Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387603946> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W4387603946 endingPage "213" @default.
- W4387603946 startingPage "190" @default.
- W4387603946 abstract "Additive manufacturing (AM) was originally developed to manufacture polymer prototypes. Today, it has been used for the manufacturing of many critical machine components. Most of the structural health monitoring (SHM) methods were developed for monitoring the condition of large and thin plates on airplane fuselages. Additively manufactured parts are generally small, thick, and have complex geometries. SHM methods have been improved to sense load, detect defects, and identify loose bolts with the help of a permanently installed sensor. In this study, the adaptability of SHM methods was researched with additively manufactured metal parts with complex geometry. Magnets were used to apply pressure to 9 different locations on the surface of a stainless steel additively manufactured thick plate with deep groves. SHM was used to estimate the magnets’ location. Many SHM (Lamb wave) methods cannot work on smaller parts since their dimensions are shorter or very close to the wavelength of the created oscillations. Surface response to excitation (SuRE) method which has similar characteristics to electromechanical impedance methods was used for data collection. To obtain descriptive features of the time domain data, fast Fourier transformation (FFT), short-time Fourier transformation (STFT), continuous wavelet transformation (CWT), and synchrosqueezing transform (SST) were applied to CWT. 1D and 2D convolutional neural networks (CNN) were used to classify the cases. When CNN was optimized for the analysis of our data, 100% location estimation accuracy was obtained by using 50% of 320 scalograms for training. The scalograms were obtained by enhancing the CWT results with SST. STFT-CNN combination was the second best. It obtained 95% accuracy with the same number of spectrograms and training allocation." @default.
- W4387603946 created "2023-10-14" @default.
- W4387603946 creator A5005243655 @default.
- W4387603946 creator A5065657263 @default.
- W4387603946 creator A5092554377 @default.
- W4387603946 creator A5093056331 @default.
- W4387603946 date "2023-10-13" @default.
- W4387603946 modified "2023-10-14" @default.
- W4387603946 title "Structural Condition Monitoring Using Deep Learning on a Metallic Part Fabricated by Additive Manufacturing" @default.
- W4387603946 doi "https://doi.org/10.37256/dmt.3220233366" @default.
- W4387603946 hasPublicationYear "2023" @default.
- W4387603946 type Work @default.
- W4387603946 citedByCount "0" @default.
- W4387603946 crossrefType "journal-article" @default.
- W4387603946 hasAuthorship W4387603946A5005243655 @default.
- W4387603946 hasAuthorship W4387603946A5065657263 @default.
- W4387603946 hasAuthorship W4387603946A5092554377 @default.
- W4387603946 hasAuthorship W4387603946A5093056331 @default.
- W4387603946 hasBestOaLocation W43876039461 @default.
- W4387603946 hasConcept C102519508 @default.
- W4387603946 hasConcept C104317684 @default.
- W4387603946 hasConcept C113556839 @default.
- W4387603946 hasConcept C11413529 @default.
- W4387603946 hasConcept C121332964 @default.
- W4387603946 hasConcept C127413603 @default.
- W4387603946 hasConcept C134306372 @default.
- W4387603946 hasConcept C154945302 @default.
- W4387603946 hasConcept C166386157 @default.
- W4387603946 hasConcept C185592680 @default.
- W4387603946 hasConcept C192562407 @default.
- W4387603946 hasConcept C196216189 @default.
- W4387603946 hasConcept C203024314 @default.
- W4387603946 hasConcept C204241405 @default.
- W4387603946 hasConcept C24890656 @default.
- W4387603946 hasConcept C2776247918 @default.
- W4387603946 hasConcept C2781067378 @default.
- W4387603946 hasConcept C33923547 @default.
- W4387603946 hasConcept C41008148 @default.
- W4387603946 hasConcept C46286280 @default.
- W4387603946 hasConcept C47432892 @default.
- W4387603946 hasConcept C55493867 @default.
- W4387603946 hasConcept C66938386 @default.
- W4387603946 hasConcept C75172450 @default.
- W4387603946 hasConcept C78519656 @default.
- W4387603946 hasConcept C95722684 @default.
- W4387603946 hasConceptScore W4387603946C102519508 @default.
- W4387603946 hasConceptScore W4387603946C104317684 @default.
- W4387603946 hasConceptScore W4387603946C113556839 @default.
- W4387603946 hasConceptScore W4387603946C11413529 @default.
- W4387603946 hasConceptScore W4387603946C121332964 @default.
- W4387603946 hasConceptScore W4387603946C127413603 @default.
- W4387603946 hasConceptScore W4387603946C134306372 @default.
- W4387603946 hasConceptScore W4387603946C154945302 @default.
- W4387603946 hasConceptScore W4387603946C166386157 @default.
- W4387603946 hasConceptScore W4387603946C185592680 @default.
- W4387603946 hasConceptScore W4387603946C192562407 @default.
- W4387603946 hasConceptScore W4387603946C196216189 @default.
- W4387603946 hasConceptScore W4387603946C203024314 @default.
- W4387603946 hasConceptScore W4387603946C204241405 @default.
- W4387603946 hasConceptScore W4387603946C24890656 @default.
- W4387603946 hasConceptScore W4387603946C2776247918 @default.
- W4387603946 hasConceptScore W4387603946C2781067378 @default.
- W4387603946 hasConceptScore W4387603946C33923547 @default.
- W4387603946 hasConceptScore W4387603946C41008148 @default.
- W4387603946 hasConceptScore W4387603946C46286280 @default.
- W4387603946 hasConceptScore W4387603946C47432892 @default.
- W4387603946 hasConceptScore W4387603946C55493867 @default.
- W4387603946 hasConceptScore W4387603946C66938386 @default.
- W4387603946 hasConceptScore W4387603946C75172450 @default.
- W4387603946 hasConceptScore W4387603946C78519656 @default.
- W4387603946 hasConceptScore W4387603946C95722684 @default.
- W4387603946 hasLocation W43876039461 @default.
- W4387603946 hasOpenAccess W4387603946 @default.
- W4387603946 hasPrimaryLocation W43876039461 @default.
- W4387603946 hasRelatedWork W1576245620 @default.
- W4387603946 hasRelatedWork W1970853565 @default.
- W4387603946 hasRelatedWork W2063802964 @default.
- W4387603946 hasRelatedWork W2088461590 @default.
- W4387603946 hasRelatedWork W2090582869 @default.
- W4387603946 hasRelatedWork W2103138788 @default.
- W4387603946 hasRelatedWork W2158824661 @default.
- W4387603946 hasRelatedWork W2168198486 @default.
- W4387603946 hasRelatedWork W2178843752 @default.
- W4387603946 hasRelatedWork W2790461051 @default.
- W4387603946 isParatext "false" @default.
- W4387603946 isRetracted "false" @default.
- W4387603946 workType "article" @default.