METHOD AND ITS APPLICATION FOR REGULATING HEAT TREATMENT DERIVED FROM IN-SITU COLLECTION OF INFORMATION

Abstract

A method and its application for regulating heat treatment derived from the in-situ collection of information. In-situ collecting information and/or data during heat treatment on a test piece, comparing the information or data with relevant information or data in a heat treatment information database, detecting or characterizing a heat treatment extent or state of the test piece, thereby optimizing a heat treatment process of the material and/or regulating the heat treatment of the test piece. The heat treatment includes homogenization, solid solution treatment, aging, recovery and recrystallization annealing. The in-situ collection is to collect information or data of the test piece in an actual heat treatment environment in real time. The heat treatment information database includes relevant information and data of material, heat treatment process, and heat treatment procedure, which can be continuously improved and optimized through subsequent detection and self-learning.

Claims

1. A method for regulating heat treatment derived from the in-situ collection of information, comprising: 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 state of the test piece, thereby optimizing a heat treatment process of material and/or regulating the heat treatment of the test piece, so that the test piece achieves a set heat treatment goal and/or microstructure and properties.

2. The method for regulating heat treatment derived from the in-situ collection of information according to claim 1, wherein the heat treatment comprises homogenization, solid solution treatment, aging, recovery and recrystallization annealing; the heat treatment process comprises at least one operation selected from the group consisting of heating-up, soaking and cooling down; preferably, the heat treatment extent or state comprises under-aged, peak-aged, over-aged, recovered, onset of recrystallization, and fully recrystallized.

3. The method for regulating heat treatment derived from the in-situ collection of information according to claim 1, wherein the in-situ collection is to collect the information and/or data of the test piece in an actual heat treatment environment in real time; preferably, the information is electrical information, comprising voltage, resistance, resistivity, electrical conductivity (in S/m), and conductivity (in % IACS); preferably, the information processing is to perform relevant processing on an electrical information-time curve and/or an electrical information-temperature curve, the relevant processing comprising 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=(E.sub.ti−E.sub.0)/(E.sub.u−E.sub.0)×100%, E.sub.0 is electrical information corresponding to an initial heat treatment extent, preferably electrical information when a temperature of the test piece meets a preset initial condition, E.sub.ti is electrical information corresponding to any moment during the heat treatment, and is electrical information corresponding to a certain extent before reaching a target heat treatment extent, and E.sub.u 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 set heat treatment goal.

4. The method for regulating heat treatment derived from the in-situ collection of information according to claim 1, wherein the heat treatment information database comprises material information and data, heat treatment process and related process parameters, and heat treatment process information and data; wherein the material information and data comprise material composition, heat treatment structure and properties; and the heat treatment process information and data comprise temperature and electrical information in different heat treatment processes.

5. The method for regulating heat treatment derived from the in-situ collection of information according to claim 1, wherein the heat treatment information database is a relational database, supporting the following database types: SQL Server, MySQL, MongoDB, SQLite, Access, H2, Oracle, and PostgreSQL; and database access technologies comprise ODBC, DAO, OLE DB, and ADO, and perform addition, deletion, modification, and query on stored content according to actual needs.

6. The method for regulating heat treatment derived from the in-situ collection of information according to claim 1, comprising: for a material that is not recorded in the heat treatment information database in different heat treatment processes, selecting characteristic points on an electrical information-time curve and an electrical information-temperature curve obtained through detection, detecting material composition, microstructure and properties, and storing material information and data, heat treatment process data, and heat treatment procedure information and data in the heat treatment information database, wherein the characteristic points comprise a starting point where the curve becomes horizontal, an inflection point on the curve, a point where a slope of the curve changes unsteadily, a point corresponding to a set heat treatment extent on the curve, points with a same time interval, and points with a same temperature interval.

7. The method for regulating heat treatment derived from the in-situ collection of information according to claim 1, wherein the heat treatment information database is continuously improved and/or optimized through subsequent detection and self-learning to improve reliability and availability of data; and the self-learning is based on at least one algorithm selected from the group consisting of neural network algorithm, random forest algorithm, and particle swarm algorithm with an operating environment supporting the following operating systems: Windows, Android, Linux, Mac OS, and IOS, and learning results provide terminal services to users through SOAP and RESTful.

8. The method for regulating heat treatment derived from the in-situ collection of information according to claim 1, wherein 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 comprising authority management, access verification, data storage, data processing, data management, and data analysis.

9. The method for regulating heat treatment derived from the in-situ collection of information according to claim 1, wherein the method is applicable to optimization of the heat treatment process of the material and/or online regulation of the heat treatment of the test piece; preferably, the method is applied to homogenization annealing, comprising determining a proper homogenization temperature, homogenization time, heating rate, and cooling rate, the homogenization comprising single-stage homogenization and multi-stage homogenization; preferably, the method is applied to solid solution treatment, comprising determining a proper solid solution temperature, solid solution time, heating rate, and cooling rate, the solid solution comprising single-stage solid solution and multi-stage solid solution; preferably, the method is applied to aging, comprising 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 comprising single-stage aging and multi-stage aging; and preferably, the method is applied to recovery and recrystallization annealing, comprising 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 under same heat treatment conditions.

10. A device and software system for regulating heat treatment derived from the in-situ collection of information according to claim 1, comprising an information collection and processing module, a self-learning module, the heat treatment information database, a heat treatment control module, and a heat treatment process, wherein the information collection and processing module is configured to perform in-situ collection and real-time processing on heat treatment information of the test piece; the self-learning module is configured to analyze logical patterns and/or data relationships, comprising analyzing logical patterns between material and heat treatment and relationships between information and information or between data and data; the heat treatment information database is configured to store data obtained by the information collection and processing module and provide terminal services; the heat treatment control module is configured to generate a control command according to analysis results of the self-learning module; and the heat treatment process executes the control command to adjust a heat treatment temperature and control a heat treatment time.

11. The method for regulating heat treatment derived from the in-situ collection of information according to claim 4, wherein the heat treatment information database is a relational database, supporting the following database types: SQL Server, MySQL, MongoDB, SQLite, Access, H2, Oracle, and PostgreSQL; and database access technologies comprise ODBC, DAO, OLE DB, and ADO, and perform addition, deletion, modification, and query on stored content according to actual needs.

12. The method for regulating heat treatment derived from the in-situ collection of information according to claim 2, comprising: for a material that is not recorded in the heat treatment information database in different heat treatment processes, selecting characteristic points on an electrical information-time curve and an electrical information-temperature curve obtained through detection, detecting material composition, microstructure and properties, and storing material information and data, heat treatment process data, and heat treatment procedure information and data in the heat treatment information database, wherein the characteristic points comprise a starting point where the curve becomes horizontal, an inflection point on the curve, a point where a slope of the curve changes unsteadily, a point corresponding to a set heat treatment extent on the curve, points with a same time interval, and points with a same temperature interval.

13. The method for regulating heat treatment derived from the in-situ collection of information according to claim 3, comprising: for a material that is not recorded in the heat treatment information database in different heat treatment processes, selecting characteristic points on the electrical information-time curve and the electrical information-temperature curve obtained through detection, detecting material composition, microstructure and properties, and storing material information and data, heat treatment process data, and heat treatment procedure information and data in the heat treatment information database, wherein the characteristic points comprise a starting point where the curve becomes horizontal, an inflection point on the curve, a point where a slope of the curve changes unsteadily, a point corresponding to a set heat treatment extent on the curve, points with a same time interval, and points with a same temperature interval.

14. The method for regulating heat treatment derived from the in-situ collection of information according to claim 4, wherein the heat treatment information database is continuously improved and/or optimized through subsequent detection and self-learning to improve reliability and availability of data; and the self-learning is based on at least one algorithm selected from the group consisting of neural network algorithm, random forest algorithm, and particle swarm algorithm with an operating environment supporting the following operating systems: Windows, Android, Linux, Mac OS, and IOS, and learning results provide terminal services to users through SOAP and RESTful.

15. The method for regulating heat treatment derived from the in-situ collection of information according to claim 5, wherein the heat treatment information database is continuously improved and/or optimized through subsequent detection and self-learning to improve reliability and availability of data; and the self-learning is based on at least one algorithm selected from the group consisting of neural network algorithm, random forest algorithm, and particle swarm algorithm with an operating environment supporting the following operating systems: Windows, Android, Linux, Mac OS, and TOS, and learning results provide terminal services to users through SOAP and RESTful.

16. The method for regulating heat treatment derived from the in-situ collection of information according to claim 6, wherein the heat treatment information database is continuously improved and/or optimized through subsequent detection and self-learning to improve reliability and availability of data; and the self-learning is based on at least one algorithm selected from the group consisting of neural network algorithm, random forest algorithm, and particle swarm algorithm with an operating environment supporting the following operating systems: Windows, Android, Linux, Mac OS, and IOS, and learning results provide terminal services to users through SOAP and RESTful.

17. The method for regulating heat treatment derived from the in-situ collection of information according to claim 4, wherein 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 comprising authority management, access verification, data storage, data processing, data management, and data analysis.

18. The method for regulating heat treatment derived from the in-situ collection of information according to claim 5, wherein 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 comprising authority management, access verification, data storage, data processing, data management, and data analysis.

19. The method for regulating heat treatment derived from the in-situ collection of information according to claim 6, wherein 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 comprising authority management, access verification, data storage, data processing, data management, and data analysis.

20. The method for regulating heat treatment derived from the in-situ collection of information according to claim 2, wherein the method is applicable to optimization of the heat treatment process of the material and/or online regulation of the heat treatment of the test piece; preferably, the method is applied to homogenization annealing, comprising determining a proper homogenization temperature, homogenization time, heating rate, and cooling rate, the homogenization comprising single-stage homogenization and multi-stage homogenization; preferably, the method is applied to solid solution treatment, comprising determining a proper solid solution temperature, solid solution time, heating rate, and cooling rate, the solid solution comprising single-stage solid solution and multi-stage solid solution; preferably, the method is applied to aging, comprising 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 comprising single-stage aging and multi-stage aging; and preferably, the method is applied to recovery and recrystallization annealing, comprising 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 under same heat treatment conditions.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

Description of the Drawings

[0042] FIG. 1 shows the electrical information-time relationship.

[0043] FIG. 2 shows an electrical information-time curve of alloy solid treatment.

[0044] FIG. 3 shows an electrical information-time curve obtained in the process of alloy aging and showing schematic diagrams of characteristic microstructures.

[0045] FIG. 4 shows an electrical information-time curve obtained during annealing of a cold-deformed material.

[0046] FIG. 5 shows a resistivity-time curve of a common alloy undergoing solid solution treatment at different temperatures.

[0047] FIG. 6 shows a resistivity-time curve of a common alloy undergoing solid solution treatment.

[0048] FIG. 7 shows a resistivity-time curve of a common alloy undergoing solid solution treatment and showing characterization of a solid solution extent FIG. 8 shows an electrical information-time curve of alloy aging and showing characterization of precipitation.

[0049] FIG. 9 shows resistivity-time curves before and after optimization of alloy composition.

[0050] FIG. 10 shows resistivity-time curves of a material annealing at different temperatures (T.sub.1>T.sub.2>T.sub.3).

[0051] FIG. 11 shows resistivity-time curves of a workpiece at different amounts of cold deformation at a set temperature.

[0052] FIG. 12 shows resistivity-time curves of two materials under the same annealing conditions.

[0053] FIG. 13 shows real-time regulation of heat treatment according to results obtained by comparing in-situ measured electrical information with reference electrical information.

[0054] FIG. 14 is a schematic diagram of obtaining reference electrical information.

[0055] FIG. 15 is a structural block diagram of modules of an application device.

[0056] FIG. 16 shows conductivity-time curves obtained through in-situ testing in Example 1.

[0057] FIGS. 17(a)-17(d) show SEM images of a test piece in Example 1.

[0058] FIG. 18 shows conductivity-time curves obtained through in-situ testing in Example 2.

[0059] FIGS. 19(a)-19(b) show TEM images of a test piece in Example 2 FIGS. 20(a)-20(f) show energy spectrum analysis results of the corresponding regions in FIGS. 19(a)-19(b).

[0060] FIG. 21 shows a resistivity-time curve obtained through in-situ testing in Example 3.

[0061] FIG. 22 shows a conductivity-time curve obtained through in-situ testing in Example 4.

[0062] FIG. 23 shows a conductivity-time curve obtained through in-situ testing in Example 5.

[0063] FIGS. 24(a)-24(b) show SEM images of a test piece in Example 5.

[0064] FIG. 25 shows a conductivity-time curve obtained through in-situ testing in Example 6.

[0065] FIGS. 26(a)-26(c) show TEM images of a test piece in Example 6.

[0066] FIG. 27 shows a conductivity-time curve obtained through in-situ testing in Example 7.

[0067] FIGS. 28(a)-28(c) show TEM images of a test piece in Example 7.

[0068] FIG. 29 shows a resistivity-time curve obtained through in-situ testing in Example 8.

[0069] FIGS. 30(a)-30(d) show TEM images of a test piece in Example 8.

[0070] FIG. 31 shows a voltage-time curve obtained through in-situ testing in Example 9.

[0071] FIGS. 32(a)-32(d) show OM images of a test piece in Example 9.

[0072] FIG. 33 shows conductivity-time curves obtained through in-situ testing in Example 10.

[0073] FIGS. 34(a)-34(d) show OM images of a test piece of Al-0.16Y alloy undergoing annealing in Example 10.

[0074] FIGS. 35(a)-35(d) show OM images of a test piece of Al-0.16Y-0.15Zr alloy undergoing annealing in Example 10.

[0075] FIGS. 36(a)-36(b) show conductivity-time curves obtained through in-situ testing in Example 11.

[0076] FIG. 37 shows a conductivity-time curve measured at 450° C. in Example 11.

[0077] FIG. 38 shows resistivity-time curves obtained through in-situ testing in Example 12 FIG. 39 shows a resistivity-time curve of the alloy measured at 475° C. in Example 12.

[0078] FIG. 40 shows an in-situ measured resistivity-time curve and a reference electrical information curve of a 7B50 alloy undergoing solid solution treatment at 470° C. in Example 13.

[0079] FIGS. 41(a)-41(b) show a measured conductivity-time curve and a reference electrical information curve of Al-0.10Zr-0.10La-0.02B alloy in Example 14.

[0080] FIGS. 42(a)-42(b) show SEM images of a test piece in Example 14.

[0081] FIGS. 43(a)-43(b) show resistivity-time curves obtained through in-situ testing in Example 15.

[0082] FIG. 44 shows a conductivity-temperature curve of Al-0.13Fe-0.33Si-0.10La alloy simulated by using the JmatPro 7.0.0 software in Comparative Example 1 FIG. 45 shows a hardness-time curve of a test piece of Al-4 wt. % Cu alloy undergoing solid solution treatment for different durations followed by aging at 170° C. for 12 h in Comparative Example 2.

[0083] FIG. 46 shows a hardness-time curve of Al-1.00Hf-0.16Y alloy undergoing homogenization in Comparative Example 3.

[0084] FIG. 47 shows a hardness-time curve of Al-4 wt. % Cu alloy undergoing aging at 190° C. in Comparative Example 4.

[0085] FIG. 48 shows a hardness-time curve and a room-temperature conductivity-time curve of Al-4.5Zn-1.2Mg alloy undergoing aging at 170° C. in Comparative Example 5.

[0086] FIG. 49 shows hardness curves of aluminum alloys undergoing isochronal annealing for 1 h at different temperatures in Comparative Example 6.

[0087] FIG. 50 shows a hardness-time curve of 7B50 alloy undergoing solid solution treatment for different durations followed by aging at 170° C. for 8 h in Comparative Example 7.

OPTIMAL EMBODIMENTS FOR IMPLEMENTING THE PRESENT INVENTION

Optimal Implementations of the Present Invention

[0088] Type here a paragraph describing the optimal implementation of the present invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Implementations of the Present Invention

[0089] 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.

[0090] 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.

[0091] 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.sup.−4 MS/(m.Math.h), the alloy reached a near-stable solid solution extent, and the required solid solution time was 6-12 h.

[0092] FIG. 16 is a graph showing conductivity-time curves of solid solution treatment at different temperatures of 510° C. 530° C. and 550° C. obtained through in-situ testing. An absolute value of a slope of the conductivity-time curve of solid solution treatment at 510° C. for 12 h is 1.20×10.sup.−4 MS/(m.Math.h), greater than 1.00×10.sup.−4 MS/(m.Math.h), indicating that the alloy has not reach the near-stable solid solution extent. The system determines that 510° C. is not the proper solid solution temperature through self-learning. The conductivity-time curve of solid solution treatment at 530° C. for 12 h has a trend towards flattening. An absolute value of a slope of the curve of solid solution treatment for 8 h is 1.00×10.sup.−4 MS/(m.Math.h), indicating that the alloy reaches the near-stable solid solution extent. The system determines that 530° C. is the proper solid solution temperature through self-learning. An absolute value of a slope of the conductivity-time curve of solid solution treatment at 550° C. for 12 h is 3.33×10.sup.−3 MS/(m.Math.h), greater than 1.00×10.sup.−4 MS/(m.Math.h). The system determines that 550° C. is not the proper solid solution temperature through self-learning.

[0093] FIGS. 17(a)-17(d) show SEM images of a test piece undergoing solid solution treatment at 550° C. for different durations (0 h, 4 h, 8 h, and 12 h) As shown in FIG. 17(a), there are many coarse second phases in the as-cast microstructure. As shown in FIG. 17(b), there are still some coarse phases after 4 h of solid solution. As shown in FIG. 17(c), some grain boundaries start to melt after 8 h of solid solution, indicating that overburning occurs. As shown in FIG. 17(d), grain boundaries are largely melted after 12 h of solid solution, indicating that severe overburning occurs.

[0094] 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.

[0095] 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.sup.−6 MS/(m.Math.s), and the required solid solution time was 1-6 h.

[0096] FIG. 18 is a graph showing a conductivity-time curve obtained through in-situ testing. After solid solution for 3600 s, an absolute value of a slope at the corresponding point on the conductivity-time curve is 3.67×10.sup.−5 MS/(m.Math.s). After solid solution for 7275 s, an absolute value of a slope at the corresponding point on the conductivity-time curve reaches 8×10.sup.−6 MS/(m.Math.s). The system automatically determines that 7275 s (or rounded to 2 h) is the proper solid solution time of the alloy at 535° C. to reach the near-stable solid solution extent through self-learning.

[0097] FIGS. 19(a)-19(b) show TEM images of a test piece undergoing solid solution treatment at 535° C. for 3600 s and 7200 s. FIGS. 20(a)-20(f) show energy spectrum analysis results of the regions marked in FIGS. 19(a)-19(b). There are a large amount of undissolved phases after solid solution for 3600 s; and the second phase basically dissolves into the matrix after solid solution for 7200 s, indicating that the alloy reaches the near-stable solid solution extent after solid solution at 535° C. for 2 h.

[0098] 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.

[0099] 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.sup.−7 Ω.Math.m, and the required solid solution time was 5-20 h.

[0100] FIG. 21 is a graph showing a resistivity-time curve obtained through in-situ testing After solid solution for 35842 s, the resistivity reaches 1.7890×10.sup.−7 Ω.Math.m. The system automatically determines that 35842 s (or rounded to 10 h) is the proper solid solution time of the alloy at 430° C. to reach the near-stable solid solution extent through self-learning.

[0101] 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.

[0102] 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.

[0103] FIG. 22 is a graph showing a conductivity-time curve obtained through in-situ testing. After homogenization for 13795 s, the conductivity reaches 4.925 MS/m. The system automatically determines that 13795 s (or rounded to 4 h) is the proper homogenization time of the alloy at 330° C. to reach the near-stable homogenization extent through self-learning.

[0104] 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.

[0105] 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.sup.−4% IACS/h, and the required homogenization time was 14-36 h.

[0106] FIG. 23 is a graph showing a conductivity-time curve obtained through in-situ testing. After homogenization for 66961 s, an absolute value of a slope of the conductivity-time curve reaches 9×10.sup.−4% IACS/h. The system automatically determines that 66961 s (or rounded to 19 h) is the proper homogenization time of the alloy at 635° C. to reach the near-stable homogenization extent through self-learning.

[0107] FIGS. 24(a)-24(b) show SEM images of a test piece undergoing homogenization at 635° C. for different durations (10 h and 19 h) After homogenization for 10 h. as shown in FIG. 24(a), there is a large amount of dendritic segregation, and after homogenization for 19 h, as shown in FIG. 24(b), the dendritic segregation is basically eliminated, indicating that the alloy reaches the near-stable homogenization extent at 635° C. for 19 h.

[0108] 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.

[0109] 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).fwdarw.θ′ phase.fwdarw.θ phase.

[0110] FIG. 25 is a graph showing a conductivity-time curve obtained through in-situ testing. The conductivity corresponding to the initial aging extent is 32.19% IACS. The conductivity after aging for 48 h increases to 33.10% IACS. There are three significant points at which the slope changes suddenly at 11 h, 20 h, and 37 h respectively on the conductivity-time curve. The system determines that the three points correspond to the precipitation of the θ″ phase (GPU zones), θ′ phase, and θ phase respectively through self-learning according to the correspondence between conductivity and precipitation of second phase in the material heat treatment information database.

[0111] FIGS. 26(a)-26(c) show TEM images (the incident direction of the electron beam is [100].sub.Al) of a test piece of the Al-4 wt. % Cu alloy undergoing aging at 150° C. for different durations (11 h, 20 h, and 37 h). After aging for 11 h, as shown in FIG. 26(a), the θ″ phase (GPII zones) is precipitated. After aging for 20 h, as shown in FIG. 26(b), the θ′ phase is precipitated. After aging for 37 h, as shown in FIG. 26(c), the θ phase is precipitated.

[0112] 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.

[0113] 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.fwdarw.θ phase.

[0114] FIG. 27 is a graph showing a conductivity-time curve obtained through in-situ testing. The conductivity corresponding to the initial aging extent is 17.15 MS/m. The conductivity after aging for 48 h increases to 17.72 MS/m There are two significant points at which the slope changes suddenly at 9 h and 32 h respectively on the conductivity-time curve. The system determines that the two points correspond to the precipitation of the θ′ phase and θ phase respectively through self-learning according to the correspondence between change of conductivity and precipitation of second phase in the material heat treatment information database.

[0115] FIGS. 28(a)-28(c) show TEM images (the incident direction of the electron beam is [100].sub.Al) of a test piece of the Al-4 wt. % Cu alloy undergoing aging at 190° C. for different durations (9 h, 32 h, and 48 h) After aging for 9 h, as shown in FIG. 28(a), the θ′ phase is precipitated. After aging for 32 h, as shown in FIG. 28(b), the θ phase is precipitated. After aging for 48 h, as shown in FIG. 28(c), the θ phase is precipitated.

[0116] 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.

[0117] 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.fwdarw.η phase, and the peak aging time was 9-24 h.

[0118] FIG. 29 is a graph showing a resistivity-time curve obtained through in-situ testing. The resistivity corresponding to the initial aging extent is 5.75×10.sup.−8 Ω.Math.m. The resistivity after aging for 48 h is 5.04×10.sup.−8 Ω.Math.m. There are three significant points at which the slope changes suddenly at 6 h, 12 h, and 19 h respectively on the resistivity-time curve. The system determines that the three points correspond to the precipitation of the atomic clusters, η′ phase, and η phase respectively through self-learning according to the correspondence between change of resistivity and precipitation of second phase in the material heat treatment information database. The alloy undergoing aging for less than 12 h is in an under-aging state, for 12 h is in a peak-aging state, and for more than 19 h is in an over-aging state.

[0119] FIGS. 30(a)-30(d) show TEM images (the incident direction of the electron beam is [100].sub.Al) of a test piece of the Al-4.5Zn-1.2Mg alloy undergoing aging at 170° C. for different durations (0 h, 6 h, 12 h, and 19 h). After aging for 0 h, as shown in FIG. 30(a), the alloy matrix is very pure. After aging for 6 h, as shown in FIG. 30(b), only small-sized punctate phases are precipitated, which is the under-aging state. After aging for 12 h, as shown in FIG. 30(c), a large amount of η′ phases are precipitated in the alloy, which is the peak-aging state. After aging for 19 h, as shown in FIG. 30(d), spherical η phase precipitates from the alloy, and the width of the precipitation-free zone of the grain boundary is more than 400 nm, indicating an over-aging state.

[0120] 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.

[0121] 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.

[0122] FIG. 31 is a graph showing a voltage-time curve obtained through in-situ testing. The voltage decreases gradually with the annealing time. The voltage before annealing is 0.6044 mV. The voltage becomes stable to 0.5973 mV after annealing for 12000 s. The voltages corresponding to annealing for 0 s, 2000 s, 6000 s, and 12000 s are 0.6044 mV, 0.5995 mV, 0.5980 mV, and 0.5974 mV respectively. The corresponding annealing extent coefficients are automatically calculated as 0%, 69.01%, 90.14%, and 98.59%, respectively. The system determines that the corresponding heat treatment extents are a rolling state, an incomplete recrystallization state, a recrystallization state, and growth of grains through self-learning.

[0123] FIGS. 32(a)-32(d) show metallographic images of a test piece undergoing annealing for different durations (0 s, 2000 s, 6000 s, and 12000 s). After annealing for 0 s, as shown in FIG. 32(a), it is a fiber structure formed through the elongation of grains. After annealing for 2000 s, as shown in FIG. 32(b), recrystallization occurs in some regions. After annealing for 6000 s, as shown in FIG. 32(c), incomplete recrystallization occurs After annealing for 12000 s, as shown in FIG. 32(d), the recrystallized grains are coarsened. It indicates that the heat treatment extents corresponding to annealing for 2000 s, 6000 s, and 12000 s are partial recrystallization, incomplete recrystallization, and growth of recrystallized grains, respectively.

[0124] 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.

[0125] FIG. 33 is a graph showing conductivity-time curves obtained through in-situ testing. For the Al-0.16Y alloy, the conductivity before annealing is 13.19 MS/m, and the conductivity becomes stable to 13.28 MS/m after annealing for 4 h. For the Al-0.16Y-0.15Zr alloy, the conductivity before annealing is 13.09 MS/m, and the conductivity becomes stable to 13.15 MS/m after annealing for 5 h. Taking a fully annealed state as a target heat treatment extent, the system automatically calculated the time required for the annealing extent coefficient of the two alloys to reach 30%, 60%, and 90%. The time required for Al-0.16Y is 0.68 h, 1.67 h, and 3.00 h, respectively. The time required for Al-0.16Y-0.15Zr is 0.70 h, 1.78 h, and 3.56 h, respectively. The Al-0.16Y-0.15Zr alloy takes a longer time to reach the same heat treatment extent, indicating that the Al-0.16Y-0.15Zr alloy has a higher resistance to recrystallization.

[0126] FIGS. 34(a)-34(d) show metallographic images of a test piece of the Al-0.16Y alloy undergoing annealing for different durations (0 h, 2 h, 4 h, and 6 h). After annealing for 0 h, as shown in FIG. 34(a), it is a fiber structure formed through the elongation of grains. After annealing for 2 h, as shown in FIG. 34(b), partial recrystallization occurs. After annealing for 4 h, as shown in FIG. 34(c), the grains merge and grow. After annealing for 8 h, as shown in FIG. 34(d), the recrystallized grains grow abnormally. FIGS. 35(a)-35(d) show metallographic images of a test piece of the Al-0.16Y-0.15Zr alloy undergoing annealing for different durations (0 h, 2 h, 4 h, and 6 h) After annealing for 0 h, as shown in FIG. 35(a), it is a fiber structure formed through the elongation of grains. After annealing for 2 h, as shown in FIG. 35(b), it is mainly a fiber structure. After annealing for 4 h, as shown in FIG. 35(c), partial recrystallization occurs in the alloy. After annealing for 8 h, as shown in FIG. 35(d), full recrystallization occurs. It indicates that the Al-0.16Y-0.15Zr alloy has a higher resistance to recrystallization (or a higher heat resistance).

[0127] 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.

[0128] It was known by searching the material heat treatment information database that FIGS. 36(a)-36(b) are graphs showing conductivity-time curves of the Al-0.1Sc alloy undergoing recrystallization annealing at 400° C. and 500° C. respectively in the material heat treatment information database. FIG. 36(a) shows that the conductivity corresponding to the initial extent of the alloy undergoing annealing at 400° C. is 23.63% IACS, the conductivity becomes stable to 23.93% IACS after annealing for 6.5 h, and the annealing time to start recrystallization is 0.61 h. FIG. 36(b) shows that the conductivity corresponding to the initial extent of the alloy undergoing annealing at 500° C. is 19.91% IACS, the conductivity becomes stable to 20.16% IACS after annealing for 5.0 h, and the annealing time to start recrystallization is 1.78 h. The system fitted a curve based on the foregoing information and data through self-learning to predict the time to start recrystallization of the alloy undergoing annealing at 450° C., which is obtained as 3883 s, that is, 64.7 min.

[0129] FIG. 37 is a graph showing a conductivity-time curve of annealing at 450° C. obtained through in-situ testing. The measured time to start recrystallization is 65.2 min, which is close to the predicted result 64.7 min.

[0130] 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.

[0131] It was known by searching the material heat treatment information database that FIG. 38 is a graph showing resistivity-time curves of the aluminum material undergoing recrystallization annealing with amounts of cold deformation of 9% and 10% in the material heat treatment information database. The resistivity corresponding to the initial annealing extent of the aluminum material with an amount of cold deformation of 9% is 8.226×10.sup.−8 Ω.Math.m, and the resistivity becomes stable to 8.122×10.sup.−8 Ω.Math.m after annealing for 4.5 h. The resistivity corresponding to the initial annealing extent of the aluminum material with an amount of cold deformation of 16% is 8.242×10.sup.−8 Ω.Math.m, and the resistivity becomes stable to 8.144×10.sup.−8 Ω.Math.m after annealing for 6.2 h. The times to start recrystallization of the two cold-deformed aluminum materials are 0.629 h and 1.101 h respectively. The system fitted a curve based on the foregoing information and data through self-learning to predict the time to start recrystallization of the aluminum material with an amount of cold deformation of 12.25%, which is obtained as 0.865 h.

[0132] 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 FIG. 39. The measured time to start recrystallization is 0.870 h, which is close to the predicted result 0.865 h.

[0133] 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.

[0134] 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.sup.−8 Ω.Math.m, the alloy reaches a near-stable solid solution extent, and the required solid solution time is 60 min.

[0135] FIG. 40 is a graph showing an in-situ measured resistivity-time curve and a reference electrical information curve of a 7B50 alloy undergoing solid solution treatment at 470° C. After solid solution treatment for 60 min, the measured resistivity is lower than the reference resistivity, and the solid solution extent coefficient is only 91.67%, so that the system determines that the heat treatment has not been completed yet. After solid solution treatment for 73 min, the measured resistivity is equal to the reference resistivity at 60 min, and the solid solution extent coefficient reaches 100%, so that the system determines that the heat treatment has been completed, and the heat treatment control module stops the heat treatment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] FIGS. 41(a)-41(b) are graphs showing a measured conductivity-time curve and a reference electrical information curve of Al-0.10Zr-0.11La-0.02B alloy. FIG. 41(a) shows a curve measured by a feedback control system. When the measured curve is lower than the reference curve, the feedback is made to reduce the furnace temperature. When the measured curve is higher than the reference curve, the feedback is made to increase the furnace temperature. Finally, the measured curve is roughly consistent with the reference curve. FIG. 41(b) shows a curve measured by a non-feedback control system. There is some deviation between the actual temperature and the set temperature, eventually resulting in a partial deviation between the measured curve and the reference curve.

[0140] FIGS. 42(a)-42(b) show microstructures after two heat treatments observed by using a scanning electron microscope. FIG. 42(a) shows the microstructure after heat treatment with feedback, which has no significant segregation and overburning, and has a good homogenization effect. FIG. 42(b) shows the microstructure after heat treatment without feedback, which has overburning at the grain boundary and segregation still existing in the grain, and has a not good homogenization effect. The reason is that the furnace temperature fluctuates and is not adjusted in time. There is overburning at the grain boundary as the temperature is excessively high, and the diffusion of elements is insufficient as the temperature is excessively low.

[0141] 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.).

[0142] 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.

[0143] The alloy was aged at 300° C. for 12 h. A resistivity-time curve shown in FIG. 43(a) was in-situ measured. The resistivity of aging for 12 h is 6.024×10.sup.−8 Ω.Math.m, and the aging extent coefficient is calculated as 60%. The self-learning module determined the temperature for the second-stage aging as 400° C., and set the resistivity corresponding to the target heat treatment states to 7.272×10.sup.−8 Ω.Math.m. The heat treatment control module heated up to 400° C. for the second-stage aging. The corresponding resistivity-time curve shown in FIG. 43(b) was in-situ measured. The resistivity of aging for 32 h reaches 7.272×10.sup.−8 Ω.Math.m, and the aging extent coefficient is calculated as 100%, so that the system automatically stops the heat treatment.

[0144] 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. FIG. 44 shows a conductivity-temperature curve simulated by using the JmatPro 7.0.0 software. The curve changes suddenly at 635° C., indicating that alloy overburning occurs at a temperature higher than this, and there is no overburning at a heat treatment temperature lower than 630° C. Example 1 demonstrates that the Al-0.1Zn-0.2Mg-0.1Fe-0.05Mn alloy overburned at 550° C., which is 85° C. lower than the overburning temperature predicted by the software.

[0145] 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. FIG. 45 is a graph showing a hardness-time curve of a test piece of Al-4 wt ° % Cu alloy undergoing solid solution treatment at 535° C. for different durations followed by aging at 170° C. for 12 h. Past the point of 2 h of solid solution treatment, the difference in the aging hardness value is not large, indicating that the alloy reaches the near-stable solid solution extent. Compared with Example 2, this comparative example is ex-situ detection, cumbersome in operation, complex in sample processing, discrete and imprecise in data, and is easily affected by differences in sampling sites.

[0146] Comparative Example 3: The proper homogenization time of Al-1.00Hf-0.16Y alloy at 635° C. was determined according to a hardness curve FIG. 46 shows the hardness of homogenization for different durations. When the homogenization time reaches and exceeds 18 h, the hardness value fluctuates slightly, indicating that the alloy reaches near-stable homogenization, and 18 h can be the proper homogenization time. Compared with Example 5, this comparative example has disadvantages such as cumbersome steps, complex sample processing, ex-situ measurement, discrete and imprecise data, easily affected by differences in sampling sites, and inability to control process parameters.

[0147] 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. FIG. 47 shows an age hardening curve. The peak hardness appears at 10 h and 36 h on the curve, respectively corresponding to the precipitation of θ′ phase and θ phase. This comparative example is easily affected by sampling sites and has low accuracy. Compared with the information of the test piece in-situ collected in Example 7, this comparative example has a small amount of experiment, intensive data and high accuracy, and can accurately detect the peak aging of the alloy.

[0148] 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. FIG. 48 shows the hardness and room-temperature conductivity of the alloy undergoing aging at 170° C. for different durations. After aging for 12 h, the peak hardness is reached, and the whole room-temperature conductivity-time curve shows an upward trend.

[0149] 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.

[0150] 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. FIG. 49 shows hardness curves of aluminum alloys with different microalloying elements added undergoing annealing for 1 h at different temperatures. The curve shows that the hardness of the Al-0.16Y alloy is lower than that of the Al-0.16Y-0.15Zr alloy. The hardness of the Al-0.16Y alloy decreases significantly in the range of 350-475° C., and becomes stable when the annealing temperature is higher than 500° C. The hardness of the Al-0.15Zr-0.16Y alloy decreases significantly when the annealing temperature reaches 450° C., and it has higher heat resistance and higher resistance to recrystallization. The results obtained in this comparative example are consistent with those in Example 10, but this comparative example takes a long time for detection, has cumbersome steps, and has the acquired hardness discrete points easily affected by accidental factors (such as sampling sites and hardness measurement errors) However, Example 10 is an in-situ detection performed at different temperatures, which has the advantages of continuous and high-precision data, short test time, and simple steps.

[0151] 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 FIG. 50 shows a hardness curve of the alloy undergoing solid solution treatment for different durations followed by aging at 170° C. for 8 h. After aging for 70 min, the hardness becomes stable, indicating that the alloy reaches the near-stable solid solution extent. However, the in-situ detection in Example 13 avoids the influence of different sampling sites, accurately determines the proper solid solution time, and can feed back in real time to online control the heat treatment process.

[0152] 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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