FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4280492023> ?p ?o ?g. }

• W4280492023 endingPage "114553" @default.
• W4280492023 startingPage "114553" @default.
• W4280492023 abstract "Board-level drop responses are critical to evaluate the mechanical reliability of solder joints to serve as electrical and mechanical connections in electronic devices to resist failure due to drop impact. A machine learning (ML) model based on the back propagation (BP) method is proposed for predicting three-dimensional board-level drop responses of the ball grid array (BGA) packaging structures. Compared with conventional finite element (FE) simulations with solid elements, the proposed approach with a deep neural network is more than three orders of magnitude faster in terms of computational efficiency. More importantly, detailed stress and strain of all solder joints and also the warpage of the printed circuit board (PCB) can be accurately predicted throughout the drop process. According to the contact type between BGA packaging structure and rigid ground, the drop conditions are divided into three types: surface contact, line contact and point contact. After training the ML model, the obtained nonlinear mapping relation can well predict the mechanical response of solder joints in BGA packaging structures. To significantly extend the application scope, the trained ML model could reasonably predict the dynamic responses of BGA structures with the drop angles which are beyond the training samples. Compared with FE results, the prediction accuracy of the proposed ML model is objectively measured by the Pearson correlation coefficient which is found to be above 0.95 after estimating the stress, strain and energy density of solder joints and PCB warpage. Except for those stress, train and energy density values close to 0, the prediction errors are mostly less than 10% compared with the finite element simulation values. Therefore, it is affirmed that the computational efficiency and accuracy of the proposed ML method are satisfactory to replace the traditional time-consuming FE modeling for predicting the dynamic responses of board-level BGA packaging structures even under more extreme and complicated loading conditions." @default.
• W4280492023 created "2022-05-22" @default.
• W4280492023 creator A5020024461 @default.
• W4280492023 creator A5036507854 @default.
• W4280492023 creator A5037691180 @default.
• W4280492023 creator A5051566266 @default.
• W4280492023 creator A5066211843 @default.
• W4280492023 date "2022-07-01" @default.
• W4280492023 modified "2023-09-27" @default.
• W4280492023 title "Machine learning for board-level drop response of BGA packaging structure" @default.
• W4280492023 cites W1963891194 @default.
• W4280492023 cites W1983187940 @default.
• W4280492023 cites W2012883830 @default.
• W4280492023 cites W2019211897 @default.
• W4280492023 cites W2024721520 @default.
• W4280492023 cites W2068187268 @default.
• W4280492023 cites W2083393033 @default.
• W4280492023 cites W2346038133 @default.
• W4280492023 cites W2465306340 @default.
• W4280492023 cites W2586822807 @default.
• W4280492023 cites W2737316333 @default.
• W4280492023 cites W2777579633 @default.
• W4280492023 cites W2793967148 @default.
• W4280492023 cites W2794230364 @default.
• W4280492023 cites W2794672244 @default.
• W4280492023 cites W2917609910 @default.
• W4280492023 cites W2937026737 @default.
• W4280492023 cites W2944960456 @default.
• W4280492023 cites W2968175525 @default.
• W4280492023 cites W3005792804 @default.
• W4280492023 cites W3020588867 @default.
• W4280492023 cites W3047474892 @default.
• W4280492023 cites W3084134041 @default.
• W4280492023 cites W3103435877 @default.
• W4280492023 cites W3153320385 @default.
• W4280492023 cites W3174028216 @default.
• W4280492023 cites W3189110740 @default.
• W4280492023 doi "https://doi.org/10.1016/j.microrel.2022.114553" @default.
• W4280492023 hasPublicationYear "2022" @default.
• W4280492023 type Work @default.
• W4280492023 citedByCount "5" @default.
• W4280492023 countsByYear W42804920232022 @default.
• W4280492023 countsByYear W42804920232023 @default.
• W4280492023 crossrefType "journal-article" @default.
• W4280492023 hasAuthorship W4280492023A5020024461 @default.
• W4280492023 hasAuthorship W4280492023A5036507854 @default.
• W4280492023 hasAuthorship W4280492023A5037691180 @default.
• W4280492023 hasAuthorship W4280492023A5051566266 @default.
• W4280492023 hasAuthorship W4280492023A5066211843 @default.
• W4280492023 hasConcept C119599485 @default.
• W4280492023 hasConcept C120793396 @default.
• W4280492023 hasConcept C121332964 @default.
• W4280492023 hasConcept C127413603 @default.
• W4280492023 hasConcept C134514944 @default.
• W4280492023 hasConcept C135628077 @default.
• W4280492023 hasConcept C138885662 @default.
• W4280492023 hasConcept C159985019 @default.
• W4280492023 hasConcept C163258240 @default.
• W4280492023 hasConcept C186260285 @default.
• W4280492023 hasConcept C192562407 @default.
• W4280492023 hasConcept C20192703 @default.
• W4280492023 hasConcept C21036866 @default.
• W4280492023 hasConcept C2775865969 @default.
• W4280492023 hasConcept C2776584680 @default.
• W4280492023 hasConcept C2780483431 @default.
• W4280492023 hasConcept C2781345722 @default.
• W4280492023 hasConcept C41895202 @default.
• W4280492023 hasConcept C43214815 @default.
• W4280492023 hasConcept C49040817 @default.
• W4280492023 hasConcept C50296614 @default.
• W4280492023 hasConcept C530198007 @default.
• W4280492023 hasConcept C62520636 @default.
• W4280492023 hasConcept C66938386 @default.
• W4280492023 hasConcept C69567186 @default.
• W4280492023 hasConcept C78519656 @default.
• W4280492023 hasConcept C94709252 @default.
• W4280492023 hasConceptScore W4280492023C119599485 @default.
• W4280492023 hasConceptScore W4280492023C120793396 @default.
• W4280492023 hasConceptScore W4280492023C121332964 @default.
• W4280492023 hasConceptScore W4280492023C127413603 @default.
• W4280492023 hasConceptScore W4280492023C134514944 @default.
• W4280492023 hasConceptScore W4280492023C135628077 @default.
• W4280492023 hasConceptScore W4280492023C138885662 @default.
• W4280492023 hasConceptScore W4280492023C159985019 @default.
• W4280492023 hasConceptScore W4280492023C163258240 @default.
• W4280492023 hasConceptScore W4280492023C186260285 @default.
• W4280492023 hasConceptScore W4280492023C192562407 @default.
• W4280492023 hasConceptScore W4280492023C20192703 @default.
• W4280492023 hasConceptScore W4280492023C21036866 @default.
• W4280492023 hasConceptScore W4280492023C2775865969 @default.
• W4280492023 hasConceptScore W4280492023C2776584680 @default.
• W4280492023 hasConceptScore W4280492023C2780483431 @default.
• W4280492023 hasConceptScore W4280492023C2781345722 @default.
• W4280492023 hasConceptScore W4280492023C41895202 @default.
• W4280492023 hasConceptScore W4280492023C43214815 @default.
• W4280492023 hasConceptScore W4280492023C49040817 @default.
• W4280492023 hasConceptScore W4280492023C50296614 @default.
• W4280492023 hasConceptScore W4280492023C530198007 @default.

• next