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吴 琪,等:

              GH4169 合金微观组织结构的超声评价与扩散生成方法

              途径,并为未来的材料微观组织建模和虚拟试验提                                 based microstructure prediction during laser sintering of
              供了参考。                                                  alumina[J]. Scientific Reports,2021,11(1):10724.
                  本文获“2024 Evident杯超声检测技术优秀论文                     [8]  LEE  K  H,YUN  G  J. Microstructure  reconstruction
              评选”活动二等奖                                               using diffusion-based generative models[J]. Mechanics of
                                                                     Advanced Materials and Structures,2024,31(18):4443-
              参考文献:                                                  4461.
                                                                  [9]  HO  J,JAIN  A,ABBEEL  P. Denoising  diffusion
                [1]  GUPTA M,KHAN M A,BUTOLA R,et al. Advances
                                                                     probabilistic  models[J]. Advances  in  Neural  Information
                   in  applications  of  non-destructive  testing(NDT):a
                                                                     Processing Systems,2020,33:6840-6851.
                   review[J]. Advances  in  Materials  and  Processing
                                                                  [10]  VAN  D  O  A,VINYALS  O. Neural  discrete
                   Technologies,2022,8(2):2286-2307.
                                                                     representation  learning[J]. Advances  in  Neural
                [2]  MCKNIGHT S,PIERCE S G,MOHSENI E,et al. A
                                                                     Information Processing Systems,2017,30:5690-5711.
                   comparison of methods for generating synthetic training
                                                                  [11]  NICHOL  A  Q,DHARIWAL  P. Improved  denoising
                   data  for  domain  adaption  of  deep  learning  models  in
                                                                     diffusion  probabilistic  models[C]//International
                   ultrasonic  non-destructive  evaluation[J]. NDT  &  E
                                                                     Conference on Machine Learning. Lille,France:PMLR,
                   International,2024,141:102978.
                                                                     2021.
                [3]  MEOLA  C,BOCCARDI  S,CARLOMAGNO  G  M,
                                                                  [12]  WANG  Z,BOVIK  A  C,SHEIKH  H  R,et  al. Image
                   et al. Nondestructive evaluation of carbon fibre reinforced
                                                                     quality  assessment:from  error  visibility  to  structural
                   composites  with  infrared  thermography  and
                                                                     similarity[J]. IEEE  Transactions  on  Image  Processing,
                   ultrasonics[J]. Composite Structures,2015,134:845-853.
                                                                     2004,13(4):600-612.
                [4]  WRÓBEL  G,STABIK  J,ROJEK  M. Non-destructive
                                                                  [13]  HORÉ  A,ZIOU  D. Image  quality  metrics:PSNR
                   diagnostic  methods  of  polymer  matrix  composites
                                                                     vs. SSIM[C]//2010  20th  International  Conference  on
                   degradation[J]. Journal of Achievements in Materials and
                                                                     Pattern Recognition. Istanbul,Turkey:IEEE,2010.
                   Manufacturing Engineering,2008,31(1):53.
                [5]  S O H L - DI CK STEI N   J ,WEI SS  E ,      [14]  ZHANG  R,ISOLA  P,EFROS  A  A,et  al. The
                   MAHESWARANATHAN N,et al. Deep unsupervised        unreasonable  effectiveness  of  deep  features  as  a
                   learning  using  nonequilibrium  thermodynamics[C]//  perceptual  metric[C]//2018  IEEE/CVF  Conference  on
                   International  Conference  on  Machine  Learning. Lille,   Computer  Vision  and  Pattern  Recognition. Salt  Lake
                   France:PMLR,2015.                                 City,UT,USA:IEEE,2018.
                [6]  YANG  Z  J,LI  X  L,CATHERINE  B  L,et  al.      [15]  CHEN  X,WU  G  H,CHEN  H,et  al. A  multi-
                   Microstructural  materials  design  via  deep  adversarial   parameter ultrasonic evaluation of mean grain size using
                   learning methodology[J]. Journal of Mechanical Design,  optimization[J]. NDT & E International,2019,106:10-17.
                   2018,140(11):111416.                           [16]  陈曦,董金龙,陈昊,等. GH4169晶粒尺寸的双目标超
                [7]  TANG J N,GENG X,LI D S,et al. Machine learning-  声评价方法[J]. 航空动力学报,2021,36(4):816-825.



























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