| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_4f9d4a007fac37228f1455e94c2ccb9c |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F2113-22
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F2119-08 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-5866
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-583
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/B22D17-2218
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-048
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/B22D17-32
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-27
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-049 |
| classificationIPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F119-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F113-22 |
| classificationIPCInventive |
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/G06F16-58
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/B22D17-32
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/B22D17-22
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-583
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate |
2021-09-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_74e90e93165ea047e7f1efe9fd8ec9ba
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e099e48c910340c83e82b44ad13f2f60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2f7024b0363a92a081343da54b298e3d
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_013015c855ea1c988bd06d0f97ef2003 |
| publicationDate |
2022-01-04-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-113887133-A |
| titleOfInvention |
An automatic cooling method of die casting system based on deep learning |
| abstract |
The invention discloses an automatic cooling method for a die casting system based on deep learning. Through the introduction of convolutional neural network (CNN) and deep learning, feature extraction is performed on the thermal image of the mold surface after the metal liquid solidifies to form a product and the thermal image of the mold surface after the cooling system is completed, so that the original input and output of the cooling system can be extracted. From the image form to the mathematical matrix form, the loss function can realize the calculation of the input and output images, and then obtain the ideal values of water flow and water flow time. In the present invention, the CNN neural network is used to determine the amount of water passing through the cooling system and the water passing time to achieve the ideal cooling effect. Compared with the empirical method, the method of the present invention is more flexible in the adjustment process in actual production; The purpose of controlling the cooling system can also achieve the goal of extending the service life of the mold and reducing the processing error. |
| priorityDate |
2021-09-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |