View in EDS

Detecting the extent of co-existing anomalies in additively manufactured metal matrix composites through explainable selection and fusion of multi-camera deep learning features.

Saved in:
Bibliographic Details
Title: Detecting the extent of co-existing anomalies in additively manufactured metal matrix composites through explainable selection and fusion of multi-camera deep learning features.
Authors: Safdar, Mutahar1,2, Wood, Gentry3, Zimmermann, Max4, Lamouche, Guy2, Wanjara, Priti2, Zhao, Yaoyao Fiona1, yaoyao.zhao@mcgill.ca
Source: Virtual & Physical Prototyping; Dec2025, Vol. 20 Issue 1, p1-39, 39p
Database: Applied Science & Technology Source
FullText Text:
  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 193165758
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Detecting the extent of co-existing anomalies in additively manufactured metal matrix composites through explainable selection and fusion of multi-camera deep learning features.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Safdar%2C+Mutahar%22">Safdar, Mutahar</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AU" term="%22Wood%2C+Gentry%22">Wood, Gentry</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AU" term="%22Zimmermann%2C+Max%22">Zimmermann, Max</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AU" term="%22Lamouche%2C+Guy%22">Lamouche, Guy</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AU" term="%22Wanjara%2C+Priti%22">Wanjara, Priti</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AU" term="%22Zhao%2C+Yaoyao+Fiona%22">Zhao, Yaoyao Fiona</searchLink><relatesTo>1</relatesTo>, <i>yaoyao.zhao@mcgill.ca</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Virtual+%26+Physical+Prototyping%22">Virtual & Physical Prototyping</searchLink>; Dec2025, Vol. 20 Issue 1, p1-39, 39p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=193165758
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/17452759.2025.2515240
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 39
        StartPage: 1
    Titles:
      – TitleFull: Detecting the extent of co-existing anomalies in additively manufactured metal matrix composites through explainable selection and fusion of multi-camera deep learning features.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Safdar, Mutahar
      – PersonEntity:
          Name:
            NameFull: Wood, Gentry
      – PersonEntity:
          Name:
            NameFull: Zimmermann, Max
      – PersonEntity:
          Name:
            NameFull: Lamouche, Guy
      – PersonEntity:
          Name:
            NameFull: Wanjara, Priti
      – PersonEntity:
          Name:
            NameFull: Zhao, Yaoyao Fiona
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 17452759
          Numbering:
            – Type: volume
              Value: 20
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Virtual & Physical Prototyping
              Type: main
ResultId 1