Patent Application
20230002851
The present invention relates to a method for regulating heat treatment derived from the in-situ collection of information, and application thereof, which belongs to the field of material hot working, and specifically, to the field of online detection and control of material heat treatment.
A material or workpiece is heat-treated to form the desired microstructure to satisfy the set requirements for performance. Process parameters of heat treatment, such as heating temperature, soaking time and temperature changing rate, affect the microstructure and properties of the material remarkably. In production, these process parameters are generally optimized and regulated, resulting in the desired microstructure and properties of the heat-treated workpiece. Conventionally, before production, heat treatment is carried out at different temperatures, different soaking times, and different temperature changing rates, and then property examination and microstructure observation are carried out at room temperature. If the microstructure and properties do not satisfy the target demands, it is necessary to adjust the process parameters and re-perform the heat treatment repeatedly, so as to get close to the target results of heat treatment through continuous optimization of the process parameters. It is not feasible to directly detect the extents or states of heat treatment during the process and control it.
At present, the detection methods related to heat treatment all have problems of being ex-situ, non-continuous, inaccurate, complex, requiring a large amount of experiment, and high cost. Patent CN109536859A disclosed a method for detection of solid solution quenching effect of 7075 aluminum alloy. The time for heat treatment was determined through detection of electrical conductivity changes of samples at different solid solution temperatures and soaking time. The electrical conductivity was measured after quenching rather than in-situ during the heat treatment. It was means that multiple groups of subsequent experiments are required to obtain a curve showing the relationship between the electrical conductivity and the heating temperature and between the conductivity and the soaking time, which resulted in the experimental steps cumbersome. In the paper “Research on Method of Quick Detection of Homogenization Effect of Round Aluminum Alloy Ingot”, the electrical conductivity and hardness of homogenized alloy 6A01, 6005A and 7B05 etc ingots from multiple heating batches were measured by using a conductivity meter and a hardness tester at room temperature, and the microstructure was observed by using a metallographic microscope and a scanning electron microscope for relevant verification. However, the detection is ex-situ and non-continuous. In the patent CN108193101 A, the optimal aging process of four Al—Mg—Cu alloys was determined based on microhardness. The samples were collected at a large time interval leads to the inaccurate peak aging time and greatly fluctuant hardness data, which is easily affected by accidental factors. In the paper “Coarsening resistance at 400° C. of precipitation-strengthened Al—Zr—Sc—Er alloys”, the optimal aging time of Al—Zr—Sc(—Er) alloys was determined based on hardness and electrical conductivity at room temperature. However, the properties were measured at room temperature, the test results were easily affected by sampling factors, and there were discrete points present in the property curve which deviate from or even go against the regular pattern. This method required a large amount of experiment and was not online. Patent CN 103175831 B provided a method suitable for analyzing and evaluating the recrystallization structure proportion of deformed aluminum alloy materials, which can distinguish between recrystallization structure and deformed structure to recognize and summarize the recrystallization states of the material. However, this method is inapplicable to the materials that are difficult to corrode or excessively easy to corrode, resulting in a limited range of applicable materials.
At present, the heat treatment information for materials is mostly collected and stored based on the microstructure and property detection after heat treatment, and the management and application system for information and data is not perfect. Patent CN 105975727 A “Processing, Generation and Application Method, Terminal, and Cloud Processing Platform for Material Data” provided a material data cloud processing platform, aiming to solve the experimental problem of disconnection between material testing and simulation calculation in material genetic engineering. However, the data generation and material preparation are not carried out at the same time, so that it is not applicable to the process control of material production. Patent CN 106447229 A “Material Data Management System and Method in Material Informatics” disclosed an informatic research framework, which can perform operations such as addition, deletion, modification, and query on material data. However, there was no systematic analysis on the information stored and no application of online feedback during production. Patent CN 110298289 A “Material Identification Method and Device, Storage Medium and Electronic Device” disclosed a device for determining material information of a target object based on ultrasonic signals, which can be used for material identification. However, ultrasonic signals are susceptible to interference and may damage a test piece, resulting in a limited application range.
The present invention can achieve high-temperature and continuous in-situ information collection while a workpiece is heat-treated. With the help of material heat treatment database resources and self-learning function, the collected information is processed, analyzed and stored in real time, and then the heat treatment extent or state of the test piece can be detected online, and the heat treatment process of the material can be optimized, thereby achieving online regulation of heat treatment of the test piece.
The present invention provides a method, a device and application for regulating heat treatment derived from the in-situ collection of information.
The method for regulating heat treatment derived from the in-situ collection of information includes continuously in-situ collecting information and/or data during heat treatment on a test piece, performing information processing and/or data analysis, then comparing the information or data with relevant information or data in a heat treatment information database, online detecting or characterizing a heat treatment extent or states of the test piece, thereby optimizing a heat treatment process of the material and/or regulating the heat treatment of the test piece, so that the test piece can achieve a set heat treatment goal and/or microstructure and properties.
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, the heat treatment includes but is not limited to homogenization, solid solution treatment, aging, recovery and recrystallization annealing; the heat treatment process includes at least one of heating-up, soaking and cooling down; and the heat treatment extent or states includes but is not limited to under-aged, peak-aged, over-aged, recovered, onset of recrystallization, and fully recrystallized.
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, the in-situ collection is to collect information and/or data of the test piece in an actual heat treatment environment in real time; preferably, the information is electrical information, including but not limited to voltage, resistance, resistivity, electrical conductivity (in S/m), and conductivity (in % IACS). Corresponding conversion can be carried out between the electrical information, and the conversion includes both numerical conversion and unit conversion. The conversion includes at least one of the following formulas.
Resistance (Ω) Voltage (V)÷Current (A).
Resistivity (Ω·m) Resistance (Ω)×Cross-sectional area (m2)÷Length (m).
Electrical conductivity (S/m)=1÷Resistivity (Ω·m).
Conductivity (% IACS)=Electrical conductivity (MS/m)÷0.58.
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, a collection method of the electrical information includes but is not limited to a four-point probes method, a single bridge method, and a double bridge method, preferably the four-point probes method, which can reduce or even eliminate the impact of wire and contact resistance on the collected information.
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, the information processing is to reduce redundant and noisy information and improve the identification of information through information screening and classification, data collection and conversion; the data analysis is to perform data dimension reduction and data processing through feature extraction, data mining and integration to improve detection accuracy; and the information processing is preferably to perform relevant processing on an electrical information-time curve and/or an electrical information-temperature curve, the relevant processing including but not limited to calculation of electrical information change value, calculation of electrical information change rate, and calculation of heat treatment extent coefficient.
Preferably, the heat treatment extent coefficient is represented by P, defined as P=(Eti−E0)/(Eu−E0)×100%, where E0 is electrical information corresponding to an initial heat treatment extent, preferably electrical information when the temperature of the test piece meets a preset initial condition, Eti is electrical information corresponding to any moment during the heat treatment, which is electrical information corresponding to a certain extent before reaching a target heat treatment extent, and Eu is electrical information corresponding to the target heat treatment extent, preferably electrical information when the properties and/or microstructure of the test piece achieves the heat treatment goal.
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, the heat treatment information database includes information and data of various materials and the corresponding heat treatment, including but not limited to material information and data, heat treatment process and related process parameters, and heat treatment process information and data, the material information and data include material composition and basic properties, heat treatment structure and property indicators; the heat treatment process includes but is not limited to a homogenization process, a solid solution treatment process, an aging process, and a soft annealing process; the related process parameters include but are not limited to a heating temperature, a soaking time, a heating rate, and a cooling rate, the heat treatment process information and data include but are not limited to temperature and electrical information in different heat treatment processes; preferably, multi-component materials are classified through a data-driven neural network, intrinsic structural features of data are extracted based on principal component analysis and association analysis, and a process-structure-property relational database with components as the main line is constructed.
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, the heat treatment information database is a relational database, supporting but not limited to the following database types: SQL Server, MySQL, MongoDB, SQLite, Access, H2, Oracle, and PostgreSQL; and database access technologies comprise but are not limited to ODBC, DAO, OLE DB, and ADO, which can perform addition, deletion, modification, and query on stored content according to actual needs.
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, the electrical information, characteristic structure and property information of a material that is already recorded in the heat treatment information database in a set heat treatment process can be directly obtained from the database. Taking the electrical information-time curve shown in
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, for homogenization, solid solution treatment, and other heat treatment, the electrical information-time curve gradually becomes horizontal as the heat treatment time increases Theoretically, at a proper solid solution temperature, a second phase gradually re-dissolves until it is sufficiently dissolved into the matrix, and the corresponding electrical information-time curve becomes horizontal, as shown in
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, the aging, recovery and recrystallization annealing include critical heat treatment states such as onset of precipitation, peak aging, onset of recrystallization, and full recrystallization. The target heat treatment extent Eu is determined according to target properties and/or microstructure of material heat treatment.
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, for a material that is not recorded in the heat treatment information database in different heat treatment processes, characteristic points are selected on an electrical information-time curve and an electrical information-temperature curve obtained through detection, material composition, microstructure and properties are detected, material information and data, heat treatment process data, and heat treatment procedure information and data are stored in the database, and the subsequent detection information for the same material can be used to supplement and improve the database. The characteristic points include but are not limited to a starting point where the curve becomes horizontal (or a starting point where an absolute value of a slope of the curve is less than a certain specified value), an inflection point on the curve (a point where the curvature of the curve changes), a point where a slope of the curve changes unsteadily (a point where the slope change rate or change value of the curve exceeds a set range), a point corresponding to a characteristic heat treatment extent or a critical heat treatment state on the curve, points with the same time interval, and points with the same temperature interval. The characteristic heat treatment extent or the critical heat treatment state includes but is not limited to onset of dissolution of a low-melting point phase, onset of re-dissolution of a second phase, onset of precipitation of a solid solution, peak aging, onset of recrystallization, full recrystallization, and growth of recrystallized grains.
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, the heat treatment information database can be continuously improved or optimized through subsequent detection and self-learning to improve the reliability and availability of data; and the self-learning is based on at least one algorithm of neural network, random forest, and particle swarm with an operating environment supporting but not limited to the following operating systems Windows, Android, Linux, Mac OS, and IOS, and learning results provide terminal services to users through SOAP and RESTful. In addition, the foregoing algorithm involved in the present invention may be connected with the Bayesian optimization algorithm, so as to optimize the algorithm.
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, the heat treatment information database is a local database or a cloud database; wherein the cloud database comprises data uploaded by different clients, with functions including but not limited to authority management, access verification, data storage, data processing, data management, and data analysis.
The method for regulating heat treatment derived from the in-situ collection of information in the present invention, there are many methods for applying the information and data in the heat treatment information database, such as detecting and characterizing the material heat treatment extent or states by calculating the slope of the electrical information-time curve. It should be considered that all methods based on this patent, that is, online detection, characterization, and regulation on the heat treatment extent by continuously in-situ collecting electrical information and performing relevant processing, shall fall within the protection scope of this patent.
In the present invention, hardware used in the information collection and processing module includes a computer, a Keithley 2450 digital source meter, a Keithley 2182A nanovoltmeter, a specific fixture, and a data cable. The computer includes a CPU, a mainboard, a graphics card, a memory stick, a display, a hard disk, and the like.
In the present invention, hardware used for constructing the self-learning module and the heat treatment information database includes a computer, a Keithley 2450 digital source meter, a Keithley 2182A nanovoltmeter, a specific fixture, and a data cable. The computer includes a CPU, a mainboard, a graphics card, a memory stick, a display, a hard disk, and the like. In the present invention, hardware used in the heat treatment control module includes a high-performance intelligent thermostat AT-708 from YUDIAN, Xiamen, a type K thermocouple, and a USB-RS485 data cable.
In the present invention, hardware used in the heat treatment process includes a 1200° C. three-temperature-zone vacuum atmosphere tube furnace from ZHONGHUAN, Tianjin.
The application of the method for regulating heat treatment derived from the in-situ collection of information in the present invention. The method is applied to optimize heat treatment process of a material and/or regulate the heat treatment of a test piece online.
The application of the method for regulating heat treatment derived from the in-situ collection of information in the present invention. The method is applied to optimize heat treatment process. An adaptive design model for efficient global optimization is established based on a basic data set of heat treatment process-characteristic microstructure-electrical information, to resolve the problem of multi-objective and multi-parameter system optimization of heat treatment.
The application of the method for regulating heat treatment derived from the in-situ collection of information in the present invention. The method is applied to homogenization, including but not limited to determining a proper homogenization temperature, homogenization time, heating rate, and cooling rate, the homogenization including single-stage homogenization and multi-stage homogenization. The specific operation is preferably as follows: carrying out homogenization at several temperatures and in-situ collecting information, and taking a temperature at which a target homogenization extent is reached within the shortest time and overburning does not occur as a proper homogenization temperature; determining a proper homogenization time according to an electrical information-time curve corresponding to the proper homogenization temperature, and taking a time when a homogenization extent coefficient reaches 100% (or an absolute value of a slope of the curve is less than a specified value) as the proper homogenization time.
The application of the method for regulating heat treatment derived from the in-situ collection of information in the present invention. The method is applied to solid solution treatment, including but not limited to determining a proper solid solution temperature, solid solution time, heating rate, and cooling rate, the solid solution including single-stage solid solution and multi-stage solid solution. The specific operation is preferably as follows: carrying out solid solution treatment at several temperatures and in-situ collecting information, and taking a temperature at which a target solid solution extent is reached within the shortest time and overburning does not occur as a proper solid solution temperature; determining a proper solid solution time according to an electrical information-time curve corresponding to the proper solid solution temperature, and taking a time when a solid solution extent coefficient reaches 100% (or an absolute value of a slope of the curve is less than a specified value) as the proper solid solution time.
The application of the method for regulating heat treatment derived from the in-situ collection of information in the present invention. The method is applied to aging, including but not limited to determining a precipitation sequence of various precipitated phases in aging and a time window of a newly precipitated phase and determining an aging time of reaching a peak strength and time points of reaching different aging extents, the aging including single-stage aging and multi-stage aging
The application of the method for regulating heat treatment derived from the in-situ collection of information in the present invention. The method is applied to recovery and recrystallization annealing, including but not limited to predicting a time required for a material to reach a specified annealing extent at a specified temperature, predicting a time required for a material to reach a specified annealing extent at a specified amount of cold deformation, and comparing recrystallization resistance of different materials wider the same heat treatment conditions.
The application of the method for regulating heat treatment derived from the in-situ collection of information in the present invention. The method is applied to predict a time required for a material to reach a specified annealing extent at a specified temperature to predict a time required for a material existing in the database to reach a specified annealing extent at an untested temperature through self-learning fitting. The specific operation is preferably as follows: retrieving known information or data at temperatures adjacent to a specified temperature from the material heat treatment information database, and predicting a time required to reach a set annealing extent at the specified temperature through self-learning.
The application of the method for regulating heat treatment derived from the in-situ collection of information in the present invention. The method is applied to predict a time required for a material to reach a specified annealing extent at a specified amount of cold deformation to predict a time required for a material existing in the database to reach a specified annealing extent at a specified amount of cold deformation at a specified temperature through self-learning fitting. The specific operation is preferably as follows: retrieving information or data corresponding to known amount of cold deformation adjacent to a specified amount of cold deformation from the material heat treatment information database, and predicting a time required to reach a set annealing extent at an amount of cold deformation that is not stored in the database through self-learning.
The application of the method for regulating heat treatment derived from the in-situ collection of information in the present invention. The method is applied to online regulate heat treatment. The specific operation is preferably as follows: continuously in-situ collecting information and/or data during heat treatment, performing real-time information processing and data analysis, comparing the information or data with relevant information or data in a heat treatment information database, detecting or characterizing a heat treatment extent or states, and adjusting process parameters of the heat treatment and controlling the heat treatment process, so that the test piece can achieve a set heat treatment goal and/or microstructure and properties.
The present invention provides a device and a software system for regulating heat treatment derived from the in-situ collection of information with a structural block diagram as shown in
In addition to the foregoing application of the method for regulating heat treatment derived from the in-situ collection of information in the present invention, the method of the present invention has various application forms in the actual production process Tt should be considered that all methods based on this patent, that is, online detection, characterization, and control on heat treatment by continuously in-situ collecting electrical information and performing relevant processing, shall fall within the protection scope of this patent.
Compared to existing technologies, the present invention provides a technical solution for regulating heat treatment based on in-situ collected information and/or data, with technical advantages as follows.
1. Online non-destructive testing can be carried out on all conductive test pieces. The shape of the test piece and heat treatment temperature are not limited. The heat treatment place is not limited, whether in the laboratory or on the production site. The motion states of the test piece is not limited, whether being stationary or moving continuously, preferably, there is no relative motion between the test piece and the detection device.
2. The present invention achieves sensitive and accurate capture of information in response to microstructure change in heat treatment through in-situ information collection and real-time information processing, and achieves effective investigation, mining and optimization of data through efficient information processing and professional data analysis, improving the effective information storage capacity of the database, reducing system errors, and improving the accuracy of detection and control.
3. The present invention has a self-learning function, achieves deep integration with the material thermodynamics and diffusion kinetics database, material heat treatment expert system, high-throughput calculation and experiment platform, and constructs a process-structure-property relational database with components as the main line. The automatic adjustment of process parameters driven by performance can be achieved through the automatic determination of microstructure development in the whole process. Through the real-time regulation of heat treatment, the heat treatment goal is achieved, and the requirements for the microstructure and properties of the test piece are accurately satisfied.
4. The informatization application of the present invention is compatible with various operating systems and application platforms. Data can be quickly transferred and operated remotely through software with a user-friendly interface combined with the Internet, and data sharing can be achieved with the big data cloud computing system, scientific research data sharing system, material gene database integration system, etc., providing support for the application of material design and development based on machine learning and artificial intelligence in material production.
Type here a paragraph describing the optimal implementation of the present invention.
The technical solution of the present invention will be further described below with reference to specific implementations. Information is collected when the heat treatment process reaches a set temperature. The electrical information is collected by a four-point probes method. The specific parameters (such as length of the electrical information collection region, constant current, and electrical information type) are adjusted according to the test piece. The material properties and microstructure obtained by conventional detection methods may be entered into the material heat treatment information database before detection, or may be recorded after the detection. It should be understood that such data is not necessary for the method of the present invention, and can be used to verify the detection results of the present invention and assist in improving the accuracy and applicability of the self-learning model. The test contents and results of the following examples are entered into the corresponding material entries of the material heat treatment information database to enrich and improve the material heat treatment information database of the present invention and continuously improve the reliability of subsequent detection and control.
Example 1: The solid solution extent of Al-0.1Zn-0.2Mg-0.1Fe-0.05Mn alloy was detected online for different solid solution durations at different temperatures to determine a proper solid solution temperature of the alloy.
The material heat treatment information database was searched for a recommended solid solution temperature of 510-540° C. When an absolute value of a slope of the conductivity-time curve was less than or equal to 1.00×10−4 MS/(m·h), the alloy reached a near-stable solid solution extent, and the required solid solution time was 6-12 h.
Example 2: The solid solution states of Al-4 wt ° Cu alloy was detected online at 535° C. for different durations to determine a proper solid solution time of the alloy at 535° C.
It was known by searching the material heat treatment information database that, when Al-4 wt. % Cu reached a near-stable solid solution extent at 535° C., an absolute value of a slope of the conductivity-time curve was less than or equal to 8×10−6 MS/(m·s), and the required solid solution time was 1-6 h.
Example 3: The solid solution states of Mg-10Al-1Zn alloy was detected online at 430° C. for different durations to determine a proper solid solution time of the alloy at 430° C.
It was known by searching the material heat treatment information database that, when the Mg-10Al-1Zn alloy reached a near-stable solid solution extent at 430° C., the resistivity was 1.7890×10−7 Ω·m, and the required solid solution time was 5-20 h.
Example 4: The homogenization states of Zn-15Al brazing filler was detected online at 330° C. for different durations to determine a proper homogenization time of the alloy at 330° C.
It was known by searching the material heat treatment information database that, when the Zn-15Al brazing filler reached a near-stable homogenization extent at 330° C., the conductivity was 4.925 MS/m, and the required homogenization time was 2-10 h.
Example 5: The homogenization states of Al-1.00Hf-0.16Y alloy was detected online at 635° C. for different durations to determine a proper homogenization time of the alloy at 635° C.
It was known by searching the material heat treatment information database that, when the Al-1.00Hf-0.16Y alloy reached a near-stable homogenization extent at 635° C., an absolute value of a slope of the conductivity-time curve was less than or equal to 9×10−4% IACS/h, and the required homogenization time was 14-36 h.
Example 6: The precipitation behavior in aging of Al-4 wt. % Cu alloy was detected online at 150° C. to determine time points at which new phases were precipitated.
It was known by searching the material heat treatment information database that the precipitation sequence of the Al-4 wt. % Cu alloy undergoing aging at 150° C. was θ″ phase (GPII zones)→θ′ phase→θ phase.
Example 7: The precipitation behavior in aging of Al-4 wt. % Cu alloy was detected online at 190° C. to determine time points at which new phases were precipitated.
It was known by searching the material heat treatment information database that the precipitation sequence of the Al-4 wt. % Cu alloy undergoing aging at 190° C. was θ′ phase→θ phase.
Example 8: The aging states of Al-4.5Zn-1.2Mg alloy was detected online at 170° C. for different durations to determine time points at which different aging extents were reached.
It was known by searching the material heat treatment information database that the precipitation sequence of the Al-4.5Zn-1.2Mg alloy undergoing aging at 170° C. was η′ phase→η phase, and the peak aging time was 9-24 h.
Example 9: The recovery and recrystallization extent or states of an as-rolled industrial pure aluminum sheet undergoing annealing was detected online at 300° C. for different durations.
It was known by searching the material heat treatment information database that, taking full recrystallization as the heat treatment goal of the as-rolled industrial pure aluminum sheet, 0%≤P<65% indicates a recovered state, 65%≤P<95% indicates a recrystallized state, and 95%≤P≤100% indicates grown grains.
Example 10: The recrystallization annealing process of an aluminum alloy with different microalloying elements added was online detected at 420° C., the recovery and recrystallization extent of two metals was compared under the same annealing conditions, and the effect of the added elements on the heat resistance of the alloy was evaluated. An alloy 1 was industrial pure aluminum with 0.16 wt. % of Y added, and an alloy 2 was industrial pure aluminum with 0.16 wt. % of Y and 0.15 wt % of Zr added.
Example 11: The time to start recrystallization of an Al-0.1Sc cold-deformed alloy undergoing annealing at 450° C. was predicted according to the existing information and data of the alloy undergoing annealing at 400° C. and 500° C. in the material heat treatment information database.
It was known by searching the material heat treatment information database that
Example 12: The time to start recrystallization of an industrial pure aluminum (containing 99.7% of aluminum) cold-worked material with an amount of cold deformation of 12.25% at the same temperature was predicted according to the existing information and data of the aluminum material undergoing annealing at 475° C. with amounts of cold deformation of 9% and 10% in the material heat treatment information database.
It was known by searching the material heat treatment information database that
The information of the aluminum material with an amount of cold deformation of 12.25% was in-situ collected in the annealing process at 475° C. to obtain a resistivity-time curve shown in
Example 13: The electrical information of a 7B50 alloy undergoing solid solution treatment at 470° C. was online detected, the detected information was compared with reference electrical information in the heat treatment information database, and self-learning was further optimized according to the feedback of the compared results.
The system obtained a reference resistivity-time curve of the 7B50 alloy undergoing solid solution treatment at 470° C. through self-learning according to the existing data in the heat treatment information database. When the resistivity reaches 9.520×10−8 Ω·m, the alloy reaches a near-stable solid solution extent, and the required solid solution time is 60 min.
The detection results were entered into the heat treatment information database, and new reference electrical information of the 7B50 alloy undergoing solid solution treatment at 470° C. was obtained through self-learning, to further optimize the parameter of time required for the alloy to reach the near-stable solid solution extent.
Example 14: The electrical information of Al-0.10Zr-0.10La-0.02B alloy undergoing homogenization was online detected, the detected information was compared with reference electrical information in the heat treatment information database, and the homogenization temperature was regulated according to the compared results, to further control the homogenization process of the Al-0.10Zr-0.10La-0.02B alloy at 620° C.
The system obtained a reference conductivity-time curve of the Al-0.10Zr-0.10La-0.02B alloy undergoing homogenization at 620° C. through self-learning according to its electrical information of homogenization at different temperatures in the heat treatment information database, and determined that the alloy can reach the near-stable homogenization extent at 620° C. for 18 h.
Example 15: The two-stage aging of Al-0.1Zr-0.1Sc alloy was detected online, and the temperature and time of the second-stage aging was automatically determined according to the heat treatment extent of the first-stage aging (300° C.).
It was known by searching the material heat treatment information database that, for the Al-0.1Zr-0.1Sc alloy, the recommended temperature for the first-stage aging was 270-350° C., and the recommended time for the first-stage aging was 8-24 h, and the recommended temperature for the second-stage aging was 370-430° C.
The alloy was aged at 300° C. for 12 h. A resistivity-time curve shown in
Comparative Example 1: The overburning temperature of Al-0.1Zn-0.2Mg-0.1Fe-0.05Mn alloy was calculated by using software for simulating material properties.
Comparative Example 2: The proper solid solution time of Al-4 wt. % Cu alloy at 535° C. was determined according to an age hardening curve.
Comparative Example 3: The proper homogenization time of Al-1.00Hf-0.16Y alloy at 635° C. was determined according to a hardness curve
Comparative Example 4: The time points at which new phases were precipitated of Al-4 wt. % Cu alloy undergoing aging at 190° C. were determined according to an age hardening curve. In this comparative example, one data was collected every 2 h.
Comparative Example 5: The peak aging time of Al-4.5Zn-1.2Mg alloy undergoing aging at 170° C. was determined according to a hardness-time curve and a room-temperature conductivity-time curve.
After aging for 21 h, the rate of change of the room-temperature conductivity decreases, corresponding to the growth and coarsening of the precipitated phase. Compared with Example 8, although this comparative example uses a large number of test pieces and requires a large amount of experiment, the obtained data is still discrete and easily affected by sampling sites.
Comparative Example 6: The recovery and recrystallization extents of Al-0.16Y alloy and Al-0.16Y-0.15Zr alloy were compared wider the same annealing conditions according to an isochronal hardness-annealing temperature curve, to evaluate the effect of added elements on the heat resistance of the alloy.
Comparative Example 7: The proper solid solution time of 7B50 alloy at 470° C. was determined according to a hardness-time curve, and the materials used and the detection environment were the same as in Example 13
The above comparative examples show the limitations of conventional methods and techniques, such as ex-situ non-continuous detection, cumbersome sampling steps, collected data that is discrete and easily affected by detection methods, and long cycle for optimizing process parameters. The examples show the technical advantages of the method in this patent, such as in-situ online detection, data directly collected during heat treatment of a test piece, simple experimental process, collected data that is accurate and continuous, and real-time monitoring of heat treatment extent or states of a test piece, so as to online regulate heat treatment.
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| Number | Date | Country | Kind |
|---|---|---|---|
| 201911155993.9 | Nov 2019 | CN | national |
This application is the national phase entry of International Application No. PCT/CN2020/101214, filed on Jul. 10, 2020, which is based upon and claims priority to Chinese Patent Application No. 201911155993.9, filed on Nov. 22, 2019, the entire contents of which are incorporated herein by reference.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/CN2020/101214 | 7/10/2020 | WO |
