http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113361209-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_2f62d1751e2557b38a9aa3cd4cf98a8b |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2411 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-086 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-27 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F30-27 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate | 2021-07-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_af0f1fd3a9390c73cdd6e9fa8b833590 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c4386dbc5ff29da8f260735ad9c71154 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ca59b2942f8026f5c75664d3368a18d5 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8e9ecb47264d8b3975d9c7f0c1ad2493 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_29e7eafc7f3a248d1f591ae1d00cd962 |
| publicationDate | 2021-09-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-113361209-A |
| titleOfInvention | A Quantitative Analysis Method for Magnetic Anomaly of Superalloy Surface Defects |
| abstract | The invention discloses a method for quantitative analysis of magnetic anomalies on the surface of superalloy surface defects. In a natural geomagnetic field environment, a weak magnetic detection instrument is used to scan the surface or near-surface of the superalloy to collect changes in the magnetic induction in the direction perpendicular to the surface of the test piece. And carry out data processing, take the eigenvalue of the defect magnetic anomaly signal as the input value, and the length, width and depth parameter values of the corresponding defect as the output value to train the support vector machine model, establish the mapping relationship with the defect parameters, and use the K-fold cross-validation method. The parameters of the support vector machine kernel function are optimized with the genetic algorithm, and the defect prediction model is established by using the optimized parameters, which improves the accuracy of defect inversion and realizes the quantitative analysis of superalloy surface defects without additional excitation source. |
| priorityDate | 2021-07-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID104727 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419583196 |
Total number of triples: 22.