Methods, systems and equipment for predicting the microstructure of large and complex forgings

Abstract

This application discloses a method, system, and equipment for predicting the microstructure of large and complex forgings, relating to machining quality prediction. Specifically addressing the problems of high false alarm rates and difficulty in capturing evolution patterns in defect identification due to process coupling and geometric complexity in existing technologies, this application improves upon the method by dividing the high-temperature austenite stable region during pre-forging and the phase transformation sensitive region during final forging cooling into two monitoring periods. It also quantifies the microstructure evolution process, achieving real-time monitoring and early warning of the forging quality status. This allows for real-time monitoring of the grain size evolution during machining, effectively identifying grain refinement and phase transformation uniformity, obtaining a comprehensive quality status, identifying potential quality hazards based on deviations, and providing recommendations. This avoids performance defects caused by normal macroscopic data but abnormal microstructure, providing a method, system, and equipment suitable for machining quality monitoring of large and complex forgings.

Description

Technical Field

[0001] This application relates to the field of machining quality prediction technology, and in particular to methods, systems and equipment for predicting the microstructure of large and complex forgings. Background Technology

[0002] Large and complex forgings are core load-bearing components in strategic industries such as aerospace, and their service performance and reliability depend heavily on the uniformity and precision of their internal microstructure. Forging, as a complex thermo-mechanical coupling process under high temperature and high pressure, requires real-time quality monitoring and dynamic process optimization to ensure product compliance and improve yield.

[0003] Current online monitoring methods primarily employ threshold judgment based on macroscopic physical quantities. These methods collect parameters such as forging force, displacement, and temperature using sensors, relying on empirical models or fixed process windows to identify anomalies. They can only identify explicit deviations like undervoltage and severe temperature drops, replacing traditional manual experience and post-event detection methods at specific stages. However, as industry demands increasingly refined microstructures, including grain size and phase distribution, the limitations of this method become apparent: macroscopic parameters and microstructure evolution have a highly nonlinear and path-dependent complex coupling relationship. Existing methods essentially focus only on monitoring external macroscopic characteristics, failing to distinguish the influence of different mechanisms on the microstructure. Even if macroscopic parameters are within acceptable limits, minute differences in the early strain path can cause the microstructure to deviate from the ideal state, leading to defects such as coarse grains and inhomogeneous performance. Traditional methods struggle to detect these potential problems, resulting in high false alarm rates for defect identification and an inability to capture evolutionary patterns.

[0004] Therefore, the need to integrate multi-source physical field information to accurately reflect the evolution of microstructures at different thermodynamic stages and to establish a mechanistic evaluation and prediction system that can deeply correlate processing processes with microstates has become a pressing technical challenge in this field. Summary of the Invention

[0005] In order to at least overcome the above-mentioned deficiencies in the prior art, the purpose of this application is to provide a method, system and equipment for predicting the microstructure of large and complex forgings to solve the above problems.

[0006] Firstly, this application provides a method for predicting the microstructure of large and complex forgings, including:

[0007] Acquire temperature field data, strain field data, and vibration acceleration data of the forging throughout the entire processing cycle;

[0008] Based on the temperature field data, a first temperature range and a second temperature range are obtained;

[0009] Within the first temperature range, the strain history path function is obtained based on the strain field data within the first temperature range;

[0010] Based on the strain history path function, the critical criterion for dynamic recrystallization is obtained;

[0011] Within the second temperature range, based on the dynamic recrystallization critical criterion and the temperature field data within the second temperature range, and using the grain size evolution model, the predicted grain size at the end of the second temperature range is obtained.

[0012] Based on the predicted grain size and vibration acceleration data within the second temperature range, a comprehensive quality status index is obtained.

[0013] In one possible implementation, obtaining the first temperature range and the second temperature range based on the temperature field data includes:

[0014] Based on the temperature field data and the stable region of austenite in the forging, the processing cycle of the forging is divided into two adjacent stages to obtain a first temperature range and a second temperature range.

[0015] The first temperature range is configured as follows: the temperature range where the forging is at high temperature during the processing and the austenite is stable. The upper limit of the temperature is set to a preset temperature below the melting point of the forging, and the lower limit is set to a temperature above the lowest stable temperature at which the forging is austenitized.

[0016] The second temperature range is configured as the temperature range corresponding to the austenite decomposition of the forging during the processing. The upper limit of the temperature range is set to the lower limit of the first temperature range, and the lower limit is set to room temperature or the set final cooling temperature.

[0017] In one possible implementation, obtaining the strain history path function based on strain field data within the first temperature range includes:

[0018] Within the first temperature range, acquire strain field data;

[0019] Based on the strain field data, the effective plastic strain rate is obtained according to the Mises yield criterion of equivalent strain.

[0020] Based on the effective plastic strain rate, the cumulative effective plastic strain of the forging within the first temperature range is obtained by time integration;

[0021] The accumulated effective plastic strain is used as the strain history path function.

[0022] One possible implementation also includes:

[0023] Within the first temperature range, acquire real-time temperature field data;

[0024] The Z parameters are obtained based on the real-time temperature field data and the strain history path function;

[0025] Based on the Z parameters and the initial grain size of the forging, the critical strain for dynamic recrystallization is obtained;

[0026] The critical criterion for dynamic recrystallization is obtained by comparing the critical strain and the cumulative effective plastic strain.

[0027] In one possible implementation, the step of comparing the critical strain and the cumulative effective plastic strain to obtain the dynamic recrystallization critical criterion further includes:

[0028] Based on the recrystallization critical criterion, determine whether the forging has already or is undergoing dynamic recrystallization within the first temperature range;

[0029] In response to the cumulative effective plastic strain being greater than or equal to the critical strain, it is determined that the forging has reached the critical condition for dynamic recrystallization.

[0030] When it is determined that the forging has reached the critical condition for dynamic recrystallization, the first temperature range ends.

[0031] At the end of the first temperature range, the grain state of the forging at this time is obtained, and the grain state includes the dynamic recrystallization volume fraction or the average grain size.

[0032] Based on the dynamic recrystallization volume fraction, the grain state is determined to be either complete dynamic recrystallization or partial recrystallization.

[0033] Based on the grain state, the initial grain size when entering the second temperature range is obtained by weighted averaging.

[0034] In one possible implementation, obtaining the predicted grain size at the end of the second temperature range within the second temperature range, based on the dynamic recrystallization critical criterion and temperature field data within the second temperature range, and using a grain size evolution model, includes:

[0035] Within the second temperature range, based on the dynamic recrystallization critical criterion, the initial grain size when entering the second temperature range is obtained through the final grain state at the end of the first temperature range.

[0036] Based on the temperature field data within the second temperature range, the cooling curve and cooling rate are obtained;

[0037] Based on the initial grain size, the cooling curve, and the cooling rate, a simulation is performed using the grain growth differential equation to obtain the evolution equation of austenite grain growth with the cooling process, so as to obtain the updated grain size.

[0038] Based on the cooling curve and a preset continuous cooling transformation diagram, phase transformation kinetics calculations are triggered in response to the banana moment to obtain the second phase grain size at the moment of phase transformation.

[0039] Based on the evolution equation of austenite grain growth and the second phase grain size, a grain size evolution model is obtained through coupling.

[0040] Based on the grain size evolution model, the predicted final average grain size at the end of the second temperature range is obtained.

[0041] In one possible implementation, obtaining the comprehensive quality status index based on the predicted grain size and vibrational acceleration data within the second temperature range includes:

[0042] Based on the predicted grain size and vibration acceleration data within the second temperature range, at least one individual quality status index is generated, including:

[0043] Based on the predicted grain size, and using a preset target average grain size, a grain refinement index is obtained.

[0044] Based on the vibration acceleration data within the second temperature range, the time-domain signal is processed to obtain the vibration energy spectrum of the current forging within a preset frequency range;

[0045] Based on the vibration energy spectrum and the preset qualified forging vibration baseline spectrum, the phase transformation uniformity index is obtained.

[0046] Based on the grain refinement index and the phase transformation uniformity index, a comprehensive quality status index is obtained through calculation.

[0047] One possible implementation also includes:

[0048] Based on real-time comprehensive quality status indicators, when they fall below a preset threshold, a defect risk warning is triggered.

[0049] In response to the triggering of a defect risk warning, it is determined whether the grain refinement index and the phase transformation uniformity index are lower than preset indexes.

[0050] If the grain refinement index is lower than the preset index, then the grain refinement is insufficient;

[0051] When the phase transition uniformity index is lower than the preset index, the phase transition uniformity is poor.

[0052] Secondly, this application provides a system for predicting the microstructure of large and complex forgings, including a sequentially electrically connected acquisition unit, a partitioning unit, a strain history path unit, a dynamic recrystallization critical criterion unit, a grain size evolution unit, and a quality evaluation unit.

[0053] The acquisition unit is configured to acquire temperature field data, strain field data, and vibration acceleration data of the forging throughout the entire processing cycle.

[0054] The partitioning unit is configured to divide the processing cycle into a first temperature range and a second temperature range based on the temperature field data.

[0055] The strain history path unit is configured to: obtain a strain history path function based on strain field data within a first temperature range.

[0056] The dynamic recrystallization critical criterion unit is configured to obtain the dynamic recrystallization critical criterion based on the strain history path function.

[0057] The grain size evolution unit is configured to: within the second temperature range, based on the dynamic recrystallization critical criterion and the temperature field data within the second temperature range, and based on the grain size evolution model, obtain the predicted grain size at the end of the second temperature range;

[0058] The quality evaluation unit is configured to obtain a comprehensive quality status index based on the predicted grain size and vibration acceleration data within the second temperature range.

[0059] Thirdly, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a method for predicting the microstructure of large and complex forgings.

[0060] In summary, the beneficial effects that this application can achieve are:

[0061] This application proposes a method, system, and equipment for real-time monitoring and early warning of forging quality by dividing the high-temperature austenite stable zone during pre-forging and the phase transformation sensitive zone during final forging cooling into two monitoring periods and quantifying the microstructure evolution process. The proposed real-time temperature stage division ensures that the subsequent microstructure evolution model accurately matches the current physical and metallurgical state of the material. The proposed integral form of the strain history path function, compared to instantaneous strain values, truly reflects the dynamic equilibrium process of dislocation accumulation and dynamic recovery within the material, providing crucial input for accurately determining the initiation of dynamic recrystallization. The proposed introduction of a recrystallization critical criterion reveals, mechanistically, the trend of microstructure refinement during high-temperature deformation, thereby assessing the degree of grain refinement within the first temperature range and providing initial state input for microstructure evolution during the phase transformation process in the second temperature range, effectively avoiding the shortcomings of traditional methods that cannot detect the starting point of microstructure evolution. This application provides a method, system, and equipment suitable for monitoring the processing quality of large and complex forgings. Attached Figure Description

[0062] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0063] Figure 1 This is a schematic diagram of the method steps in an embodiment of this application;

[0064] Figure 2 This is a schematic diagram of the system structure according to an embodiment of this application. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0066] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0067] Example 1

[0068] Please refer to the following: Figure 1 The diagram below illustrates the steps of the method for predicting the microstructure of large and complex forgings provided in this embodiment of the invention. Further, the method for predicting the microstructure of large and complex forgings may specifically include the contents described in steps S1 to S7.

[0069] Step S1: According to the time series, acquire the temperature field data, strain field data and vibration acceleration data of the forging surface throughout the entire processing cycle;

[0070] Step S2: Based on the temperature field data, obtain the first temperature range and the second temperature range, wherein the first temperature range corresponds to the high-temperature austenite stable region and the second temperature range corresponds to the phase transformation sensitive region.

[0071] Step S3: Within the first temperature range, obtain the strain history path function based on the strain field data within the first temperature range;

[0072] Step S4: Obtain the dynamic recrystallization critical criterion based on the strain history path function;

[0073] Step S5: Within the second temperature range, based on the dynamic recrystallization critical criterion and the temperature field data within the second temperature range, and using the grain size evolution model, obtain the predicted grain size at the end of the second temperature range.

[0074] Step S6: Obtain the comprehensive quality status index based on the predicted grain size and vibration acceleration data within the second temperature range.

[0075] Step S7: Based on the real-time comprehensive quality status index, when it falls below the preset threshold, trigger a defect risk warning and output suggestions for adjusting the current process parameters.

[0076] In the implementation of this application, since grain size classification is directly related to the mechanical properties of forgings, the specific level selection needs to be combined with the material type and actual working conditions. Therefore, this application starts with the grain size of the grain size for monitoring and design.

[0077] The grain size of forgings is divided into 12 grades according to the national standard GB / T6394, with different grades corresponding to different grain sizes and performance characteristics.

[0078] Coarse grains correspond to levels 1-4, with grain diameters greater than 150μm. For example, level 1 grains have a diameter of approximately 600μm. They are suitable for high-temperature environments, such as turbine disks in aero engines, which commonly use levels 3-4, taking advantage of the high-temperature creep resistance of coarse grain boundaries.

[0079] Fine grains correspond to grades 5-8, with grain sizes between 30-150μm in diameter. They are suitable for structural components, such as automotive crankshaft forgings, which typically require grades 6-7. Fine grain strengthening can increase tensile strength by 15% to 20%.

[0080] Ultrafine grains correspond to grades 9-12, with grain sizes less than 30μm in diameter. They are suitable for precision components requiring high strength and toughness. For example, nuclear power plant valve forgings use grade 9 grain size, which can reduce the crack propagation rate to 1 / 3 of that of coarse-grained materials.

[0081] In the implementation of this application embodiment, the temperature field data, strain field data and vibration acceleration data of the forging during the entire processing cycle are first obtained according to the time series. The temperature field data reflects the changes in thermodynamic state during the processing, the strain field data characterizes the strain accumulation history of the material during plastic deformation, and the vibration acceleration data captures the dynamic disturbance characteristics in the phase transformation sensitive area.

[0082] Then, based on the temperature field data, the processing cycle is divided into a first temperature range and a second temperature range. The first temperature range corresponds to the high-temperature austenite stable region, and the second temperature range corresponds to the phase transformation sensitive region. This partitioning process enables the monitoring to perform differentiated analysis for different physical metallurgical mechanisms.

[0083] Within the first temperature range, a strain history path function is obtained based on the strain field data within the first temperature range. This function quantifies the historical path dependence of strain accumulation. Then, based on the strain history path function, a dynamic recrystallization critical criterion is obtained to determine whether the material has reached the critical condition for dynamic recrystallization.

[0084] Within the second temperature range, combining the dynamic recrystallization critical criterion and temperature field data within the second temperature range, the grain size at the end of the second temperature range is predicted based on a grain size evolution model. Then, a comprehensive quality status index can be generated based on the predicted grain size and vibrational acceleration data within the second temperature range. This index integrates multi-dimensional characteristics of microstructure uniformity and phase transformation processes. When the comprehensive quality status index falls below a preset threshold, a defect risk warning is triggered, and suggestions for adjusting the current process parameters are output.

[0085] This method effectively solves the problem that existing monitoring methods cannot reflect the microstructure evolution mechanism of forgings at different thermodynamic stages, leading to the inability to provide timely early warning of microstructure defects. Specifically, by distinguishing between the high-temperature deformation stage dominated by dynamic recrystallization and the cooling stage dominated by phase transformation kinetics, it avoids treating the entire processing cycle as a homogenization process and can accurately predict the microstructure state. The comprehensive quality status index generated based on the prediction results enables early identification of potential microstructure defects, allowing for timely adjustment of the process while macroscopic process parameters remain within acceptable ranges. This ensures the uniformity and fineness of the microstructure inside the forging, thereby improving the accuracy and reliability of processing quality monitoring.

[0086] Example 2

[0087] Based on Example 1, please refer to the following: Figure 1 Furthermore, the method for predicting the microstructure of large and complex forgings may specifically include the following:

[0088] Step S1: Obtain the temperature field data, strain field data, and vibration acceleration data of the forging throughout the entire processing cycle according to the time series.

[0089] Step S2: Based on the temperature field data, obtain the first temperature range and the second temperature range, where the first temperature range corresponds to the high-temperature austenite stable region and the second temperature range corresponds to the phase transformation sensitive region.

[0090] Step S3: Within the first temperature range, obtain the strain history path function based on the strain field data within the first temperature range.

[0091] Step S4: Obtain the dynamic recrystallization critical criterion based on the strain history path function.

[0092] Step S5: Within the second temperature range, based on the dynamic recrystallization critical criterion and the temperature field data within the second temperature range, and using the grain size evolution model, obtain the predicted grain size at the end of the second temperature range.

[0093] Step S6: Obtain the comprehensive quality status index based on the predicted grain size and vibration acceleration data within the second temperature range.

[0094] Step S7: Based on the real-time comprehensive quality status index, when it falls below the preset threshold, trigger a defect risk warning and output suggestions for adjusting the current process parameters.

[0095] Because GH4169 nickel-based superalloy is more commonly used in the specific implementation of this application, and this alloy has the highest yield strength among wrought superalloys below 650℃, we take the material of large and complex forgings as GH4169 nickel-based superalloy, and the scenario of manufacturing turbine disks as an example, to test the final microstructure, especially the grain size and phase transformation uniformity, in order to monitor its performance quality during the high-temperature and high-stress forging process.

[0096] Before the processing begins, the material of large and complex forgings needs to be initialized and calibrated. For example, the material properties of GH4169 alloy, such as dynamic recrystallization activation energy, grain boundary mobility activation energy, constants in the Z-parameter model, and constants in the grain growth kinetics model, are all pre-determined through a series of precise Gleeble thermal simulation compression experiments and metallographic analysis. Then, by collecting and analyzing the vibration signals of multiple known qualified GH4169 forgings during the processing under standard processes, a vibration baseline spectrum for evaluating phase transformation uniformity is constructed and stored.

[0097] In step S1, temperature field data, strain field data, and vibration acceleration data of the forging throughout the entire processing cycle are obtained according to the time series.

[0098] In the implementation of this application embodiment, multi-source physical field data of the forging is collected in real time throughout the entire processing cycle, including temperature field data of the forging surface or near the surface, strain field data of the forging interior or key areas, and vibration acceleration data of the forging equipment or forging. All collected data are time-series stored at a preset sampling frequency to form real-time data.

[0099] In the implementation of this application embodiment, after the forging is heated to the forging temperature and sent into the forging equipment, real-time data acquisition is started in a synchronized manner.

[0100] An infrared thermal imager installed above the forging equipment, with a temperature measurement range covering 500℃ to 2000℃, continuously captures the complete temperature distribution on the surface of the forging at a set frame rate, forming time-series two-dimensional temperature field data.

[0101] Meanwhile, multiple grid-shaped strain gauge arrays are pre-attached in the stress concentration areas of the forging, and can work in high-temperature environments. In this embodiment, it can actually be done at temperatures up to 1200℃. The strain signal is recorded in real time at a set sampling frequency, showing the multiaxial strain components of each measuring point.

[0102] Simultaneously, one or more accelerometers need to be fixed on the lower die base or frame column of the forging equipment to capture the weak vibration signals generated by the evolution of the internal structure of the material and the operation of the equipment during the forging process. The sampling frequency is set to 51.2kHz. All collected temperature, strain, and vibration data are accurately timestamped to form synchronous multi-source heterogeneous real-time data, which undergoes preliminary filtering, noise reduction, and format standardization processing.

[0103] In step S2, based on the temperature field data, a first temperature range and a second temperature range are obtained, wherein the first temperature range corresponds to the high-temperature austenite stable region and the second temperature range corresponds to the phase transformation sensitive region.

[0104] Based on the temperature field data, a first temperature range and a second temperature range are obtained, including:

[0105] Based on temperature field data and the stable region of austenite in the forging, the processing cycle of the forging is divided into two adjacent stages to obtain the first temperature range and the second temperature range.

[0106] The first temperature range is configured as follows: the temperature range where the forging is at high temperature during the processing and the austenite is stable. The upper limit of the temperature is set to a preset temperature below the melting point of the forging, and the lower limit is set to the minimum stable temperature above the austenitizing temperature of the forging.

[0107] The second temperature range is configured as the temperature range corresponding to the austenite decomposition of the forging during the processing. The upper limit of the temperature range is set as the lower limit of the first temperature range, and the lower limit is set as room temperature or the final cooling temperature required by the special process.

[0108] In the implementation of this application embodiment, based on the temperature field data collected in step S1, the entire processing cycle of the forging is divided into two stages with significant metallurgical characteristics, and the processing cycle is divided into a first temperature range and a second temperature range.

[0109] Then, our first temperature range corresponds to the high-temperature austenite stable region of the forging during the processing. The microstructure evolution of the material in this region is mainly dominated by dynamic recrystallization behavior. The second temperature range corresponds to the phase transformation sensitive region of the forging during the processing. The microstructure evolution of the material in this region is mainly dominated by the phase transformation kinetics process of austenite decomposition, new phase nucleation, and growth.

[0110] Furthermore, the upper limit of the first temperature range is set to a specific safety margin below the melting point of the material, and the lower limit is set to above the lowest stable temperature for austenitization of the material; the upper limit of the second temperature range is set to the lower limit of the first temperature range, and the lower limit is set to room temperature or the final cooling temperature required by a specific process.

[0111] In the implementation of this application embodiment, for the nickel-based high-temperature alloy of this embodiment, the first temperature range can be set to 1000℃ to 1200℃, and the second temperature range can be set to 600℃ to 1000℃. The specific temperature threshold depends on the material type of the forging and its phase transformation critical point. For example, different alloy steels Ac1, Ac3, Ar1, etc. are different. The corresponding material nameplate can be found first to make the corresponding accurate calibration.

[0112] The dominant physical mechanisms of microstructure evolution in GH4169 nickel-based superalloys differ fundamentally across different temperature ranges.

[0113] Based on the real-time temperature field data of the forging surface collected by the infrared thermal imager, the entire hot working cycle is dynamically divided into two core metallurgical stages.

[0114] For alloys, the critical points of phase transformation, such as the solution temperatures of the first γ' phase and the second γ'' phase, and the temperature range in which austenite is stable are known.

[0115] Therefore, in this embodiment, the first temperature range, namely the high-temperature austenite stability region, is set to 1000℃ to 1200℃. Within this range, the material is a single austenite phase, and the microstructure evolution caused by plastic deformation is mainly dominated by dynamic recovery and dynamic recrystallization behavior. The real-time temperature of any region on the forging... satisfy At that time, the data in that region is included in the analysis process of the first temperature range.

[0116] The second temperature range, the phase transformation sensitive region, is set to 600℃ to 1000℃. Within this range, as the temperature decreases, the austenite matrix becomes unstable and undergoes a precipitation transformation into strengthening phases such as γ' and γ'' phases. This is accompanied by complex processes such as static or subdynamic recrystallization and grain growth, which together determine the final room temperature microstructure. The real-time temperature of any region on the forging... satisfy At that time, the data from that region is then incorporated into the analysis process for the second temperature range.

[0117] In step S3, within the first temperature range, the strain history path function is obtained based on the strain field data within the first temperature range.

[0118] Within the first temperature range, based on the strain field data within the first temperature range, the strain history path function is obtained, including:

[0119] Within the first temperature range, acquire strain field data;

[0120] Based on strain field data, the effective plastic strain rate is obtained according to the Mises yield criterion of equivalent strain.

[0121] Based on the effective plastic strain rate, the cumulative effective plastic strain of the forging within the first temperature range is obtained by time integration;

[0122] The cumulative effective plastic strain is used as the strain history path function.

[0123] In the implementation of this application, because this application considers the ability to track the actual deformation degree of the material during the high-temperature deformation process, this invention establishes a strain history path function. Compared with the method that only focuses on the instantaneous strain value, we can comprehensively reflect the cumulative effect of the deformation process, transform the macroscopic strain into the microscopic austenite change, and then transfer it to the prediction of recrystallization behavior, providing input for the prediction of recrystallization behavior, thereby avoiding evaluation errors caused by differences in deformation history.

[0124] The strain history path function of the key region of the forging is calculated and obtained in real time. The strain history path function is used to characterize the accumulation of plastic deformation and its rate characteristics experienced by the forging in the high-temperature austenitic stable region.

[0125] Within the first temperature range, the real-time data stream from the high-temperature strain gauge array is continuously processed based on the strain field data acquired in step S1. For each measuring point, the acquired multiaxial strain components are first converted to at least the components in the x, y, and z axes of the strain, and then the multiaxial strain components are transformed into strain rate tensors.

[0126] Then, the effective plastic strain rate at that point can be directly calculated using readily available effect rate criteria, such as the Mises effect rate criterion. By performing a numerical integration of the effective plastic strain rate over time, for example assuming that at a certain time t, the effective plastic strain rate at a certain point in the forging is... Then from the initial deformation moment The accumulated effective plastic strain at this point during the time interval up to the current time t is obtained through integration, that is, from the start time of this deformation pass. Cumulative effective plastic strain up to the current time t That is, the strain history path function, which can then be expressed as:

[0127] ;

[0128] in, For integration time variable, Indicates time The effective plastic strain rate at the location.

[0129] In step S4, the dynamic recrystallization critical criterion is obtained based on the strain history path function.

[0130] Based on the strain history path function, the critical criteria for dynamic recrystallization are obtained, including:

[0131] Within the first temperature range, acquire real-time temperature field data;

[0132] We consider the hot deformation behavior of forging materials, i.e. alloys, which, under microscopic conditions, soften and refine in the form of dynamic recrystallization. The Z parameter is essential for quantifying this. As the Z parameter decreases, the volume fraction of dynamic recrystallization increases accordingly. Therefore, to obtain the Z parameter, we establish a relationship between the real-time temperature field data of the collected time series data and the obtained real-time effective plastic strain rate. Conversely, we can then calculate the Zener-Hollomon parameter.

[0133] The Z parameters are obtained based on real-time temperature field data and strain history path function;

[0134] Based on the Z-parameter and the initial grain size of the forging, the critical strain for dynamic recrystallization is obtained;

[0135] By comparing the critical strain and the cumulative effective plastic strain, the critical criterion for dynamic recrystallization is obtained.

[0136] The dynamic recrystallization critical criterion is obtained by comparing the critical strain and the cumulative effective plastic strain, and also includes:

[0137] Based on the recrystallization critical criterion, determine whether dynamic recrystallization has occurred or is occurring in the forging within the first temperature range;

[0138] When the cumulative effective plastic strain is greater than or equal to the critical strain, the forging is determined to have reached the critical condition for dynamic recrystallization.

[0139] When it is determined that the forging has reached the critical condition for dynamic recrystallization, the first temperature range ends.

[0140] At the end of the first temperature range, the grain state of the forging at this time is obtained, including the dynamic recrystallization volume fraction or the average grain size.

[0141] Based on the volume fraction of dynamic recrystallization, determine whether the grain state is complete or partial dynamic recrystallization at this time;

[0142] Based on the grain state, the initial grain size when entering the second temperature range is obtained by weighted averaging.

[0143] In the implementation of this application embodiment, because of the later grain refinement, we consider that the occurrence of dynamic recrystallization is a prerequisite for grain refinement. Therefore, by introducing the recrystallization critical criterion, we can reveal from the mechanism a trend that the microstructure will soon be refined during the high-temperature deformation process, and then evaluate the degree of grain refinement in the first temperature range, so as to provide the initial state input for the microstructure evolution during the phase transformation process in the second temperature range.

[0144] Based on the strain history path function obtained in step S3, and combined with the real-time temperature of the forging within the first temperature range and the preset material parameters, the recrystallization critical criterion of the forging is calculated.

[0145] The recrystallization critical criterion is used to determine whether dynamic recrystallization has occurred or is occurring in the forging within a first temperature range. Dynamic recrystallization is a prerequisite for grain refinement, and its critical strain... It typically exhibits a power-law relationship with the Zener-Hollomon parameter, also known as the Z parameter, and may be affected by the initial grain size. The Z-parameter takes into account both the deformation temperature T and the effective plastic strain rate. The effect can then be expressed as:

[0146] ;

[0147] in, R is the apparent activation energy for dynamic recrystallization, and its value is an inherent property of a specific material; T is the universal gas constant; and T is the absolute temperature.

[0148] Critical strain The empirical formula can then be expressed as:

[0149] ;

[0150] in, , C is a material-related constant. The average grain size of the forging before deformation is determined by the real-time accumulated effective plastic strain obtained in step S3. With critical strain When comparing, If the condition is met, then the region is determined to have reached the critical condition for dynamic recrystallization.

[0151] In the embodiments of this application, the critical strain for dynamic recrystallization is calculated in parallel. This calculation integrates real-time temperature T and real-time effective plastic strain rate. and the initial grain size of the forging In this embodiment, the initial grain size of the GH4169 forging before entering the first deformation pass... The measured value is 120 μm. First, the real-time temperature T (in Kelvin) and effective plastic strain rate were used. The Zener-Hollomon parameters can be calculated as follows:

[0152] ;

[0153] Wherein, the universal gas constant R is taken as 8.3 J / (mol·K), and the apparent activation energy of dynamic recrystallization is... The preset value for GH4169 alloy is 410 kJ / mol.

[0154] Then, based on the pre-calibrated material model, the critical strain is calculated, which can be expressed as:

[0155] ;

[0156] In a specific computational scenario, specifically in this embodiment, if we obtain a set of constants for the forging based on historical material data... , , If the measured real-time temperature is 1100℃, the effective plastic strain rate is 0.5. Then the calculation yields Then the critical strain is calculated. .

[0157] Then the cumulative effective plastic strain calculated in real time The critical strain calculated at this time Conduct continuous comparisons, and once discovered... This means that the region has met the triggering conditions for dynamic recrystallization and has entered the calculation stage of the dynamic recrystallization kinetic evolution model. This introduces the dynamic recrystallization critical judgment, which allows us to capture the moment when grain refinement begins, thus providing an accurate initial state for subsequent microstructure evolution prediction.

[0158] In step S5, within the second temperature range, based on the dynamic recrystallization critical criterion and the temperature field data within the second temperature range, and using the grain size evolution model, the predicted grain size at the end of the second temperature range is obtained.

[0159] Within the second temperature range, based on the dynamic recrystallization critical criterion and temperature field data within the second temperature range, and using a grain size evolution model, the predicted grain size at the end of the second temperature range is obtained, including:

[0160] Within the second temperature range, based on the dynamic recrystallization critical criterion, the initial grain size when entering the second temperature range is obtained through the final grain state at the end of the first temperature range.

[0161] Because the austenitic matrix becomes unstable as the temperature decreases, the second temperature range we set corresponds to the phase transformation sensitive region. The second temperature range is configured to be from processing to cooling to the set cooling value of the specific process or cooling to room temperature. Therefore, the cooling curve and cooling rate are obtained based on the temperature field data in the second temperature range.

[0162] Based on the initial grain size, cooling curve, and cooling rate, a simulation is performed using the grain growth differential equation to obtain the evolution equation of austenite grain growth with the cooling process, so as to obtain the updated grain size.

[0163] Based on the cooling curve and a preset continuous cooling transformation diagram, phase transformation kinetics calculations are triggered in response to the banana moment to obtain the second phase grain size at the moment when a phase transformation may occur.

[0164] Based on the evolution equation of austenite grain growth and the size of the second phase grain, a grain size evolution model is obtained through coupling.

[0165] Based on the grain size evolution model, the predicted final average grain size is obtained at the end of the second temperature range, for example, when cooled to 650°C.

[0166] In the implementation of this application embodiment, when the temperature of the forging enters the second temperature range, i.e., the phase transformation sensitive region, our core focus now shifts to obtaining a grain size evolution model. The initial condition of this model is the initial grain size upon entering the second temperature range. It depends directly on the degree of completion of dynamic recrystallization within the first temperature range.

[0167] Within the second temperature range, based on the recrystallization critical criterion calculated in step S4, i.e., the grain state at the end of the first temperature range (such as average grain size or recrystallization fraction), and combined with the temperature field data collected in step S1 within the second temperature range, a grain size evolution model for the forging is constructed and obtained. Then, using this model, the final microstructure of the forging within the phase transformation sensitive zone can be predicted. The grain size evolution model is used to predict the final average grain size and distribution of the forging during cooling to the phase transformation sensitive zone and up to room temperature.

[0168] First, based on the degree of dynamic recrystallization within the first temperature range, determine the initial grain size when entering the second temperature range. .

[0169] For example, if fully dynamic recrystallization occurs, then This can be confirmed by the grain size model after dynamic recrystallization, and can then be expressed as:

[0170] ;

[0171] in It predicts the grain size after dynamic recrystallization under given thermodynamic conditions; , denoted as a material constant, DXR refers to dynamic recrystallization, and Z is the Zener-Hollomon parameter, which is the strain rate factor considering temperature compensation.

[0172] Simultaneously, the dynamic recrystallization fraction is calculated using the Avrami-type equation, and can then be expressed as:

[0173] ;

[0174] in It is the volume fraction of dynamic recrystallization, which quantifies the proportion of material that has undergone dynamic recrystallization, and is between 0 and 1; Yes, the cumulative effective plastic strain, It is the critical strain at which dynamic recrystallization occurs. A reference strain value is set based on historical data. and These are material-related constants.

[0175] If recrystallization is incomplete, the initial grain size... The average size of a region consisting of the original unrecrystallized grains and newly formed fine recrystallized grains, weighted by the mixing law or considering the unrecrystallized region, can be expressed as:

[0176] ;

[0177] When dynamic recrystallization is complete ,but ;

[0178] When partial recrystallization occurs, It is the average value of the fine new grains obtained by dynamic recrystallization and the original coarse old grains. In this way, we get a single initial value, which can represent the overall grain state of the material when entering the second temperature range.

[0179] Then, within the second temperature range, a continuous process that progresses over time, the evolution of grain size will be simultaneously affected by the growth of austenite grains during static recrystallization and the nucleation and growth of new grains during phase transformation. We then establish a coupled grain size evolution model that comprehensively considers the effects of time, temperature history (which is actually the cooling rate here), initial grain size, and strain state on the final average grain size.

[0180] For example, in the second temperature range, temperature field data from an infrared thermal imager is received in real time, and then a temperature history is constructed to create an actual cooling curve. Then we construct the actual cooling curve along the temperature history measured by the infrared thermal imager. Numerical integration of this equation allows for the prediction of grain evolution in the absence of a phase transition, specifically the average grain size. The evolution can then be expressed as:

[0181] ;

[0182] in, Let be the grain boundary mobility, which is a function of temperature and can be expressed as:

[0183] ;

[0184] in, For the alloy in this embodiment, the grain growth activation energy is a material constant. Approximately 350 kJ / mol, where R is the universal gas constant and T is the absolute temperature. It is a material constant;

[0185] The saturated grain size represents the limiting size of grain growth at a given temperature, and it is itself a function of temperature.

[0186] We can then use numerical integration to solve this differential equation, starting from the initial time and initial grain size, for each tiny time step. Get the current absolute temperature. ,according to Calculate the current Then you get The current rate of change is used to update the grain size. By repeating this process, the continuous evolution of grain size during cooling can be simulated, while simultaneously recording the absolute temperature at each moment. Forming a cooling curve .

[0187] When the forging undergoes a phase transformation, we designed it to check the current cooling profile at each step of the numerical integration. Whether it intersects with the preset continuous cooling transformation diagram, once intersecting, it marks the start of the phase transformation. In response to the intersection moment, the phase transformation kinetics calculation is triggered to obtain the second phase grain state at the moment when the phase transformation may occur. By using historical data to back-calculate, an empirical value of the grain size of the second phase grain state is obtained.

[0188] The new phases that precipitate during the phase transformation process are called the second phase, such as the γ' phase and the γ'' phase. The nucleation and growth of the new phase will result in grain pinning. Grain pinning will hinder the movement of austenite grain boundaries, thereby inhibiting the growth of austenite and potentially forming new, finer grain structures.

[0189] By incorporating these competing mechanisms, a predicted final average grain size at the end of the second temperature range is ultimately output. For example, to predict the final average grain size after cooling to 650°C, the grain size evolution model can be expressed as:

[0190] ;

[0191] in, This represents the predicted grain size at the end of the second temperature range. The cooling profile was also recorded, along with real-time temperatures. For cooling rate, This represents the total time spent within the second temperature range.

[0192] In step S6, a comprehensive quality status index is obtained based on the predicted grain size and vibration acceleration data within the second temperature range.

[0193] Based on the predicted grain size and vibrational acceleration data within the second temperature range, a comprehensive quality status index is obtained, including:

[0194] Based on the predicted grain size and vibrational acceleration data within the second temperature range, at least one individual quality status index is generated, including:

[0195] Based on the predicted grain size, and using a preset target average grain size, a grain refinement index is obtained.

[0196] Based on the vibration acceleration data within the second temperature range, the time-domain signal is processed to obtain the vibration energy spectrum of the current forging within the preset frequency range;

[0197] Based on the vibration energy spectrum and the preset vibration baseline spectrum of qualified forgings, the phase transformation uniformity index is obtained.

[0198] Based on the grain refinement index and the phase transformation uniformity index, the comprehensive quality status index is obtained through calculation.

[0199] In the implementation of this application embodiment, based on the grain size evolution model prediction results obtained in step S5 and the vibration acceleration data collected in the second temperature range in step one, multiple individual quality status indicators are generated, and a comprehensive quality status indicator is further calculated.

[0200] One single quality status indicator is the degree of grain refinement. It compares the predicted final average grain size. With the target grain size Quantification can be expressed as:

[0201] ;

[0202] in, The higher the value, the better the grain refinement and the higher the processing quality.

[0203] Another individual quality status indicator is the phase transition uniformity indicator. It is obtained by analyzing vibration acceleration data within a second temperature range. In the phase transformation sensitive region, phase transformations within the material, such as the change from austenite to ferrite, pearlite, or bainite, and the phase transformations within our material are usually accompanied by microscale volume changes, stress release, or formation, can be captured as specific vibration signal characteristics by a highly sensitive accelerometer.

[0204] By performing time-frequency analysis on vibration acceleration data, common short-time Fourier transforms or wavelet transforms can be used to extract the vibration energy spectral density within a specific frequency range. This density is then compared with the vibration baseline spectrum under a pre-defined ideal phase transition process. The vibration baseline spectrum is obtained from historical data of a large number of qualified forgings, and the phase transition uniformity index... It can be represented as:

[0205] ;

[0206] in, This represents the vibrational energy spectral density currently monitored at frequency f. For baseline spectral density, This refers to the key frequency range related to phase transitions. The higher the value, the more uniform and stable the phase transition process, and the smaller the deviation from the ideal state. Using vibration acceleration data to assess phase transition uniformity overcomes the problem that traditional monitoring methods cannot directly perceive the dynamic characteristics of the internal phase transition process, and provides a sensitive means to identify local non-uniform phase transitions or the formation of abnormal phases.

[0207] Comprehensive quality status indicators This is based on the grain refinement index. and phase transition uniformity index The weighted combination calculation yields the following result, which can be expressed as:

[0208] ;

[0209] in, and These are the weighting coefficients, and The weighting coefficients are set based on empirical data or numerical simulation results regarding the specific forging process requirements, material properties, and the influence of different quality indicators on the final mechanical properties of the target forging. This invention, by calculating a comprehensive quality status index, can provide a more comprehensive, objective, and quantitative assessment of the forging processing quality, avoiding potential misjudgments caused by a single indicator, thereby achieving accurate insight into the quality status of the processing.

[0210] In the implementation of this application's embodiments, after obtaining the predicted final average grain size... Then, the process proceeds to the quality status index generation and comprehensive evaluation step, calculating individual quality status indices in at least two dimensions.

[0211] The first indicator is the degree of grain refinement. For aero-engine turbine disks, the target average grain size is... Typically, a size between 20μm and 30μm is required; in this embodiment... Set to 25μm, this indicator is obtained through the formula. Calculate, if the prediction If it is 24μm, then The value was 104.17%, indicating that the grain refinement effect was better than the target value.

[0212] The second indicator is the phase transition uniformity index. The data source is vibration signals collected by an accelerometer, and the time-domain vibration acceleration signals collected within the second temperature range are processed. Research shows that during the phase transformation process, the GH4169 alloy generates acoustic emission signals in a specific frequency band due to changes in lattice constant and the release of latent heat of phase transformation. This manifests as an instantaneous increase in vibration energy. By comparing the current forging cycle within this key frequency range... Vibrational energy spectrum within Compared with the pre-stored vibration baseline spectrum of qualified forgings This can quantify the uniformity and stability of the phase transition process. The phase transition uniformity index is given by the formula... If the vibrational spectrum produced by a stable and homogeneous phase transition process is in high agreement with the baseline spectrum, then... When the value approaches 100%, if abnormalities such as rapid local cooling leading to martensitic phase transformation occur, it will generate violent vibrational signals that deviate from the baseline spectrum, resulting in... The value decreased significantly.

[0213] Finally, these two individual indicators are weighted and merged to generate a comprehensive quality status indicator:

[0214] ;

[0215] Considering the final mechanical properties, especially fatigue life, which are more sensitive to grain size, a weighting coefficient is set in this embodiment. =0.6, =0.4. This comprehensive index provides a complete and quantitative evaluation of the internal microstructure quality of forgings.

[0216] In step S7, based on the real-time comprehensive quality status index, when it falls below a preset threshold, a defect risk warning is triggered, and suggestions for adjusting the current process parameters are output.

[0217] Also includes:

[0218] Based on real-time comprehensive quality status indicators, when they fall below a preset threshold, a defect risk warning is triggered.

[0219] In response to the triggering of a defect risk warning, it is determined whether the grain refinement index and the phase transformation uniformity index are lower than the preset indexes.

[0220] When the grain refinement index is lower than the preset index, the grain refinement is insufficient, and the corresponding suggestions are output: it is recommended to increase the deformation amount or extend the deformation time, or to appropriately adjust the deformation temperature to a range with higher dynamic recrystallization efficiency.

[0221] When the phase transformation uniformity index is lower than the preset index, the phase transformation uniformity is poor, and the corresponding suggestions are output: it is recommended to adjust the flow rate and temperature of the cooling medium, or change the cooling path of the forging to optimize the cooling rate and ensure uniform phase transformation.

[0222] In the implementation of this application embodiment, the comprehensive quality status index calculated in step six is ​​continuously monitored and updated in real time. When the comprehensive quality status index Quality below the preset threshold In certain situations, a defect risk warning will be automatically triggered, and suggestions for adjusting current process parameters and quality thresholds will be output based on real-time analysis results. The determination is based on the design requirements of the forging, material performance standards, and historical data analysis.

[0223] The mechanism for generating process parameter adjustment suggestions is based on a pre-established process adjustment strategy library. When a suggestion is detected... At that time, firstly according to and The specific values ​​and their deviations from their respective target values ​​are used to diagnose the main quality problems.

[0224] For example, if A significantly low value may indicate insufficient strain within the first temperature range or a deformation temperature deviating from the optimal range, leading to inadequate dynamic recrystallization; if A significantly low reading may indicate that the cooling rate in the second temperature range is too fast or too slow, leading to uneven phase transformation. For different diagnostic results, appropriate adjustment suggestions are selected from a pre-defined process adjustment strategy library.

[0225] For example: if the grain refinement is insufficient, then... If the deformation is low, it is recommended to increase the amount of deformation or extend the deformation time, or appropriately adjust the deformation temperature to a range with higher dynamic recrystallization efficiency.

[0226] If the phase transition uniformity is poor If the temperature is low, it is recommended to adjust the flow rate and temperature of the cooling medium, or change the cooling path of the forging, in order to optimize the cooling rate and ensure uniform phase transformation.

[0227] Adjustment suggestions may include, for example, increasing the reduction by X%, adjusting the deformation temperature to Y℃, and adjusting the cooling rate to Z℃ / s, among other specific parameter values. This invention, by providing defect risk warnings and specific process parameter adjustment suggestions, achieves online closed-loop control and optimization of the machining process for large and complex forgings. It fundamentally solves the technical problem that existing methods can only identify macroscopic defects retrospectively and cannot effectively guide process optimization, significantly improving the manufacturing consistency, yield, and final service performance of forgings.

[0228] In the implementation of this application's embodiments, the final step of the method of the present invention is to provide defect risk warnings and process parameter adjustment suggestions based on comprehensive quality status indicators, thus forming a closed-loop control system.

[0229] In this embodiment, the set quality threshold At that time, it was 90%. After a certain forging pass, if the calculated... If the percentage falls below 90%, an audible and visual alarm will be immediately triggered at the central control console, indicating a potential risk of microscopic structural defects. Simultaneously, the system will analyze which individual indicator caused the overall indicator to fail to meet the standard. For example, if a diagnosis is made... Only 75%, while If the failure rate is as high as 98%, the root cause is identified as insufficient dynamic recrystallization within the first temperature range. Based on this diagnosis, specific adjustment suggestions are calculated using the process adjustment strategy library and the current process parameters. For the problem of insufficient dynamic recrystallization, the following suggestions might be output: In the next processing pass, it is recommended to increase the pressing speed of the forging equipment by 10% to increase the strain rate; and at the same time, increase the pressing amount per pass by 10% so that the accumulated effective strain can stably exceed the critical strain value. These specific and quantitative adjustment suggestions will be directly displayed to the field engineers to guide them in optimizing the subsequent processing technology online.

[0230] Taking the monitoring of the machining process of a GH4169 nickel-based high-temperature alloy turbine disk forging as an example, we set a target final average grain size. Set to 25μm, comprehensive quality index threshold Set to 90%.

[0231] Then, during the first forging process, temperature, strain, and vibration data were collected in real time. Based on the real-time temperature data, the forging was divided into a high-temperature austenitic stable region of 1000℃-1200℃ and a phase transformation sensitive region of 600℃-1000℃.

[0232] In the high-temperature austenite stability region, the cumulative effective plastic strain in the key area is calculated by integrating the strain gauge data. And the critical strain for recrystallization is calculated dynamically based on real-time temperature and strain rate. Comparison. Determine at the end of the race that most areas... All exceeded Dynamic recrystallization has fully occurred.

[0233] Entering the cooling stage in the second temperature range, the grain size evolution model uses the recrystallization state of the previous stage as initial conditions and combines it with the real-time cooling curve to calculate and predict the final average grain size. The value is 24.5 μm, and then the grain refinement index is calculated. =(25 / 24.5)×100%=102.04%.

[0234] Simultaneously, time-frequency analysis was performed on the vibration signal during the cooling stage, and its energy spectrum was compared with the standard baseline spectrum to calculate the phase transition uniformity index. =98.5%.

[0235] Finally, calculate the comprehensive quality status index. =0.6×102.04%+0.4×98.5%=100.62%.

[0236] Since this value is much higher than the 90% threshold, the processing quality of this pass is considered excellent, and no process adjustment is required.

[0237] This monitoring and assessment was repeated in all subsequent sessions.

[0238] If, during the third pass, the initial temperature of the forging is slightly lower than normal due to fluctuations in the furnace temperature, this will be detected. The percentage dropped to 90%, and the diagnosis was insufficient grain refinement.

[0239] We immediately suggested increasing the reduction amount in the fourth pass by 5%. After the operator adopted this suggestion, the reduction amount in the fourth pass... It has rebounded to 99%.

[0240] After the entire processing was completed, the forging was dissected, and the microstructure and grain size were observed at multiple locations using a metallographic microscope. The measured actual average grain size was 24.0 μm, with a small error compared to the predicted value of 24.5 μm. Furthermore, the grain size distribution was uniform, the standard deviation was small, and no abnormal structures were found.

[0241] In this way, the method of the present invention not only realizes passive monitoring of processing quality, but also realizes active, physical mechanism-based online intervention and closed-loop control, thereby effectively avoiding the scrapping of the entire batch of forgings and significantly improving the yield and quality consistency of large and complex forging manufacturing.

[0242] Example 3

[0243] This is the third embodiment of the present invention. Based on embodiments 1 and 2, please refer to the following references. Figure 2 This embodiment provides a system structure diagram of a large and complex forging microstructure prediction system. Further, the large and complex forging microstructure prediction system may specifically include a sequentially electrically connected acquisition unit, a partitioning unit, a strain history path unit, a dynamic recrystallization critical criterion unit, a grain size evolution unit, and a quality evaluation unit.

[0244] The acquisition unit is configured to acquire temperature field data, strain field data, and vibration acceleration data of the forging throughout the entire processing cycle.

[0245] The partitioning unit is configured to divide the processing cycle into a first temperature range and a second temperature range based on temperature field data.

[0246] The strain history path unit is configured to obtain the strain history path function based on the strain field data within the first temperature range.

[0247] The dynamic recrystallization critical criterion unit is configured to obtain the dynamic recrystallization critical criterion based on the strain history path function.

[0248] The grain size evolution unit is configured to: within the second temperature range, based on the dynamic recrystallization critical criterion and the temperature field data within the second temperature range, obtain the predicted grain size at the end of the second temperature range based on the grain size evolution model;

[0249] The quality evaluation unit is configured to obtain a comprehensive quality status index based on the predicted grain size and vibration acceleration data within the second temperature range.

[0250] This embodiment also includes:

[0251] The early warning unit is configured to trigger a defect risk warning when the real-time comprehensive quality status index falls below a preset threshold.

[0252] In the implementation of this application, by dividing the processing cycle into a first temperature range and a second temperature range, and employing corresponding microstructure evolution models for different thermodynamic stages, combined with the fusion analysis of multi-source physical field data, real-time capture and precise evaluation of the microstructure evolution dynamics of forgings in the high-temperature austenite stable region and phase transformation sensitive region are achieved. This results in early warning of potential quality hazards such as coarse grains and mixed crystal structure. Specifically, this scheme breaks through the "black box" bottleneck between macroscopic process parameters and internal microstructure evolution, avoids the defect of treating the entire processing cycle as a homogenization process, and can distinguish between the high-temperature deformation stage dominated by dynamic recrystallization and the cooling stage dominated by phase transformation kinetics, making the quality monitoring depth closely match the inherent physical metallurgical laws of the material. The path dependence of strain accumulation history on dynamic recrystallization is quantified by strain history path function, and the dynamic recrystallization critical criterion is used to accurately identify the microstructure softening mechanism in the high-temperature region. At the same time, the temperature field data and vibration acceleration data of the phase transformation sensitive region are combined to predict the grain size evolution. The resulting comprehensive quality status index truly reflects the microstructure uniformity and effectively prevents the microstructure evolution deviation caused by small differences in the early strain history path.

[0253] Example 4

[0254] The fourth embodiment of the present invention differs from the previous embodiments in that:

[0255] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0256] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0257] The units described as separate components may or may not be physically separate. As will be apparent to those skilled in the art, the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0258] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0259] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0260] This embodiment uses a GH4169 nickel-based high-temperature alloy turbine disk forging as the processing object, and provides a computer device for predicting the microstructure of large and complex forgings. Through multi-source data acquisition, real-time analysis, and closed-loop control, it achieves accurate monitoring and process optimization of the forging's microstructure quality. This computer device consists of a hardware system and a software system working together, as detailed below:

[0261] The computer device hardware system is configured with a data acquisition module, a data processing module, a storage module, and an output and control module; the computer device hardware system is also configured with a data preprocessing unit, a temperature range division unit, a strain path and critical criterion calculation unit, a grain size evolution model unit, and an early warning and adjustment suggestion unit.

[0262] Computer device hardware systems include:

[0263] The data acquisition module is configured with an infrared thermal imager, a strain gauge array, and an accelerometer. The infrared thermal imager is mounted above the forging equipment, with a temperature measurement range of 500℃-2000℃, capturing two-dimensional temperature field data of the forging surface at a set frame rate. Multiple grid-like strain gauge arrays, capable of withstanding temperatures up to 1200℃, are attached to the stress concentration areas of the forging, recording multi-axis strain components at a preset sampling frequency. An accelerometer is fixed to the lower die base of the forging equipment, with a sampling frequency of 51.2kHz, capturing phase transition and vibration signals generated during equipment operation. All acquisition devices have time synchronization capabilities, and the data includes precise timestamps.

[0264] The data processing module is configured to use an industrial-grade high-performance computer with a multi-core processor and GPU acceleration unit, supporting parallel computing of multi-source heterogeneous data, meeting the requirements of real-time integration, time-frequency analysis and model solving, and its computing power can cover the millisecond-level response of Z-parameter, critical strain and grain size evolution models.

[0265] The storage module is configured to include a high-speed solid-state drive and a large-capacity hard disk drive, pre-store GH4169 alloy physical property parameters, vibration baseline spectrum of qualified forgings, continuous cooling transformation diagram and process adjustment strategy library, and simultaneously store the collected time-series data and calculation results in real time.

[0266] The output and control module is configured as follows: a central control console, an audible and visual alarm, and a process parameter interaction interface. It can display comprehensive quality status indicators in real time, output visual alarm signals and quantitative adjustment suggestions when an early warning is triggered, and supports linkage with the forging equipment control system.

[0267] The software system includes:

[0268] The data preprocessing unit is configured to filter, reduce noise, and standardize the format of the collected temperature, strain, and vibration data to ensure data integrity and consistency.

[0269] The temperature range division unit is configured to automatically identify 1000℃-1200℃ (first temperature range, high-temperature austenite stable region) and 600℃-1000℃ (second temperature range, phase transformation sensitive region) based on the preset phase transformation critical point of GH4169 alloy, thereby realizing dynamic segmentation of the processing cycle.

[0270] The strain path and critical criterion calculation unit is configured to: convert multiaxial strain data through the Mises equivalent strain rate criterion, obtain the cumulative effective plastic strain through time integration; calculate the Z parameters in combination with real-time temperature, solve for the critical strain, and determine the dynamic recrystallization trigger state.

[0271] The grain size evolution model unit is configured to: take the recrystallization state of the first interval as the initial condition, couple the grain growth differential equation with the phase transformation kinetic model, and predict the final average grain size at the end of the second interval based on the real-time cooling curve.

[0272] The quality index evaluation unit is configured to calculate the grain refinement index and the phase transformation uniformity index, and generate a comprehensive quality index through a weighted formula.

[0273] The warning and adjustment suggestion unit is configured as follows: preset comprehensive quality threshold of 90%, when the indicator is lower than the threshold, diagnose the cause of deviation of individual indicators, and call quantitative adjustment suggestions from the strategy library.

[0274] During the initialization phase, the computer device loads the calibration parameters and vibration baseline spectrum of the GH4169 alloy, completes the synchronous calibration of the acquisition equipment and processing module, and sets the target grain size and weighting coefficient.

[0275] Data acquisition and preprocessing: After the forging is heated to the initial forging temperature, the computer device synchronously starts each acquisition device to receive temperature, strain, and vibration data in real time, completes filtering and noise reduction and timestamp alignment, and forms a standardized data stream.

[0276] Then, the temperature range is divided. Based on real-time temperature data, the software automatically divides the range into two intervals: 1000℃-1200℃ and 600℃-1000℃, and allocates subsequent computing resources accordingly.

[0277] Within the first interval, strain path and critical criterion calculations are performed. The computer integrates the strain data in real time to obtain the cumulative effective plastic strain. Combined with temperature calculations, Z parameters and critical strain are used to determine the degree of dynamic recrystallization completion and output the initial grain size for entering the second interval.

[0278] In the second interval, grain size prediction is performed by using software to solve the grain evolution model based on the cooling curve to predict the final average grain size.

[0279] The overall quality assessment is obtained through calculation; if the overall index is higher than the threshold.

[0280] The system will issue an early warning and adjust the process. If the third pass fails to meet the threshold due to temperature fluctuations, the computer will diagnose it as insufficient grain refinement and output an adjustment suggestion to "increase the reduction amount in the fourth pass". After execution, the indicators will recover.

[0281] This computer device enables full online monitoring of the GH4169 turbine disk forging process, predicts grain size and actual measured values, improves the phase transformation uniformity compliance rate, and increases the forging yield compared with traditional methods through closed-loop process adjustment, effectively avoiding forging scrap caused by microstructural defects.

[0282] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of predicting the microstructure of a large complex forging, characterized by, The method comprises: acquiring temperature field data, strain field data and vibration acceleration data of the forging during the whole processing cycle; obtaining a first temperature interval and a second temperature interval based on the temperature field data; in the first temperature interval, obtaining a strain history path function according to the strain field data in the first temperature interval; obtaining a dynamic recrystallization critical criterion according to the strain history path function; in the second temperature interval, obtaining a predicted grain size at the end of the second temperature interval based on a grain size evolution model according to the dynamic recrystallization critical criterion and the temperature field data in the second temperature interval; obtaining a comprehensive quality state index according to the predicted grain size and the vibration acceleration data in the second temperature interval. The method comprises: dividing the processing cycle of the forging into two adjacent stages to obtain the first temperature interval and the second temperature interval based on the temperature field data and the stable zone of austenite of the forging; the first temperature interval is configured to correspond to the temperature zone of the high temperature of the forging in the processing and the stability of the austenite, the upper limit of the temperature is set to be a preset temperature below the melting point of the forging, and the lower limit is set to be above the lowest stable temperature of the austenitizing of the forging; the second temperature interval is configured to correspond to the temperature zone of the decomposition of the austenite of the forging in the processing, the upper limit of the temperature is set to be the lower limit of the first temperature interval, and the lower limit is set to be room temperature or a set final cooling temperature.

2. The method of claim 1, wherein The method comprises: acquiring the strain field data in the first temperature interval; obtaining an effective plastic strain rate based on the Mises yield criterion of equivalent strain according to the strain field data; obtaining the cumulative effective plastic strain of the forging in the first temperature interval by time integration according to the effective plastic strain rate; taking the cumulative effective plastic strain as the strain history path function.

3. The method of claim 2, wherein the large complex forging is a large complex aluminum forging. The method further comprises: acquiring real-time temperature field data in the first temperature interval; obtaining a Z parameter according to the real-time temperature field data and the strain history path function; obtaining a critical strain of dynamic recrystallization according to the Z parameter and the initial grain size of the forging; comparing the critical strain and the cumulative effective plastic strain to obtain the dynamic recrystallization critical criterion.

4. The method of claim 3, wherein The method further comprises: judging whether the dynamic recrystallization of the forging has occurred or is occurring in the first temperature interval according to the recrystallization critical criterion; determining that the forging has reached the critical condition of dynamic recrystallization when the cumulative effective plastic strain is greater than or equal to the critical strain; the first temperature interval ends when it is determined that the forging has reached the critical condition of dynamic recrystallization; obtaining the grain state of the forging at this time when the first temperature interval ends, the grain state comprising a dynamic recrystallization volume fraction or an average grain size; judging whether the grain state at this time is complete dynamic recrystallization or partial recrystallization according to the dynamic recrystallization volume fraction. According to the grain state, an initial grain size at the beginning of the second temperature interval is obtained by weighted average.

5. The method of claim 4, wherein According to the dynamic recrystallization critical criterion and the temperature field data in the second temperature interval, a predicted grain size at the end of the second temperature interval is obtained based on a grain size evolution model, including: According to the dynamic recrystallization critical criterion, an initial grain size at the beginning of the second temperature interval is obtained from the final grain state at the end of the first temperature interval; According to the temperature field data in the second temperature interval, a cooling curve and a cooling rate are obtained; According to the initial grain size, the cooling curve and the cooling rate, a simulation is performed based on a grain growth differential equation to obtain an evolution equation of austenite grain growth with the cooling process, so as to obtain an updated grain size; According to the cooling curve, based on a preset continuous cooling transformation diagram, a phase transformation kinetics calculation is triggered in response to the intersection time to obtain a second phase grain size at the time of phase transformation; According to the evolution equation of austenite grain growth and the second phase grain size, a grain size evolution model is obtained by coupling; According to the grain size evolution model, a predicted final average grain size at the end of the second temperature interval is obtained.

6. The method of claim 1, wherein According to the predicted grain size and the vibration acceleration data in the second temperature interval, a comprehensive quality state index is obtained, including: According to the predicted grain size and the vibration acceleration data in the second temperature interval, at least one single quality state index is generated, including: According to the predicted grain size, a grain refinement degree index is obtained based on a preset target average grain size; According to the vibration acceleration data in the second temperature interval, a time domain signal is processed to obtain a vibration energy spectrum of the current forging in a preset frequency range; According to the vibration energy spectrum, a phase transformation uniformity index is obtained based on a preset qualified forging vibration baseline spectrum; According to the grain refinement degree index and the phase transformation uniformity index, a comprehensive quality state index is obtained by calculation.

7. The method of claim 6, wherein Further comprising: According to the real-time comprehensive quality state index, when it is lower than a preset threshold, a defect risk warning is triggered; In response to triggering the defect risk warning, it is judged whether the grain refinement degree index and the phase transformation uniformity index are lower than preset indexes respectively; When the grain refinement degree index is lower than the preset index, the grain refinement is insufficient; When the phase transformation uniformity index is lower than the preset index, the phase transformation uniformity is poor.

8. A system for microstructure prediction of large complex forgings, characterized in that, The system comprises sequentially connected acquisition units, partition units, strain history path units, dynamic recrystallization critical criterion units, grain size evolution units and quality evaluation units; The acquisition unit is configured to obtain temperature field data, strain field data and vibration acceleration data of the forging in the entire processing period; The partition unit is configured to divide the processing period into a first temperature interval and a second temperature interval based on the temperature field data; The strain history path unit is configured to obtain a strain history path function according to the strain field data in the first temperature interval in the first temperature interval; The dynamic recrystallization critical criterion unit is configured to obtain a dynamic recrystallization critical criterion according to a strain history path function; The grain size evolution unit is configured to obtain a predicted grain size at the end of the second temperature interval according to the dynamic recrystallization critical criterion and temperature field data in the second temperature interval, and based on a grain size evolution model; The quality evaluation unit is configured to obtain a comprehensive quality state index according to the predicted grain size and vibration acceleration data in the second temperature interval; The partition unit comprises: The temperature field data is used to divide the forging processing cycle into two adjacent stages based on the stable region of the austenite of the forging, thereby obtaining the first temperature interval and the second temperature interval; The first temperature interval is configured to correspond to a temperature region in which the austenite of the forging is stable during the processing, and the upper limit of the temperature is set to be a preset temperature below the melting point of the forging, and the lower limit is set to be above the lowest stable temperature of the austenitizing of the forging; The second temperature interval is configured to correspond to a temperature region in which the austenite of the forging is decomposed during the processing, and the upper limit of the temperature is set to be the lower limit of the first temperature interval, and the lower limit is set to be room temperature or a set final cooling temperature.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-8. The processor executes the computer program to implement the steps of the method of any one of claims 1 to 7.

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