Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285271824> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W4285271824 endingPage "1343" @default.
- W4285271824 startingPage "1330" @default.
- W4285271824 abstract "Deep learning technology has been more and more used in traffic flow prediction, weather prediction, mechanical system fault diagnosis and other fields, and has achieved good results through engineering verification. In this paper, a method of Surface Mounted Technology (SMT) solder joint morphology and reliability prediction based on Gated Recurrent Unit (GRU) neural network is proposed using deep learning as a theoretical guide. Firstly, a large amount of data is obtained by combining finite element simulation and experiment. Then, the three main parameters (peak temperature, cooling rate and solder paste thickness) in welding process are taken as the input characteristics of neural network, and the three main shape parameters (solder joint thickness, solder joint climbing width and solder joint climbing height) after solder joint formation are taken as the output characteristics of neural network, and the shape prediction model of solder joint is established. Then, the reliability prediction model of solder joints is established by taking solder joint morphology parameters as the new input characteristics and the fatigue cycle limit times of solder joints under thermal cycle as the output of the neural network. Compared with the prediction model established by Recurrent Neural Network (RNN) and Long short-term Memory Networks (LSTM), the results show that GRU Neural Network can predict solder joint morphology and reliability more accurately. The training time of the model is also shorter. Through engineering verification, the proposed method has certain reference value for actual production." @default.
- W4285271824 created "2022-07-14" @default.
- W4285271824 creator A5000549161 @default.
- W4285271824 creator A5013015058 @default.
- W4285271824 creator A5019492969 @default.
- W4285271824 creator A5043928475 @default.
- W4285271824 creator A5046732096 @default.
- W4285271824 creator A5049131970 @default.
- W4285271824 creator A5055881641 @default.
- W4285271824 creator A5067183406 @default.
- W4285271824 creator A5075668721 @default.
- W4285271824 creator A5078674724 @default.
- W4285271824 date "2022-01-01" @default.
- W4285271824 modified "2023-09-29" @default.
- W4285271824 title "Fast Prediction Method of SMT Solder Joint Shape and Reliability Based on Gated Recurrent Unit (GRU)" @default.
- W4285271824 cites W2024493895 @default.
- W4285271824 cites W2274616290 @default.
- W4285271824 cites W2345972302 @default.
- W4285271824 cites W2691864041 @default.
- W4285271824 cites W3048271490 @default.
- W4285271824 cites W3083839406 @default.
- W4285271824 cites W3088433393 @default.
- W4285271824 cites W3097923824 @default.
- W4285271824 cites W3127627567 @default.
- W4285271824 doi "https://doi.org/10.1007/978-981-19-1309-9_129" @default.
- W4285271824 hasPublicationYear "2022" @default.
- W4285271824 type Work @default.
- W4285271824 citedByCount "0" @default.
- W4285271824 crossrefType "book-chapter" @default.
- W4285271824 hasAuthorship W4285271824A5000549161 @default.
- W4285271824 hasAuthorship W4285271824A5013015058 @default.
- W4285271824 hasAuthorship W4285271824A5019492969 @default.
- W4285271824 hasAuthorship W4285271824A5043928475 @default.
- W4285271824 hasAuthorship W4285271824A5046732096 @default.
- W4285271824 hasAuthorship W4285271824A5049131970 @default.
- W4285271824 hasAuthorship W4285271824A5055881641 @default.
- W4285271824 hasAuthorship W4285271824A5067183406 @default.
- W4285271824 hasAuthorship W4285271824A5075668721 @default.
- W4285271824 hasAuthorship W4285271824A5078674724 @default.
- W4285271824 hasConcept C108583219 @default.
- W4285271824 hasConcept C121332964 @default.
- W4285271824 hasConcept C127413603 @default.
- W4285271824 hasConcept C154945302 @default.
- W4285271824 hasConcept C163258240 @default.
- W4285271824 hasConcept C18555067 @default.
- W4285271824 hasConcept C191897082 @default.
- W4285271824 hasConcept C192562407 @default.
- W4285271824 hasConcept C41008148 @default.
- W4285271824 hasConcept C43214815 @default.
- W4285271824 hasConcept C50296614 @default.
- W4285271824 hasConcept C50644808 @default.
- W4285271824 hasConcept C62520636 @default.
- W4285271824 hasConcept C66938386 @default.
- W4285271824 hasConceptScore W4285271824C108583219 @default.
- W4285271824 hasConceptScore W4285271824C121332964 @default.
- W4285271824 hasConceptScore W4285271824C127413603 @default.
- W4285271824 hasConceptScore W4285271824C154945302 @default.
- W4285271824 hasConceptScore W4285271824C163258240 @default.
- W4285271824 hasConceptScore W4285271824C18555067 @default.
- W4285271824 hasConceptScore W4285271824C191897082 @default.
- W4285271824 hasConceptScore W4285271824C192562407 @default.
- W4285271824 hasConceptScore W4285271824C41008148 @default.
- W4285271824 hasConceptScore W4285271824C43214815 @default.
- W4285271824 hasConceptScore W4285271824C50296614 @default.
- W4285271824 hasConceptScore W4285271824C50644808 @default.
- W4285271824 hasConceptScore W4285271824C62520636 @default.
- W4285271824 hasConceptScore W4285271824C66938386 @default.
- W4285271824 hasLocation W42852718241 @default.
- W4285271824 hasOpenAccess W4285271824 @default.
- W4285271824 hasPrimaryLocation W42852718241 @default.
- W4285271824 hasRelatedWork W2155264472 @default.
- W4285271824 hasRelatedWork W2319117344 @default.
- W4285271824 hasRelatedWork W2348775318 @default.
- W4285271824 hasRelatedWork W2349054874 @default.
- W4285271824 hasRelatedWork W2359830805 @default.
- W4285271824 hasRelatedWork W2366276408 @default.
- W4285271824 hasRelatedWork W2376378544 @default.
- W4285271824 hasRelatedWork W2809431270 @default.
- W4285271824 hasRelatedWork W2899084033 @default.
- W4285271824 hasRelatedWork W3208844382 @default.
- W4285271824 isParatext "false" @default.
- W4285271824 isRetracted "false" @default.
- W4285271824 workType "book-chapter" @default.