A method for quickly generating non-uniform structure characteristics and mechanical properties of a weld zone

Abstract

The application discloses a kind of non-uniform organization characteristics and mechanical properties of weld zone fast generation method, belong to the technical field of welding quality evaluation, comprising the following steps: S1, by dynamic temperature measurement method, the characteristic temperature-time curve of the weld thermal history is obtained;S2, the characteristic temperature-time curve is input into the thermomechanical coupling model with the chemical composition of base material, the non-uniform microstructure phase distribution map of weld area is predicted;S3, based on semi-empirical mechanical model, the non-uniform microstructure phase distribution map is mapped into the mechanical property distribution map of weld area, and the fast identification of organization characteristics and mechanical properties is completed.The application adopts the above-mentioned non-uniform organization characteristics and mechanical properties of weld zone fast generation method, realizes the fast, non-destructive identification of weld non-uniform organization and performance, overcomes the shortcomings of traditional method lag and damage, and provides an effective means for welding quality online evaluation and process optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of welding quality evaluation, in particular to a method for rapidly generating non-uniform microstructure characteristics and mechanical properties of a weld zone. BACKGROUND

[0002] Welding is a key process for connecting steel structures. The microstructure (such as ferrite, pearlite, bainite, martensite, etc.) of the weld zone is significantly different from that of the base material due to the complex thermal cycle, which often becomes a weak link of the structure. The mechanical properties of the weld depend directly on its microstructure.

[0003] Evaluating the quality of the weld depends on destructive or non-destructive testing after welding, which has a lag and cannot predict the final performance in real time during welding. Although existing numerical simulation techniques can predict microstructure, the input is usually preset and idealized welding parameters, rather than the actual temperature field affected by various random factors during welding. This leads to a deviation between the predicted results and the actual situation, limiting the prediction accuracy and engineering application value of the model.

[0004] Therefore, there is an urgent need for a technology that can capture the real welding thermal history in real time and accurately predict the evolution of microstructure and mechanical properties of the weld based on it, to realize online, advanced, and non-contact intelligent evaluation of welding quality. SUMMARY

[0005] The purpose of the present application is to provide a method for rapidly generating non-uniform microstructure characteristics and mechanical properties of a weld zone, to overcome the shortcomings of existing technologies that rely on destructive testing after welding and idealized numerical simulation, and to achieve rapid, advanced, and non-destructive accurate evaluation of weld quality, which can be widely used in the welding manufacturing and in-service safety evaluation of steel structures such as bridges, buildings, and pipelines.

[0006] To achieve the above purpose, the present application provides a method for rapidly generating non-uniform microstructure characteristics and mechanical properties of a weld zone, which specifically includes the following steps:

[0007] S1. Obtain a characteristic temperature-time curve representing the thermal history of the weld by a dynamic temperature measurement method;

[0008] S2. Input the characteristic temperature-time curve and the chemical composition of the base material into a thermo-metallurgical coupling model to predict a non-uniform microstructure phase distribution map of the weld zone;

[0009] S3. Map the non-uniform microstructure phase distribution map to a mechanical property distribution map of the weld zone based on a semi-empirical mechanical model, and complete the rapid identification of microstructure characteristics and mechanical properties.

[0010] Preferably, the specific steps for obtaining the characteristic temperature-time curves characterizing the thermal history of the weld in S1 are as follows:

[0011] S1.1, Area Scanning and Highest Temperature Point Location: Control the temperature measuring instrument to quickly scan the weld pool and its surrounding area, and lock the spatial coordinates of the highest temperature point at the current moment;

[0012] S1.2 Single-point continuous tracking and data recording: The instrument switches to single-point continuous measurement mode, performs high-frequency data acquisition on the locked highest temperature point, and records its complete temperature-time curve;

[0013] S1.3 Dynamic Update: Repeatedly perform scanning and tracking at a frequency of 10 times per second to ensure that the highest temperature point is continuously locked throughout the welding process.

[0014] Preferably, the thermo-metallurgical coupling model in S2 is based on the Kirkaldy-type phase transition kinetic equation, and the isothermal transformation process is shown below:

[0015] ;

[0016] in, To maintain a constant temperature Below, the phase transition reaches a certain specific phase change. The time required To match the chemical composition of the steel and the original austenite grain size Related functions, Supercooling refers to the difference between the equilibrium phase transition temperature and the actual isothermal temperature. The difference, The reaction index is related to the form of the precipitates. Activation energy represents the energy barrier that an atom needs to overcome to cross a phase transition interface. This is the universal gas constant. To transform the fractional function.

[0017] Preferably, the Kirkaldy-type phase transformation kinetic equations specifically include ferrite, pearlite, and bainite transformation kinetic models, as shown below:

[0018] Ferrite transformation kinetic model:

[0019] ;

[0020] Pearlite transformation kinetic model:

[0021] ;

[0022] kinetic model of bainitic transformation:

[0023] ;

[0024] wherein, Afo is the transformation start temperature of ferrite, Ape is the transformation start temperature of pearlite, Ab is the transformation start temperature of bainite;

[0025] The transformation fraction function The calculation formula is as follows:

[0026] ;

[0027] For continuous cooling transformation of the welding process, the phase change amount is calculated by using the additive rule The calculation formula is as follows:

[0028] ;

[0029] wherein, A is the phase change rate, and the calculation formula is as follows:

[0030] ;

[0031] The Leblond-Devaux model is used to describe the formation of austenite in the heating stage of the thermo-metallurgical coupling model, and the specific description is as follows:

[0032] ;

[0033] wherein, Aa is the volume fraction of austenite, Ae is the equilibrium fraction, At is the characteristic transformation time.

[0034] Preferably, the mechanical property in S3 is microhardness, and the semi-empirical mechanical model uses the Maynier hardness formula and synthesizes the macro microhardness through the linear mixing law The calculation formula is as follows:

[0035] ;

[0036] wherein, Afo is the volume fraction of ferrite, Ape is the volume fraction of pearlite, Afp is the hardness of ferrite-pearlite mixed structure, Ab is the volume fraction of bainite, Ab is the hardness of bainite;

[0037] The calculation formula of the hardness of ferrite-pearlite mixed structure and bainite is as follows:

[0038] ;

[0039] ;

[0040] wherein, is the cooling rate at 700℃.

[0041] Therefore, the present application adopts the above-mentioned one kind of non-uniform structure characteristics and mechanical properties of the weld zone fast generation method, has the following beneficial effects:

[0042] (1) The core innovation of the present application is to first couple the measured dynamic thermal history data with the Kirkaldy type phase transition kinetics model and the Maynier semi-empirical hardness model, realizing the full-link prediction from the real welding temperature field to the microstructure and then to the mechanical properties. Unlike the traditional research which relies on ideal heat input assumption or static heat source model, the present application obtains the real temperature-time response curve in the welding process through the non-contact dual-color infrared temperature measurement technology, so that the prediction model has real-time and authenticity.

[0043] (2) By combining the measured characteristic thermal history with the physical metallurgical model, the non-uniform spatial distribution of the microstructure and mechanical properties of the weld zone can be accurately predicted, and the distribution graph is directly presented, which can directly lock the weak area of over-hardness or over-softness.

[0044] (3) The method is based on the universal physical metallurgical principle, and can be predicted by inputting specific material composition and measured thermal cycle, and does not depend on specific welding method or material, and has good universality and engineering application prospect.

[0045] The technical solutions of the present application will be further described in detail below through the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is a flow chart of an embodiment of the present application one kind of non-uniform structure characteristics and mechanical properties of the weld zone fast generation method;

[0047] Figure 2 is a temperature-time curve graph of the highest temperature point of the weld zone of the present application one kind of non-uniform structure characteristics and mechanical properties of the weld zone fast generation method;

[0048] Figure 3 is a ferrite volume fraction distribution graph of the present application one kind of non-uniform structure characteristics and mechanical properties of the weld zone fast generation method;

[0049] Figure 4 is a pearlite volume fraction distribution graph of the present application one kind of non-uniform structure characteristics and mechanical properties of the weld zone fast generation method;

[0050] Figure 5This is a martensite volume fraction distribution diagram in the weld zone of the method for rapidly generating non-uniform microstructure and mechanical properties of the weld zone according to the present invention.

[0051] Figure 6 This is a diagram showing the austenite volume fraction distribution in the weld zone, which is part of the present invention, a method for rapidly generating non-uniform microstructure and mechanical properties of the weld zone.

[0052] Figure 7 This invention relates to a method for rapidly generating microhardness maps of weld zones to assess the non-uniform microstructure and mechanical properties of weld zones. Detailed Implementation

[0053] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0054] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0055] Example

[0056] like Figure 1 As shown, this invention provides a method for rapidly generating the non-uniform microstructure and mechanical properties of weld zones, specifically including the following steps:

[0057] S1. Obtain characteristic temperature-time curves that characterize the thermal history of the weld by using dynamic temperature measurement methods;

[0058] The dynamic temperature measurement method uses a CTratio2M dual-color colorimetric infrared thermometer. The specific steps for obtaining the characteristic temperature-time curves representing the thermal history of the weld are shown below:

[0059] S1.1, Area Scanning and Highest Temperature Point Location: Control the temperature measuring instrument to quickly scan the weld pool and its surrounding area, and lock the spatial coordinates of the highest temperature point at the current moment;

[0060] S1.2 Single-point continuous tracking and data recording: The instrument switches to single-point continuous measurement mode, performs high-frequency data acquisition on the locked highest temperature point, and records its complete temperature-time curve;

[0061] S1.3 Dynamic Update: Repeatedly perform scanning and tracking at a frequency of 10 times per second to ensure that the highest temperature point is continuously locked throughout the welding process.

[0062] S2. Input the characteristic temperature-time curve and the chemical composition of the base material into the thermo-metallurgical coupling model to predict the non-uniform microstructure phase distribution map of the weld area;

[0063] The thermo-metallurgical coupling model was implemented using Abaqus software. It is based on Kirkaldy-type phase transition kinetic equations, and the isothermal transformation process is shown below:

[0064] ;

[0065] in, To maintain a constant temperature Below, the phase transition reaches a certain specific phase change. The time required To match the chemical composition of the steel and the original austenite grain size Related functions, Supercooling refers to the difference between the equilibrium phase transition temperature and the actual isothermal temperature. The difference, The reaction index is related to the form of the precipitates. Activation energy represents the energy barrier that an atom needs to overcome to cross a phase transition interface. This is the universal gas constant. To transform the fractional function.

[0066] The Kirkaldy-type phase transformation kinetic equations specifically include ferrite, pearlite, and bainite transformation kinetic models, as shown below:

[0067] Ferrite transformation kinetic model:

[0068] ;

[0069] Pearlite transformation kinetic model:

[0070] ;

[0071] kinetic model of bainitic transformation:

[0072] ;

[0073] in, This is the ferrite transformation initiation temperature. This is the initiation temperature of the pearlite transformation. This is the bainite transformation initiation temperature;

[0074] Transformation of fractional functions The calculation formula is as follows:

[0075] ;

[0076] For the continuous cooling transformation in the welding process, the additivity rule is used to calculate the phase change. The calculation formula is as follows:

[0077] ;

[0078] in, The phase transition rate is calculated using the following formula:

[0079] ;

[0080] The thermo-metallurgical coupling model uses the Leblond-Devaux model to describe austenite formation during the heating stage, as shown below:

[0081] ;

[0082] in, This represents the volume fraction of austenite. Equilibrium fraction, For feature transition time.

[0083] S3. Based on a semi-empirical mechanical model, the non-uniform microstructure phase distribution map is mapped to the mechanical property distribution map of the weld area, thus enabling rapid identification of microstructure characteristics and mechanical properties.

[0084] The mechanical property is microhardness. The semi-empirical mechanical model uses the Maynier hardness formula and synthesizes the macroscopic and microhardness through a linear mixing law. The calculation formula is as follows:

[0085] ;

[0086] in, This represents the volume fraction of ferrite. This represents the volume fraction of pearlite. The hardness is for a ferrite-pearlite mixed structure. This represents the volume fraction of bainite. The hardness of bainite;

[0087] The formulas for calculating the hardness of ferrite-pearlite mixed microstructures and bainite are as follows:

[0088] ;

[0089] ;

[0090] in, The cooling rate is at 700℃.

[0091] Application examples

[0092] Taking the submerged arc welding process of 316L steel as an example, the chemical composition of 316L steel is 0.0173% C, 0.493% Si, 1.377% Mn, 0.0312% P, 10.17% Ni, 16.54% Cr, and the balance is Fe.

[0093] S1. Obtain characteristic temperature-time curves that characterize the thermal history of the weld by using dynamic temperature measurement methods;

[0094] The welding process of 316L steel was monitored using a CTratio 2M dual-color colorimetric infrared thermometer. The specific steps are as follows:

[0095] S1.1 Area Scanning and Highest Temperature Point Location: Control the temperature measuring instrument to quickly scan the weld pool and its surrounding area, and lock the spatial coordinates of the highest temperature point at the current moment; the highest temperature point is located in the center area of ​​the weld pool.

[0096] S1.2 Single-point continuous tracking and data recording: The instrument switches to single-point continuous measurement mode, performs high-frequency data acquisition on the locked highest temperature point, and records its complete temperature-time curve, such as... Figure 2 As shown in the figure; the measured peak temperature at this point is approximately 960.8℃.

[0097] S1.3 Dynamic Update: Repeatedly perform scanning and tracking at a frequency of 10 times per second to ensure that the highest temperature point is continuously locked throughout the welding process.

[0098] S2. Input the characteristic temperature-time curve and the chemical composition of the base material into the thermo-metallurgical coupling model to predict the non-uniform microstructure phase distribution map of the weld area;

[0099] The characteristic temperature-time curve obtained in step S1 and the chemical composition of 316L steel are input into the thermo-metallurgical coupling model, and the model automatically calculates the phase transformation behavior under this thermal process.

[0100] Model calculations show that after this thermal cycle, the microstructure distribution in the weld region exhibits a significant non-uniformity. The phase distribution diagram uses different colors to represent the volume fraction of each phase. Figure 3As shown, ferrite is mainly concentrated in the base metal and areas far from the molten pool, exhibiting a high value range of deep red to orange, with a volume fraction of approximately 0.8-0.95. As the weld approaches its center, the ferrite content rapidly decreases to below 0.1%, showing a typical softening trend in the weld heat-affected zone; for example... Figure 4 As shown, pearlite is mainly concentrated in the matrix region, with a volume fraction of approximately 0.06-0.11. In the high-temperature region near the molten pool, the pearlite content significantly decreases to below 0.01; for example... Figure 5 As shown, martensite is mainly distributed in the weld and its near-fusion line region, with a volume fraction between 0.8% and 0.95%, making it the dominant microstructure in the entire weld zone; Figure 6 As shown, austenite is mainly concentrated in the intermediate layer of the molten pool transition zone and the heat-affected zone, with a volume fraction of approximately 0.015-0.025. This phase distribution diagram clearly shows the gradient variation characteristics of phase composition in various regions of the weld.

[0101] S3. Based on a semi-empirical mechanical model, the non-uniform microstructure phase distribution map is mapped to the mechanical property distribution map of the weld area, thus enabling rapid identification of microstructure characteristics and mechanical properties.

[0102] The mechanical property mapping module reads the non-uniform microstructure phase distribution map obtained in step S2. Based on the integrated Maynier hardness formula and linear mixing law, it maps the predicted phase fraction map to a local microhardness map, such as... Figure 7 The different colors shown represent the hardness values ​​(HV) of each region.

[0103] The hardness of the weld area exhibits a gradient distribution, with higher hardness at the center and lower hardness at the sides. The hardness is highest at the weld center, approximately 800-1700 HV, corresponding to a martensitic predominant structure; the heat-affected zone has moderate hardness, approximately 400-700 HV, containing some bainite and retained austenite; the base metal region has the lowest hardness, approximately 200-300 HV, mainly consisting of ferrite-pearlite structure. Overall distribution trend and phase distribution... Figure 1 This verifies the reliability of the model's mapping between organization and performance.

[0104] Therefore, this invention employs the aforementioned method for rapidly generating the non-uniform microstructure and mechanical properties of the weld zone. Driven by the measured characteristic thermal history of the 316L steel welding process, it successfully achieves rapid and accurate prediction and identification of the microstructure and hardness of the heat-affected zone. The prediction results clearly show the phase composition distribution and hardness variation trend in each region of the weld, consistent with the typical characteristics of 316L steel welded joints. This method eliminates the need for complex and time-consuming post-weld destructive testing, providing key technical support for online evaluation of welding quality and process optimization.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for rapidly generating the non-uniform microstructure and mechanical properties of weld zones, characterized in that: Specifically, the following steps are included: S1. Obtain characteristic temperature-time curves that characterize the thermal history of the weld by using dynamic temperature measurement methods; S2. Input the characteristic temperature-time curve and the chemical composition of the base material into the thermo-metallurgical coupling model to predict the non-uniform microstructure phase distribution map of the weld area; The thermo-metallurgical coupling model is based on Kirkaldy-type phase transition kinetic equations, and the isothermal transformation process is shown below: ； in, To maintain a constant temperature Below, the phase transition reaches a certain specific phase change. The time required To match the chemical composition of the steel and the original austenite grain size Related functions, Supercooling refers to the difference between the equilibrium phase transition temperature and the actual isothermal temperature. The difference, The reaction index is related to the form of the precipitates. Activation energy represents the energy barrier that an atom needs to overcome to cross a phase transition interface. This is the universal gas constant. To transform the fractional function; The Kirkaldy-type phase transformation kinetic equations specifically include ferrite, pearlite, and bainite transformation kinetic models, as shown below: Ferrite transformation kinetic model: ； Pearlite transformation kinetic model: ； kinetic model of bainitic transformation: ； in, This is the ferrite transformation initiation temperature. This is the initiation temperature of the pearlite transformation. This is the bainite transformation initiation temperature; Transformation of fractional functions The calculation formula is as follows: ； For the continuous cooling transformation in the welding process, the additivity rule is used to calculate the phase change. The calculation formula is as follows: ； in, The phase transition rate is calculated using the following formula: ； The thermo-metallurgical coupling model uses the Leblond-Devaux model to describe austenite formation during the heating stage, as shown below: ； in, This represents the volume fraction of austenite. Equilibrium fraction, For feature transition time; S3. Based on a semi-empirical mechanical model, the non-uniform microstructure phase distribution map is mapped to the mechanical property distribution map of the weld area, thus enabling rapid identification of microstructure characteristics and mechanical properties. The mechanical property is microhardness. The semi-empirical mechanical model uses the Maynier hardness formula and synthesizes the macroscopic and microhardness through a linear mixing law. The calculation formula is as follows: ； in, This represents the volume fraction of ferrite. This represents the volume fraction of pearlite. The hardness is for a ferrite-pearlite mixed structure. This represents the volume fraction of bainite. The hardness of bainite; The formulas for calculating the hardness of ferrite-pearlite mixed microstructures and bainite are as follows: ； ； in, The cooling rate is at 700℃.

2. The method for rapidly generating non-uniform microstructure and mechanical properties of weld zones according to claim 1, characterized in that: The specific steps for obtaining the characteristic temperature-time curves representing the thermal history of the weld in S1 are as follows: S1.1, Area Scanning and Highest Temperature Point Location: Control the temperature measuring instrument to quickly scan the weld pool and its surrounding area, and lock the spatial coordinates of the highest temperature point at the current moment; S1.2 Single-point continuous tracking and data recording: The instrument switches to single-point continuous measurement mode, performs high-frequency data acquisition on the locked highest temperature point, and records its complete temperature-time curve; S1.3 Dynamic Update: Repeatedly perform scanning and tracking at a frequency of 10 times per second to ensure that the highest temperature point is continuously locked throughout the welding process.

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