CN103352192A - Method for designing single-crystal superalloy solid solution system - Google Patents
Method for designing single-crystal superalloy solid solution system Download PDFInfo
- Publication number
- CN103352192A CN103352192A CN201310289294XA CN201310289294A CN103352192A CN 103352192 A CN103352192 A CN 103352192A CN 201310289294X A CN201310289294X A CN 201310289294XA CN 201310289294 A CN201310289294 A CN 201310289294A CN 103352192 A CN103352192 A CN 103352192A
- Authority
- CN
- China
- Prior art keywords
- temperature
- solid solution
- phase solvent
- initial melting
- solution system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 229910000601 superalloy Inorganic materials 0.000 title claims abstract description 60
- 239000006104 solid solution Substances 0.000 title claims abstract description 51
- 239000013078 crystal Substances 0.000 title claims abstract description 50
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000002844 melting Methods 0.000 claims abstract description 77
- 230000008018 melting Effects 0.000 claims abstract description 76
- 238000013528 artificial neural network Methods 0.000 claims abstract description 39
- 238000010438 heat treatment Methods 0.000 claims abstract description 26
- 239000002904 solvent Substances 0.000 claims description 41
- 230000008014 freezing Effects 0.000 claims description 19
- 238000007710 freezing Methods 0.000 claims description 19
- 239000011159 matrix material Substances 0.000 claims description 16
- 210000002569 neuron Anatomy 0.000 claims description 13
- 230000006870 function Effects 0.000 claims description 8
- 229910052750 molybdenum Inorganic materials 0.000 claims description 2
- 229910052758 niobium Inorganic materials 0.000 claims description 2
- 229910052707 ruthenium Inorganic materials 0.000 claims description 2
- 229910052715 tantalum Inorganic materials 0.000 claims description 2
- 229910052719 titanium Inorganic materials 0.000 claims description 2
- 229910052759 nickel Inorganic materials 0.000 claims 1
- 229910045601 alloy Inorganic materials 0.000 abstract description 14
- 239000000956 alloy Substances 0.000 abstract description 14
- 239000007787 solid Substances 0.000 abstract description 4
- 238000009776 industrial production Methods 0.000 abstract description 3
- 238000007711 solidification Methods 0.000 abstract description 2
- 230000008023 solidification Effects 0.000 abstract description 2
- 239000000203 mixture Substances 0.000 description 12
- 238000005275 alloying Methods 0.000 description 8
- 239000000470 constituent Substances 0.000 description 6
- 230000009897 systematic effect Effects 0.000 description 4
- 229910000995 CMSX-10 Inorganic materials 0.000 description 3
- 238000007669 thermal treatment Methods 0.000 description 3
- 238000005266 casting Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000010587 phase diagram Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 229910001011 CMSX-4 Inorganic materials 0.000 description 1
- 229910001027 TMS-162 Inorganic materials 0.000 description 1
- 229910052782 aluminium Inorganic materials 0.000 description 1
- 230000003078 antioxidant effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002051 biphasic effect Effects 0.000 description 1
- 210000001787 dendrite Anatomy 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000155 melt Substances 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 229910052702 rhenium Inorganic materials 0.000 description 1
- 229910052721 tungsten Inorganic materials 0.000 description 1
Images
Landscapes
- Crystals, And After-Treatments Of Crystals (AREA)
Abstract
The invention discloses a method for designing a single-crystal superalloy solid solution system. The method for designing the single-crystal superalloy solid solution system comprises the following steps: firstly, through a database of characteristic temperatures of single-crystal superalloys with different components, training a Back-Propagation artificial neural network; secondly, inputting an alloy component to be measured into the trained artificial neural network, calculating to obtain a solidus temperature, a liquidus temperature, a solidification interval, a second phase melting temperature, a low-melting point initial melting temperature and a heat-treatment initial melting temperature; finally, according to the above characteristic temperatures, designing the solid solution system. The input end of the trained neural network covers most of the component range of a superalloy; by the method, whether the superalloy can be dissolved in a solid state can be quickly determined and the solid solution system is accurately designed; the method can be used for guiding the design and the industrial production of the superalloy.
Description
Technical field
The present invention relates to design the nonlinear method of single crystal super alloy solid solution system, particularly a kind of method of the design single crystal super alloy solid solution system based on artificial neural network.
Background technology
Single crystal super alloy is owing to having higher warm ability, outstanding military service performance and the good antioxidant property of holding, and is the advanced engine preferred material of holding warm turbine blade at present and in the future.Because more and more higher to the requirement of use temperature, the alloying consumption of refractory element such as W, Mo, Re, Ru etc. is more and more large, the refractory element total amount is about 14w.t.% in the first-generation single crystal super alloy, brings up to approximately 20w.t.% during to the third generation such as CMSX-10.Simultaneously, the use of the second-phase forming elements such as Al, Ti, Ta, Nb is also near solid solubility limit.Also brought thus following shortcoming: solid solubility temperature is too high, and low melting point phase initial melting temperature is excessively low, and the solid solution window is narrow, and the solid solution process is slow, and the solid solution system design is difficult even can not realize solid solution.For example, the standard solid solution system of CMSX-10 is from 1315 ℃ of ladder-elevating temperatures to 1365 ℃, and 50 ℃ of temperature spans reach more than 40 hour when total.This shows, realize judging fast that can superalloy solid solution and Exact Design solid solution system, for superalloy design and industrial production important in inhibiting.
And traditional high temperature alloy research method, generally to pass through on alloy designs, mother alloy melting, single crystal casting, first fusing point metallographic, the solid solubility temperature roll off the production line, solid solution system design and adjustment, just can obtain the solid solution system.An entire flow generally needs half a year even time more of a specified duration.The situation that material can not be realized solid solution inevitably appears in the conventional alloys method of design simultaneously, causes unnecessary loss.
Simultaneously, some linear systems that are used for simulating heterogeneity superalloy performance are developed, but along with the alloy element range extension, Alloying Amount is near the superalloy design limit, and its composition and performance more show nonlinear feature.Major cause is that single crystal super alloy is γ-γ ' biphasic system, all alloy elements influence each other in two-phase, so that each element distribution constant all changes, cause linear system interalloy element prematrix to occur obviously to change and the increase simulation error.So for the wide variation of composition, the nonlinear system simulation result is closing to reality more, it is more accurate to judge.In non-linear system, the Back-Propagation artificial neural network technology is the most ripe, and compatible and error shows more outstanding.
Summary of the invention
The objective of the invention is to realize to judge fast that can superalloy solid solution and Exact Design solid solution system, propose a kind of method of the design single crystal super alloy solid solution system based on artificial neural network.
The invention provides a kind of novel method of the single crystal super alloy solid solution system design based on artificial neural network, pass through first the characteristic temperature database of heterogeneity single crystal super alloy, training Back-Propagation artificial neural network, afterwards, the artificial neural network of having trained is inputted alloying constituent to be measured, calculate characteristic temperature and design the solid solution system.
Here the characteristic temperature of selecting is solidus temperature, liquidus temperature, freezing range, second-phase solvent temperature, low melting point phase initial melting temperature and heat treatment state initial melting temperature.Wherein, solidus temperature is the temperature that begins to melt on the single crystal alloy equilibrium phase diagram; Liquidus temperature is the temperature that melts fully on the single crystal alloy equilibrium phase diagram; Dendrite form nuclear temperature was to the temperature head of interdendritic complete solidification temperature when the freezing range was single crystal casting; The second-phase solvent temperature is the solvent temperature of coherence γ ' in the single crystal super alloy; Low melting point phase initial melting temperature is the fusing point that the low melting point phase that forms latter stage is solidified in the interdendritic; The heat treatment state initial melting temperature is the initial melting temperature of alloy behind the elimination as cast condition microsegregation.
These solid solution system design method concrete steps are as follows:
Step 1, set up the database of the single crystal super alloy characteristic temperature of following composition range (w.t.%):
| W | 0～8% | Mo | 0～16% | Ta | 0～9% |
| Al | 4～8.5% | Ti | 0～4% | Nb | 0～2% |
| Re | 0～7% | Ru | 0～3% | Co | 0～10% |
| Cr | 0～10% | Y | 0～0.1% | Ni | bal. |
Wherein, " bal. " is the weight percentage of Ni element, and its numerical value is 100% to deduct other element wt per-cent sums.
Select artificial neural network to be input as the single crystal super alloy composition, be output as solidus temperature, liquidus temperature, freezing range, second-phase solvent temperature, low melting point phase initial melting temperature and heat treatment state initial melting temperature.Setting up artificial neural network is the Back-Propagation neural network, adopts three-decker, comprises input layer, hidden layer and output layer; The input layer number is 12, and the output layer neuron number is 6,14 of hidden layer neuron numbers, and input layer and output layer neurone use the purelin function representation, and hidden layer uses the log-sigmoid function representation.
Wherein, t
kBe the value of learning sample, y
kBe the output of artificial neural network output layer, k is number of training.
Design requirements is that the training objective error is e＜0.001.When output error satisfied the target error of setting requirement, training stopped, and preserved weight matrix and threshold values matrix.
Step 3: utilize the artificial neural network of having trained in the step 2, input alloying constituent to be measured, calculate the characteristic temperature of this alloy to be measured, and design superalloy solid solution system.Concrete grammar is as follows:
Situation one: when the second-phase solvent temperature〉during the heat treatment state initial melting temperature, single crystal super alloy can not solid solution;
Situation two: when second-phase solvent temperature≤heat treatment state initial melting temperature and low melting point phase initial melting temperature〉second-phase solvent temperature and freezing range〉25 ℃ times, single crystal super alloy solid solution system is: second-phase solvent temperature/16h;
Situation three: when second-phase solvent temperature≤heat treatment state initial melting temperature and low melting point phase initial melting temperature〉when second-phase solvent temperature and freezing range≤25 ℃, single crystal super alloy solid solution system is: second-phase solvent temperature/10h;
Situation four: when second-phase solvent temperature≤heat treatment state initial melting temperature and low melting point phase initial melting temperature≤second-phase solvent temperature and freezing range〉25 ℃ times, single crystal super alloy solid solution system is: low melting point phase initial melting temperature/4h+ second-phase solvent temperature/16h;
Situation five: when second-phase solvent temperature≤heat treatment state initial melting temperature and low melting point phase initial melting temperature≤second-phase solvent temperature and freezing range≤25 ℃, single crystal super alloy solid solution system is: low melting point phase initial melting temperature/4h+ second-phase solvent temperature/10h;
The invention has the beneficial effects as follows:
The artificial neural network input terminus of the present invention's training has been contained most alloying constituent and content range that superalloy adopts, realized that can quick judgement single crystal super alloy solid solution and Exact Design solid solution system, can be used for instructing single crystal super alloy design and industrial production.
Description of drawings
Fig. 1 is solid solution system design method flow diagram provided by the invention;
Fig. 2 is the scatter diagram that systematic error changes with the variation of hidden layer neuron number among the embodiment, and an error hour hidden layer neuron number is 14;
Fig. 3 a is the as-cast structure pattern of single crystal super alloy described in the embodiment 1;
Fig. 3 b is the microstructure morphology of the heat treatment state of single crystal super alloy described in the embodiment 1;
Fig. 3 c is the as-cast structure pattern of single crystal super alloy among the embodiment 2;
Fig. 3 d is the heat treatment state microstructure morphology of single crystal super alloy among the embodiment 2;
Fig. 3 e is the as-cast structure pattern of single crystal super alloy among the embodiment 3;
Fig. 3 f is the heat treatment state microstructure morphology of single crystal super alloy among the embodiment 3.
Embodiment
The present invention is further described below in conjunction with drawings and Examples.
Embodiment 1
Adopt method of design provided by the invention to carry out single crystal super alloy solid solution system design, concrete steps are as follows:
Step 1, set up the database of the single crystal super alloy characteristic temperature of following composition range (w.t.%):
| W | 0～8% | Mo | 0～16% | Ta | 0～9% |
| Al | 4～8.5% | Ti | 0～4% | Nb | 0～2% |
| Re | 0～7% | Ru | 0～3% | Co | 0～10% |
| Cr | 0～10% | Y | 0～0.1% | Ni | bal. |
Select artificial neural network to be input as the single crystal super alloy composition, be output as solidus temperature, liquidus temperature, freezing range, second-phase solvent temperature, low melting point phase initial melting temperature and heat treatment state initial melting temperature.Setting up artificial neural network is the Back-Propagation neural network, adopts three-decker, comprises input layer, hidden layer and output layer; The input layer number is 12, and the output layer neuron number is 6,14 of hidden layer neuron numbers.Input layer and output layer neurone use the purelin function representation, and hidden layer uses the log-sigmoid function representation.
This hidden layer neuron number selection principle is: systematic error minimizes.Systematic error is that the artificial neural network frequency of training is greater than the error amount of 1000 back balance systems.As shown in Figure 2, the artificial neural network that obtains for single crystal super alloy characteristic temperature database training is when the hidden layer neuron number is 14() time, systematic error is minimum, and therefore selecting the hidden layer neuron number is 14.
Step 3: utilize the artificial neural network of having trained in the step 2, input following alloying constituent to be measured:
| W | 0% | Mo | 11% | Ta | 6% |
| Al | 7.6% | Ti | 0% | Nb | 0% |
| Re | 3% | Ru | 0% | |
5% |
| Cr | 0% | Y | 0% | Ni | 67.4% |
The characteristic temperature that calculates this composition is 1360 ℃ of 1389 ℃ of solidus temperatures, 1411.2 ℃ of liquidus temperatures, 22.2 ℃ of freezing ranges, 1371.2 ℃ of second-phase solvent temperatures, 1351 ℃ of low melting point phase initial melting temperatures and heat treatment state initial melting temperatures.Can obtain 1371.2 ℃ of second-phase solvent temperatures〉1360 ℃ of heat treatment state initial melting temperatures, namely meet situation one, alloy can't be realized solid solution.Experimental result as shown in Figure 3, as-cast structure is seen Fig. 3 a, 1360 ℃/50 hours microtextures of thermal treatment are seen Fig. 3 b, not solid solution.Experimental result conforms to design solid solution system.
Step 1, set up the database of the single crystal super alloy characteristic temperature of following composition range (w.t.%):
| W | 0～8% | Mo | 0～16% | Ta | 0～9% |
| Al | 4～8.5% | Ti | 0～4% | Nb | 0～2% |
| Re | 0～7% | Ru | 0～3% | Co | 0～10% |
| Cr | 0～10% | Y | 0～0.1% | Ni | bal. |
Select artificial nerve network model to be input as the single crystal super alloy composition, be output as solidus temperature, liquidus temperature, freezing range, second-phase solvent temperature, low melting point phase initial melting temperature and heat treatment state initial melting temperature.Setting up artificial neural network is the Back-Propagation neural network, adopts three-decker, comprises input layer, hidden layer and output layer; The input layer number is 12, and the output layer neuron number is 6,14 of hidden layer neuron numbers, and input layer and output layer neurone use the purelin function representation, and hidden layer uses the log-sigmoid function representation.
Step 3: utilize the artificial neural network of having trained in the step 2, input following alloying constituent to be measured:
| |
5% | |
2% | Ta | 6% |
| Al | 7.6% | Ti | 1% | Nb | 0% |
| Re | 1.5% | Ru | 0% | Co | 0% |
| Cr | 0% | Y | 0% | Ni | 76.9% |
The characteristic temperature that calculates this alloy to be measured is 1345 ℃ of 1355.3 ℃ of solidus temperatures, 1391.2 ℃ of liquidus temperatures, 35 ℃ of freezing ranges, 1336.6 ℃ of second-phase solvent temperatures, 1333 ℃ of low melting point phase initial melting temperatures and heat treatment state initial melting temperatures.There is following relation:
1345 ℃ of 1336.6 ℃≤heat treatment state of second-phase solvent temperature initial melting temperatures;
1336.6 ℃ of 1333 ℃≤second-phase of low melting point phase initial melting temperature solvent temperatures;
The freezing range〉25 ℃
Namely meet situation four, single crystal super alloy solid solution system to be measured is: 1333 ℃/4h+1336.6/16h.As-cast structure is seen Fig. 3 c, and 1333 ℃/4h+1336.6/16h thermal treatment microtexture is seen Fig. 3 d, realizes solid solution.Experimental result conforms to the design result of solid solution system of the present invention.
Embodiment 3
The present embodiment and embodiment 1,2 difference only exist: step 3: utilize the artificial neural network of having trained in the step 2, input following target component:
| W | 0% | Mo | 9.5% | Ta | 3% |
| Al | 7.8% | Ti | 0% | Nb | 0% |
| Re | 1.5% | Ru | 0% | Co | 0% |
| Cr | 1.5% | Y | 0.05% | Ni | 76.65% |
The characteristic temperature that calculates this composition is 1352 ℃ of 1371.7 ℃ of solidus temperatures, 1401.8 ℃ of liquidus temperatures, 30 ℃ of freezing ranges, 1326.1 ℃ of second-phase solvent temperatures, 1330 ℃ of low melting point phase initial melting temperatures and heat treatment state initial melting temperatures.There is following relation:
1352 ℃ of 1326.1 ℃≤heat treatment state of second-phase solvent temperature initial melting temperatures;
1330 ℃ of low melting point phase initial melting temperatures〉1326.1 ℃ of second-phase solvent temperatures;
The freezing range〉25 ℃,
Namely meet situation two, superalloy solid solution system is: 1326.1 ℃/16h.As-cast structure is seen Fig. 3 e, and 1327 ℃/16h thermal treatment microtexture is seen Fig. 3 f, realizes solid solution.Experimental result conforms to the design result of solid solution system of the present invention.
The A that relates in the specification sheets ℃/Bh is illustrated under the A temperature and is incubated B hour.
The single crystal super alloy characteristic temperature that is used for the training of human artificial neural networks derives from published or certified single crystal super alloy characteristic temperature data, and the predictable single crystal super alloy composition range of artificial neural network is the published or certified high temperature alloy composition scope of typing learning sample.For example, Re element published or confirmed that superalloy addition (w.t.%) is 0(trade mark PWA1480), 0(trade mark SRR99), 0(trade mark CMSX-6), 1.5(IC27 confirms), 1.5(IC21 confirms), 3(trade mark PWA1484), 3(trade mark ReneN5), 3(trade mark CMSX-4), 4(trade mark MC-NG), 5(trade mark ReneN6), 5(trade mark TMS-75), 5(trade mark TMS-162), 7(trade mark CMSX-10) etc., then the predictable Re elemental range of artificial neural network is 0～7(w.t.%).In like manner obtain the predictable scope of other elements, this predictable range has contained most alloying constituent and content range that superalloy adopts.
Claims (7)
1. method that designs single crystal super alloy solid solution system, it is characterized in that: the method may further comprise the steps:
Step 1: set up the database of the single crystal super alloy characteristic temperature of heterogeneity, set up artificial neural network;
Step 2: from database, select sample as learning sample, the training of human artificial neural networks, when the satisfied setting of output error required, training stopped, and preserves weight matrix and threshold values matrix;
Step 3: utilize the artificial neural network of having trained in the step 2, obtain single crystal super alloy characteristic temperature to be measured and design the solid solution system.
2. method according to claim 1, it is characterized in that: the scope of heterogeneity is in the step 1:
W(w.t.%) 0～8% Mo(w.t.%) 0～16% Ta(w.t.%) 0～9%
Al(w.t.%) 4～8.5% Ti(w.t.%) 0～4% Nb(w.t.%) 0～2%
Re(w.t.%) 0～7% Ru(w.t.%) 0～3% Co(w.t.%) 0～10%
Cr(w.t.%) 0～10% Y(w.t.%) 0～0.1% Ni(w.t.%) bal。
3. method according to claim 1, it is characterized in that: the described characteristic temperature of step 1 is solidus temperature, liquidus temperature, freezing range, second-phase solvent temperature, low melting point phase initial melting temperature and heat treatment state initial melting temperature.
4. method according to claim 1, it is characterized in that: the selected artificial neural network of step 1 is the Back-Propagation neural network, adopts three-decker, comprises input layer, hidden layer and output layer; The input layer number is 12, and the output layer neuron number is 6,14 of hidden layer neuron numbers, and input layer and output layer neurone use the purelin function representation, and hidden layer uses the log-sigmoid function representation.
5. method according to claim 1 is characterized in that: step 2 learning sample quantity is for greater than 300.
6. method according to claim 1, it is characterized in that: step 2 training of human artificial neural networks use error back-propagation algorithm, the artificial neural network initialization condition is: (0,0.5) random assignment is to weight matrix, (0,1) random assignment is to the threshold values matrix; The training objective error e is set and is required to be e＜0.001.
7. method according to claim 1 is characterized in that: the described design solid solution of step 3 system, specific as follows:
Situation one: when the second-phase solvent temperature〉during the heat treatment state initial melting temperature, superalloy can not solid solution;
Situation two: when second-phase solvent temperature≤heat treatment state initial melting temperature and low melting point phase initial melting temperature〉second-phase solvent temperature and freezing range〉25 ℃ times, single crystal super alloy solid solution system is: second-phase solvent temperature/16h;
Situation three: when second-phase solvent temperature≤heat treatment state initial melting temperature and low melting point phase initial melting temperature〉when second-phase solvent temperature and freezing range≤25 ℃, single crystal super alloy solid solution system is: second-phase solvent temperature/10h;
Situation four: when second-phase solvent temperature≤heat treatment state initial melting temperature and low melting point phase initial melting temperature≤second-phase solvent temperature and freezing range〉25 ℃ times, single crystal super alloy solid solution system is: low melting point phase initial melting temperature/4h+ second-phase solvent temperature/16h;
Situation five: when second-phase solvent temperature≤heat treatment state initial melting temperature and low melting point phase initial melting temperature≤second-phase solvent temperature and freezing range≤25 ℃, single crystal super alloy solid solution system is: low melting point phase initial melting temperature/4h+ second-phase solvent temperature/10h.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310289294.XA CN103352192B (en) | 2013-07-11 | 2013-07-11 | Method for designing single-crystal superalloy solid solution system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310289294.XA CN103352192B (en) | 2013-07-11 | 2013-07-11 | Method for designing single-crystal superalloy solid solution system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103352192A true CN103352192A (en) | 2013-10-16 |
| CN103352192B CN103352192B (en) | 2014-10-22 |
Family
ID=49308602
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310289294.XA Active CN103352192B (en) | 2013-07-11 | 2013-07-11 | Method for designing single-crystal superalloy solid solution system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103352192B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104975248A (en) * | 2015-06-30 | 2015-10-14 | 西北工业大学 | Solution treatment method of third generation nickel-base single crystal high temperature alloy |
| CN105806878A (en) * | 2016-03-08 | 2016-07-27 | 西北工业大学 | Method for determining initial melting temperature of nickel-based superalloy |
| CN111074332A (en) * | 2019-12-31 | 2020-04-28 | 东莞材料基因高等理工研究院 | Heat treatment method for rapidly eliminating microsegregation in single crystal high-temperature alloy |
| CN111640472A (en) * | 2020-06-23 | 2020-09-08 | 清华大学 | Solid solution heat treatment simulation method of nickel-based single crystal superalloy |
| CN111763897A (en) * | 2020-07-24 | 2020-10-13 | 中国航发北京航空材料研究院 | Method for determining homogenization/solution heat treatment schedule of nickel-based single crystal superalloy |
| US11676009B2 (en) | 2019-10-04 | 2023-06-13 | Raytheon Technologies Corporation | Machine learning based rotor alloy design system |
| US11859267B2 (en) | 2016-10-12 | 2024-01-02 | Oxford University Innovation Limited | Nickel-based alloy |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09259156A (en) * | 1996-03-21 | 1997-10-03 | Hitachi Ltd | Material designing method and material designing device for performing the same method |
| CN102663498A (en) * | 2012-04-28 | 2012-09-12 | 武汉大学 | Method for forecasting Ac1 point of martensite refractory-steel weld metal with 9 percent of Cr |
-
2013
- 2013-07-11 CN CN201310289294.XA patent/CN103352192B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09259156A (en) * | 1996-03-21 | 1997-10-03 | Hitachi Ltd | Material designing method and material designing device for performing the same method |
| CN102663498A (en) * | 2012-04-28 | 2012-09-12 | 武汉大学 | Method for forecasting Ac1 point of martensite refractory-steel weld metal with 9 percent of Cr |
Non-Patent Citations (1)
| Title |
|---|
| 郭鹏: "基于神经网络的高温合金组织性能分析", 《西北工业大学硕士学位论文》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104975248A (en) * | 2015-06-30 | 2015-10-14 | 西北工业大学 | Solution treatment method of third generation nickel-base single crystal high temperature alloy |
| CN104975248B (en) * | 2015-06-30 | 2017-01-25 | 西北工业大学 | Solution treatment method of third generation nickel-base single crystal high temperature alloy |
| CN105806878A (en) * | 2016-03-08 | 2016-07-27 | 西北工业大学 | Method for determining initial melting temperature of nickel-based superalloy |
| CN105806878B (en) * | 2016-03-08 | 2018-11-06 | 西北工业大学 | A method of measuring nickel base superalloy initial melting temperature |
| US11859267B2 (en) | 2016-10-12 | 2024-01-02 | Oxford University Innovation Limited | Nickel-based alloy |
| US11676009B2 (en) | 2019-10-04 | 2023-06-13 | Raytheon Technologies Corporation | Machine learning based rotor alloy design system |
| CN111074332A (en) * | 2019-12-31 | 2020-04-28 | 东莞材料基因高等理工研究院 | Heat treatment method for rapidly eliminating microsegregation in single crystal high-temperature alloy |
| CN111640472A (en) * | 2020-06-23 | 2020-09-08 | 清华大学 | Solid solution heat treatment simulation method of nickel-based single crystal superalloy |
| CN111763897A (en) * | 2020-07-24 | 2020-10-13 | 中国航发北京航空材料研究院 | Method for determining homogenization/solution heat treatment schedule of nickel-based single crystal superalloy |
| CN111763897B (en) * | 2020-07-24 | 2021-10-15 | 中国航发北京航空材料研究院 | Method for determining homogenization/solution heat treatment schedule of nickel-based single crystal superalloy |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103352192B (en) | 2014-10-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103352192B (en) | Method for designing single-crystal superalloy solid solution system | |
| Scheppe et al. | Nickel aluminides: a step toward industrial application | |
| Rappaz et al. | Probabilistic modelling of microstructure formation in solidification processes | |
| US4853044A (en) | Alloy suitable for making single crystal castings | |
| Giamei | Development of single crystal superalloys: a brief history | |
| Ma et al. | Microstructural evolution in AISI 304 stainless steel during directional solidification and subsequent solid-state transformation | |
| Gu et al. | A three-dimensional cellular automaton model of dendrite growth with stochastic orientation during the solidification in the molten pool of binary alloy | |
| Hurst et al. | The contribution of small punch testing towards the development of materials for aero-engine applications | |
| Walter et al. | Simulation of the solidification of CMSX-4 | |
| Yu et al. | Anomalous overgrowth of converging dendrites during directional solidification | |
| CN111640472B (en) | Solid solution heat treatment simulation method of nickel-based single crystal superalloy | |
| EP2402473A2 (en) | Process for producing a single-crystal component made of a nickel-based superalloy | |
| Hu et al. | Competitive converging dendrites growth depended on dendrite spacing distribution of Ni-based bi-crystal superalloys | |
| CN107904447A (en) | A kind of nickel-base high-temperature single crystal alloy series Nideal2 alloy series and its application | |
| Guo et al. | High temperature creep behavior of a cast polycrystalline nickel-based superalloy K465 under thermal cycling conditions | |
| Yang et al. | Simulation of stray grain formation during single crystal seed melt-back and initial withdrawal in the Ni-base superalloy CMSX4 | |
| Shajari et al. | Effect of solution temperature of rejuvenation heat treatment on the stability of γ′ precipitates in Ni-base superalloy IN738LC during long-term heating | |
| Wang et al. | Effect of directional solidification variables on the microstructures of single‐crystal turbine blades of nickel‐based superalloy | |
| US4345950A (en) | Method for making a composite grained cast article | |
| Zhang et al. | Dendritic growth pattern and dendritic network distortion in the platform of a Ni-based superalloy | |
| JP4088223B2 (en) | Method for predicting susceptibility to solidification cracking of alloy and method for producing alloy with improved susceptibility to solidification cracking | |
| Dong et al. | alpha-Cr precipitation behavior and its effect on high Cr-containing superalloys | |
| Franke et al. | Tertiary dendritic instability in late stage solidification of Ni-based superalloys | |
| Maldini et al. | Modelling creep of single crystal CM186LC alloy under constant and variable loading | |
| Maguin et al. | Finite diffusion microsegregation model applied to multicomponent alloys |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20210115 Address after: 611936 group 4, Baihe community, Lichun Town, Chengdu City, Sichuan Province Patentee after: Pengzhou Hangda New Material Co.,Ltd. Address before: 100191 No. 37, Haidian District, Beijing, Xueyuan Road Patentee before: BEIHANG University |
|
| TR01 | Transfer of patent right | ||
| CP03 | Change of name, title or address |
Address after: 610000 Baihe Community Group 4, Lichun Town, Pengzhou City, Chengdu, Sichuan Province Patentee after: Sichuan Hangda New Materials Co.,Ltd. Country or region after: China Address before: 611936 group 4, Baihe community, Lichun Town, Chengdu City, Sichuan Province Patentee before: Pengzhou Hangda New Material Co.,Ltd. Country or region before: China |
|
| CP03 | Change of name, title or address |