http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114707398-A
Outgoing Links
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|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_64c08dbbd66b52c9a43aa3682ae4aafe |
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| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F17-11 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-27 |
| classificationIPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F111-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F119-08 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F30-27 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F17-11 |
| filingDate | 2022-02-22^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3a3ba8f1c0cd245073a2d33ac60a66b2 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f7b3d8b18853866abe32833029fcc22e http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_81a3486d5219f91228b125c4c494bc5d http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_443ae597ba16c355264e7c1ebee23f23 |
| publicationDate | 2022-07-05^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-114707398-A |
| titleOfInvention | A method for predicting creep properties of metal structural materials based on hard-constrained neural network model |
| abstract | The invention discloses a method for predicting the creep performance of metal structural materials based on a hard-constrained neural network model. The steps include: S1, establishing constraints of the first derivative and second derivative of the creep strength creep life curve; S2, combining the constraints, establishing a hard-constrained neural network model, including the establishment of the network structure and the derivation of the network structure, constraints Loss function, etc.; S3, set the structure of the hard-constrained neural network model, input and output parameters, training methods, etc., fit the experimental data, get the fitting results and prediction results, and compare with the experimental data; S4, the final analysis is obtained the accuracy of the results. The method of the invention can be used to predict the long-term creep properties of most commercial austenitic stainless steels, nickel-based alloys, high-chromium steels, and high-temperature alloys currently in the research and development stage, and the results are stable and reliable. |
| priorityDate | 2022-02-22^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
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|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID412550040 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID935 |
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