CN115329475A - Part preparation method and equipment based on partitioned multistage cryogenic treatment - Google Patents

Abstract

The invention belongs to the technical field of cryogenic treatment, and discloses a method and equipment for preparing parts based on sub-regional multi-stage cryogenic treatment, comprising the following steps: (1) establishing a multi-stage cryogenic process parameter-microstructure-performance relationship through a neural network (2) Divide the target part into multiple sub-regions, invert the multi-level cryogenic treatment parameters required by each sub-region, and then obtain a series of corresponding cryogenic process parameter distribution combinations inside the target part; ( 3) Carry out high-throughput numerical simulation of the partitioned multi-stage cryogenic treatment process to determine the optimal combination of cryogenic treatment parameters for different areas inside the target part; (4) Perform partition-independent cryogenic treatment on the part to be treated to obtain the target Components. The invention solves the technical problems in the prior art that the applicability to the cryogenic treatment of the parts is not strong and the process is complicated.

Description

Part preparation method and equipment based on partitioned multi-stage cryogenic treatment

technical field

The invention belongs to the technical field related to cryogenic treatment, and more specifically relates to a part preparation method and equipment based on multi-stage cryogenic treatment in different regions.

Background technique

Lightweight structural parts, such as blisks and turbine disks, can reduce weight by about 30%, improving the engine's thrust-to-weight ratio and reliability. The overall blisk and turbine disk parts have large temperature gradients and stress gradients in the radial direction, and different regions have different requirements for material properties. The blades emphasize excellent high-cycle fatigue resistance, and the disk body emphasizes high-temperature creep Resistance and damage tolerance performance. The working temperature of the spokes of the turbine disk is relatively low, and the fine-grained structure is more in line with the high yield strength and low cycle fatigue performance requirements of the spokes; the edge temperature is relatively high, and the coarse-grained structure has high creep and damage tolerance performance. Possible microcracks in the tongue and groove can be avoided. In order to further develop the performance potential of parts such as blisks, appropriate alloy materials and microstructures should be selected according to the actual service environment in different regions of the parts. For this reason, scientists have proposed the design idea of dual-performance blisks and turbine disks, breaking through the traditional thermal Processing technology pursues the inertial thinking of uniform organization, and has developed a series of single-alloy dual-performance or double-alloy gradient structure parts with gradient microstructure characteristics.

At present, the manufacturing methods of gradient structure metal parts, especially the representative dual-performance integral blisks and turbine disks, mainly include welding method, zoned temperature controlled forging and zoned temperature controlled heat treatment. The welding method can realize the connection of dissimilar materials, but its biggest problem is that the connection area often becomes the weak link of the whole component, which is an important hidden danger for high-speed rotating parts of aero-engines that emphasize high reliability and long life. Compared with the zoned temperature control forging process, the zoned temperature controlled heat treatment process is easier to form a stable and controllable temperature gradient between the blade and the disc, so as to obtain the required double structure, and the process operation is relatively easy and consistent. it is good. After traditional heat treatment, the material still has some shortcomings, such as unstable structure after quenching, high thermal stress and structural stress, uneven structure, etc., which will deteriorate the material performance and affect the service life of the material. Generally speaking, it is difficult to solve such problems through a single heat treatment process. As an important additional process of heat treatment, cryogenic treatment can effectively re-optimize the performance of materials after heat treatment, and has a significant effect on prolonging the service life of materials.

Cryogenic treatment refers to placing materials (or workpieces) in a certain low temperature (below -130°C) environment, and processing materials by controlling process parameters such as cryogenic treatment temperature, heating and cooling rate, holding time, and treatment times, so that the material's Different degrees of irreversible changes occur in the microstructure, so as to achieve the purpose of improving the comprehensive performance. Studies have shown that cryogenic treatment has a significant modification effect on materials such as die steel, superalloys, titanium alloys, hard alloys, amorphous alloys and high-entropy alloys, and can promote the transformation of retained austenite, refine grains, and improve Dislocation density, promote the formation of twins and subgrain structures, promote phase transformation and carbide precipitation, and promote texture formation.

For parts with gradient structure, different parts have different organizational requirements and even different alloy types, so the requirements for cryogenic treatment are quite different. The general cryogenic treatment method for such parts is to use heat insulating material to wrap the area that does not undergo cryogenic treatment at a certain stage, which often requires multiple processes, the process is complicated and the effect is not good. Existing cryogenic treatment processes and devices are only suitable for single-material, uniform parts, and it is difficult to meet the cryogenic requirements of different regions of parts with gradient structures such as dual-performance blisks and turbine disks.

Contents of the invention

In view of the above defects or improvement needs of the prior art, the present invention provides a part preparation method and equipment based on partitioned multi-stage cryogenic treatment. The preparation method obtains by controlling the temperature gradient field distribution inside the sample during the multi-stage cryogenic treatment process. Parts with corresponding tissue characteristics and performance distribution can achieve differentiated cryogenic treatment of different regions of the part through the optimization of process parameters such as temperature and holding time in the multi-stage cryogenic treatment process, so as to obtain the target tissue and performance distribution to solve the problem. In the prior art, the applicability to cryogenic treatment of parts is not strong and the technical problems are complicated.

In order to achieve the above object, according to one aspect of the present invention, a method for preparing parts based on subregional multi-stage cryogenic treatment is provided, the preparation method mainly includes the following steps:

(1) Obtain the microstructure-property evolution law of the material composition of the target part through multi-stage cryogenic treatment experiments, and establish the non-linear mapping relationship between multi-stage cryogenic process parameters-microstructure-property through neural network;

(2) Divide the target part into multiple sub-regions according to the distribution requirements of the target part's organization and performance, and deduce the multi-level cryogenic treatment parameters required for each sub-region by combining the obtained nonlinear mapping relationship, and then obtain the multi-level cryogenic treatment target part A series of cryogenic process parameter distribution combinations corresponding to the inside of the target part;

(3) Combining the obtained cryogenic process parameter distribution combination to carry out high-throughput numerical simulation on the partitioned multi-stage cryogenic treatment process, the temperature distribution or microstructure distribution in the numerical simulation is closest to the cryogenic treatment corresponding to the microstructure and performance distribution requirements of the target part The combination of process parameters is determined as the best combination of cryogenic treatment parameters for different regions inside the target part;

(4) Combining the geometrically divided regions of the target part and the optimal cryogenic treatment parameter combination to perform partitioned and independent cryogenic treatment on the part to be treated to obtain the target part.

Furthermore, the acquisition of the structure-property evolution law of the material composition of the target part includes the following steps: taking the material of the target part as the initial experimental object, within the preset cryogenic treatment parameter range, setting up an orthogonal experiment or a single factor test, in Perform multi-stage cryogenic treatment on the initial test object under different cryogenic treatment process parameters, and perform microstructure characterization and performance testing on the samples obtained from cryogenic treatment, and obtain microstructural characteristic parameters and performance data of samples under different cryogenic treatment parameters , and then obtain the multi-stage cryogenic treatment process parameter-microstructure-property database.

Furthermore, multi-stage cryogenic treatment refers to a process that undergoes several heat preservation and heating and cooling processes from the initial treatment temperature to the lowest treatment temperature; the microstructure characteristic parameters include grain size, dislocation density, retained austenite volume fraction , one or more of the size and volume fraction of the carbide phase; the multi-stage cryogenic treatment process parameters include one or more of temperature, holding time, heating and cooling rate, and treatment times.

Further, the established nonlinear mapping relationship between multi-stage cryogenic process parameters-microstructure-performance includes cryogenic treatment process parameters-microstructure relationship model, microstructure-performance relationship model, and cryogenic treatment process parameter-performance relationship Model.

Further, each relationship model is described by a BP neural network model containing multiple hidden layers, and the establishment of the cryogenic process parameter-microstructure nonlinear mapping relationship includes the following steps:

Taking the set of cryogenic process parameters obtained from multi-level cryogenic treatment experiments as input, and the obtained set of microstructure characteristic parameters as output, a BP neural network model with multiple hidden layers is constructed and trained, and each hidden layer and output layer selects an appropriate activation function; optimizes the initial weights and thresholds of the BP neural network using a genetic algorithm to obtain the weights and thresholds of the optimal individual; assigns the weights and thresholds of the obtained optimal individual to the BP neural network In the network model, the error backpropagation algorithm is used to update the weights and thresholds of each hidden layer during the training process, until the cost function J is less than the set accuracy or reaches the maximum number of iterations, then the training ends.

Further to the end, the activation function of the hidden layer is selected as a logistic function, and the activation function of the output layer is selected as a linear function g(x)=x.

Further, using genetic algorithm to optimize the initial weights and thresholds of the BP neural network, specifically includes the following steps:

S1: First, determine the number of weights and thresholds of the neural network according to the topological diagram of the BP neural network model, following the following formula:

Where N _um is the total number of weights and thresholds, i represents the i-th layer of neurons, H _i is the number of nodes of the i-th layer of neurons;

S2: Use the real number encoding method to encode the weight threshold of the neural network, initialize the population, the initial weight threshold is randomly selected between (-1,1), and set the fitness function of the population to F1;

Among them, F1 is the fitness value, ρ1 is the enhanced phase component predicted by the neural network using the initial weight and threshold, η1 is the enhanced phase volume fraction predicted by the neural network using the initial weight and threshold, and δ1 is the initial weight The average size of the enhanced phase predicted by the neural network of value and threshold, _ρ0 is the expected value of the enhanced phase composition, _η0 is the expected value of the volume fraction of the enhanced phase, and _δ0 is the expected value of the average size of the enhanced phase;

S3: Calculate the fitness value of all individuals in the population, and use the roulette algorithm to select individuals with high fitness from the parent generation to generate the next generation of individuals. The probability of each individual being selected follows the following formula:

Among them, p _k is the probability that the kth individual is selected, F _k is the fitness value of the kth individual, and K is the total number of individuals in the population;

S4: Perform a crossover operation on the individuals in the population, set the crossover probability as pc, generate a random number that is less than the crossover probability, then perform the crossover operation, randomly select two individuals and randomly select the crossover position during the crossover, and perform crossover according to the following formula operate:

Among them, a _kj is the real number of the k-th chromosome at the j-position, a _lj is the real number of the l-th chromosome at the j-position, and b is a random number between (0,1);

S5: Perform a mutation operation on the individuals in the population, set the mutation probability to pm, generate a random number that is less than the mutation probability, then perform the mutation operation, randomly select an individual and randomly select the mutation bit during mutation, and perform the mutation operation according to the following formula :

Among them, a _ij is the real number of the i-th chromosome at position j, g is the current iteration number, f(g) is the variation factor, G _max is the maximum iteration number, a _max is the upper limit of the value of a _ij , and a _min is The lower limit of the value of a _ij , r and r' are random numbers between (0,1);

S6: Repeat steps S3 to S5 until a satisfactory fitness value is obtained or a limited number of iterations is reached, and the optimal individual is output, that is, the individual with the largest fitness value.

According to another aspect of the present invention, there is provided a kind of parts preparation equipment based on subregional multi-stage cryogenic treatment, the preparation equipment adopts the above-mentioned parts preparation method based on subregional multi-stage cryogenic treatment to prepare parts; the equipment forms There are multiple independent cryogenic treatment chambers, and each cryogenic treatment chamber can independently perform cryogenic treatment on a section of the part to be prepared.

Further, each cryogenic treatment chamber is provided with an independent resistance heating wire, a temperature sensor, a cooling medium inlet and outlet pipe, and a moving mechanism, so that each cryogenic treatment chamber can work independently to deep-dry a local area of the target part. Cold treatment.

Further, the device also includes a plurality of heat insulation boards, and the device is formed with a storage chamber, and a plurality of the heat insulation boards are arranged at intervals in the storage chamber to divide the storage chamber into a plurality of independent The cryogenic treatment chamber; the heat shield is also used to carry the parts to be prepared; each heat shield is connected to one of the moving mechanisms, and the movement mechanism is used to drive the heat shield to move to change The space size of the cryogenic processing chamber on both sides of the heat shield.

Generally speaking, compared with the prior art through the above technical solutions conceived by the present invention, the part preparation method and equipment based on subregional multi-stage cryogenic treatment provided by the present invention mainly have the following beneficial effects:

1. The preparation method uses the fact that the microstructure and performance evolution of metal materials during cryogenic treatment directly depends on the temperature field distribution in cryogenic treatment, and according to the microstructure and performance requirements of the target part, it is inversely deduced that the use of multi-stage cryogenic treatment The gradient temperature field distribution required for the target part, and the geometric division scheme that can obtain the target gradient temperature field distribution is determined through numerical simulation, and finally the back-calculated multi-stage cryogenic treatment parameters and geometric division scheme are used to adjust the cryogenic treatment device For each chamber space, set the cryogenic treatment program for each chamber to obtain the target parts.

2. The present invention adopts a multi-stage cooling method. Compared with the traditional method of directly immersing in liquid nitrogen and other cooling media, the thermal shock to the parts is small, and the processing process is not easy to produce cracks and deformations, and is suitable for thin-walled parts and brittle materials; At the same time, according to the cryogenic treatment requirements of different regions of the parts, the sub-regional cryogenic treatment method is adopted, and the microstructure and mechanical properties of the entire part can be adjusted in one cryogenic treatment, which significantly simplifies the cryogenic treatment process of this type of parts and improves the processing efficiency. The transition of microstructure and mechanical properties between different regions is good, which can avoid the interface stress concentration during the part service process and improve the overall service life.

3. The present invention first carries out multi-stage cryogenic treatment orthogonal experiments or single factor experiments on the material composition of the target part, obtains its microstructure and performance data under different cryogenic treatment parameters, and then uses neural network technology to establish deep Cold treatment parameters such as temperature, holding time, heating and cooling rate, treatment times and microstructure parameters such as grain size, dislocation density, retained austenite volume fraction, carbide and other precipitated phase volume fraction, etc., performance parameters such as yield strength, room temperature The complex nonlinear mapping relationship of plasticity, fracture strength, flexural strength, hardness, etc., can fully reflect the cryogenic treatment parameters, the complex influence law between microstructure and performance, and help reverse the required gradient temperature field and optimize process parameters. Reduce the number of pre-tests, and realize the fast and intelligent formulation of multi-stage cryogenic treatment processes for parts partitions.

4. The equipment changes the single chamber of the traditional cryogenic device into a multi-chamber design. The temperature of each chamber is controlled independently, the space is flexible and adjustable, and the heat insulation layer is set between each chamber, which can meet the requirements of single alloy gradient structure or multi-chamber structure. The cryogenic requirement of complex shape parts composed of alloy structure significantly simplifies the cryogenic treatment process, and the cryogenic treatment effect is better.

Description of drawings

Fig. 1 is a schematic flow chart of a part preparation method based on partitioned multi-stage cryogenic treatment provided by the present invention;

Fig. 2 is a schematic structural view of parts preparation equipment based on subregional multi-stage cryogenic treatment provided by an embodiment of the present invention;

Fig. 3 is a cross-sectional view of the parts preparation equipment based on subregional multi-stage cryogenic treatment in Fig. 2;

Fig. 4 is a partial schematic diagram of the parts preparation equipment based on subregional multi-stage cryogenic treatment in Fig. 2;

Fig. 5 is a schematic diagram of the parts preparation equipment based on subregional multi-stage cryogenic treatment in Fig. 2 when it is in an open and closed state;

Fig. 6 is a schematic structural diagram of parts preparation equipment based on zoned multi-stage cryogenic treatment provided by another embodiment of the present invention;

Fig. 7 is a cross-sectional view of the parts preparation equipment based on subregional multi-stage cryogenic treatment in Fig. 6;

Fig. 8 is a partial schematic diagram of the parts preparation equipment based on zoned multi-stage cryogenic treatment in Fig. 6;

Fig. 9 is a schematic diagram of the parts preparation equipment based on multi-stage cryogenic treatment in Fig. 6 when it is in an open and closed state.

In all the drawings, the same reference numerals are used to denote the same elements or structures, wherein: 1-upper pipeline chamber, 2-upper chamber, 3-lower chamber, 4-lower pipeline chamber, 5-connection mechanism , 6-liquid nitrogen tube, 7-moving mechanism, 8-heat insulation layer, 9-resistance heating wire, 10-thermocouple, 11-plate gradient structure parts, 12-thermocouple signal line, 13-temperature control system, 14-insulation plate, 15-disc-shaped gradient structural parts.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

Please refer to Fig. 1, the present invention provides a part preparation method based on multi-stage cryogenic treatment in different regions, which can realize the flexible setting of the microstructure of parts, rapid optimization of performance partitions, better continuity of tissue performance between different regions, and multiple The thermal shock generated by the grade treatment is small, and the parts are not easy to crack during the treatment process, and the applicable parts types and materials are wide. Among them, the preparation method can prepare gradient structure and uniform structure parts.

The method for preparing parts based on partitioned multi-stage cryogenic treatment provided by the present invention mainly includes the following steps:

Step 1: Obtain the microstructure-property evolution law of the material composition of the target part through multi-stage cryogenic treatment experiments, and establish the nonlinear mapping relationship between multi-stage cryogenic process parameters-microstructure-property through neural network.

Obtain the microstructure-property evolution law of the material composition of the target part through the multi-stage cryogenic treatment experiment, which specifically includes the following steps: taking the material of the target part as the initial experimental object, within the preset cryogenic treatment parameter range, set up an orthogonal experiment or a single In the factor test, the initial test object is subjected to multi-stage cryogenic treatment under different cryogenic treatment process parameters, and the microstructure characterization and performance test are carried out on the samples obtained from the cryogenic treatment, so as to obtain the microstructural characteristics of the samples under different cryogenic treatment parameter conditions Parameter and performance data, and then obtain multi-stage cryogenic treatment process parameter-microstructure-performance database.

Multi-stage cryogenic treatment refers to a process that undergoes several heat preservation, heating and cooling processes from the initial treatment temperature to the lowest treatment temperature. The characteristic parameters of microstructure include one or more of grain size, dislocation density, volume fraction of retained austenite, size and volume fraction of carbide phase. The multi-stage cryogenic treatment process parameters include one or more of temperature, holding time, heating and cooling rate, and treatment times. The performance data includes mechanical properties of the material, such as yield strength and fracture strength, and electromagnetic properties such as one or more of electrical conductivity, magnetic permeability, and thermal conductivity.

Determine the phase composition and dislocation density in cryogenically treated samples by X-ray diffraction analysis (XRD), and obtain the grain size, retained austenite volume fraction, and carbide phase in cryogenically treated samples by means of metallographic microscopy, scanning electron microscopy, and EBSD. size and volume fraction etc.

The established nonlinear mapping relationship between the cryogenic treatment process parameters-microstructure-performance of the material composition of the target part, including the cryogenic treatment process parameters-microstructure relationship model, the microstructure-performance relationship model, the cryogenic treatment process parameter-performance 3 relational models including relational model.

Each relational model is described by a BP neural network model containing multiple hidden layers, and the establishment of the cryogenic process parameter-microstructure nonlinear mapping includes the following steps:

The set of cryogenic process parameters obtained by the multi-stage cryogenic treatment experiment is input, and the obtained microstructure characteristic parameter set is output, and a BP neural network model containing multiple hidden layers is constructed and trained to give each hidden layer and the output layer to select a suitable excitation function; utilize the genetic algorithm to optimize the initial weight and threshold of the BP neural network to obtain the weight and threshold of the optimal individual; assign the weight and threshold of the optimal individual obtained to the In the BP neural network model, the error back propagation algorithm is used to update the weights and thresholds of each hidden layer during the training process, and the training ends until the cost function J is less than the set accuracy or reaches the maximum number of iterations.

The activation functions of the hidden layer all select the logistic function, and the activation functions of the output layer select the linear function g(x)=x.

Optimizing the initial weights and thresholds of the BP neural network using a genetic algorithm specifically includes the following steps:

S1: First, determine the number of weights and thresholds of the neural network according to the topological diagram of the BP neural network model, following the following formula:

Where N _um is the total number of weights and thresholds, i represents the i-th layer of neurons, H _i is the number of nodes of the i-th layer of neurons;

S2: Use the real number encoding method to encode the weight threshold of the neural network, initialize the population, the initial weight threshold is randomly selected between (-1,1), and set the fitness function of the population to F1;

Among them, F1 is the fitness value, ρ1 is the enhanced phase component predicted by the neural network using the initial weight and threshold, η1 is the enhanced phase volume fraction predicted by the neural network using the initial weight and threshold, and δ1 is the initial weight The average size of the enhanced phase predicted by the neural network of value and threshold, _ρ0 is the expected value of the enhanced phase composition, _η0 is the expected value of the volume fraction of the enhanced phase, and _δ0 is the expected value of the average size of the enhanced phase;

S3: Calculate the fitness value of all individuals in the population, and use the roulette algorithm to select individuals with high fitness from the parent generation to generate the next generation of individuals. The probability of each individual being selected follows the following formula:

Among them, p _k is the probability that the kth individual is selected, F _k is the fitness value of the kth individual, and K is the total number of individuals in the population;

S4: Perform a crossover operation on the individuals in the population, set the crossover probability as pc, generate a random number that is less than the crossover probability, then perform the crossover operation, randomly select two individuals and randomly select the crossover position during the crossover, and perform crossover according to the following formula operate:

Among them, a _kj is the real number of the k-th chromosome at the j-position, a _lj is the real number of the l-th chromosome at the j-position, and b is a random number between (0,1);

S5: Perform a mutation operation on the individuals in the population, set the mutation probability to pm, generate a random number that is less than the mutation probability, then perform the mutation operation, randomly select an individual and randomly select the mutation bit during mutation, and perform the mutation operation according to the following formula :

Among them, a _ij is the real number of the i-th chromosome at position j, g is the current iteration number, f(g) is the variation factor, G _max is the maximum iteration number, a _max is the upper limit of the value of a _ij , and a _min is The lower limit of the value of a _ij , r and r' are random numbers between (0,1);

S6: Repeat steps S3 to S5 until a satisfactory fitness value is obtained or a limited number of iterations is reached, and the optimal individual is output, that is, the individual with the largest fitness value.

Step 2: Divide the target part into multiple sub-regions according to the distribution requirements of the tissue performance of the target part, combine the obtained nonlinear mapping relationship to deduce the multi-level cryogenic treatment parameters required by each sub-region, and then obtain the multi-level cryogenic treatment target part A series of cryogenic process parameter distribution combinations corresponding to the inside of the target part.

Step 3: Combining the obtained cryogenic process parameter distribution combination to carry out high-throughput numerical simulation on the partitioned multi-stage cryogenic treatment process, the temperature distribution or microstructure distribution in the numerical simulation is closest to the cryogenic treatment corresponding to the structure and performance distribution requirements of the target part The combination of process parameters is determined as the optimal combination of cryogenic treatment parameters for different regions inside the target part.

In numerical simulation, commercial numerical simulation software such as Deform, Abaqus, Ansys or Comsol is used to perform heat transfer simulation or heat transfer-microstructure coupling simulation on the multi-stage cryogenic treatment process.

Step 4: Combining the geometrically divided regions of the target part and the optimal cryogenic treatment parameter combination to perform partitioned and independent cryogenic treatment on the part to be treated, so as to obtain the target part.

The present invention will be further described in detail with several embodiments below.

Example 1

The target part is a high-temperature alloy GH2132 dual-performance plate-shaped part. The middle part of the part has a low working temperature and requires a small grain size to ensure sufficient strength, creep resistance and fatigue resistance. The edge part bears high working temperature and requires coarse grain size. Granular structure, to ensure room temperature plasticity, creep resistance and fatigue expansion resistance. Therefore, different regions of the target plate-shaped part are required to have microstructures with different grain sizes in order to obtain corresponding mechanical properties. According to the actual service conditions of the parts, the target gradient structure parts are divided into 4 characteristic areas, the central part of the grain is ultra-fine grain structure, the average grain size is less than 1 μm, the edge part is coarse grain structure, the average grain size is 100 μm, The grain size in the middle region gradually transitions. At the same time, the Vickers hardness of the central part is not less than 380HV, the tensile strength at room temperature is not less than 800MPa, the tensile plasticity at room temperature is not less than 20%, the Vickers hardness of the edge area is not less than 300HV, and the tensile strength at room temperature is not less than 500MPa. Tensile plasticity at room temperature is not less than 40%. Its divisional multi-stage cryogenic treatment method includes the following steps:

( ₁ ) _The microstructure-property evolution law of the superalloy GH2132 was obtained through multi-stage cryogenic treatment experiments. Particle size, performance parameters are selected as Vickers hardness, tensile strength and tensile plasticity at room temperature. And the non-linear mapping relationship between multi-level cryogenic process parameters-microstructure-performance is established through BP neural network.

The cryogenic treatment temperature is set to -50°C, -75°C, -100°C, -125°C, -150°C, -175°C, -196°C, the holding time is uniformly 1h, the heating and cooling rate is 50°C/min, and the number of treatments is 1. The average grain size after multi-stage cryogenic treatment was determined by taking metallographic photos after metallographic grinding and polishing, and the grain size distribution and average grain size were counted by ImageJ image software. The Vickers hardness is tested by the 430SVD Vickers hardness tester of Wilson Hardness Company in the United States. The loading load is 1kg. Five points are taken for each sample for hardness testing. The maximum and minimum values of each data are removed and the average value is calculated. as the hardness of the sample. The tensile bar is a standard tensile sample with a diameter of 10mm. Specifically, refer to the requirements of GB/T228-2010 "Metallic Materials Tensile Test Method at Room Temperature" to conduct quasi-static tensile tests at room temperature and calculate the room temperature tensile strength and room temperature tensile plasticity. data.

The nonlinear mapping relationship between temperature, grain size and Vickers hardness, room temperature tensile strength and room temperature tensile plasticity is established through BP neural network, and the experimental data obtained in the pre-experiment is used as the training sample until the ideal prediction accuracy is achieved.

(2) According to the actual service conditions of the parts, the target gradient structural plate parts are divided into four characteristic areas, combined with the obtained nonlinear mapping relationship, the multi-level cryogenic treatment parameters required for each sub-area are reversely deduced, and then the multi-level cryogenic treatment parameters required by the multi-level sub-area are obtained. A series of cryogenic process parameter distribution combinations inside when the target part is treated in the first-level cryogenic treatment.

(3) Combining step (2) to obtain a series of cryogenic process parameter combinations inside the target part, and perform high-throughput numerical simulation on the partitioned multi-stage cryogenic treatment process, using comsol software for numerical simulation. The combination of cryogenic treatment process parameters corresponding to the temperature distribution or microstructure distribution closest to the target part's tissue performance distribution requirements during numerical simulation is determined as the best combination of cryogenic treatment parameters for different regions inside the target part. In this embodiment, the parameters of multi-stage cryogenic treatment in each sub-region are as follows: Region _I : T1 is -175°C, T2 is -196°C, Region _{II :} T1 is -150°C, T2 is _-175 °C, Zone III _: T1 is -125°C, T2 is _-150 °C, Zone IV _: T1 is -100°C, T2 is _-125 °C. The heating and cooling rate is uniformly 50°C/min, and the holding time of each temperature stage is 1h.

(4) In conjunction with the optimal cryogenic treatment parameter combination determined in the step target gradient structure plate-shaped part geometric division area and step (3), adjust the geometric space of each chamber of the multi-chamber partition cryogenic device, and each chamber is according to the step (3) ) to determine the optimal cryogenic treatment parameters to carry out subarea independent cryogenic treatment, and then obtain the target gradient structure plate-shaped part.

Example 2

The target part is a high-temperature alloy GH2132 dual-performance disc-shaped part. The middle part of the same part has a low working temperature, and the grain size of the structure is required to be small to ensure sufficient strength, creep resistance and fatigue resistance. The working temperature of the edge part is high, requiring Coarse grain structure ensures room temperature plasticity, creep resistance and fatigue expansion resistance. Therefore, different regions of the target plate-shaped part are required to have microstructures with different grain sizes in order to obtain corresponding mechanical properties. According to the actual service conditions of the parts, the target gradient structure parts are divided into three characteristic areas. The central part of the grain is ultra-fine grain structure, the average grain size is less than 1 μm, and the edge part is coarse grain structure, with an average grain size of 100 μm. The grain size in the middle region gradually transitions. At the same time, the Vickers hardness of the central part is not less than 380HV, the tensile strength at room temperature is not less than 800MPa, the tensile plasticity at room temperature is not less than 20%, the Vickers hardness of the edge area is not less than 300HV, and the tensile strength at room temperature is not less than 500MPa. Tensile plasticity at room temperature is not less than 40%.

The pre-experiment obtains the experimental data samples, and uses the data samples to train the neural network until the ideal prediction accuracy is achieved. According to the nonlinear mapping relationship between process parameters-tissue-performance established by the neural network, the multi-level cryogenic treatment parameters of each region are reversed. Combining with high-throughput numerical simulation to determine the optimal combination of process parameters; using a partitioned multi-stage cryogenic treatment device suitable for disc-shaped parts to carry out cryogenic treatment on the target gradient structure parts, and taking out the parts after the treatment can obtain the gradient grain structure. Object gradient structure disk-shaped part.

The present invention also provides a parts preparation equipment based on subregional multi-stage cryogenic treatment, which adopts the above-mentioned part preparation method based on subregional multi-stage cryogenic treatment to prepare parts. The equipment is formed with a plurality of independent cryogenic treatment chambers, and each cryogenic treatment chamber can independently perform cryogenic treatment on a section of the part to be prepared.

Please refer to Fig. 2, Fig. 3, Fig. 4 and Fig. 5, an embodiment of the present invention provides parts preparation equipment based on partitioned multi-stage cryogenic treatment, the equipment includes an upper pipeline chamber 1, an upper chamber 2, and a lower chamber 3 , lower pipeline chamber 4, connection mechanism 5, liquid nitrogen tube 6, moving mechanism 7, heat insulation layer 8, resistance heating wire 9, thermocouple 10, thermocouple signal line 12, temperature control system 13 and heat shield 14.

The upper pipeline chamber 1, the upper chamber 2, the lower chamber 3 and the lower pipeline chamber 4 are sequentially connected together from top to bottom through a connecting mechanism 5, and the upper pipeline chamber 1 and the upper pipeline chamber The chamber 2 is fixedly connected, the lower chamber 3 and the lower pipeline chamber 4 are fixedly connected together, and the upper chamber 2 is rotatably connected to one side of the lower chamber 3 . Both the upper pipeline chamber 1 and the lower pipeline chamber 4 are boxes, and the upper chamber 2 and the lower chamber 3 are rectangular frames, and the structures and sizes of the two are corresponding to realize closure.

The upper chamber 2 is formed with a first receiving chamber, and the lower chamber 3 is formed with a second receiving chamber. When the upper chamber 2 is turned against the lower chamber 3, the second A storage cavity communicates with the second storage cavity to form a storage cavity. A plurality of heat insulation boards 14 are arranged at intervals in the first storage chamber, and a plurality of heat insulation boards 14 are also arranged at intervals in the second storage chamber. The number and positions of the heat insulation boards 14 in the first storage chamber The number and position of the heat insulation boards 14 in the second storage chamber correspond respectively to divide the storage chamber into a plurality of independent cryogenic treatment chambers. The opposite two heat shields 14 are provided with receiving grooves respectively, and the shapes and sizes of the draw-in grooves formed by the two receiving grooves are respectively corresponding to the shapes and sizes of the regions corresponding to the parts to be prepared, and the draw-in grooves are used for Clamp the corresponding area of the part to facilitate independent cryogenic treatment of this area. In this embodiment, the part to be prepared is a plate-shaped gradient structure part 11 .

The liquid nitrogen pipe 6 is divided into an inlet pipe, an outlet pipe and a branch pipe, and the number of the inlet pipe and the outlet pipe are the same, and both are the same as the number of the cryogenic treatment chambers. One ends of the plurality of inlet pipes extend into the upper pipeline chamber 1 and then are connected to the branch pipes. The branch pipes are n-shaped, and their two ends respectively pass through the bottom plate of the upper pipeline chamber 1 and then extend into the corresponding Cryogenic processing chamber. One end of a plurality of outlet pipes stretches into the lower pipeline chamber 4 and is connected to the branch pipe, and the two ends of the branch pipe respectively pass through the bottom plate of the lower pipeline chamber 4 and then enter the cryogenic treatment chamber, so The inlet pipe, the cryogenic treatment chamber and the outlet pipe are connected.

Both ends of each cryogenic treatment chamber are respectively symmetrically provided with resistance heating wires 9 and thermocouples 10 . The inner wall of each cryogenic treatment chamber is laid with a thermal insulation layer 8, and the thermal insulation layer 8 can be a vacuum insulation board, airgel felt, aluminum silicate fiber, etc.; different cryogenic treatment chambers are isolated with sealing gaskets between the inner walls For the cooling medium, conformal gaskets are used at the contact parts between the inner wall and the parts to ensure the sealing between the chambers. Rubber gaskets, asbestos gaskets and other gaskets with poor thermal conductivity and good sealing effect can be used.

Each heat shield 14 is respectively connected with a moving mechanism 7 for supporting and driving the heat shield 14 to move so as to change the space of the cryogenic treatment chamber on both sides of the heat shield 14 . Wherein, the thermocouple 10 is connected to the temperature control system 13 through the thermocouple signal line 12, and the temperature control system 13 is also connected to the resistance heating wire 9, the cooling medium supply system and the cooling medium supply system. The inlet pipe of the system.

In this embodiment, the cooling medium can be liquid nitrogen, liquid helium, or a mixed liquid or gas thereof with alcohol and other media, and the cooling medium is passed into the cryogenic processing chamber through a cooling pipe; the temperature sensor can be a thermocouple, symmetrically installed At both ends of the gradient part to be cryogenically treated, other types of temperature sensors can also be used. The temperature of the cryogenically treated chamber is determined as the average value of the temperature measurements of the two temperature sensors. The temperature control system adjusts the corresponding resistance heating wire. The temperature of the corresponding cryogenic treatment chamber is controlled by the electric heating power and the flow rate of the cooling medium; the moving mechanism is responsible for adjusting the position of the heat-insulating inner wall between the chambers, flexibly adjusting the space size of each chamber, and being responsible for the parts in the Support and movement inside a cryogenic unit.

Please refer to Fig. 6, Fig. 7, Fig. 8 and Fig. 9, the parts preparation equipment based on partitioned multi-stage cryogenic treatment provided by another embodiment of the present invention is basically the same as the part preparation equipment based on partitioned multi-stage cryogenic treatment provided in the above embodiment , the difference lies in the shape of the heat shield and the setting of the liquid nitrogen tube. The part to be prepared in this embodiment is a disc-shaped gradient structure part 15, and the corresponding heat shield is formed by an arc-shaped sheet. In the cavity and the second storage cavity, the heat insulation plate located in the first storage cavity is coaxially arranged, and the heat insulation plate located in the second storage cavity is also coaxially arranged, so that the storage cavity is divided into a plurality of annular cryogenic processing chamber. Wherein, the space size of the corresponding cryogenic processing chamber is changed by changing the size of the non-overlapping area at opposite ends of the arc-shaped sheet. The outlet pipe and the inlet pipe are respectively connected with the corresponding cryogenic treatment chamber through inline branch pipes.

It is easy for those skilled in the art to understand that the above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, All should be included within the protection scope of the present invention.

Claims (10)

1. A method for preparing parts based on subregional multistage cryogenic treatment, characterized in that the method may further comprise the steps: (1) Obtain the microstructure-property evolution law of the material composition of the target part through multi-stage cryogenic treatment experiments, and establish the non-linear mapping relationship between multi-stage cryogenic process parameters-microstructure-property through neural network; (2) Divide the target part into multiple sub-regions according to the distribution requirements of the target part's organization and performance, and deduce the multi-level cryogenic treatment parameters required for each sub-region by combining the obtained nonlinear mapping relationship, and then obtain the multi-level cryogenic treatment target part A series of cryogenic process parameter distribution combinations corresponding to the inside of the target part; (3) Combining the obtained cryogenic process parameter distribution combination to carry out high-throughput numerical simulation on the partitioned multi-stage cryogenic treatment process, the temperature distribution or microstructure distribution in the numerical simulation is closest to the cryogenic treatment corresponding to the microstructure and performance distribution requirements of the target part The combination of process parameters is determined as the best combination of cryogenic treatment parameters for different regions inside the target part; (4) Combining the geometrically divided regions of the target part and the optimal cryogenic treatment parameter combination to perform partitioned and independent cryogenic treatment on the part to be treated to obtain the target part. 2. The part preparation method based on partitioned multi-stage cryogenic treatment as claimed in claim 1, characterized in that: the acquisition of the organization-performance evolution law of the material composition of the target part comprises the following steps: taking the material of the target part as the initial experimental object , within the pre-set range of cryogenic treatment parameters, an orthogonal experiment or a single factor test is set up, and the initial test object is subjected to multi-stage cryogenic treatment under different cryogenic treatment process parameters, and the microstructure of the sample obtained from the cryogenic treatment is analyzed. Characterization and performance testing, to obtain microstructure characteristic parameters and performance data in samples under different cryogenic treatment parameters, and then obtain a multi-level cryogenic treatment process parameter-microstructure-performance database. 3. The method for preparing parts based on partitioned multi-stage cryogenic treatment as claimed in claim 2, characterized in that: multi-stage cryogenic treatment refers to the process of several times of heat preservation and heating and cooling processes from the initial treatment temperature to the lowest treatment temperature. Process; microstructure characteristic parameters include one or more of grain size, dislocation density, retained austenite volume fraction, carbide phase size and volume fraction; the multi-stage cryogenic treatment process parameters include temperature, heat preservation One or more of time, heating and cooling rate, and processing times. 4. The method for preparing parts based on partitioned multi-stage cryogenic treatment as claimed in claim 1, characterized in that: the established multi-stage cryogenic process parameters-microstructure-performance nonlinear mapping relationship includes cryogenic treatment process parameters -Microstructure relationship model, microstructure-performance relationship model, and cryogenic treatment process parameter-performance relationship model. 5. The method for preparing parts based on partitioned multi-stage cryogenic treatment as claimed in claim 4, wherein: each relational model is described by a BP neural network model containing a plurality of hidden layers, wherein the cryogenic process parameter- The establishment of the nonlinear mapping relationship of the microstructure includes the following steps: Taking the set of cryogenic process parameters obtained from multi-level cryogenic treatment experiments as input, and the obtained set of microstructure characteristic parameters as output, a BP neural network model with multiple hidden layers is constructed and trained, and each hidden layer and output layer selects an appropriate activation function; optimizes the initial weights and thresholds of the BP neural network using a genetic algorithm to obtain the weights and thresholds of the optimal individual; assigns the weights and thresholds of the obtained optimal individual to the BP neural network In the network model, the error backpropagation algorithm is used to update the weights and thresholds of each hidden layer during the training process, until the cost function J is less than the set accuracy or reaches the maximum number of iterations, then the training ends. 6. The part preparation method based on partitioned multi-stage cryogenic treatment as claimed in claim 5, characterized in that: the activation function of the hidden layer is selected from the logistic function, and the activation function of the output layer is selected from the linear function g(x)=x. 7. The method for preparing parts based on subregional multistage cryogenic treatment as claimed in claim 5, characterized in that: adopt genetic algorithm to optimize the initial weight and threshold of the BP neural network, specifically comprising the steps of: S1: First, determine the number of weights and thresholds of the neural network according to the topological diagram of the BP neural network model, following the following formula:

Where N _um is the total number of weights and thresholds, i represents the i-th layer of neurons, H _i is the number of nodes of the i-th layer of neurons; S2: Use the real number encoding method to encode the weight threshold of the neural network, initialize the population, the initial weight threshold is randomly selected between (-1,1), and set the fitness function of the population to F1;

Among them, F1 is the fitness value, ρ1 is the enhanced phase component predicted by the neural network using the initial weight and threshold, η1 is the enhanced phase volume fraction predicted by the neural network using the initial weight and threshold, and δ1 is the initial weight The average size of the enhanced phase predicted by the neural network of value and threshold, _ρ0 is the expected value of the enhanced phase composition, _η0 is the expected value of the volume fraction of the enhanced phase, and _δ0 is the expected value of the average size of the enhanced phase; S3: Calculate the fitness value of all individuals in the population, and use the roulette algorithm to select individuals with high fitness from the parent generation to generate the next generation of individuals. The probability of each individual being selected follows the following formula:

Among them, p _k is the probability that the kth individual is selected, F _k is the fitness value of the kth individual, and K is the total number of individuals in the population; S4: Perform a crossover operation on the individuals in the population, set the crossover probability as pc, generate a random number that is less than the crossover probability, then perform the crossover operation, randomly select two individuals and randomly select the crossover position during the crossover, and perform crossover according to the following formula operate:

Among them, a _kj is the real number of the k-th chromosome at the j-position, a _lj is the real number of the l-th chromosome at the j-position, and b is a random number between (0,1); S5: Perform a mutation operation on the individuals in the population, set the mutation probability to pm, generate a random number that is less than the mutation probability, then perform the mutation operation, randomly select an individual and randomly select the mutation bit during mutation, and perform the mutation operation according to the following formula :

Among them, a _ij is the real number of the i-th chromosome at position j, g is the current iteration number, f(g) is the variation factor, G _max is the maximum iteration number, a _max is the upper limit of the value of a _ij , and a _min is The lower limit of the value of a _ij , r and r' are random numbers between (0,1); S6: Repeat steps S3 to S5 until a satisfactory fitness value is obtained or a limited number of iterations is reached, and the optimal individual is output, that is, the individual with the largest fitness value. 8. A parts preparation equipment based on partitioned multi-stage cryogenic treatment, characterized in that: said equipment adopts the part preparation method based on partitioned multi-stage cryogenic treatment according to any one of claims 1-7 to prepare parts; The above equipment is formed with a plurality of independent cryogenic treatment chambers, and each cryogenic treatment chamber can independently perform cryogenic treatment on a section of the part to be prepared. 9. The parts preparation equipment based on partitioned multi-stage cryogenic treatment as claimed in claim 8, characterized in that: each cryogenic treatment chamber is provided with independent resistance heating wires, temperature sensors, cooling medium inlet and outlet pipes and moving mechanisms , enabling each cryogenic chamber to work independently to cryogenically treat a localized area of the target part. 10. The parts preparation equipment based on partitioned multi-stage cryogenic treatment as claimed in claim 9, characterized in that: the equipment also includes a plurality of heat insulation boards, and the equipment is formed with a storage chamber, and a plurality of the heat insulation plates Plates are arranged at intervals in the storage cavity to divide the storage cavity into a plurality of independent cryogenic treatment chambers; the heat insulation plates are also used to carry the parts to be prepared; each heat insulation plate is connected to a The moving mechanism is used to drive the heat shield to move, so as to change the space size of the cryogenic treatment chamber on both sides of the heat shield.

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