating and controlling heat treatment based on in-situ collected information, belonging to the field of material thermal processing, in particular to the field of material thermal processing on-line detection and control.
  • the material or workpiece is heat-treated to form the expected organizational structure to meet the set performance requirements.
  • Heat treatment process parameters such as heating temperature, holding time, and temperature change rate have a greater impact on the structure and properties of the material.
  • the optimization and regulation of these process parameters are used in production to obtain the required structure and properties of the workpiece after heat treatment.
  • the traditional method is to perform heat treatments with different temperatures, different holding times, and different temperature changing rates before production, and then perform performance testing and microstructure observation at room temperature. If the structure performance does not meet the target requirements, it is necessary to repeatedly adjust the process parameters and perform the heat treatment again. By continuously optimizing the process parameters to approach the target requirements of the heat treatment, it is impossible to directly detect the degree or state of the heat treatment and control the heat treatment process during the heat treatment process.
  • Patent CN 109536859 A discloses a method for detecting the effect of solution quenching of 7075 aluminum alloy.
  • the heat treatment time is determined by detecting the conductivity changes of samples with different solution temperatures and holding times.
  • the measured conductivity is the conductivity after quenching, not heat treatment
  • In-situ measurement in the process requires multiple sets of subsequent experiments to obtain the curves of conductivity, heating temperature, and holding time, and the experimental steps are more complicated.
  • Patent CN 103175831 B proposes a method suitable for the analysis and evaluation of the proportion of the recrystallized structure of deformed aluminum alloy materials, which can distinguish the recrystallized structure from the deformed structure, and then distinguish and count the recrystallization status of the material.
  • this method is not suitable for difficult-to-corrode or For materials that are too corrosive, the applicable range of materials is limited.
  • Patent CN 105975727 A "Material data processing, generation, application methods and terminals, transport and processing platform” proposed a material data cloud processing platform, the goal is to solve the experimental problem of material testing and simulation calculation in the material genetic engineering technology is disconnected, but its The data generation and material preparation process are not carried out at the same time, and cannot be applied to the production process control of materials; patent CN 106447229 A "A Material Data Management System and Method in Material Informatics” discloses an informatics research framework that can add, delete, modify, and check material data, but does not perform a systematic analysis of the stored information.
  • patent CN 110298289 A “Material Recognition Methods, Devices, Storage Media, and Electronic Equipment” discloses a device that determines the material information of a target object based on ultrasonic signals. It can be used for material recognition, but ultrasonic signals are susceptible to interference and may be The parts are damaged and the scope of application is limited.
  • the present invention can realize high-temperature, continuous in-situ information collection while heat-treating the workpiece. At the same time, it can use material heat-treatment database resources and self-learning functions to process, analyze, and store the collected information in real time, and then perform online detection and testing. The heat treatment degree or state of the parts, and the heat treatment process of the optimized materials, to realize the online control of the heat treatment of the tested parts.
  • the present invention provides a method, device and application for regulating and controlling heat treatment based on in-situ collected information.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collection of information; in the process of heat treatment of a test piece, information and/or data are continuously collected in situ, and after information processing and/or data analysis are performed, it is related to the heat treatment information database Information or data comparison, online detection or characterization of the heat treatment degree or status of the tested part, and then optimize the heat treatment process of the material and/or regulate the heat treatment of the tested part, so that the tested part can reach the set heat treatment target and/or organization performance.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collection of information;
  • the heat treatment includes, but is not limited to, homogenization, solid solution, aging, and recovery recrystallization annealing;
  • the heat treatment process includes at least one of operations such as heating, heat preservation, and cooling
  • the degree or state of the heat treatment includes, but is not limited to, underaging, peak aging, overaging, recovery, beginning to recrystallize, and complete recrystallization.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collection of information; said in-situ collection is the real-time collection of information and/or data of the test piece in the actual heat treatment environment; preferably, the information is electrical information, including but not Limited to voltage, resistance, resistivity, conductivity, and conductivity, various electrical information can be converted accordingly.
  • the conversion includes both numerical conversion and unit conversion. The conversion uses at least one of the following formulas.
  • Resistance ( ⁇ ) voltage (V) ⁇ current (A).
  • Resistivity ( ⁇ •m) resistance ( ⁇ ) ⁇ cross-sectional area (m 2 ) ⁇ length (m).
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collection information; the collection methods of electrical information include but are not limited to the DC four-point method, the single bridge method, and the double bridge method; preferably, the DC four-point method can be used. Reduce or even eliminate the influence of wire and contact resistance on the collection of information.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collection of information; the information processing is to reduce redundancy and noise information through information screening and classification processing, data acquisition and conversion, and improve the recognition of information; the data analysis is Through feature extraction, data mining and integration, data dimensionality reduction and data processing are performed to improve the accuracy of detection; the information processing is preferably related to electrical information-time curve and/or electrical information-temperature curve; said correlation Processing includes but is not limited to calculating the change value of electrical information, calculating the rate of change of electrical information, and calculating the coefficient of heat treatment degree.
  • the E 0 is the electrical information corresponding to the initial heat treatment degree, preferably The electrical information corresponding to the temperature of the test piece reaching the preset initial conditions;
  • the E ti is the electrical information corresponding to any time in the heat treatment process, which is the electrical information corresponding to a certain degree before the target heat treatment degree is reached;
  • the E u is the target heat treatment
  • the electrical information corresponding to the degree is preferably the electrical information corresponding to the performance and/or structure of the test piece reaching the heat treatment target.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collection of information;
  • the heat treatment information database stores a variety of materials and their heat treatment information and data, including but not limited to material information and data, heat treatment systems and related process parameters, Heat treatment process information and data;
  • the material information and data include material composition and basic properties, heat treatment organization and performance indicators;
  • the heat treatment system includes, but is not limited to, the homogenization treatment system, the solution treatment system, the aging system, and the softening annealing system;
  • the relevant process parameters include but are not limited to heating temperature, holding time, heating rate, and cooling rate;
  • the heat treatment process information and data include, but are not limited to, the temperature and electrical information of different heat treatment processes; preferably, a neural network driven by data Multi-component materials are classified, based on principal component analysis and correlation analysis, the internal essential structural features of the data are extracted, and a process-organization-performance relational database with components as the main line is constructed.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collected information;
  • the relational database support is not limited to the following database types: SQL Server, MySQL, MongoDB, SQLite, Access, H2, Oracle, PostgreSQL; database access technologies include but not Limited to ODBC, DAO, OLE DB, ADO, the storage content can be added, deleted, modified, and inquired according to actual needs.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collected information; for materials that have been recorded in a heat treatment information database, electrical information, characteristic organization and performance information of the material in a set heat treatment process can be directly obtained from the database.
  • electrical information-time curve shown in Figure 1 Take the electrical information-time curve shown in Figure 1 as an example.
  • t 0 is the starting time point of the heat treatment (the starting point of the abscissa of the curve)
  • E 0 is the electrical level corresponding to the initial heat treatment.
  • t 1 , t 2 , t 3 ... are different moments in the heat treatment process
  • E t1 , E t2 , E t3 ... are electrical information corresponding to different heat treatment moments, corresponding to the degree of heat treatment, tu , E u To achieve the target heat treatment time and the corresponding electrical information.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collection of information; for heat treatment such as homogenization and solid solution, as the heat treatment time is prolonged, the electrical information-time curve gradually tends to be horizontal. Theoretically, at an appropriate solid solution temperature, the second phase gradually re-dissolves until it is completely dissolved into the matrix, and the corresponding electrical information-time curve tends to be horizontal, as shown in Figure 2. However, there are often insoluble or poorly soluble phases in actual production. After solution treatment for a certain period of time, the degree of solid solution no longer changes or the rate of change is relatively small. Preferably, the rate of change of the slope of the electrical information-time curve is relatively small.
  • a near-stable solid solution degree is defined as the target heat treatment degree.
  • the solid solution degree of the near-stable solid solution degree and the corresponding electrical information are similar to the stable solid solution degree, but the required heat treatment time is greatly shortened.
  • Methods for determining the degree of near-stable solid solution include, but are not limited to: setting the absolute value of the measured electrical information-time curve to the degree of solid solution corresponding to the starting point of the set value as the near-stable solid solution degree, and setting the measured electrical information with The difference of the stable electrical information recorded in the material heat treatment information database reaches the set value and the corresponding solid solution degree is set to the near stable solid solution degree, and the material performance or structure during the heat treatment process reaches the target solid solution degree. Set as near-stable solid solution degree.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collection of information; for aging and recovery recrystallization annealing, there are critical heat treatment states such as the beginning of desolvation, peak aging, the beginning of recrystallization, and complete recrystallization.
  • the target heat treatment degree Eu is based on The target performance and/or structure of the material heat treatment are determined;
  • Figure 3 is the electrical information-time curve and characteristic structure diagram measured during the alloy aging process.
  • the characteristic structure and corresponding properties of the material's recovery, start of recrystallization, complete recrystallization, and secondary recrystallization can be retrieved in the heat treatment information database.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collection information; for materials not recorded in the heat treatment information database, for different heat treatment processes, characteristic points are selected on the electrical information-time curve and electrical information-temperature curve obtained by the detection, Detect the composition, organization and performance of the material separately, and then store the material information and data, heat treatment process data, heat treatment process information and data, etc.
  • the characteristic points Including but not limited to the starting point where the curve becomes a horizontal line (or the starting point where the absolute value of the slope of the curve is less than a certain set value), the inflection point of the curve (the point where the unevenness of the curve changes), the point where the slope of the curve changes non-steadyly (the rate of change of the curve slope or The point where the change value exceeds the set range), the corresponding point on the curve of the characteristic heat treatment degree or critical heat treatment state, the point with the same time interval, and the point with the same temperature interval; the characteristic heat treatment degree or critical heat treatment state includes but is not limited to low
  • the melting point phase begins to dissolve, the second phase begins to re-dissolve, the solid solution begins to desolvate, peak aging, recrystallization begins, complete recrystallization, and recrystallized grains grow.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collection information; the heat treatment information database can be continuously improved or optimized through subsequent detection and self-learning to improve the credibility and usability of the data; the self-learning is based on neural network algorithms, random At least one of forest algorithm and particle swarm algorithm; its operating environment support is not limited to the following operating systems: Windows, Android, Linux, Mac OS, IOS, and the learning results provide terminal services to users through SOAP and RESTful; at the same time, the present invention
  • the above-mentioned algorithms involved can be connected with the Bayesian optimization algorithm to achieve the purpose of the optimization algorithm.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collection information;
  • the heat treatment information database is a local database or a cloud database;
  • the cloud database is composed of data uploaded by different users, and its functions include, but are not limited to, management authority, verification access, and storage Data, process data, manage data, analyze data.
  • the present invention is a method for regulating and controlling heat treatment based on in-situ collected information; there are many ways to apply the information and data in the heat treatment information database, such as detecting and characterizing the degree of heat treatment of materials by calculating the slope of the electrical information-time curve, etc. State, etc., it should be considered that any method based on the method described in this patent, that is, continuous in-situ collection of electrical information and related processing, on-line detection, characterization and regulation of the degree of heat treatment, all belong to the scope of protection of this patent.
  • the hardware used in the information acquisition and processing module includes a computer eithley 2450 digital source meter; Keithley 2182A nanovoltmeter; a special fixture; a data cable; the computer contains a CPU, a motherboard, a graphics card, a memory bar, a display, a hard disk, and so on.
  • the hardware used for self-learning module and heat treatment information database construction includes computer eithley 2450 digital source meter; Keithley 2182A nanovoltmeter; special fixture; data cable; the computer contains CPU, motherboard, graphics card, memory bar, display, hard disk Wait.
  • the hardware used in the heat treatment control module includes: Xiamen Yudian high-performance intelligent thermostat AI-708; K-type thermocouple; USB to RS485 data line.
  • the hardware used in the heat treatment system includes: Tianjin Zhonghuan furnace 1200°C three-temperature zone vacuum atmosphere tubular electric furnace.
  • the present invention is an application of a method for regulating and controlling heat treatment based on in-situ collection of information; the method can be applied to optimize the heat treatment process of materials and/or online regulation and control of the heat treatment of the test piece.
  • the present invention is an application of a method for regulating and controlling heat treatment based on in-situ collected information; the method is applied to heat treatment process optimization, and based on the basic data set of heat treatment process-characteristic organization-electrical information, an efficient global optimization adaptive design model is established to solve heat treatment The optimization problem of multi-objective and multi-parameter system.
  • the present invention is an application of a method for regulating and controlling heat treatment based on in-situ collection of information; the method is applied to homogenization processing, including but not limited to determining a suitable homogenization temperature, homogenization time, heating rate, and cooling rate, and the homogenization includes Single-stage homogenization and multi-stage homogenization; the specific operation is preferably: select several temperatures for homogenization and collect information in situ, and use the temperature that takes the shortest time to reach the target homogenization degree without overburning as the appropriate homogenization temperature; The appropriate homogenization time is determined according to the electrical information-time curve corresponding to the appropriate homogenization temperature, and the corresponding time when the homogenization degree coefficient reaches 100% (or the absolute value of the curve slope is less than the set value) is determined as the appropriate homogenization time.
  • the present invention is an application of a heat treatment method based on in-situ collection of information; the method is applied to solid solution treatment, including but not limited to determining an appropriate solid solution temperature, solid solution time, heating rate, and cooling rate, and the solid solution includes Single-stage solid solution, multi-stage solid solution; the specific operation is preferably: select several temperatures for solid solution while collecting information in situ, and use the temperature that takes the shortest time to reach the target solid solution degree without over-burning as the appropriate solid solution temperature; Determine the appropriate solution time according to the electrical information-time curve corresponding to the appropriate solution temperature, and determine the appropriate solution time when the solution degree coefficient reaches 100% (or the absolute value of the curve slope is less than the set value); 5 is a schematic diagram of the resistivity-time curve of a typical alloy solid solution at different temperatures, T 1 >T 2 >T 3 >T 4 >T 5 , the resistivities of the corresponding curves of T 1 and T 5 cannot tend to Stable, the resistivity of the corresponding curves of T 2 , T 3 and T 4
  • Figure 7 shows the resistivity-time curve of a typical alloy solid solution and the characterization of the solid solution degree Schematic diagram, when the absolute value of the preset resistivity-time curve slope is less than the set value k, the solid solution is completed, point A
  • the present invention is an application of a method for regulating and controlling heat treatment based on in-situ collection of information; the method is applied to aging treatment, including but not limited to determining the desolvation sequence of various aging precipitated phases and the time window for precipitation of new phases, and determining the peak intensity
  • the aging time of the alloy and the time nodes to reach different aging degrees.
  • the aging includes single-stage aging and multi-stage aging;
  • Figure 8 is the electrical information-time curve of alloy aging and the schematic diagram of the precipitation of the corresponding phases, the ⁇ phase and ⁇ phase are separated out Both the growth and growth will cause the slope of the curve to change.
  • Figure 9 shows the resistivity-time curve before and after the optimization of the alloy composition.
  • the alloy composition changes slightly, and the resistivity-time curve changes significantly.
  • the composition change causes the peak aging time to change. Therefore, according to the aging resistivity-time curve, it can be Determine the time node at which the alloy reaches different aging degrees at the set temperature.
  • the present invention is an application of a heat treatment method based on in-situ collection of information; the method is applied to the recovery recrystallization annealing, including but not limited to predicting the time required for the material to reach the specified annealing degree at the specified temperature, and predicting the specified cold deformation of the material to reach the specified The time required for the degree of annealing, compare the ability of different materials to resist recrystallization under the same heat treatment conditions.
  • the present invention is an application of a method for regulating and controlling heat treatment based on in-situ collection of information; predicting the time required for a material to reach a specified annealing degree at a specified temperature refers to predicting the material that has not been tested by self-learning and fitting in the database.
  • the time required for temperature annealing to reach the specified annealing degree, the specific operation is preferably: call the known information or data of the specified temperature adjacent to the specified temperature in the material heat treatment information database, and predict the annealing at the specified temperature to reach the set annealing degree through self-learning Time;
  • Figure 10 shows the resistivity-time curve of annealing of the same material at different temperatures (T 1 >T 2 >T 3 ).
  • the present invention is an application of a method for regulating and controlling heat treatment based on in-situ collection of information; predicting the time required for a specified cold deformation material to reach a specified annealing degree refers to the prediction of the material in the database through self-learning and fitting.
  • the time required for annealing at the specified temperature after cold deformation to reach the specified annealing degree is preferably: in the material heat treatment information database, call the information or data corresponding to the known cold deformation amount adjacent to the specified cold deformation amount, Through self-learning, predict the time required for the annealing to reach the set annealing degree without storing the cold deformation in the database;
  • the present invention is an application of a heat treatment method based on in-situ collection of information; the specific operation of comparing the ability of different materials to resist recrystallization under the same heat treatment conditions is preferably: multiple metals are placed in a heat treatment system for detection at the same time or separately in the same heat treatment system. Test under heat treatment conditions and compare the annealing degree coefficients at the same time point in the electrical information-time curve. The larger the value, the higher the annealing softening degree and the weaker the anti-recrystallization ability of the material. The longer the time to reach the same annealing degree coefficient, the material The stronger the resistance to recrystallization.
  • Figure 12 is the resistivity-time curve of the two materials under the same annealing conditions. To reach the same annealing degree, the time required for alloy 1 is shorter than that of alloy 2, indicating that the anti-recrystallization ability of alloy 1 is weaker than that of alloy 2.
  • the present invention is an application of a method for regulating and controlling heat treatment based on in-situ collection of information; the method is applied to online regulating and controlling heat treatment, and the specific operation is preferably: continuous collection of information and/or data in situ during the heat treatment process, through real-time information processing and data analysis After that, it is compared with related information or data in the heat treatment information database to detect or characterize the degree or state of heat treatment, and then adjust the heat treatment process parameters and control the heat treatment process, so that the test piece can achieve the set heat treatment target and/or tissue performance.
  • Figure 13 is a schematic diagram of real-time control of heat treatment based on the comparison results of in-situ measured electrical information and reference electrical information.
  • Point A is the point where the measured resistivity-time curve overlaps with the reference resistivity-time curve, keeping the heat treatment parameters unchanged; at B Point, the measured resistivity-time curve deviates from the reference resistivity-time curve, adjust the heat treatment parameters, at point C, the measured resistivity-time curve returns to the reference resistivity-time curve, at point D, the heat treatment target is reached and the heat treatment is stopped.
  • the reference electrical information is obtained from the heat treatment information database.
  • FIG. 14 is a schematic diagram of obtaining the reference electrical information.
  • the obtaining method is preferably: based on the electrical information of the same material and the same heat treatment process in the database, obtaining electrical information and heat treatment parameters through self-learning
  • the logical rules and/or data relationships between the data are stored in the heat treatment information database as a sample, and the database is continuously optimized through subsequent testing.
  • the present invention is an application device and software system for regulating and controlling heat treatment method based on in-situ collection of information. Its structural block diagram is shown in Figure 15, including an information collection and processing module, a self-learning module, a heat treatment information database, a heat treatment control module, and a heat treatment system;
  • the information collection and processing module is used for in-situ collection and real-time processing of the heat treatment information of the test piece, the collection frequency is adjustable, and the electrical information used can be converted in real time;
  • the self-learning module is used to analyze logic laws and/or
  • the data relationship includes, but is not limited to, analyzing the logical laws between materials and heat treatment, information and information, or the association between data and data;
  • the heat treatment information database is used to store the data obtained by the information collection and processing module and provide terminal services;
  • the heat treatment control module is used to generate a control command according to the analysis result of the self-learning module, which can be operated according to a preset mode or can be adjusted online; the heat treatment system execute
  • the present invention is an application of a heat treatment method based on in-situ collection of information; in addition to the above applications, the method of the present invention can be applied in various forms in the actual production process; it should be considered that all based on the method described in this patent, that is, through continuous in-situ collection Electrical information and related processing, on-line detection, characterization and control of heat treatment are all within the protection scope of this patent.
  • the present invention proposes a technical solution based on in-situ collection of information and/or data to regulate heat treatment, which has the following technical advantages.
  • Online non-destructive testing can be performed on all conductive tested parts.
  • the shape of the tested part is not restricted, the heat treatment temperature is not restricted, and the heat treatment location is not restricted. It can be applied in the laboratory and the production site, and the movement state of the tested part is not restricted. Restricted, it can be stationary or can move continuously, preferably there is no relative movement between the test piece and the detection device.
  • the present invention realizes the sensitive and accurate capture of heat treatment organization change response information.
  • efficient information processing and professional data analysis it realizes effective data investigation, mining and optimization, and improves
  • the effective information storage capacity of the database reduces system errors and improves the accuracy of detection and control.
  • the present invention has a self-learning function, and realizes deep integration with material thermodynamics and diffusion kinetics database, material heat treatment expert system, high-throughput calculation and experimental platform, and constructs a process-organization-performance relational database with composition as the main line , Through the automatic judgment of the organization evolution of the whole process, the automatic adjustment of performance-driven process parameters can be realized, and the heat treatment target can be achieved through real-time adjustment of heat treatment, and the organization and performance requirements of the tested part can be accurately met.
  • the informatization application of the present invention is compatible with a variety of operating systems and application platforms.
  • the user-friendly software combined with the Internet can carry out rapid data transfer and remote operation, and can be used with big data cloud computing systems, scientific research data sharing systems, and material genes.
  • the database integration system realizes data sharing, and provides support for material design and development based on machine learning, and the application of artificial intelligence in material production.
  • Figure 1 is a schematic diagram of the relationship between electrical information and time.
  • Figure 2 shows the electrical information-time curve during the solid solution process of the alloy.
  • Figure 3 is a schematic diagram of electrical information-time curve and characteristic structure measured during alloy aging.
  • Figure 4 shows the electrical information-time curve measured during the annealing process of the cold deformed material.
  • Figure 5 is a schematic diagram of the resistivity-time curve of a typical alloy solid solution at different temperatures.
  • Figure 6 shows the resistivity-time curve of a typical alloy solid solution.
  • Figure 7 is a schematic diagram showing the resistivity-time curve and the degree of solid solution of a typical alloy solid solution.
  • Figure 8 is the electrical information-time curve of alloy aging and the schematic diagram of the precipitation behavior.
  • Figure 9 shows the resistivity-time curve before and after optimization of alloy composition.
  • Figure 10 shows the resistivity-time curve of annealing of the same material at different temperatures (T 1 > T 2 > T 3 ).
  • Figure 11 is the resistivity-time curve of the workpiece with different cold deformation under the set temperature.
  • Figure 12 shows the resistivity-time curves of the two materials under the same annealing conditions.
  • Fig. 13 is a schematic diagram of real-time control of heat treatment by comparing the results of in-situ measured electrical information and reference electrical information.
  • Fig. 14 is a schematic diagram of obtaining reference electrical information.
  • Figure 15 is a block diagram of the module structure of the application device.
  • Fig. 16 is the conductivity-time curve obtained by the in-situ test in Example 1.
  • Fig. 17 is an SEM photograph of a sample of Example 1.
  • FIG. 18 is the conductivity-time curve obtained by the in-situ test in Example 2.
  • Figure 19 is a TEM photograph of a sample of Example 2.
  • Fig. 20 is the energy spectrum analysis result of the corresponding area in Fig. 19.
  • FIG. 21 is the resistivity-time curve obtained by the in-situ test in Example 3.
  • FIG. 23 is the conductivity-time curve obtained by the in-situ test in Example 5.
  • Fig. 24 is an SEM photograph of a sample of Example 5.
  • FIG. 25 is the conductivity-time curve obtained by the in-situ test in Example 6.
  • Fig. 26 is a TEM photograph of a sample of Example 6.
  • Fig. 27 is the conductivity-time curve obtained by the in-situ test in Example 7.
  • Fig. 28 is a TEM photograph of a sample of Example 7.
  • Fig. 29 is the resistivity-time curve obtained by the in-situ test in Example 8.
  • Figure 30 is a TEM photograph of a sample of Example 8.
  • Fig. 31 is the voltage-time curve obtained by the in-situ test in Example 9.
  • Figure 32 is an OM photograph of the sample of Example 9.
  • Fig. 33 is a conductivity-time curve obtained by the in-situ test of Example 10.
  • FIG. 34 is an OM photograph of an annealed sample of Al-0.16Y alloy in Example 10.
  • 35 is an OM photograph of an annealed sample of Al-0.16Y-0.15Zr alloy in Example 10.
  • Fig. 36 is the conductivity-time curve obtained by the in-situ test in Example 11.
  • Fig. 37 is the conductivity-time curve measured in Example 11 at 450°C.
  • Fig. 38 is the resistivity-time curve obtained by the in-situ test of Example 12.
  • Fig. 39 is a resistivity-time curve of the alloy of Example 12 measured at 475°C.
  • Figure 40 shows the measured resistivity-time curve and reference electrical information curve of the 7B50 alloy in Example 13 during the solid solution process at 470°C.
  • Figure 41 shows the measured conductivity-time curve and reference electrical information curve of the Al-0.10Zr-0.10La-0.02B alloy in Example 14.
  • Fig. 42 is an SEM photograph of a sample of Example 14.
  • FIG. 43 is the resistivity-time curve obtained in the in-situ test of Example 15.
  • Figure 44 shows the conductivity-temperature curve of Al-0.13Fe-0.33Si-0.10La alloy simulated by JmatPro7.0.0 software in Comparative Example 1.
  • Figure 45 is the hardness curve of Al-4wt.%Cu alloy in Comparative Example 2 after solid solution for different time and aging at 170°C/12h.
  • Fig. 46 shows the homogenization hardness-time curve of Al-1.00Hf-0.16Y alloy in Comparative Example 3.
  • Figure 47 shows the hardness curve of Al-4wt.%Cu alloy aged at 190°C in Comparative Example 4.
  • Figure 48 shows the hardness curve and room temperature conductivity curve of Al-4.5Zn-1.2Mg alloy aged at 170°C in Comparative Example 5.
  • Figure 49 shows the hardness change curve of the aluminum alloy in Comparative Example 6 when annealed at different temperatures and isochronous for 1 h.
  • Figure 50 shows the hardness curve of 7B50 alloy in Comparative Example 7 after solid solution for different time and aging at 170°C/8h.
  • the temperature of the heat treatment system reaches the set temperature, information is collected; electrical information is collected using the DC four-point method, and the specific parameters (length of electrical information collection area, constant current, electrical information type, etc.) are adjusted according to the test piece.
  • the material properties and microstructure obtained by traditional testing methods can be entered into the material heat treatment information database before testing, or can be added after testing. It should be understood that such data is not necessary for the patented method and can be used to verify the patented testing. As a result, assist in improving the accuracy and applicability of the self-learning model.
  • the detection content and results of the following embodiments are all entered under the corresponding material entry in the material heat treatment information database, so as to enrich and perfect the material heat treatment information database of the present invention, and continuously improve the reliability of subsequent detection and control.
  • Example 1 Online detection of the degree of solid solution of Al-0.1Zn-0.2Mg-0.1Fe-0.05Mn alloy at different temperatures and different times to determine the appropriate solid solution temperature of the alloy.
  • the recommended solution temperature range is 510°C ⁇ 540°C.
  • the absolute value of the slope of the conductivity-time curve is less than or equal to 1.00 ⁇ 10 -4 MS/(m ⁇ h)
  • the alloy reaches a near stable solid solution.
  • the degree of dissolution, the required solid solution time is 6-12h.
  • Figure 16 shows the conductivity-time curves of solid solution at different temperatures obtained by in-situ tests.
  • the solid solution temperatures are 510°C, 530°C, and 550°C, respectively.
  • the absolute value of the slope of the conductivity-time curve of solid solution at 510°C for 12h is 1.20 ⁇ 10 -4 MS/(m ⁇ h), which is greater than 1.00 ⁇ 10 -4 MS/(m ⁇ h), indicating that the near-stable solid solution has not yet been reached.
  • the degree of solubility is determined by the system through self-learning that 510°C is not a suitable solution temperature.
  • the conductivity-time curve of solid solution at 530°C for 12 hours tends to be stable, and the absolute value of the curve slope corresponding to 8 hours of solid solution is 1.00 ⁇ 10 -4 MS/(m ⁇ h), indicating that the nearly stable solid solution degree has been reached, and the system has passed Self-study identified 530°C as the appropriate solution temperature.
  • the absolute value of the slope of the conductivity-time curve of solid solution at 550°C for 12h is 3.33 ⁇ 10 -3 MS/(m ⁇ h), which is greater than 1.00 ⁇ 10 -4 MS/(m ⁇ h), and the system has passed self-learning as 550 °C is not a suitable solid solution temperature.
  • Figure 17 is the SEM photograph of the sample solution at 550°C for different time (0h, 4h, 8h, 12h). There are more coarse second phases in the as-cast structure, as shown in Figure 17(a), after solid solution for 4h There are still some coarse phases, as shown in Figure 17(b). After 8 hours of solid solution, part of the grain boundaries began to melt. As shown in Figure 17(c), it indicates that over-sintering occurred. After 12 hours of solid solution, the grain boundaries melted a lot. As shown in Figure 17(d), it indicates that a severe burn has occurred.
  • Example 2 Online detection of the state of Al-4wt.%Cu alloy solid solution at 535°C for different times to determine the appropriate time for the alloy to be solid solution at 535°C.
  • Figure 18 shows the conductivity-time curve obtained by the in-situ test, solid solution for 3600s, the absolute value of the slope at the corresponding point of the conductivity-time curve is 3.67 ⁇ 10 -5 MS/(m ⁇ s), after solution solution for 7275s, the conductivity The absolute value of the slope of the corresponding point of the rate-time curve reaches 8 ⁇ 10 -6 MS/(m ⁇ s).
  • the system recognizes that it has reached a near-stable solid solution degree through self-learning, and automatically takes 7275s (or rounded to 2h) as the alloy in 535. Suitable time for solid solution at °C.
  • Fig. 19 is a TEM photograph of the sample solution for 3600s and 7200s at 535°C
  • Fig. 20 is the energy spectrum analysis result of the marked area in Fig. 19.
  • Solid solution for 3600s there is a large amount of undissolved phase structure; solid solution for 7200s, the second phase basically dissolves into the matrix, which proves that the alloy has reached a nearly stable solid solution degree at 535°C for 2h.
  • Example 3 On-line detection of the state of the Mg-10Al-1Zn alloy solid solution at 430°C for different times to determine the appropriate time for the alloy to be solid solution at 430°C.
  • Figure 21 is the resistivity-time curve obtained by the in-situ test. After solid solution for 35842s, the resistivity reaches 1.7890 ⁇ 10 -7 ⁇ m, and the system recognizes that it has reached a near-stable solid solution degree through self-learning. 10h) as a suitable time for the alloy to solid-dissolve at 430°C.
  • Example 4 Online detection of the state of homogenization treatment of Zn-15Al solder at 330°C for different times to determine the appropriate time for homogenization of the alloy at 330°C.
  • Figure 22 shows the conductivity-time curve obtained by the in-situ test. After homogenizing 13795s, the conductivity reaches 4.925MS/m. The system recognizes that it has reached a nearly stable homogenization degree through self-learning, and automatically takes 13795s (or rounded to 4h) as Suitable time for homogenization of the alloy at 330°C.
  • Example 5 On-line detection of the Al-1.00Hf-0.16Y alloy at 635°C homogenization treatment for different time states, to determine the appropriate time for the alloy to homogenize at 635°C.
  • the Al-1.00Hf-0.16Y alloy reaches a near stable homogenization degree at 635°C, and the absolute value of the slope of the conductivity-time curve is less than or equal to 9 ⁇ 10 -4 %IACS/h, which is required to be uniform
  • the transformation time is 14 ⁇ 36h.
  • Figure 23 shows the conductivity-time curve obtained by the in-situ test. After homogenizing 66961s, the absolute value of the slope of the conductivity-time curve reaches 9 ⁇ 10 -4 %IACS/h, and the system has achieved a nearly stable homogenization degree through self-learning. , Automatically use 66961s (or rounded to 19h) as the appropriate homogenization time for the alloy at 635°C.
  • Figure 24 is a SEM photograph of samples homogenized for different times (10h, 19h) at 635°C.
  • the homogenization time is 10h, as shown in Figure 24(a)
  • the dendrite segregation is basically eliminated, indicating that a nearly stable homogenization level has been reached after 635°C/19h.
  • Example 6 On-line detection of the desolvation behavior of Al-4wt.%Cu alloy aging at 150°C to determine the time node for the precipitation of a new phase.
  • the desolubilization sequence of Al-4wt.%Cu alloy aging at 150°C is ⁇ ’’ phase (GPII zone) ⁇ ⁇ ’ phase ⁇ ⁇ phase.
  • Figure 25 shows the conductivity-time curve obtained by the in-situ test.
  • the conductivity corresponding to the initial aging degree is 32.19%IACS, and the corresponding conductivity rises to 33.10%IACS after 48h aging.
  • the conductivity-time curve corresponds to aging 11h and 20h. There are 3 obvious slope mutation points at 37h.
  • the system determines through self-learning that they correspond to the ⁇ '' phase (GPII zone), ⁇ 'phase and ⁇ respectively. The phase begins to precipitate.
  • Figure 26 is the TEM photograph of the Al-4wt.%Cu alloy aged at 150°C for different times (11h, 20h, 37h) (the electron beam incident direction is [100] Al ), when the aging time is 11h, as shown in Figure 26 As shown in (a), the ⁇ '' phase (GPII zone) is precipitated. When the aging time is 20h, as shown in Figure 26(b), the ⁇ 'phase is precipitated. When the aging time is 37h, as shown in Figure 26 ( As shown in c), the ⁇ phase is precipitated.
  • Example 7 On-line detection of the desolubilization behavior of Al-4wt.%Cu alloy aging at 190°C to determine the time node for the precipitation of a new phase.
  • the desolubilization sequence of Al-4wt.%Cu alloy aging at 190°C is ⁇ 'phase ⁇ ⁇ phase.
  • Figure 27 shows the conductivity-time curve obtained by the in-situ test.
  • the conductivity corresponding to the initial aging degree is 17.15 MS/m, and the corresponding conductivity rises to 17.72 MS/m after aging for 48 hours.
  • On the conductivity-time curve there are two obvious slope mutation points at 9h and 32h corresponding to the aging.
  • the system determines the corresponding relationship between the conductivity change and the second phase precipitation in the material heat treatment information database through self-learning. The ⁇ phase begins to precipitate.
  • Figure 28 is the TEM photograph of the Al-4wt.%Cu alloy aged at 190°C for different times (9h, 32h, 48h) (the electron beam incident direction is [100] Al ), when the aging time is 9h, as shown in Figure 28 As shown in (a), the ⁇ 'phase is precipitated. When the aging time is 32h, as shown in Figure 28(b), the ⁇ phase is precipitated. When the aging time is 48h, as shown in Figure 28(c), Theta phase.
  • Example 8 On-line detection of the aging state of Al-4.5Zn-1.2Mg alloy aging at 170°C for different times, and determining the time nodes for reaching different degrees of aging.
  • the desolubilization sequence of Al-4.5Zn-1.2Mg alloy aging at 170°C is ⁇ 'phase ⁇ ⁇ phase, and the peak aging time is 9-24h.
  • Figure 29 shows the resistivity-time curve obtained by the in-situ test.
  • the resistivity corresponding to the initial aging degree is 5.75 ⁇ 10 -8 ⁇ m
  • the resistivity after 48h aging is 5.04 ⁇ 10 -8 ⁇ m.
  • the system identifies the corresponding atomic clusters and ⁇ through self-learning. The'phase and ⁇ phase begin to separate out, and the aging time is less than 12h as the under-aging state, the aging time is 12h to reach the peak aging state, and the aging time is higher than 19h as the over-aging state.
  • Figure 30 is the TEM photograph of the Al-4.5Zn-1.2Mg alloy aged at 170°C for different times (0h, 6h, 12h, 19h) (the electron beam incident direction is [100] Al ), when the aging time is 0h, As shown in Figure 30(a), the alloy matrix is very pure, and when the ageing time is 6h, as shown in Figure 30(b), only small point-like phases are precipitated, which is under-aged. The ageing time is At 12h, as shown in Figure 30(c), a large amount of ⁇ 'phase is precipitated in the alloy, corresponding to the peak aging state. When the aging time is 19h, as shown in Figure 30(d), the alloy precipitates a spherical ⁇ phase, and the grain boundary The width of the precipitation-free band is above 400nm, which is an over-aged state.
  • Example 9 On-line detection of the degree or state of recovery and recrystallization of the rolled industrial pure aluminum sheet annealing at 300°C for different times .
  • Search material heat treatment information database For rolled industrial pure aluminum sheet, when complete recrystallization is the heat treatment target, 0% ⁇ P ⁇ 65% corresponds to the recovery state, 65% ⁇ P ⁇ 95% corresponds to the recrystallization state, 95% ⁇ P ⁇ 100% corresponds to grain growth.
  • Figure 31 shows the voltage-time curve obtained by the in-situ test.
  • the voltage gradually decreases with the extension of the annealing time.
  • the voltage before annealing is 0.6044mV, and it stabilizes after 12000s. Its stable voltage is 0.5973mV.
  • the corresponding voltages for annealing 0s, 2000s, 6000s, and 12000s are 0.6044mV, 0.5995mV, 0.5980mV, 0.5974mV, respectively, and the corresponding voltages are automatically calculated.
  • the annealing degree coefficients are 0%, 69.01%, 90.14%, 98.59%, and the system recognizes that the corresponding heat treatment degrees are rolled, incompletely recrystallized, recrystallized, and grain growth through self-learning.
  • Figure 32 is the metallographic photo of the samples corresponding to different annealing times (0s, 2000s, 6000s, 12000s).
  • the annealing time is 0s, as shown in Figure 32(a), it is a fiber structure with elongated grains.
  • the annealing time is 2000s, as shown in Figure 32(b), recrystallization occurs in a local area.
  • the annealing time is 6000s, as shown in Figure 32(c) incomplete recrystallization occurs.
  • Example 10 On-line detection of the recrystallization annealing process of aluminum alloy with different microalloying elements added at 420°C, comparing the degree of recovery and recrystallization of the two metals under the same annealing conditions, and evaluating the effect of the added elements on the heat resistance of the alloy, where alloy 1 is 0.16wt.%Y is added to industrial pure aluminum, and alloy 2 is 0.16wt.%Y and 0.15wt.%Zr to industrial pure aluminum.
  • Figure 33 shows the conductivity-time curve obtained by the in-situ test.
  • the conductivity of the Al-0.16Y alloy before annealing is 13.19MS/m, and it becomes stable after annealing for 4h.
  • the corresponding conductivity is 13.28MS/m
  • Al-0.16 The conductivity of Y-0.15Zr alloy before annealing is 13.09MS/m, and it becomes stable after 5h annealing, and the corresponding conductivity is 13.15MS/m.
  • the system automatically calculates the time required for the annealing degree coefficient of the two alloys to reach 30%, 60%, and 90%.
  • the time required for Al-0.16Y is 0.68h, 1.67h, 3.00h, respectively.
  • the time required for -0.16Y-0.15Zr is 0.70h, 1.78h, 3.56h, respectively, to reach the same degree of heat treatment.
  • Al-0.16Y-0.15Zr alloy takes longer time. It is determined that Al-0.16Y-0.15Zr alloy has more High resistance to recrystallization.
  • Figure 34 is the metallographic photo of the samples corresponding to the annealing of Al-0.16Y alloy at different times (0h, 2h, 4h, 6h).
  • the annealing time is 0h, as shown in Figure 34(a)
  • the grains are elongated
  • the annealing time is 2h, as shown in Figure 34(b)
  • partial recrystallization occurs.
  • the annealing time is 4h, as shown in Figure 34(c)
  • the grains merge and grow.
  • the annealing time is 8h, as shown in Figure 34(d)
  • the recrystallized grains grow abnormally.
  • Figure 35 is the metallographic photo of the Al-0.16Y-0.15Zr alloy annealed at different times (0h, 2h, 4h, 6h).
  • the grains are The elongated fiber structure, when the annealing time is 2h, as shown in Figure 35(b), is mainly fiber structure, when the annealing time is 4h, as shown in Figure 35(c), the alloy is partially recrystallized. When the annealing time is 8h, as shown in Figure 35(d), complete recrystallization has occurred. The results show that the Al-0.16Y-0.15Zr alloy has better resistance to recrystallization (or heat resistance).
  • Example 11 Information and data on annealing of Al-0.1Sc cold-shifted alloy at 400°C and 500°C are already stored in the material heat treatment information database, and the time for the annealing to start recrystallization at 450°C is predicted.
  • Figure 36 shows the conductivity-time curve of the Al-0.1Sc alloy recrystallization annealing at 400°C and 500°C in the material heat treatment information database.
  • Figure 36(a) shows the alloy annealing at 400°C corresponding to the initial degree The conductivity is 23.63%IACS, and the conductivity becomes stable after 6.5h of annealing, the corresponding conductivity is 23.93%IACS, and the annealing time corresponding to the start of recrystallization is 0.61h.
  • Figure 36(b) shows that the initial conductivity of the alloy is 19.91%IACS when annealed at 500°C. After 5.0h, the conductivity becomes stable, and the corresponding conductivity is 20.16%IACS.
  • the annealing time corresponding to the start of recrystallization is 1.78h. .
  • the system fits the alloy annealing at 450°C through self-learning, and predicts that the corresponding time to start recrystallization is 3883s, which is 64.7min.
  • the conductivity-time curve was tested in situ during the annealing process at 450°C. The result is shown in Figure 37.
  • the actual measured time to start recrystallization is 65.2min, which is similar to the predicted result of 64.7min.
  • Example 12 The material heat treatment information database already has 9% and 10% cold deformation information and data of industrial pure aluminum (aluminum content of 99.7%) annealing at 475°C.
  • the predicted cold deformation is 12.25% The time when the aluminum material starts to recrystallize at the same temperature.
  • Figure 38 shows the resistivity-time curve of the recrystallization annealing of aluminum with 9% and 10% cold deformation in the material heat treatment information database.
  • the initial annealing degree resistivity of the aluminum with 9% deformation is 8.226 ⁇ 10 -8 ⁇ •m, the resistivity tends to be stable after annealing for 4.5h, the corresponding resistivity is 8.122 ⁇ 10 -8 ⁇ •m;
  • the initial annealing degree resistivity of aluminum with a deformation of 16% is 8.242 ⁇ 10 -8 ⁇ •m, the resistivity tends to be stable after 6.2h annealing, the corresponding resistivity is 8.144 ⁇ 10 -8 ⁇ •m, the corresponding time for the two cold deformation aluminum materials to reach the start of recrystallization is 0.629h and 1.101h, respectively .
  • the system fits the annealing process of aluminum with a cold deformation of 12.25% through self-learning, and predicts that the corresponding time to start recrystallization is 0.865h
  • Example 13 Online detection of the electrical information of 7B50 alloy solid solution at 470°C, comparing the detection information with the reference electrical information in the heat treatment information database, and further feedback and optimizing self-learning based on the comparison results.
  • the reference resistivity-time curve of solid solution at 470°C is obtained through self-learning.
  • the resistivity reaches 9.520 ⁇ 10 -8 ⁇ m
  • the alloy reaches nearly To stabilize the degree of solid solution, the required solid solution time is 60min.
  • Figure 40 shows the in-situ measured resistivity-time curve and reference electrical information curve of 7B50 alloy during solid solution at 470°C.
  • the measured electrical resistivity of 60min solid solution is lower than the reference electrical information curve resistivity value, and the solid solution degree coefficient is only 91.67 %, the system determines that the heat treatment has not been completed, and the measured resistivity of 73 min of solid solution is equal to the resistivity value at 60 min of the reference electrical information curve, and the coefficient of solid solution reaches 100%.
  • the system determines that the heat treatment has been completed, and the heat treatment control module stops the heat treatment.
  • Example 14 Online detection of the homogenization of Al-0.10Zr-0.10La-0.02B alloy, the detection information is compared with the reference electrical information in the heat treatment information database, and the homogenization temperature is adjusted according to the comparison result, thereby controlling the Al- The homogenization process of 0.10Zr-0.10La-0.02B alloy at 620°C.
  • the system Based on the electrical information of the Al-0.10Zr-0.10La-0.02B alloy homogenized at different temperatures in the heat treatment information database, the system obtains its homogenized reference conductivity-time curve at 620°C through self-learning, and determines that it is homogenized at 620°C 18h can reach a nearly stable degree of homogenization.
  • Figure 41 is the measured conductivity-time curve and reference electrical information curve of Al-0.10Zr-0.10La-0.02B alloy.
  • Figure 41(a) is the measured curve with feedback control system. When the measured curve is lower than the reference curve, feedback Lower the furnace temperature. When the measured curve is higher than the reference curve, the furnace temperature will be increased by feedback. The final measured curve is roughly in line with the reference curve;
  • Figure 41(b) is the measured curve of the non-feedback control system. The actual temperature is somewhat different from the set temperature. Deviations result in a partial deviation between the measured curve and the reference curve.
  • Figure 42 shows the microstructures after the two heat treatments are observed by scanning electron microscope.
  • Figure 42(a) shows the microstructures after heat treatment with feedback. There is no obvious segregation and overburning, and the homogenization effect is good;
  • Figure 42(b) It is the microstructure after the non-feedback heat treatment is completed. There is overburning at the grain boundary, and there is still segregation in the grain, and the homogenization effect is not good. The reason is that the furnace temperature fluctuates and is not adjusted in time. When the temperature is too high, the grain boundary is overburned. , When the temperature is too low, the element diffusion is insufficient.
  • Example 15 The two-stage aging of Al-0.1Zr-0.1Sc alloy is detected online, and the temperature and time of the second-stage aging are automatically determined according to the degree of heat treatment of the first-stage aging (300°C).
  • the recommended first-stage aging temperature range for Al-0.1Zr-0.1Sc alloy is 270°C ⁇ 350°C
  • the aging time is 8 ⁇ 24h
  • the recommended second-stage aging temperature range is 370 ⁇ 430°C.
  • the alloy is aged at 300°C for 12h, the resistivity-time curve is measured in situ, as shown in Figure 43(a), the resistivity corresponding to 12h aging is 6.024 ⁇ 10 -8 ⁇ •m, and the aging degree coefficient is calculated as 60 %, the self-learning module determines that the second-level aging temperature is 400°C, and sets the resistivity corresponding to the target heat treatment state to 7.272 ⁇ 10 -8 ⁇ •m, the heat treatment control module heats up to 400°C for the second-level aging, in-situ
  • the corresponding resistivity-time curve is measured, as shown in Figure 43(b), the resistivity corresponding to aging for 32h reaches 7.272 ⁇ 10 -8 ⁇ •m, and the system calculates the aging degree coefficient as 100%, and the heat treatment is automatically terminated.
  • Comparative example 1 Use material performance simulation software to calculate the over-firing temperature of Al-0.1Zn-0.2Mg-0.1Fe-0.05Mn alloy.
  • Figure 44 shows the conductivity-temperature curve simulated by JmatPro7.0.0 software, the curve is at 635°C There is a sudden change in the temperature, the alloy will overfire above this temperature, and the overfire will not occur when the heat treatment temperature is below 630°C.
  • Example 1 shows that the Al-0.1Zn-0.2Mg-0.1Fe-0.05Mn alloy is solid solution overburned at 550°C, which is 85°C lower than the overburning temperature predicted by the software.
  • Comparative Example 2 Determine the appropriate time for Al-4wt.%Cu alloy to be solid-dissolved at 535°C by using the aging hardness curve.
  • Figure 45 shows the Al-4wt.%Cu alloy samples that were solid-dissolved at 535°C for different times and then subjected to 170°C/12h In the aging hardness curve, after the solid solution time exceeds 2h, the difference in the aging hardness value is not large, indicating that it has reached a nearly stable solid solution degree.
  • the non-in-situ detection of this comparative example is cumbersome in operation, complicated in sample processing, discrete and inaccurate in data, and is susceptible to differences in sampling locations.
  • Comparative Example 3 Using the hardness curve to determine the appropriate time for homogenization of Al-1.00Hf-0.16Y alloy at 635°C, Figure 46 shows the hardness of homogenization at different times. When the homogenization time reaches and exceeds 18h, the hardness value fluctuates slightly. It shows that nearly stable homogenization is achieved, and 18h can be used as a suitable uniform time.
  • this comparative example has disadvantages such as cumbersome steps, complicated sample processing process, non-in-situ measurement, discrete and insufficient data, susceptibility to differences in sampling positions, and inability to control process parameters.
  • Comparative example 4 Using the aging hardness curve to determine the time node for the precipitation of new phases in Al-4wt.%Cu alloy at 190°C, this comparative example collects a data every 2h.
  • Figure 47 shows the aging hardening curve. The aging of 10h and 36h corresponds to the aging time. The curve has a peak hardness, which corresponds to the ⁇ 'phase and the ⁇ phase precipitation.
  • This comparative example is affected by the sampling location, and the accuracy is not high.
  • the sample information was collected in situ, with small amount of experiment, dense data, high accuracy, and can accurately detect the peak aging of the alloy.
  • Comparative Example 5 Determine the peak aging time of Al-4.5Zn-1.2Mg alloy aged at 170°C by using the hardness curve and the room temperature conductivity curve.
  • Figure 48 shows the hardness and room temperature conductivity after aging at 170°C for different times. The hardness is reached after aging for 12 hours. At the peak value, the room temperature conductivity curve shows an upward trend as a whole. After the ageing time reaches 21h, the room temperature conductivity change rate decreases, corresponding to the growth and coarsening of the precipitated phase.
  • Example 8 although this comparative example uses a large number of samples and requires a large amount of experimentation, the data obtained is still discrete, and the sampling position is still taken.
  • Comparative Example 6 The isochronous annealing hardness curve was used to compare the degree of recovery and recrystallization of Al-0.16Y and Al-0.16Y-0.15Zr alloys under the same annealing conditions, and to evaluate the effect of added elements on the heat resistance of the alloy.
  • Figure 49 shows the hardness curve of aluminum alloy with different microalloying elements annealed at different temperatures for 1h. The curve shows that the hardness of Al-0.16Y alloy is lower than that of Al-0.16Y-0.15Zr alloy, and the hardness of Al-0.16Y alloy is 350 ⁇ The decrease in the range of 475°C is very significant.
  • Example 10 is an in-situ detection performed at different temperatures, which has the advantages of continuous data and high accuracy, short test time, simple steps, and the like.
  • Comparative Example 7 The hardness curve was used to determine the appropriate time for the 7B50 alloy to be solid-dissolved at 470°C.
  • the materials used and the testing environment were the same as in Example 13.
  • Figure 50 shows the hardness curve of the alloys after aging at 170°C/8h for different time of solid solution. After the aging time reaches 70min, the hardness value tends to be stable, indicating that it has reached a nearly stable solid solution degree.
  • the in-situ detection in Example 13 avoids the influence of different sampling positions, the determined suitable solution time is relatively accurate, and feedback control of the heat treatment process can be implemented online.
  • the above-mentioned comparative examples show the limitations of traditional methods and means, non-in-situ intermittent detection, cumbersome sample preparation steps, discrete data collected and easily affected by detection methods, and a long period for optimizing process parameters.
  • the embodiments reflect the technical advantages of the patented method.
  • In-situ online detection can directly obtain data during the heat treatment of the test piece.
  • the experimental process is simple, and the collected data is accurate and continuous. It can monitor the degree or status of the heat treatment of the test piece in real time, and then Online control of heat treatment.

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Abstract

一种基于原位采集信息调控热处理的方法及应用。在受测件进行热处理时原位采集信息和/或数据，比对热处理信息数据库中相应信息或数据，检测或表征受测件的热处理程度或状态，进而优化材料的热处理工艺和/或调控受测件的热处理。所述热处理包含但不局限于均匀化、固溶、时效、回复再结晶退火；所述原位采集是实时采集受测件在实际热处理环境中的信息或数据；所述热处理信息数据库中包括但不限于材料、热处理工艺、热处理过程的相关信息及数据，可以通过后续检测及自学习不断完善和优化。可以在线实现受测件的无损检测、热处理参数的实时优化和灵敏调控，进而使受测件达到设定热处理目标和/或组织性能。

Description

一种基于原位采集信息调控热处理的方法及应用 技术领域
本发明涉及一种基于原位采集信息调控热处理的方法及应用，属于材料热加工领域，具体属于材料热处理在线检测及控制领域。
 背景技术
材料或工件通过热处理形成预期组织结构，进而满足设定性能要求。加热温度、保温时间、变温速率等热处理工艺参数对材料组织性能的影响较大，生产中一般通过这些工艺参数的优化及调控使工件热处理后获得需要的组织性能。传统做法是在生产前进行不同温度、不同保温时间、不同变温速率的热处理，然后在室温进行性能检测和微观组织观察，如果组织性能没有达到目标要求，还须反复调整工艺参数并再次进行热处理，通过不断优化工艺参数逼近热处理的目标要求，不能直接在热处理过程中检测热处理程度或状态并控制热处理过程。
目前与热处理有关的检测方法均存在非原位、不连续、不准确、过程复杂、实验量大、成本过高等问题。专利CN 109536859 A公开了一种检测7075铝合金固溶淬火效果的方法，利用检测不同固溶温度和保温时间试样的电导率变化确定热处理时间，所测电导率为淬火后电导率，并非热处理过程中的原位测量，需要后续进行多组实验才能获得电导率与加热温度、保温时间曲线，实验步骤较复杂。论文《一种铝合金圆铸锭均匀化效果快速检测方法研究》中使用室温电导率仪及硬度计检测了6A01、6005A、7B05等合金多个炉次铸锭均匀化后的电导率及硬度值，并用金相显微镜及扫描电镜观察组织来进行相关验证，但其检测过程非原位、不连续。专利CN108193101A中利用显微硬度确定了4种Al-Mg-Cu系合金的最佳时效热处理工艺，其样品选取时间间隔大，最终确定的峰时效时间不精确，测量硬度数据波动大且易受偶发因素影响。论文《Coarsening resistance at 400°C of precipitation-strengthened Al-Zr-Sc-Er alloys》中利用硬度和室温电导率确定了Al-Zr-Sc(-Er)合金的最佳时效时间，但所测性能均为室温性能，且测试结果易受取样因素影响，性能曲线中出现离散点偏离甚至违背整体规律，该方法所需实验量大，非在线测试。专利CN 103175831 B中提出了一种适于变形铝合金材料再结晶组织比例分析评价的方法，可以区分再结晶组织与变形组织，进而分辨及统计材料的再结晶状况，但是该方法不适用于难腐蚀或过于易腐蚀的材料，适用的材料范围受限。
对于材料热处理信息的采集和储存，目前大多基于热处理后的组织和性能检测，对信息和数据的管理及应用体系并不完善。专利CN 105975727 A《材料数据的处理、生成、应用方法及终端、运处理平台》中提出了一种材料数据云处理平台，目标在于解决材料基因工程技术中材料试验和仿真计算脱节的实验问题，但其数据产生与材料制备过程并非同时进行，无法应用于材料的生产工艺控制；专利CN 106447229 A《一种材料信息学中的材料数据管理系统及方法》中公开了一种信息学研究框架，能够对材料数据进行增、删、改、查操作，但未对所存信息进行系统分析，未涉及生产过程在线反馈应用；专利CN 110298289 A《材料识别方法、装置、存储介质和电子设备》中公开了一种基于超声波信号确定目标物体的材料信息的设备，可用于材料识别，但是超声波信号容易受到干扰，并有可能对受测件产生破坏，应用范围受到限制。
 技术问题
本发明可在对工件进行热处理的同时，实现高温、连续的原位信息采集，同时可借助材料热处理数据库资源和自学习功能，对采集的信息进行即时处理和分析、存储，进而在线检测受测件的热处理程度或状态、优化材料的热处理工艺，实现受测件热处理的在线调控。
 技术解决方案
本发明提出一种基于原位采集信息调控热处理的方法和装置及应用。
本发明一种基于原位采集信息调控热处理的方法；在对受测件进行热处理的过程中原位连续采集信息和/或数据，在进行信息处理和/或数据分析后，与热处理信息数据库中相关信息或数据进行比对，在线检测或表征受测件的热处理程度或状态，进而优化材料的热处理工艺和/或调控受测件的热处理，从而使受测件达到设定热处理目标和/或组织性能。
本发明一种基于原位采集信息调控热处理的方法；所述热处理包含但不限于均匀化、固溶、时效、回复再结晶退火；所述热处理过程包含升温、保温、降温等操作的至少一种；所述热处理程度或状态包含但不限于欠时效、峰时效、过时效、回复、开始再结晶、完全再结晶。
本发明一种基于原位采集信息调控热处理的方法；所述原位采集是实时采集受测件在实际热处理环境中的信息和/或数据；优选的，所述信息为电学信息，包括但不限于电压、电阻、电阻率、电导率、导电率，各电学信息之间可以进行相应换算，换算既包括数值换算，也包括单位换算，所述换算采用下述公式中的至少一种。
电阻（Ω）=电压（V）÷电流（A）。
电阻率（Ω•m）=电阻（Ω）×截面积（m 2）÷长度（m）。
电导率（S/m）=1÷电阻率（Ω•m）。
导电率(%IACS)=电导率（MS/m）÷0.58。
本发明一种基于原位采集信息调控热处理的方法；所述电学信息的采集方法包括但不限于直流四点法、单电桥法、双电桥法；优选的，采用直流四点法，可以减少甚至排除导线和接触电阻对采集信息的影响。
本发明一种基于原位采集信息调控热处理的方法；所述信息处理是通过信息筛选及归类处理、数据获取及转换，减少冗余和噪音信息，提高信息的识别度；所述数据分析是通过特征量提取、数据挖掘和集成，进行数据降维和数据处理，提高检测的精准性；所述信息处理优选为对电学信息-时间曲线和/或电学信息-温度曲线进行相关处理；所述相关处理包括但不限于计算电学信息变化值、计算电学信息变化率、计算热处理程度系数。
优选的，所述热处理程度系数用P表示，定义P=(E ti-E 0)/(E u-E 0)×100%；所述E 0为初始热处理程度对应的电学信息，优选为受测件温度达到预设初始条件时对应的电学信息；所述E ti为热处理过程中任意时刻对应的电学信息，是达到目标热处理程度之前某个程度对应的电学信息；所述E u为目标热处理程度对应的电学信息，优选为受测件的性能和/或组织达到热处理目标时对应的电学信息。
本发明一种基于原位采集信息调控热处理的方法；所述热处理信息数据库中存有多种材料及其热处理的信息和数据，包括但不限于材料的信息和数据、热处理制度及相关工艺参数、热处理过程信息及数据；所述材料信息和数据包括材料成分及基本性质、热处理组织及性能指标；所述热处理制度包括但不限于均匀化处理制度、固溶处理制度、时效制度、软化退火制度；所述相关工艺参数包括但不限于加热温度、保温时间、升温速率、降温速率；所述热处理过程信息及数据包括但不限于不同热处理过程的温度、电学信息；优选的，通过数据驱动的神经网络对多组元材料进行分类，基于主成分分析、关联分析，进行数据内部本质结构特征提取，构建以成分为主线的工艺-组织-性能的关系型数据库。
本发明一种基于原位采集信息调控热处理的方法；所述关系型数据库支持并不限于下列数据库类型：SQL Server、MySQL、MongoDB、SQLite、Access、H2、Oracle、PostgreSQL；数据库访问技术包括但不限于ODBC、DAO、OLE DB、ADO，可以根据实际需要对存储内容进行增加、删除、修改、查询。
本发明一种基于原位采集信息调控热处理的方法；对于热处理信息数据库中已记录材料，可以直接从数据库中获取该种材料在设定热处理过程的电学信息、特征组织及性能信息。以图1所示的电学信息-时间曲线为例，当受测件温度达到预设初始条件时，t 0为热处理起始时间点（曲线横坐标起点）、E 0为初始热处理程度对应的电学信息，t 1、t 2、t 3…为热处理过程中的不同时刻，E t1、E t2、E t3….为对应不同热处理时刻的电学信息，与热处理程度一一对应，t u、E u为达到目标热处理程度的时间及相应的电学信息。
本发明一种基于原位采集信息调控热处理的方法；对于均匀化、固溶等热处理，随着热处理时间延长，电学信息-时间曲线逐渐趋于水平。理论上，在适当的固溶温度下，第二相逐渐回溶直至完全溶入基体中，对应的电学信息-时间曲线趋于水平，如图2所示。但是，实际生产中往往存在不溶相或难溶相，固溶处理一定时间后，固溶程度不再发生变化或变化速率相当小，优选的，对于电学信息-时间曲线的斜率变化率相当小的热处理过程，为了节省能源和缩短生产时间，定义1个近稳定固溶程度作为目标热处理程度。所述近稳定固溶程度的固溶程度及相应的电学信息与稳定固溶程度相近，但所需热处理时间大大缩短。确定近稳定固溶程度的方法包括但不限于：将实测的电学信息-时间曲线的斜率绝对值小于设定值起点所对应的固溶程度设定为近稳定固溶程度、将实测电学信息与材料热处理信息数据库中记录的稳定电学信息的差值达到设定值所对应的固溶程度设定为近稳定固溶程度、将热处理过程中材料的性能或组织达到目标时所对应的固溶程度定为近稳定固溶程度。
本发明一种基于原位采集信息调控热处理的方法；对于时效、回复再结晶退火，存在开始脱溶、峰值时效、开始再结晶、完全再结晶等临界热处理状态，所述目标热处理程度E u根据材料热处理的目标性能和/或组织确定；图3为合金时效过程中测得的电学信息-时间曲线及特征组织示意图，曲线上存在斜率非平稳变化点，分别对应开始脱溶、峰值时效；对于热处理信息数据库中已经存在的材料及其热处理条目，可以检索到该材料开始脱溶、欠时效、峰值时效、过时效的组织特征，也可以检索到T6、T79、T76、T74、T73等热处理状态的电导率（电阻率）、强度（或硬度）、，可以根据电导率（或电阻率）、强度（或硬度）表征其热处理程度，进而控制受测件的热处理过程。图4为冷变形材料退火过程中测得的电学信息-时间曲线，在热处理信息数据库中可以检索到该材料的回复、开始再结晶、完全再结晶、二次再结晶的特征组织及对应性能。用热处理程度系数P表示退火程度，例如，以完全再结晶为热处理目标，对应的热处理程度系数P=100%，P＜100%则对应部分再结晶。
本发明一种基于原位采集信息调控热处理的方法；对于热处理信息数据库中未记录的材料，针对不同的热处理过程，在检测获得的电学信息-时间曲线、电学信息-温度曲线上选取特征点，分别检测材料的成分、组织和性能，然后将材料信息和数据、热处理工艺数据、热处理过程信息及数据等存入数据库，后续针对同一材料的检测信息可以用来补充和完善数据库；所述特征点包括但不限于曲线变为水平线的起点（或曲线斜率绝对值小于某设定值的起点）、曲线的拐点（曲线凹凸性改变的点）、曲线斜率非平稳变化的点（曲线斜率变化率或变化值超过设定范围的点）、特征热处理程度或临界热处理状态在曲线上的对应点、时间间隔相同的点、温度间隔相同的点；所述特征热处理程度或临界热处理状态包括但不限于低熔点相开始溶解、第二相开始回溶、固溶体开始脱溶、峰值时效、开始再结晶、完全再结晶、再结晶晶粒长大。
本发明一种基于原位采集信息调控热处理的方法；所述热处理信息数据库可以通过后续检测及自学习不断完善或优化，提高数据的可信度及可用性；所述自学习基于神经网络算法、随机森林算法、粒子群算法中的至少一种算法；其运行环境支持并不限于下列操作系统：Windows、Android、Linux、Mac OS、IOS，学习结果通过SOAP、RESTful对用户提供终端服务；同时本发明所涉及的上述算法可以和贝叶斯优化算法对接，进而达到优化算法的目的。
本发明一种基于原位采集信息调控热处理的方法；所述热处理信息数据库是本地数据库或云端数据库；所述云端数据库由不同用户端上传数据组成，功能包括但不限于管理权限、验证访问、保存数据、处理数据、管理数据、分析数据。
本发明一种基于原位采集信息调控热处理的方法；所述热处理信息数据库中的信息和数据的应用方式还有很多，如通过计算电学信息-时间曲线斜率等方法检测和表征材料热处理的程度或状态等，应当认为，凡是基于本专利所述方法，即通过原位连续采集电学信息并进行相关处理，对热处理程度进行在线检测、表征和调控，均属于本专利的保护范围。
在本发明中，信息采集及处理模块所用硬件包括计算机eithley 2450 数字源表；Keithley 2182A 纳伏表；特制夹具；数据线；其中计算机含有CPU、主板， 显卡、内存条、显示器，硬盘等。
在本发明中，自学习模块以及热处理信息数据库搭建所用硬件包括计算机eithley 2450 数字源表；Keithley 2182A 纳伏表；特制夹具；数据线；其中计算机含有CPU、主板， 显卡、内存条、显示器，硬盘等。
在本发明中，热处理控制模块所用硬件包括：厦门宇电高性能智能温控器AI-708；K型热电偶；USB转RS485数据线。
在本发明中，热处理系统所用硬件包括：天津中环furnace 1200℃三温区真空气氛管式电炉。
本发明一种基于原位采集信息调控热处理方法的应用；所述方法可应用于优化材料的热处理工艺和/或在线调控受测件的热处理。
本发明一种基于原位采集信息调控热处理方法的应用；所述方法应用于热处理工艺优化，基于热处理工艺-特征组织-电学信息基本数据集，建立高效全局寻优的自适应设计模型，解决热处理的多目标、多参数系统优化问题。
本发明一种基于原位采集信息调控热处理方法的应用；所述方法应用于均匀化处理，包括但不限于确定适宜的均匀化温度、均匀化时间、升温速率、降温速率，所述均匀化包括单级均匀化、多级均匀化；具体操作优选为：选择几个温度进行均匀化并原位采集信息，将达到目标均匀化程度用时最短且不会过烧的温度作为适宜的均匀化温度；根据适宜均匀化温度对应的电学信息-时间曲线确定适宜的均匀化时间，将均匀化程度系数达到100%（或曲线斜率绝对值小于设定值）的对应时间确定为适宜的均匀化时间。
本发明一种基于原位采集信息调控热处理方法的应用；所述方法应用于固溶处理，包括但不限于确定适宜的固溶温度、固溶时间、升温速率、降温速率，所述固溶包括单级固溶、多级固溶；具体操作优选为：选择几个温度进行固溶同时原位采集信息，将达到目标固溶程度用时最短且不会过烧的温度作为适宜的固溶温度；根据适宜固溶温度对应的电学信息-时间曲线确定适宜的固溶时间，将固溶程度系数达到100%（或曲线斜率绝对值小于设定值）的对应时间确定为适宜的固溶时间；图5为典型合金在不同温度固溶的电阻率-时间曲线示意图，T 1＞T 2＞T 3＞T 4＞T 5，T 1和T 5对应曲线的电阻率在较长时间内无法趋于稳定，T 2、T 3和T 4对应曲线的电阻率能够在规定时间内趋于稳定，均可以作为该合金的适宜固溶温度；图6为典型合金固溶的电阻率-时间曲线，当系统检测到合金达到近完全固溶程度时，设定固溶程度系数P=100%、对应的时间为适宜固溶时间；图7为典型合金固溶的电阻率-时间曲线及固溶程度表征示意图，预设电阻率-时间曲线斜率绝对值小于设定值k时固溶完成，A点|dP/dt|＞k，合金处于不完全固溶态，B点|dP/dt|＜k，合金达到目标固溶程度。
本发明一种基于原位采集信息调控热处理方法的应用；所述方法应用于时效处理，包括但不限于判定多种时效析出相的脱溶贯序及析出新相的时间窗口、确定达到强度峰值的时效时间、达到不同时效程度的时间节点，所述时效包括单级时效、多级时效；图8为合金时效的电学信息-时间曲线及对应相的析出示意图，α相、β相脱溶析出和长大均会引起曲线斜率变化，可根据斜率变化和合金的脱溶规律判定该合金的脱溶贯序及析出新相的时间窗口；图9为合金成分优化前后的电阻率-时间曲线，合金成分发生细微的变化，电阻率-时间曲线发生明显变化，B、B’点分别对应成分优化前后的峰值时效点，成分变化导致峰值时效时间改变，因此，根据时效的电阻率-时间曲线可以确定合金在设定温度达到不同时效程度的时间节点。
本发明一种基于原位采集信息调控热处理方法的应用；所述方法应用于回复再结晶退火，包括但不限于预测材料在指定温度达到指定退火程度所需时间、预测指定冷变形量材料达到指定退火程度所需时间、比较不同材料在相同热处理条件下抵抗再结晶的能力。
本发明一种基于原位采集信息调控热处理方法的应用；预测材料在指定温度达到指定退火程度所需时间，是指对数据库中已存有的材料通过自学习拟合预测其在未检测过的温度退火达到指定退火程度所需的时间，具体操作优选为：在材料热处理信息数据库中调取指定温度相邻温度的已知信息或数据，通过自学习预测指定温度退火达到设定退火程度所需时间；图10为同种材料在不同温度下（T 1＞T 2＞T 3）退火的电阻率-时间曲线，退火温度越高，达到设定再结晶程度用时越短，在T 1、T 2、T 3温度对应的电阻率-时间曲线上分别找到P=50%点并拟合连线，根据拟合曲线可以对不同温度下达到P=50%再结晶程度所需退火时间进行预测。
本发明一种基于原位采集信息调控热处理方法的应用；预测指定冷变形量材料达到指定退火程度所需时间，是指对数据库中已有材料通过自学习拟合预测其在经过指定变形量的冷变形后于指定温度下退火达到指定退火程度所需的时间，，具体操作优选为：在材料热处理信息数据库中调取指定冷变形量相邻的已知的冷变形量对应的信息或数据，通过自学习预测数据库中未存储冷变形量退火达到设定退火程度所需时间；图11为不同冷变形量工件在设定温度下的电阻率-时间曲线，在3根电阻率-时间曲线上分别找到P=50%对应点并拟合连线，据此可以预测设定温度下不同冷变形量受测件达到P=50%退火程度的所需时间。本发明一种基于原位采集信息调控热处理方法的应用；比较不同材料在相同热处理条件下抵抗再结晶的能力的具体操作优选为：将多个金属同时放在一个热处理系统进行检测或分别在相同热处理条件下进行检测，比较电学信息-时间曲线相同时间点的退火程度系数，数值越大，退火软化程度越高，材料的抗再结晶能力越弱，达到相同退火程度系数的时间越长，材料的抗再结晶能力越强。图12为两种材料在相同退火条件下的电阻率-时间曲线，达到相同退火程度，合金1所需时间比合金2短，说明合金1的抗再结晶能力弱于合金2。
本发明一种基于原位采集信息调控热处理方法的应用；所述方法应用于在线调控热处理，具体操作优选为：在热处理过程中原位连续采集信息和/或数据，通过即时的信息处理和数据分析后，与热处理信息数据库中相关信息或数据进行比对，检测或表征其热处理程度或状态，进而调整热处理工艺参数、控制热处理过程，从而使受测件达到设定热处理目标和/或组织性能。图13为通过原位实测电学信息和参照电学信息比对结果实时调控热处理的示意图，A点为实测电阻率-时间曲线与参照电阻率-时间曲线重合的点，保持热处理参数不变；在B点，实测电阻率-时间曲线偏离参照电阻率-时间曲线，调整热处理参数，在C点，实测电阻率-时间曲线回归参照电阻率-时间曲线，在D点，达到设定热处理目标，停止热处理；所述参照电学信息从热处理信息数据库中获取，图14为获取参照电学信息的示意图，获取方法优选为：基于数据库中同一材料及同一热处理过程的电学信息，通过自学习获取电学信息与热处理参数的之间的逻辑规律和/或数据关系，并作为样本储存在热处理信息数据库中，数据库通过随后的检测不断优化。
本发明一种基于原位采集信息调控热处理方法的应用装置及软件系统，其结构框图如图15所示，包括信息采集及处理模块、自学习模块、热处理信息数据库、热处理控制模块、热处理系统；所述信息采集及处理模块用于对受测件的热处理信息进行原位采集和即时处理，其采集频率可调，所用电学信息可实时换算；所述自学习模块用于分析逻辑规律和/或数据关系，包括但不限于分析材料与热处理之间的逻辑规律、信息与信息或数据与数据之间的关联；所述热处理信息数据库用于存储信息采集与处理模块得到的数据并提供终端服务；所述热处理控制模块用于根据自学习模块的分析结果生成控制命令，既可以根据预先设定模式运行，也可以进行在线调整；所述热处理系统执行所述控制命令，调整热处理温度、控制热处理时间。
本发明一种基于原位采集信息调控热处理方法的应用；除了上述应用外，本发明方法在实际生产过程中的应用形式多样；应当认为，凡是基于本专利所述方法，即通过原位连续采集电学信息并进行相关处理，对热处理进行在线检测、表征和控制，均属于本专利的保护范围。
 有益效果
相对于现有技术，本发明提出一种基于原位采集信息和/或数据调控热处理的技术方案，其技术优势为。
1. 可以对所有导电的受测件进行在线无损检测，受测件形状不受限制，热处理温度不受限制，热处理场所不受限制，实验室和生产现场均可应用，受测件运动状态不受限制，可以静止也可以连续运动，优选为受测件和检测装置之间不存在相对运动。
2. 本发明通过原位信息采集和即时信息处理，实现热处理组织变化响应信息的灵敏、准确捕捉，通过高效的信息处理和专业化的数据分析，实现对数据的有效排查、挖掘和优化，提高数据库的有效信息存储量，减少系统误差、提高检测和控制的精准性。
3. 本发明具有自学习功能，与材料的热力学和扩散动力学数据库、材料热处理专家系统、高通量计算和实验平台实现深度融合，构建以成分为主线的工艺-组织-性能的关系型数据库，通过全流程组织演变的自动判断，可实现性能驱动的工艺参数的自动调整，通过热处理实时调控达成热处理目标、准确满足受测件的组织性能要求。
4. 本发明的信息化应用兼容多种操作系统和应用平台，通过界面友好的软件结合互联网可进行数据的快速流转和远程操作，可与大数据云计算系统、科研数据共享系统、材料基因大数据库集成系统等实现数据共享，为基于机器学习的材料设计与开发、人工智能在材料生产中应用提供支撑。
 附图说明
图1为电学信息-时间关系示意图。
图2为合金固溶过程中的电学信息-时间曲线。
图3为合金时效过程中测得的电学信息-时间曲线及特征组织示意图。
图4为冷变形材料退火过程中测得的电学信息-时间曲线。
图5为典型合金在不同温度固溶的电阻率-时间曲线示意图。
图6为典型合金固溶的电阻率-时间曲线。
图7为典型合金固溶的电阻率-时间曲线及固溶程度表征示意图。
图8为合金时效的电学信息-时间曲线及表征析出行为示意图。
图9为合金成分优化前后的电阻率-时间曲线。
图10为同种材料在不同温度下（T 1＞T 2＞T 3）退火的电阻率-时间曲线。
图11为不同冷变形量工件在设定温度下的电阻率-时间曲线。
图12为两种材料在相同退火条件下的电阻率-时间曲线。
图13为通过原位实测电学信息和参照电学信息比对结果实时调控热处理的示意图。
图14为获取参照电学信息的示意图。
图15为应用装置的模块结构框图。
图16为实施例1原位测试得到的电导率-时间曲线。
图17为实施例1样品的SEM照片。
图18为实施例2原位测试得到的电导率-时间曲线。
图19为实施例2样品的TEM照片。
图20为图19中对应区域的能谱分析结果。
图21是实施例3中原位测试得到的电阻率-时间曲线。
图22是实施例4中原位测试得到的电导率-时间曲线。
图23是实施例5原位测试得到的导电率-时间曲线。
图24为实施例5样品的SEM照片。
图25为实施例6原位测试得到的导电率-时间曲线。
图26为实施例6样品的TEM照片。
图27为实施例7原位测试得到的电导率-时间曲线。
图28为实施例7样品的TEM照片。
图29为实施例8原位测试得到的电阻率-时间曲线。
图30为实施例8样品的TEM照片。
图31为实施例9原位测试得到的电压-时间曲线。
图32为实施例9样品的OM照片。
图33为实施例10原位测试得到的电导率-时间曲线。
图34为实施例10中Al-0.16Y合金退火样品的OM照片。
图35为实施例10中Al-0.16Y-0.15Zr合金退火样品的OM照片。
图36为实施例11原位测试得到的导电率-时间曲线。
图37为实施例11在450℃实测得到的导电率-时间曲线。
图38为实施例12原位测试得到的电阻率-时间曲线。
图39为实施例12合金在475℃实测得到的电阻率-时间曲线。
图40为实施例13中7B50合金在470℃固溶过程中原位实测的电阻率-时间曲线和参照电学信息曲线。
图41为实施例14中Al-0.10Zr-0.10La-0.02B合金实测电导率-时间曲线和参照电学信息曲线。
图42为实施例14样品的SEM照片。
图43为实施例15原位测试得到的电阻率-时间曲线。
图44为对比例1中利用JmatPro7.0.0软件模拟出的Al-0.13Fe-0.33Si-0.10La合金电导率-温度曲线。
图45为对比例2中Al-4wt.%Cu合金固溶不同时间样品再经170℃/12h时效后的硬度曲线。
图46为对比例3中Al-1.00Hf-0.16Y合金均匀化的硬度-时间曲线。
图47为对比例4中Al-4wt.%Cu合金在190℃时效的硬度曲线。
图48为对比例5中Al-4.5Zn-1.2Mg合金在170℃时效的硬度曲线和室温导电率曲线。
图49为对比例6中铝合金在不同温度等时退火1h的硬度变化曲线。
图50为对比例7中7B50合金固溶不同时间再经170℃/8h时效后的硬度曲线。
 本发明的最佳实施方式
在此处键入本发明的最佳实施方式描述段落。
 本发明的实施方式
下面结合具体实施方式来进一步说明本发明的技术方案。待热处理系统温度达到设定温度开始采集信息；电学信息使用直流四点法进行采集，具体参数（电学信息采集区域长度、恒定电流、电学信息类型等）根据受测件进行调整。传统检测方法得到的材料性能、微观组织既可以在检测前录入材料热处理信息数据库中，也可以在检测完成后补录，应理解，此类数据并非本专利方法必须，可以用于验证本专利检测结果、辅助提高自学习模型的精确度和适用性。下列实施例检测内容及结果均录入材料热处理信息数据库相应材料条目下，以丰富和完善本发明的材料热处理信息数据库，并持续提高后续检测和控制的可靠性。
实施例1：在线检测Al-0.1Zn-0.2Mg-0.1Fe-0.05Mn合金在不同温度固溶不同时间的固溶程度，确定合金适宜的固溶温度。
检索材料热处理信息数据库，推荐的固溶温度范围为510℃~540℃，当电导率-时间曲线斜率的绝对值小于等于1.00×10 -4MS/(m·h)时，合金达到近稳定固溶程度，所需固溶时间为6~12h。
图16为原位测试得到的不同温度下固溶的电导率-时间曲线，其固溶温度分别为510℃、530℃、550℃。在510℃固溶12h的电导率-时间曲线的斜率绝对值为1.20×10 -4MS/(m·h)，大于1.00×10 -4MS/(m·h)，表明尚未达到近稳定固溶程度，系统通过自学习认定510℃不是适宜的固溶温度。在530℃固溶12h的电导率-时间曲线趋于稳定，固溶8h对应曲线斜率的绝对值为1.00×10 -4MS/(m·h)，表明达到了近稳定固溶程度，系统通过自学习认定530℃为适宜的固溶温度。在550℃固溶12h的电导率-时间曲线的斜率绝对值为3.33×10 -3MS/(m·h)，大于1.00×10 -4MS/(m·h)，系统通过自学习认定550℃不是适宜的固溶温度。
图17为在550℃固溶不同时间（0h，4h，8h，12h）试样的SEM照片，铸态组织中存在较多粗大第二相，如图17(a)所示，固溶4h后仍有部分粗大相，如图17(b)所示，固溶8h后，部分晶界开始熔化，如图17(c)所示，表明发生了过烧，固溶12h后晶界大量熔化，如图17(d)所示，表明发生了严重过烧。
实施例2：在线检测Al-4wt.%Cu合金在535℃固溶不同时间的状态，确定合金在535℃固溶的适宜时间。
检索材料热处理信息数据库，获知Al-4wt.%Cu在535℃达到近稳定固溶程度时，其电导率-时间曲线的斜率绝对值小于等于8×10 -6MS/(m·s)，所需固溶时间为1~6h。
图18为原位测试得到的电导率-时间曲线，固溶3600s，在电导率-时间曲线对应点的斜率绝对值为3.67×10 -5 MS/(m·s)，固溶7275s后，电导率-时间曲线对应点的斜率绝对值达到8×10 -6MS/(m·s)，系统通过自学习认定达到近稳定固溶程度，自动将7275s（或取整为2h）作为合金在535℃固溶的适宜时间。
图19为在535℃固溶3600s和7200s试样的TEM照片，图20为图19中标示区域的能谱分析结果。固溶3600s，存在大量未溶相组织；固溶7200s，第二相基本溶入基体，证明合金在535℃固溶2h达到了近稳定固溶程度。
实施例3：在线检测Mg-10Al-1Zn合金在430℃固溶不同时间的状态，确定合金在430℃固溶的适宜时间。
检索材料热处理信息数据库，获知Mg-10Al-1Zn合金在430℃达到近稳定固溶程度时对应的电阻率为1.7890×10 -7Ω·m，所需固溶时间为5~20h。
图21为原位测试得到的电阻率-时间曲线，固溶35842s后，电阻率达到1.7890×10 -7Ω·m，系统通过自学习认定达到近稳定固溶程度，自动将35842s（或取整为10h）作为合金在430℃固溶的适宜时间。
实施例4：在线检测Zn-15Al钎料在330℃均匀化处理不同时间的状态，确定合金在330℃均匀化的适宜时间。
检索材料热处理信息数据库，获知Zn-15Al钎料在330℃达到近稳定均匀化程度时对应的电导率为4.925MS/m，所需均匀化时间为2~10h。
图22为原位测试得到的电导率-时间曲线，均匀化13795s后，电导率达到4.925MS/m，系统通过自学习认定达到近稳定均匀化程度，自动将13795s（或取整为4h）作为合金在330℃均匀化的适宜时间。
实施例5：在线检测Al-1.00Hf-0.16Y合金在635℃均匀化处理不同时间的状态，确定合金在635℃均匀化的适宜时间。
检索材料热处理信息数据库，获知Al-1.00Hf-0.16Y合金在635℃达到近稳定均匀化程度，其导电率-时间曲线的斜率绝对值小于等于9×10 -4%IACS/h，所需均匀化时间为14~36h。
图23为原位测试得到的导电率-时间曲线，均匀化66961s后，导电率-时间曲线的斜率绝对值达到9×10 -4%IACS/h，系统通过自学习认定达到近稳定均匀化程度，自动将66961s（或取整为19h）作为合金在635℃的适宜均匀化时间。
图24为在635℃均匀化不同时间（10h，19h）试样的SEM照片，当均匀化时间为10h时，如图24(a)所示，晶内存在大量枝晶偏析，当均匀化时间为19h时，如图24(b)所示，枝晶偏析基本消除，表明经过635℃/19h达到了近稳定均匀化程度。
实施例6：在线检测Al-4wt.%Cu合金在150℃时效的脱溶行为，确定析出新相的时间节点。
检索材料热处理信息数据库，Al-4wt.%Cu合金在150℃时效的脱溶序列为θ’’相（GPII区）→θ’相→θ相。
图25为原位测试得到的导电率-时间曲线，初始时效程度对应的导电率为32.19%IACS，时效48h后对应的导电率上升为33.10%IACS，导电率-时间曲线上对应时效11h、20h、37h处存在3个明显的斜率突变点，系统根据材料热处理信息数据库中导电率与第二相析出的对应关系，通过自学习认定分别对应θ’’相（GPII区）、θ’相和θ相的开始析出。
图26为Al-4wt.%Cu合金在150℃时效不同时间（11h，20h，37h）试样的TEM照片（电子束入射方向为[100] Al），当时效时间为11h时，如图26(a)所示，析出了θ’’相（GPII区），当时效时间为20h时，如图26(b)所示，析出了θ’相，当时效时间为37h时，如图26(c)所示，析出了θ相。
实施例7：在线检测Al-4wt.%Cu合金在190℃时效的脱溶行为，确定析出新相的时间节点。
检索材料热处理信息数据库，Al-4wt.%Cu合金在190℃时效的脱溶序列为θ’相→θ相。
图27为原位测试得到的电导率-时间曲线，初始时效程度对应的电导率为17.15MS/m，时效48h后对应的电导率上升为17.72MS/m。电导率-时间曲线上对应时效9h、32h处存在2个明显的斜率突变点，系统根据材料热处理信息数据库中电导率变化与第二相析出的对应关系，通过自学习认定分别对应θ’相和θ相的开始析出。
图28为Al-4wt.%Cu合金在190℃时效不同时间（9h，32h，48h）试样的TEM照片（电子束入射方向为[100] Al），当时效时间为9h时，如图28(a)所示，析出了θ’相，当时效时间为32h时，如图28(b)所示，析出了θ相，当时效时间为48h时，如图28(c)所示，析出了θ相。
实施例8：在线检测Al-4.5Zn-1.2Mg合金在170℃时效不同时间的时效状态，确定达到不同时效程度的时间节点。
检索材料热处理信息数据库，Al-4.5Zn-1.2Mg合金170℃时效的脱溶序列为η’相→η相，峰时效时间为9~24h。
图29为原位测试得到的电阻率-时间曲线，初始时效程度对应的电阻率为5.75×10 -8Ω·m，时效48h后对应的电阻率为5.04×10 -8Ω·m，电阻率-时间曲线上对应时效6h、12h、19h处存在3个明显的斜率突变点，系统根据材料热处理信息数据库中电阻率变化与第二相析出的对应关系，通过自学习认定分别对应原子团簇、η’相、η相开始析出，时效时间少于12h为欠时效状态，时效12h达到峰时效状态，时效时间高于19h为过时效状态。
图30为Al-4.5Zn-1.2Mg合金在170℃时效不同时间（0h，6h，12h，19h）试样的TEM照片（电子束入射方向为[100] Al），当时效时间为0h时，如图30(a)所示，合金基体十分纯净，当时效时间为6h时，如图30(b)所示，仅有尺寸很小的点状相析出，为欠时效状态，当时效时间为12h时，如图30(c)所示，合金析出大量η’相，对应峰时效状态，当时效时间为19h时，如图30(d)所示，合金析出圆球状的η相，晶界无沉淀析出带宽度在400nm以上，为过时效状态。
实施例9：在线检测轧制态工业纯铝板在300℃退火不同时间的回复再结晶程度或状态 。
检索材料热处理信息数据库，对于轧制态工业纯铝板，以完全再结晶为热处理目标时，0%≤P＜65%对应回复态，65%≤P＜95%对应再结晶状态，95%≤P≤100%对应晶粒长大。
图31为原位测试得到的电压-时间曲线，电压随退火时间延长逐渐降低。退火前的电压为0.6044mV，在12000s后趋于稳定，其稳定的电压为0.5973mV，退火0s、2000s、6000s、12000s对应电压分别为0.6044mV、0.5995mV、0.5980mV、0.5974mV，自动计算对应的退火程度系数分别为0%、69.01%、90.14%、98.59%，系统通过自学习认定对应的热处理程度分别为轧制态、不完全再结晶态、再结晶态和晶粒长大。
图32为对应退火不同时间（0s，2000s，6000s，12000s）试样的金相照片，当退火时间为0s时，如图32(a)所示，为晶粒被拉长的纤维组织，当退火时间为2000s时，如图32(b)所示，局部区域发生了再结晶，当退火时间为6000s时，如图32(c)所示，发生了不完全再结晶，当退火时间为12000s时，如图32(d)所示，再结晶晶粒出现粗化，表明退火2000s、6000s、12000s对应的热处理程度分别为部分再结晶、不完全再结晶、再结晶晶粒长大。
实施例10：在线检测加入不同微合金元素铝合金在420℃再结晶退火过程，比较相同退火条件下两种金属的回复再结晶程度，评估添加元素对合金耐热性的影响，其中合金1为工业纯铝中加入0.16wt.%Y，合金2为工业纯铝中加入0.16wt.%Y和0.15wt.%Zr。
图33为原位测试得到的电导率-时间曲线，Al-0.16Y合金退火前的电导率为13.19MS/m，退火4h后趋于稳定，对应的电导率为13.28MS/m，Al-0.16Y-0.15Zr合金退火前的电导率为13.09MS/m，退火5h后趋于稳定，对应的电导率为13.15MS/m。以完全退火态为目标热处理程度，系统自动计算两种合金退火程度系数达到30%、60%、90%所需时间，Al-0.16Y所需时间分别为0.68h、1.67h、3.00h，Al-0.16Y-0.15Zr所需时间分别为0.70h、1.78h、3.56h，达到相同热处理程度，Al-0.16Y-0.15Zr合金经历的时间更长，认定Al-0.16Y-0.15Zr合金具有更高的抵抗再结晶的能力。
图34为对应Al-0.16Y合金退火不同时间（0h，2h，4h，6h）试样的金相照片，当退火时间为0h时，如图34(a)所示，为晶粒被拉长的纤维组织，当退火时间为2h时，如图34(b)所示，发生了部分再结晶，当退火时间为4h时，如图34(c)所示，晶粒发生合并长大，当退火时间为8h时，如图34(d)所示，再结晶晶粒异常长大。图35为对应Al-0.16Y-0.15Zr合金退火不同时间（0h，2h，4h，6h）试样的金相照片，当退火时间为0h时，如图35(a)所示，为晶粒被拉长的纤维组织，当退火时间为2h时，如图35(b)所示，主要为纤维组织，当退火时间为4h时，如图35(c)所示，合金发生部分再结晶，当退火时间为8h时，如图35(d)所示，发生了完全再结晶，结果表明Al-0.16Y-0.15Zr合金具有更好的抵抗再结晶的能力（或耐热性）。
实施例11：材料热处理信息数据库中已经存有Al-0.1Sc冷变形态合金在400℃、500℃退火的信息和数据，预测其在450℃退火开始再结晶的时间。
检索材料热处理信息数据库，图36为材料热处理信息数据库中Al-0.1Sc合金在400℃、500℃再结晶退火的导电率-时间曲线，图36(a)显示合金在400℃退火对应初始程度的导电率为23.63%IACS，退火6.5h后导电率趋于稳定，对应的导电率为23.93%IACS，开始再结晶对应的退火时间为0.61h。图36(b)显示合金在500℃退火对应初始程度的导电率为19.91%IACS，退火5.0h后导电率趋于稳定，对应导电率为20.16%IACS，开始再结晶对应的退火时间为1.78h。系统通过自学习对合金在450℃退火进行拟合，预测开始再结晶对应时间为3883s，即64.7min。
在450℃退火过程中原位测试导电率-时间曲线，结果如图37所示，实测开始再结晶对应时间为65.2min，与预测结果64.7min相近。
实施例12：材料热处理信息数据库中已经存有冷变形量为9%和10%的工业纯铝（铝含量为99.7%）冷加工材在475℃退火的信息和数据，预测冷变形量为12.25%的铝材在相同温度开始再结晶的时间。
检索材料热处理信息数据库，图38为材料热处理信息数据库中冷变形量为9%和10%的铝材再结晶退火的电阻率-时间曲线，变形量为9%铝材的初始退火程度电阻率为8.226×10 -8Ω•m，退火4.5h后电阻率趋于稳定，对应的电阻率为8.122×10 -8Ω•m；变形量为16%铝材的初始退火程度电阻率为8.242×10 -8Ω•m，退火6.2h后电阻率趋于稳定，对应的电阻率为8.144×10 -8Ω•m，两种冷变形量铝材达到开始再结晶对应时间分别为0.629h、1.101h。系统通过自学习对冷变形量为12.25%铝材退火过程进行拟合，预测开始再结晶对应时间为0.865h。
对冷变形量为12.25%的铝材在475℃退火过程进行原位信息采集，电阻率-时间曲线如图39所示，分析出开始再结晶对应时间为0.870h，与预测结果0.865h相近。
实施例13：在线检测7B50合金470℃固溶的电学信息，将检测信息与热处理信息数据库中参照电学信息进行比对，根据比对结果，进一步反馈优化自学习。
系统根据热处理信息数据库中7B50合金固溶的现有数据，通过自学习得到其在470℃固溶的参照电阻率-时间曲线，当电阻率达到9.520×10 -8Ω·m时，合金达到近稳定固溶程度，所需固溶时间为60min。
图40为7B50合金在470℃固溶过程中原位实测的电阻率-时间曲线和参照电学信息曲线，实测固溶60min的电阻率低于参照电学信息曲线电阻率数值，固溶程度系数仅为91.67%，系统认定热处理未完成，实测固溶73min的电阻率与参照电学信息曲线60min处的电阻率数值相等，固溶程度系数达到100%，系统认定热处理已完成，热处理控制模块停止热处理。
将本次检测结果录入热处理信息数据库中，并再次通过自学习得到7B50合金470℃固溶的参照电学信息，可进一步优化合金达到近稳定固溶程度所需的时间参数。
实施例14：在线检测Al-0.10Zr-0.10La-0.02B合金的均匀化，将检测信息与热处理信息数据库中参照电学信息进行比对，根据比对结果，调控均匀化温度，进而控制Al-0.10Zr-0.10La-0.02B合金在620℃的均匀化过程。
系统根据热处理信息数据库中Al-0.10Zr-0.10La-0.02B合金在不同温度均匀化的电学信息，通过自学习得到其在620℃均匀化的参照电导率-时间曲线，认定在620℃均匀化18h可以达到近稳定均匀化程度。
图41为Al-0.10Zr-0.10La-0.02B合金实测的电导率-时间曲线和参照电学信息曲线，图41(a)为有反馈控制系统测得曲线，当实测曲线低于参照曲线时反馈降低炉温，当实测曲线高于参照曲线时反馈升高炉温，最终得到的实测曲线与参照曲线大致相符；图41(b)为无反馈控制系统测得曲线，实际温度与设定温度存在一些偏差，导致最终得到的实测曲线与参照曲线存在部分偏离。
图42为利用扫描电镜观察到的两种热处理完成后的微观组织，图42(a)为有反馈热处理完成后的微观组织，无明显偏析和过烧，均匀化效果好；图42(b)为无反馈热处理完成后的微观组织，晶界处带有过烧，晶内仍存在偏析，均匀化效果不好，原因是炉温发生波动且未及时调整，温度过高时晶界发生过烧，温度过低时元素扩散不充分。
实施例15：在线检测Al-0.1Zr-0.1Sc合金的双级时效，根据第一级时效（300℃）的热处理程度自动确定第二级时效的温度和时间。
检索材料热处理信息数据库，Al-0.1Zr-0.1Sc合金推荐的第一级时效温度范围为270℃~350℃，时效时间为8~24h，推荐的第二级时效温度范围为370~430℃。
合金在300℃进行12h的时效，原位测得电阻率-时间曲线，如图43(a)所示，时效12h对应的电阻率为6.024×10 -8Ω•m，计算时效程度系数为60%，自学习模块确定第二级时效温度为400℃，并设定目标热处理状态对应的电阻率为7.272×10 -8Ω•m，热处理控制模块升温至400℃进行第二级时效，原位测得相应的电阻率-时间曲线，如图43(b)所示，时效32h对应的电阻率达到7.272×10 -8Ω•m，系统计算时效程度系数为100%，自动终止热处理。
对比例1：利用材料性能模拟软件计算Al-0.1Zn-0.2Mg-0.1Fe-0.05Mn合金的过烧温度，图44为利用JmatPro7.0.0软件模拟出的电导率-温度曲线，曲线在635℃处发生突变，高于此温度合金发生过烧，热处理温度低于630℃不会发生过烧。实施例1表明，Al-0.1Zn-0.2Mg-0.1Fe-0.05Mn合金在550℃固溶发生过烧，相比软件预测的过烧温度低85℃。
对比例2：利用时效硬度曲线确定Al-4wt.%Cu合金在535℃固溶的适宜时间，图45为在535℃固溶不同时间的Al-4wt.%Cu合金样品再经170℃/12h时效的硬度曲线，固溶时间超过2h后，时效硬度数值差距不大，表明达到近稳定固溶程度。相比实施例2，本对比例非原位检测，操作繁琐、样品处理过程复杂、数据离散且不够精确，易受取样部位差异的影响。
对比例3：利用硬度曲线确定Al-1.00Hf-0.16Y合金在635℃均匀化的适宜时间，图46为均匀化不同时间的硬度，当均匀化时间达到和超过18h后，硬度值小幅波动，表明达到近稳定均匀化，18h可以作为适宜均匀时间。对应实施例5，本对比例存在步骤繁琐、样品处理过程复杂、非原位测量、数据离散且不够精确，易受取样部位差异的影响、无法调控工艺参数等缺点。
对比例4：利用时效硬度曲线确定Al-4wt.%Cu合金在190℃时效析出新相的时间节点，本对比例间隔2h采集一个数据，图47为时效硬化曲线，时效10h和36h对应的时效曲线出现峰值硬度，分别对应θ’相、θ相析出。本对比例受取样部位影响，精确度不高，对应实施例7原位采集样品信息，实验量小、数据密集、精确度高，能精准检测出合金的峰时效。
对比例5：利用硬度曲线和室温导电率曲线确定Al-4.5Zn-1.2Mg合金在170℃时效的峰时效时间，图48为在170℃时效不同时间的硬度和室温导电率，时效12h达到硬度峰值，室温导电率曲线整体呈上升趋势，当时效时间达到21h后，室温导电率变化率降低，对应析出相长大粗化。相比实施例8，本对比例虽然采用了大量样品，所需实验量大，但获得的数据仍是离散的，受取样位置。
对比例6：利用等时退火硬度曲线比较相同退火条件下Al-0.16Y和Al-0.16Y-0.15Zr合金的回复再结晶程度，评估添加元素对合金耐热性的影响。图49为加入不同微合金元素铝合金在不同温度下退火1h的硬度曲线，曲线显示Al-0.16Y合金的硬度低于Al-0.16Y-0.15Zr合金，Al-0.16Y合金的硬度在350~475℃区间降低非常显著，当温度退火温度高于500℃后趋于稳定，Al-0.15Zr-0.16Y合金的硬度在退火温度达到450℃才有明显下降，具有更高的耐热性和抵抗再结晶的能力。本对比例得到的结果与实施例10一致，但本对比例检测过程耗时较长，步骤繁琐，所采集硬度离散点易受偶发因素（如样品取样位置、硬度测量误差等）影响。而实施例10是在不同温度进行的原位检测，具有数据连续且精度高，测试耗时短，步骤简单等优点。
对比例7：利用硬度曲线确定7B50合金在470℃固溶的适宜时间，所用材料和检测环境与实施例13相同。图50为固溶不同时间合金经过170℃/8h时效后的硬度曲线，当时效时间达到70min后，硬度值趋于稳定，表明达到近稳定固溶程度。而实施例13原位检测避免了不同取样部位的影响，确定的适宜固溶时间比较准确，而且能在线实施反馈控制热处理进程。
上述对比例表现出传统方法和手段的局限性，非原位的断续检测，样品制备步骤繁琐，采集数据离散且易受检测方法影响，优化工艺参数周期长。实施例体现出本专利方法的技术优势，原位在线检测，直接在受测件热处理过程中获得数据，实验过程简单，采集数据精确且连续，可实时监测受测件的热处理程度或状态，进而在线调控热处理。
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  1. 一种基于原位采集信息调控热处理的方法，其特征在于：在对受测件进行热处理的过程中原位连续采集信息和/或数据，在进行信息处理和/或数据分析后，与热处理信息数据库中相关信息或数据进行比对，在线检测或表征受测件的热处理程度或状态，进而优化材料的热处理工艺和/或调控受测件的热处理，从而使受测件达到设定热处理目标和/或组织性能。
  2. 根据权利要求1所述的一种基于原位采集信息调控热处理的方法，其特征在于：所述热处理包含但不限于均匀化、固溶、时效、回复再结晶退火；所述热处理过程包含升温、保温、降温中的至少一种操作。优选的，所述热处理程度或状态包含但不限于欠时效、峰时效、过时效、回复、开始再结晶、完全再结晶。
  3. 根据权利要求1所述的一种基于原位采集信息调控热处理的方法，其特征在于：所述原位采集是实时采集受测件在实际热处理环境中的信息和/或数据；优选的，所述信息为电学信息，包括但不限于电压、电阻、电阻率、电导率、导电率；
    优选的，所述信息处理为对电学信息-时间曲线和/或电学信息-温度曲线进行相关处理，所述相关处理包括但不限于计算电学信息变化值、计算电学信息变化率、计算热处理程度系数；
    优选的，所述热处理程度系数用字母P表示，定义P=(E ti-E 0)/(E u-E 0)×100%；所述E 0为初始热处理程度对应的电学信息，优选为受测件温度达到预设初始条件时对应的电学信息；所述E ti为热处理过程中任意时刻对应的电学信息，是达到目标热处理程度之前某个程度对应的电学信息；所述E u为目标热处理程度对应的电学信息，优选为受测件的性能和/或组织达到热处理目标时对应的电学信息。
  4. 根据权利要求1所述的一种基于原位采集信息调控热处理的方法，其特征在于：所述热处理信息数据库中包括但不限于材料信息和数据、热处理制度及相关工艺参数、热处理过程信息及数据；所述材料信息和数据包括材料成分、热处理组织及性能；所述热处理过程信息及数据包括但不限于不同热处理过程的温度、电学信息。
  5. 根据权利要求1或4所述的一种基于原位采集信息调控热处理的方法，其特征在于：所述热处理信息数据库为关系型数据库，支持并不限于下列数据库类型：SQL Server、MySQL、MongoDB、SQLite、Access、H2、Oracle、PostgreSQL；数据库访问技术包括但不限于ODBC、DAO、OLE DB、ADO，可以根据实际需要对存储内容进行增加、删除、修改、查询。
  6. 根据权利要求1-3任意一项所述的一种基于原位采集信息调控热处理的方法，其特征在于：对于热处理信息数据库中未记录的材料，针对不同热处理过程，在检测获得的电学信息-时间曲线、电学信息-温度曲线上选取特征点，分别检测材料的成分、组织和性能，然后将材料信息和数据、热处理工艺数据、热处理过程的信息及数据存入数据库；所述特征点包括但不限于曲线变为水平线的起点、曲线的拐点、曲线斜率非平稳变化的点、设定热处理程度在曲线上的对应点、时间间隔相同的点、温度间隔相同的点。
  7. 根据权利要求1、4、5、6任意一项所述的一种基于原位采集信息调控热处理的方法，其特征在于：所述热处理信息数据库可以通过后续检测及自学习不断完善和/或优化，提高数据的可信度及可用性；所述自学习基于神经网络算法、随机森林算法、粒子群算法中的至少一种算法，其运行环境支持并不限于下列操作系统：Windows、Android、Linux、Mac OS、IOS，学习结果通过SOAP、RESTful对用户提供终端服务。
  8. 根据权利要求1、4、5、6任意一项所述的一种基于原位采集信息调控热处理的方法，其特征在于：所述热处理信息数据库是本地数据库或云端数据库；所述云端数据库由不同用户端上传数据组成，功能包括但不限于管理权限、验证访问、保存数据、处理数据、管理数据、分析数据。
  9. 一种如权利要求1-8任一项所述基于原位采集信息调控热处理方法的应用，其特征在于：所述方法可应用于优化材料的热处理工艺和/或在线调控受测件的热处理；
    优选的，所述方法应用于均匀化退火，包括但不限于确定适宜的均匀化温度、均匀化时间、升温速率、降温速率，所述均匀化包括单级均匀化、多级均匀化；
    优选的，所述方法应用于固溶处理，包括但不限于确定适宜的固溶温度、固溶时间、升温速率、降温速率，所述固溶包括单级固溶、多级固溶；
    优选的，所述方法应用于时效处理，包括但不限于判定多种时效析出相的脱溶贯序及析出新相的时间窗口、确定达到强度峰值的时效时间、达到不同时效程度的时间节点，所述时效包括单级时效、多级时效；
    优选的，所述方法应用于回复再结晶退火，包括但不限于预测材料在指定温度达到指定退火程度所需时间、预测材料在指定冷变形量达到指定退火程度所需时间、比较不同材料在相同热处理条件下抵抗再结晶的能力。
  10. 一种如权利要求1-9任一项所述基于原位采集信息调控热处理方法所用装置及软件系统，其特征在于：所述装置及软件系统包括信息采集及处理模块、自学习模块、热处理信息数据库、热处理控制模块、热处理系统；所述信息采集及处理模块用于对受测件的热处理信息进行原位采集和即时处理；所述自学习模块用于分析逻辑规律和/或数据关系，包括但不限于分析材料与热处理之间的逻辑规律、信息与信息或数据与数据之间的关联；所述热处理信息数据库用于存储信息采集与处理模块得到的数据并提供终端服务；所述热处理控制模块用于根据自学习模块的分析结果生成控制命令；所述热处理系统执行所述控制命令，调整热处理温度、控制热处理时间。
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