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Journalof Computationaland Applied Mathematics427(2023)115166 Contents lists available at Science Direct Journalof Computationaland Applied Mathematics journal homepage: www. elsevier. com/locate/cam Approaching STEPfileanalysisasalanguageprocessingtask: Arobustandscale-invariantsolutionformachiningfeature recognition ... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 Fig. 1. Segmentofa STEPfile. inwhichartificialintelligenceisusedtoaidinthedesignprocessbyinterfacingbetween CAD(computeraideddesign) modelsandphysicalmanufacturingprocesses[1]. There are many potential applications for int... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 Fig. 2. 24classesofmachiningfeature,from[3]. Thevoxel-basedapproach[6-8]involvesconverting3Dobjectsinto3Dgridsof'voxels'andusingtheseasinputto a3Dneuralnetwork,suchasa3DCNN. Thismethodhasasevereresolutionissue,asthe3Dneura... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 relevanttothe V-bendingprocess[20]. Evenmoregeneralsolutions,suchasthefeaturerecognitionsystemforrotational partspresentedin[21],relyonlimitedfeaturesetswhichcanonlybeexpandedthroughtheproductionofanewsetof definitiverules... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 Fig. 3. Neuralnetworkarchitecturefortraditionalandadapteddecodernetworks,showingfourpredictionsteps,where iiareinputvectors, oioutput vectors, hetheoutputhiddenstatefromtheencodernetworkand c0theinitialcellstate. 2. 2. Dec... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 Fig. 4. Processeswithinan LSTMcell,where htisthehiddenstate, ctisthecellstate, xtthenewinputand ft,itand otareoutputsoftheforget, inputandoutputgatesattimestep t. cellstate. Finally,theoutputgatecontrolshowmuchofthenewcell... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 Table 1 Train-timefeaturerecognitionaccuracyforatraditional LSTMarchitectureandafterimplementingeach alterationtothearchitecture,eachmodellistedalsoincludesalladaptationslistedabove. Decoderarchitecture Validationaccuracy ... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 Fig. 5. Validationf-scoresplottedagainstnumberoftrainingstepsrequiredforvariousmodelsizes. targetvectorissetasalistofallpresentfeatureclasses. Subsequenttargetvectorsareinitialisedtorepresentonlyfeature classeswhichappearm... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 Table 2 Parametersusedtogeneratetestdatasets. Testset Numberofmodels Scalefactor(Minimumsize) 1 400 1 2 200 1/2 3 200 1/3 4 200 1/4 Table 3 Parametersusedtogeneratedatasubsets. Datasubset 1 2 3 4 5 6 7 8 9 10 Numberoffeatu... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 Fig. 6. Example CADmodelsfromeachtestdataset. Table 4 F-scoresfor Test Set1,separatedbydatasubset. Datasubset 1 2 3 4 5 6 7 8 9 10 Total Msv Net[12] 0. 7205 0. 8481 0. 7699 0. 7752 0. 7399 0. 6866 0. 6994 0. 7219 0. 6657 0... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 Table 5 F-scoresforeachtestdataset. Model Test Set1 Test Set2 Test Set3 Test Set4 Msv Net 0. 7156 0. 6101 0. 6023 0. 5713 Ssd Net 0. 9217 0. 8367 0. 7657 0. 7413 Ours 0. 8906 0. 8976 0. 8831 0. 8780 Fig. 7. Overallf-scoreo... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 Table 6 Networkperformanceacrossalltestsubsets. Model Datasubset 1 2 3 4 5 6 7 8 9 10 Total Msv Net[12] Scale1 0. 7205 0. 8481 0. 7699 0. 7752 0. 7399 0. 6866 0. 6994 0. 7219 0. 6657 0. 6779 0. 7156 Scale2 0. 8052 0. 6053 ... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 Fig. 9. Featurerecognitionperformanceacrossalltestsubsets. Data availability Alinktothereserveddoiforthedatasetisincludedintheacknowledgementssection Acknowledgements Thisworkhasused Durham University's NCCcluster. NCChasb... | survey3DCADfeatureExtractors.pdf |
V. Miles, S. Giani and O. Vogt Journal of Computational and Applied Mathematics 427 (2023) 115166 [17] C. Yeo,B. C. Kim,S. Cheon,J. Lee,D. Mun,Machiningfeaturerecognitionbasedondeepneuralnetworkstosupporttightintegrationwith3D CADsystems,Sci. Rep. 11(1)(2021)1-20. [18] B. K. Venu,V. Rao,D. Srivastava,STEP-basedfeaturer... | survey3DCADfeatureExtractors.pdf |
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